DE202016009077U1 - Segmentation and stratification of composite portfolios of investment securities - Google Patents

Abstract

A system for executing an instruction in a computing environment to build a representation of an index or portfolio of investments in a database, the system
a computerized processor configured to
one or more data entities electronically tagged with one or more functional attributes of corresponding economic entities,
wherein the functional attributes describe the roles of the respective economic entities in one or more processes that convert inputs into outputs;
selects multiple investment securities represented by the data entities for inclusion in an index or portfolio of investment securities;
defines at least a first group and a second group of assets based on the electronic tags or the functional attributes associated with the respective economic entities;
segmented the selected assets into the two or more groups based on the electronic tags or the functional attributes,
wherein the assets in the first segmented group share a first common or close functional attribute and the assets in the second segmented group share a second common or close functional attribute;
electronically access the database representation of the segmented groups; electronically traverses the accessed representations to calculate a negative or positive weight for one or more of the investments based on the one or more segmented groups into which the investments are segmented; and
assigns the negative or positive weight to the one or more of the investment securities; and
an electronic data memory which is designed in such a way that it
storing one or more items of data electronically in a database system, the items of data identifying an investment security identify which investment security is linked to a relevant economic entity, and
stores the assigned weight electronically in the database system.

Description

FIELD OF THE INVENTION

The present invention relates generally to computerized techniques that use a logical data model to construct a segmented or layered composite portfolio of investments.

BACKGROUND ART

Investment portfolio management has been the subject of much theory and research. Portfolio theory considers how assets should be invested and how, by carefully choosing the proportions of different assets, the expected return of a portfolio can be maximized for a given level of portfolio liquidity-adjusted risk, or liquidity-adjusted, respectively risk can be minimized for a certain level of expected return. Although a specific return can be expected, the valuation of individual positions in the portfolio may deviate up or down from this expected return. This swing up and down from the expected value is known as variance or volatility. Over time, securities should, in theory, exhibit an efficient frontier in terms of volatility and return. According to the theory, securities with a higher expected risk will have a higher expected return.

Financial indices are often used to measure the performance of a financial instrument. The S&P 500® Index is an example of such a benchmark for equity-oriented funds and the Barclays Aggregate Bond Index is an example of a benchmark for bond funds. The S&P 500 is the largest stock benchmark in the world. Trillions of dollars are invested either against this benchmark or in funds that follow it. Since the end of 1999, broad-based US indices such as the S&P 500® have experienced lengthy periods of underperformance. For example, an investor in the S&P 500® at year-end 1999 was down about 20% nominally ten years later at year-end 2009, depending on fees and dividend treatment. It wasn't until late 2012 that the S&P 500® was showing a positive nominal return at the end of 1999 for these investors, including many large pension funds and endowments. In October 2014, the S&P 500® showed negative real returns since year-end 1999. During this same period, broad funds holding US Treasuries or corporate bonds posted positive real returns, with corporate bonds outperforming government bonds during this period. This added value was due to the additional risk posed by a corporate bond versus a US Treasury bond of comparable maturity. These markets had their annual fluctuations but were largely stable; over a reasonable period of time, these investments have shown both positive returns and differences that would be expected based on risk. Neither of these statements can be made for stock indices such as the S&P 500®, which have declined in value on an absolute basis and have materially underperformed the less risky indices that hold investment grade corporate or government bonds over a long period of time.

Under a cap-weighted methodology, a change in the market value of a relatively large company will have a disproportionate impact on an equity index, while a change in the liabilities of a relatively highly indebted issuer will have a disproportionate impact on a bond index. Funds that track these indices also experience corresponding fluctuations in value as the instruments representing the relatively larger companies fluctuate in value.

The S&P 500®, like most market-wide indices, is capitalization-weighted. This means that an individual company's weight in the index relative to the other components is related to its market capitalization. There are no controls in the S&P 500® to ensure that an individual security or groups of securities that share a common risk are not or become overweight so that they represent too large a portion of the portfolio. That is, the types of controls used in scientific fields and engineering processes to limit the impact that a portion of a population can have on a measured total population are not used in the broad market indices. Such controls limit both positive and negative influences. In population studies, controls are used to create a normative model of an underlying population. Given that the benchmarks currently used to invest in equity securities are not controls, there is no assurance that historical returns from year-end 1999 to the present are representative of equity securities generally. The capitalization weighting strategy with no controls has produced underperforming returns for long periods.

The results of the major broad US equity indices since 1999 appear to be inconsistent with the main theories of asset pricing and efficient market theory. Much of the work on efficient markets and asset pricing followed the pioneering work of Markowitz and Shape, with later notable extensions by others such as Fama and French. Their theories state that individual securities are priced at a level expected to produce a risk-adjusted return relative to other securities, and that by following certain rules, a portfolio of securities is more likely than a single one to grow Security has the ability to provide that risk-adjusted return in any given time period or over multiple time periods.

The principles proposed by Markowitz and others have been used to assist investors and managers in choosing the most efficient portfolio design by analyzing various possible portfolios of a given set of securities. By describing a process for constructing a portfolio that involves selecting securities with significantly different risk-reward profiles, the models show investors how their risk can be reduced. The basic model in this area is known as the mean-variance model because it is based on expected returns (mean) and the distribution of expected returns (variance) of the different portfolios. In developing the original mean-variance model, Markowitz assumed that a portfolio that maximizes return for a given risk or minimizes risk for a given return is an efficient portfolio. Therefore, portfolios are chosen using the following rules: (a) among portfolios with the same expected return, the investor will prefer the portfolio with a lower risk; and (b) among portfolios with the same level of risk, an investor will prefer the portfolio with the higher expected return.

To facilitate portfolio construction, Markowitz used the expected covariance or correlation between securities as additional information that would allow investors to maximize their risk-adjusted portfolio-level return. Although a single security can underperform for a long period of time, the rules developed for efficient portfolio construction are designed to reduce this likelihood of underperforming the portfolio of securities through diversification. According to these basic theories, investors might expect to be compensated only for systematic, or broad market, risk by a premium commensurate with the risks in a given asset class, and should they be able to limit the exposure of nonsystematic risk to it efficient frontier consisting of the hypothesized market portfolio to diversify away.

One explanation for the inconsistency between modern portfolios and the theoretical portfolios on which the efficient market hypothesis was developed is that modern portfolios operate at a much larger scale and level of complexity than the theoretical examples. The early theoretical models, based on the Efficient Market Hypothesis and the Capital Asset Pricing Model, tend to use single securities and describe diversification within portfolios composed of single-digit or low double-digit numbers of securities. Many of the basic documents were written before the mutual fund boom of the 1980s and 1990s, following the creation of individual retirement accounts (IRAs) by the Employee Retirement Income Security Act (ERISA) in 1974, as well as the introduction of the first index fund in 1976 written. For example, Markowitz's paper on portfolio selection, published in the Journal of Finance, was written in 1952. According to the first stockholder survey conducted by the New York Stock Exchange (NYSE) in 1952, at that time only 6.5 million Americans (about 4.2% of the US population) owned common stock, and each owned an average of four shares. Sharpe's document "A Simplified Model for Portfolio Analysis" was written in 1963 and his book "Portfolio Theory and Capital Markets" was written in 1970, well before the mutual fund boom created by ERISA, the onset of globalization and modern technology, the development of exchange-traded products that have enabled en masse retail investors to hold thousands of securities at once, or the widespread recognition by institutional investors of the unique problems involved in managing such large funds.

Modern portfolios manage trillions of dollars altogether. Total investment in US mutual funds in 2012 was $13 trillion. To reduce exposure to non-systematic risk while avoiding relatively illiquid positions, portfolios require thousands of securities in various risk groups. At this scale, building efficient portfolios presented a challenge in the absence of applicable finance theory to guide selection and weightings, as portfolio theory was developed for portfolios of a much smaller scale. Today's absolute scale of investments by very large institutions has been since exponentially in the aforementioned mutual fund boom of the 1980s and 1990s. In addition, the underlying population of securities has become more heterogeneous and complex. This diversity and connection increases year after year. The need to manage the non-systematic risks embedded in this portfolio of firms is also increasing every year.

There is a need for a framework that allows for the systematic comparison and contextualization of all types of securities in today's complex heterogeneous global marketplace. In particular, there is a great need for a framework that allows systematic comparison and contextualization of all types of stocks in today's complex heterogeneous global market. A systems approach to organizing economic and financial information would accomplish this, allowing us to correlate the vast amounts of data associated with these activities and analyze economic and financial interdependencies.

In addition, there is a need for a new normative methodology for constructing investment portfolios, one that addresses the complexities of today's firms and the increasing size and diversity of today's funds, by adapting the approach and fundamental principles of Markowitz and Sharpe to the complexities of today's large format fund applies.

Some portfolio construction efforts seek to address the complex, heterogeneous global marketplace by relying on existing company grading systems. Current rating systems, such as the Global Industry Classification Standard (GICS), are not well suited to building new models of potentially efficient portfolios of these large-scale modern investment vehicles that rely on complex and globally cohesive universes of equity securities. The NAICS or the GICS relate companies by their positions in a fixed hierarchy. There are two major limitations to the NAICS and GICS fixed hierarchies: 1) all elements with no common parent are unrelated and cannot be compared using constraints in the hierarchy; 2) all elements with a common parent can only be used in accordance with the terms used by GICS or NAICS to designate that group (to the extent that the groups' designations indicate the condition separating them, e.g. “consumer” versus “commercial “ relate to the customer base) are used to be compared.

These systems, like the basic documents in finance, were created before the advent of large digital databases; they are modeled after the frameworks of the time, such as the Dewey Decimal System and the Standard Industry Classification System. These systems rely on a fixed hierarchy in which each unit has a single mother, that mother has a single mother, and so on. Each mother has descriptions but not concepts of certain attributes that would allow an entity under one mother to be associated with an entity under another mother.

Without the ability in the data structure to relate an entity under one parent to an entity under another parent, it is difficult to understand the multi-dimensional risks firms face and how many securities in a large portfolio or index may share a similar or related risk. The weaknesses of current classification systems are becoming increasingly apparent given the complexities of today's firms and the increasing size and diversity of today's funds. Although many of the biggest risks in a cap-weighted strategy stem from a lack of controls over individual risk levels, bubbles, or massive non-systematic price corrections, there are currently limited tools to systematically address these issues. Therefore, there is a need for a multi-dimensional attribute-driven categorization system enabled by current data processing capable of using these tools as well as the ability to build multiple different portfolios to assess the effectiveness of each and test it for a normative case. to provide.

benchmarks

In addition to the systems used to organize securities and the information about them, modern portfolio construction is hampered by another step in the process that was very slow to develop, the benchmarks against which their performance is compared. In other economic and financial areas, the role of benchmarks is established. Central banks routinely use inflation targeting to guide policy, which has proven crucial in increasing the predictability of price changes. This has enabled consumers, traders and investors to consume, save and invest with a high degree of confidence in short- to medium-term price changes. National economy ministries routinely plan and attempt to achieve their future annual gross domestic products, which multilateral institutions, banks and investors rely on as an index of a country's economic health.

As far as corporate finance is concerned, publicly traded companies regularly issue earnings forecasts and have quarterly profit targets, which are the chief responsibility of CFOs to achieve. Firms are measured against their profit targets and held accountable by boards of directors and financial analysts, with even minor shortfalls in earnings often leading to rapid falls in their share price. CFOs are also expected to deliver on target returns on shares which, due to the fact that this is junior to bonds in the capital structure, has a higher cost of capital for a given company and should have higher yields than the bonds issued by a company. In any case, modern technology has enabled decision makers to more accurately predict future economic and financial outcomes, manage risk and achieve their benchmarks with a high degree of predictability.

However, at the portfolio level, there is no comparable accountability for equity benchmarks. Because equity investing carries more risk at the portfolio level than bond investing, all equity indices should strive to earn a consistent premium over long-dated corporate bonds. Just as all companies will expect higher costs for stocks than for debt financing, all indices for stock investors, like the companies they invest in, should anticipate a higher return from investing in a company's stock than in its bond issues. Because of the statistical nature of large sets of investment securities, investors should expect to see this risk premium more consistently in an index portfolio. This risk premium should be realized at the portfolio level; Equity index investors should seek to outperform long-dated corporate bond returns for their constituent group on a consistent basis.

The Capital Asset Pricing Model uses the term "alpha" to describe the excess return of a benchmark; from a company's perspective, generating "alpha" entails exceeding their earnings forecasts. For any given company, a stock award equates to meeting earnings estimates and beating borrowing rates. The same principle should apply at the portfolio and index level; Investors in equity portfolios should expect returns in excess of the average borrowing rate for bonds in a given constituent group. When an index or portfolio fails to meet the performance target predicted by theory, a new methodology is needed that will more consistently and predictably achieve the target.

The S&P 500 is widely accepted as an equity benchmark, although it continues to lack risk controls and exhibits higher volatility than theoretical models predict. It does not achieve the returns predicted for it by basic financial theory and asset pricing models. Still, the methodology of the S&P 500 has not changed significantly since its inception, and it has failed to leverage the tools of modern technology and data analytics to manage risk and deliver more predictable, reliable returns. There is therefore a need for a reconsideration of how equity benchmarks and the standards for them should be constructed.

conglomerates

Companies have attempted to achieve diversification at the firm level through the conglomerate form, which involves the acquisition and management by a parent company of several independently operated and often functionally unrelated businesses. Conglomerate owners have attempted to manage the volatility in earnings associated with business cycles in different industries by organizing relatively uncorrelated revenue streams under the same corporate structure to decrease; some have also sought cost savings through sourcing, branding and sales synergies, or by aggregating interests across sectors to avoid antitrust restrictions on expansion and consolidation in a particular industry.

Although conglomerates have enjoyed considerable popularity in certain wealthy countries following long periods of high economic growth - the US in the 1960s, Japan in the 1980s, and most recently South Korea - they have largely fallen out of favor in high-yield markets please. The added layers of bureaucracy and lack of sufficient industry knowledge at the holding or parent company level have often made conglomerates too complex to manage efficiently.

Recently, private equity firms have attempted to achieve goals similar to those of conglomerate managers by acquiring and managing older companies, often in a wide variety of industries. The significant fees charged by such companies, which typically include 2% of assets under management and 20% of earnings above a benchmark, alongside transaction-specific fees, have hampered their ability as a group to generate strong returns for investors, while other companies have owed similar challenges faced by conglomerates, failed to capitalize on potential marketing, sales and operational synergies, or incurred excessive leverage that contributed to large losses during economic downturns.

Although some private equity firms have consistently performed very well, most of them are limited partnerships closed to the general public due to regulatory restrictions and information regarding their operations, strategy and investments is largely unclear and often not accessible. The lack of transparency and liquidity in these funds, as well as the challenges associated with managing industries across different sectors, have hampered these companies' capacity to scale. Currently, the largest traditional investment firm itself manages more capital than the entire global private equity industry combined.

volatility

Volatility in pricing refers to fluctuations in price. Volatility is a significant factor in portfolio performance and these price fluctuations can create resistance to portfolio growth. For example, daily volatility has been shown to hurt leveraged exchange traded fund returns. Random movements in assets without portfolio-level controls, particularly large downward movements caused by unpredictable events or the bursting of non-systematic bubbles, reduce risk- and liquidity-adjusted returns. In these cases, there is little to no expectation that portfolios and their constituent investments will rebound to pre-existing levels. In both cases, the impaired securities are repriced based on new information or the market's sudden realization that they were overpriced.

Various weighting schemes have been proposed in the investment industry in an effort to reduce the impact of volatility on a portfolio. For example, a method that works in the U.S. Patent No. 8,306,892 is described by calculating weights based on market capitalization, gross domestic product, and geographic area. In another example, found in the U.S. Patent No. 8,131,620 , weights in a portfolio of securities are based on market capitalization and dividend yield. There are numerous other portfolio weighting schemes. However, none of these weighting schemes fully addresses the shortcomings of the prior art portfolio theory as discussed above. Some examples like that in the U.S. Patent No. 8,005,740 uses accounting-based metrics to value universes of securities.

In traditional portfolio construction, random groups of securities are likely to have periods of significant swings in valuation, both up and down, from one time period to another. These massive fluctuations in the value of random groups of securities may not be caused by variables such as accounting attributes or their designation as "growth" or "value" stocks. The fluctuations in value can be caused, for example, by Fir men who have been engaged in a particular commodity for a long time, in the event of a sudden decrease in the value of the commodity; an exuberance in demand prospects for a company's or industry's product that does not meet demand; long term fixed cost contracts when actual costs available to their competitors change; an overweight to a particular asset in the product mix when that asset is declining in value; or other idiosyncratic reasons.

There are many reasons for seemingly random bubbles. In some cases they are systematic or broad-market bubbles, in others they are largely confined to a group of components (such as an asset class or industry). There are certain events that appeared systematic because they impacted index and portfolio returns as massively as the internet bubble of the late 1990's, but are non-systematic. In any event, the impact on an investor's returns when the bubble bursts can be extremely negative as a result of portfolio biases and overexposures to components particularly impacted by the bubble burst.

The random walk hypothesis in financial theory represents the inability to address the apparent randomness of the volatility and returns in equity-based investments Fee adjustment will perform as well as an actively managed selection of equity-based investments. The random walk hypothesis is the underlying reason for the spread of index funds and the widespread support of passive index funds by the academic community. Taken to extremes, the hypothesis proposes that a blindfolded monkey throwing darts at the price charts could choose a portfolio that would do as well as one chosen by the pundits.

Many different weighting strategies have been proposed to address the problem of random volatility in equity-based investments. The recent underperformance of these passive cap and equally weighted indices versus indices tracking comparable universes of companies has demonstrated that these passive indices continue to validate the same randomness hypothesis.

A major problem in risk management of large portfolios of securities is the inability of existing systems to control for the occurrence of these types of events without a framework for defining homogeneous subpopulations. If a portfolio is inadvertently overweight a security or group of securities with a common bubble or risk of failure, returns may be materially affected by a relatively small number of securities in the portfolio. Non-systematic bubbles and bankruptcies are related to non-systematic factors in the industries, companies, or assets associated with particular investments. In some cases, an overweight to certain non-systematic variables has caused significant negative impacts on a portfolio. This was clearly the case with the internet bubble. In calendar year 2000, the cap-weighted S&P 500® declined 9.09%. That year, there were sixteen stocks that were down 49.8% while the rest of the mark was up 4.28%. Unfortunately for investors in funds tracking this index, these sixteen companies, all of which were in the industry of moving, storing or processing information, comprised 24.8% of the total portfolio. The underperformance of these chosen securities had an extremely disproportionate impact on the index and the trillions of dollars in funds that were measured against it, due to the lack of controls on the underlying index.

Previous efforts to improve portfolio returns appear to have at least three problems, 1) a suboptimal number of groups; 2) insufficient ability to control in-group covariance or inter-group correlation to ensure that each group operates in a predictable group-specific manner; and 3) no way to define a group in a systematic way that is applicable across an entire economy and allows all groups to be correlated. Existing large-scale heterogeneous indices and portfolios of securities lack controls over their component groups, and neither capitalization weighting nor equal weighting can reduce the impact of group-specific risks at the portfolio level on a population of securities.

covariance and correlation

Although financial theorists have made significant breakthroughs in predicting return and variance for individual securities, there has been little progress in finding reliable indicators of the pairwise correlations or covariances between securities, a required input to the Markowitz model. In 1973, finance scholars Edwin Elton and Martin Gruber considered why quantitative solutions are unlikely to be practical on a large scale, and found that obtaining efficient portfolios of 200 stocks would require generating 19,900 correlation coefficients.

There are also institutional obstacles to finding generally applicable and sufficiently explanatory indicators, since it is highly unlikely that there would be a person in a financial institution who is sufficiently conversant with the mathematical analysis of each component of a substantial stock universe to be able to approximate it capable of a quantitative solution. Elton and Gruber concluded that there is no non-overlapping organizational structure that would allow securities analysts in a financial institution to produce estimates of correlation coefficients between all relevant pairs of stocks, since each analyst represents a subset of the stocks in which the institution is interested , follows.

In an effort to address the lack of reliable indicators of correlation in terms of security performance, traditional models such as the Capital Asset Pricing Model (CAPM) assume that all remaining pairwise correlations are zero. That is, each security is assumed to have no relation to any other security beyond synchronicity with the market as a whole. This assumption lacks realism, a simple likelihood ratio test for zero correlations rejects the null hypothesis of zero remaining pairwise correlations at the 0.000001 significance level.

Elton and Gruber explain that the CAPM can only be improved by assuming that a single non-zero pairwise correlation is allocated across an entire portfolio, but recognize the severe limitations of this approach. The challenges noted above and the lack of a well-developed field-specific framework to address the issue of covariance on a large scale have left the problem unresolved. The increasing scale, complexity and heterogeneity of modern portfolios have exacerbated this challenge.

Purely quantitative measures of correlation have proven to be the least accurate and least predictive with precision when they are most needed during bubbles, crashes and other periods of high market volatility when these measures have deviated far from their historical norms. Investors looking to diversify primarily based on quantitative historical covariance have suffered extraordinary losses during recent bouts of market volatility.

factor models

Asset pricing models such as CAPM have often failed to accurately describe or predict performance characteristics of securities, groups of securities, or portfolios. These models isolate a very small number of factors believed to drive security price returns and rely on the assumption that they can be purely quantified.

The CAPM relies on the security's risk-free rate, market return, and idiosyncratic risk; in other words, it rests on the assumptions (among others) that there is a factor F common to all securities in the market, a set of factors f _1,2 ... _n related to the set of securities s _1,2...n accurately in a one-to-one correspondence, it is true that these factors and their weights are essentially stable over time, and that the relationship between these factors and their weights is entirely unknown .

Fama and French's three-factor model adds size and book-to-market value to the above factors, while their postulated five-factor model, which as of November 2013 also adds profitability and asset growth, their earlier model not yet improving (Eugene Fama and Kenneth French, “A Five-Factor Asset Pricing Model”, working paper, September 2014). Carhart's postulated four-factor model adds dynamics to the three-factor model (Carhart, MM, "On Persistence in Mutual Fund Performance", The Journal of Finance 52; 57 - 82 (1987)). Tobias Adrian, Emanuel Moench, and Hyun Song Shin point to the systematic impact of aggregate broker-dealer capital structure and asset growth in non-bank financial institutions on stock and bond prices (Tobias Adrian, Emanuel Moench, and Hyun Song Shin, "Financial Intermediation, Asset Prices, and Macroeconomic Dynamics”, Federal Reserve Bank of New York, 2010). Adding common factors such as general market volatility and commodity prices, Andrew Lo and Amir Khandani emphasize liquidity as an additional layer of safety factor needed in studies of large and mid-cap stocks in developed markets during periods of low turbulence when liquidity factors are less relevant, has been neglected (Andrew Lo and Amir Khandani, “Illiquidity Premia in Asset Returns”, Concept Paper, June 2009).

Methodologies that initially focus on a quantitative analysis and that have failed to identify any factors or risks other than systematic and idiosyncratic factors or risks, or the relationship between the various idiosyncratic factors or risks, and the lack of computational power than many of the Key paradigms of finance have been formulated, leading to portfolio and index construction based on the assumption that all drivers of security price returns affect either a) every security in the entire market in exactly the same way, or b) only one security in the entire affect the market in any way that is predicted. This untenable assumption has made effective portfolio and index construction extremely difficult.

problem of scale

1. (a) Chartergrenzen auf Eigentum: Für viele Fonds und Fondsverwalter gibt es Grenzen für den Prozentsatz einer Firma, den sie besitzen können. Zum Beispiel sind für jeden Fonds, der eine 5%ige Beteiligung von öffentlichen US-Aktien erlangen möchte, 13-D-Einreichungen und eine umfangreichere Regulierungsaufsicht nötig. Viele Fonds wollen oder können diese Grenze nicht überschreiten.
2. (b) Liquiditätsgrenzen auf Eigentum: Je mehr eines einzelnen Wertpapiers ein Fonds besitzt, desto schwerer ist es insbesondere für große Beteiligungen im Allgemeinen, zu verkaufen. Die Wirkung ist für geringe Dollarwertbeteiligungen an liquiden Wertpapieren häufig belanglos, kann aber für größere Beteiligungen oder verhältnismäßig illiquide Wertpapiere bedeutend sein.
3. (c) Große Fonds benötigen eine große Anzahl an Wertpapieren, um ein Portfolio auszufüllen: Aufgrund der oben identifizierten Faktoren wie auch anderer praktischer Probleme benötigt ein großer Fonds infolge Liquiditäts- und Eigentumsfragen eine große Anzahl von Firmen, in die investiert wird. In einer Wirtschaft gibt es viele Verflechtungen unter Firmen, und je größer die Anzahl der unter Bewertung stehenden Firmen ist, desto schwieriger ist es, die Verfechtungen und die Risiken, die davon stammen, zu verfolgen und zu überblicken.
4. (d) Große Fonds können sich einer begrenzten Auswahl an Wertpapieren gegenübersehen: Aufgrund der oben identifizierten Faktoren wie auch vieler weiterer praktischer Probleme müssen große Fonds häufig unverhältnismäßig in große Firmen oder andere Fonds investieren. Die verfügbaren Firmen in dieser Gruppe ändern sich im Lauf der Zeit. Zudem weisen diese Wertpapiere abhängig davon, welche Firmen zu einem beliebigen gegebenen Zeitpunkt in welcher Kategorie vorhanden sind, variable Gewichte auf und aggregieren anders.
5. (e) Geographische Abweichungen: Zusätzlich zu Veränderungen im Lauf der Zeit variiert diese Branchen-, Sektor oder Firmenwahl je nach der Geographie; in großen Portfolios, Indizes oder Fonds, die aus Wertpapieren bestehen, ist das Bestimmen der geographischen Einwirkung von Vermögenswerten, Tätigkeiten und Produkten als nicht beschränkende Beispiele unter Verwendung herkömmlicher Verfahren unpraktisch. Die Sektordifferenzierung kann eine größere Ursache für Preisbewegungen zwischen Geographien als die zugrundeliegende Währung, die die Produkte antreibt, darstellen. Zum Beispiel werden Portfolios von US-Wertpapieren häufig schwerer in Technologiewerten als Portfolios von europäischen oder lateinamerikanischen Wertpapieren gewichtet. Europa und Lateinamerika sind verhältnismäßig schwer, was die Herstellung und den Finanzsektor betrifft.

For a number of reasons, the problems described above are particularly acute in large-scale portfolios of securities. Various reasons why the large-scale management is difficult are given below.

1. (a) Charter Limits on Ownership: Many funds and fund managers have limits on the percentage of a company they can own. For example, any fund seeking a 5% interest in US public stocks will require 13-D filings and greater regulatory oversight. Many funds do not want to or cannot exceed this limit.
2. (b) Liquidity Limits on Ownership: The more of a single security a Fund owns, the more difficult it is to sell, particularly for large holdings in general. The effect is often insignificant for small dollar value holdings in liquid securities, but can be significant for larger holdings or relatively illiquid securities.
3. (c) Large funds require a large number of securities to fill a portfolio: Due to the factors identified above, as well as other practical issues, a large fund requires a large number of companies to invest in due to liquidity and ownership issues. In an economy there are many interdependencies among firms, and the greater the number of companies under assessment, the more difficult it is to track and oversee the interdependencies and the risks that arise from them.
4. (d) Large funds may face a limited choice of securities: Due to the factors identified above, as well as many other practical issues, large funds often have to invest disproportionately in large companies or other funds. The available companies in this group will change over time. In addition, these securities have variable weights and aggregate differently depending on which companies are in which category at any given time.
5. (e) Geographic Variations: In addition to changing over time, this industry, sector or company choice will vary by geography; in large portfolios, indexes or funds composed of securities, determining the geographic exposure of assets, activities and products as non-limiting examples using conventional methods is impractical. Sector differentiation can be a bigger cause of price movements between geographies than the underlying currency that drives the products. For example, portfolios of US securities are often more heavily weighted in technology stocks than portfolios of European or Latin American securities. Europe and Latin America are relatively heavy in terms of manufacturing and finance.

When a fund, index or portfolio manager's goal is currency differentiation, it is important to manage these sector fluctuations. Not just understanding the different potential risk groups that exist at any given time and in any particular geography or category, but the ability to control for these risks is difficult using current or prior art techniques.

(e) Attribute and over-concentration risk are multidimensional: Single and multiple attributes are useful for distinguishing risks at individual firms, but attributes that are clear at an individual level are lost in larger classification systems. These diverse, yet critical, attributes that drive security price returns are often grouped together into a technology meta-category in large-scale funds. The existing categories in current systems tend to be standardized on a global basis and do not allow differentiation among these attributes, which aggregate to denote each metacategory. The inability to represent linked multi-attribute risks is a major limitation for existing large-scale investment portfolios.

If portfolios, and particularly large-scale portfolios, are not better managed and the linkages between firms are not well understood, it appears that non-systematic events can have a systematic impact. Examples of non-systematic events are given below. Known and existing classification systems do not address the underlying statistical reasons for the systematic influence of the volatility of the components of large-scale portfolios of securities. However, with improved controls, the influence of non-systematic events could be limited.

SUMMARY OF THE INVENTION

Without both a reliable and validated classification system that uses functional attributes and a computerized system that has a layered or segmented (or blocked) composite structure, conventional systems are incapable of control over the various attributes that associated with the Securities. A stratified or segmented composite portfolio may be formed by selecting a group of securities, segmenting the securities into subgroups according to attributes that correlate to one or more identified securities risks, and assigning portfolio weights to one or more subgroups based on their stratified or segmented positions . The attributes can be chosen from a universe of possible values. Other positive or negative alignments can also be applied to individual assets, groups of assets chosen at will, or positions in the architecture at any point, layer, or segment.

The specific functional attributes associated with the assets can be used to segment, stratify and weight the asset holdings in a portfolio by assigning specific weights to the risk groups in which the underlying securities are held to determine the constructed meet risk targets for the entire portfolio. As a non-limiting example, one of the goals of segmenting or stratifying risk groups may be to reduce the impact of attribute-specific resistance to volatility on the portfolio as a whole. As non-limiting examples, the systems and methods described herein may be used in wealth management by controlling for specific types of random events that reflect the overall randomness of risk, reward, skewness and kurtosis in large portfolios or groups of affect investments, is made.

Multi-attribute risk composites can provide a tool for managing risk by reducing or minimizing the potential risk arising from those attributes and/or augmenting or maximizing the potential return from those types of risks by constructing the composite such that an event, which an administrator expects to occur is used.

In some embodiments, a layered composite portfolio may be created by tagging securities with risk attributes based on functional attributes and applying a weighting scheme that limits the impact of individual attributes. The result of this process is a weighted portfolio that stratifies or segments risk exposure across a number of risk attribute categories and diversifies risk in the individual groups and subgroups according to attribute categories into groups to achieve a desired risk profile represented by a target score can.

According to one aspect of the disclosure, there is provided a computer-implemented method for storing in a database a representation of an index or portfolio of investment securities, the method comprising electronically storing in a database system one or more data items, each of the data items representing the identity of an investment security, the investment document is associated with a corresponding economic entity, electronically tagging each data item with one or more functional attributes of the corresponding economic entities, the functional attributes describing the roles of each of the economic entities in one or more processes that convert inputs into outputs; selecting multiple investments represented by the data entities for inclusion in an index or portfolio of investments; defining at least a first group and a second group of assets based on the electronic tags or the functional attributes associated with the respective economic entities; segmenting the selected assets into the two or more groups based on the electronic tags or the functional attributes, the assets in the first segmented group sharing a first common or close attribute and the assets in the second segmented group sharing a second common or close have attribute in common; electronically accessing the database representation of the segmented groups, electronically cycling through the representations accessed to assign negative or positive weight to one or more of the securities based on the one or more segmented groups into which the securities are segmented calculate; and assigning the negative or positive weight to the one or more of the investments; and electronically storing the assigned weight in the database system.

Further embodiments include selecting one of the segmented groups of assets that have a first common or close functional attribute; segmenting the selected group of investments into two or more subgroups, the subgroups being subsets of the segmented groups; weighting the two or more segmented subsets, wherein the assets in a first subset share a third common or close functional attribute and the assets in a second subset share a fourth common or close functional attribute.

In other embodiments, the common intersection of each set of groups is the empty set and the common intersection of each set of subsets is the empty set. In further embodiments, one or more groups, subgroups, or attachments are weighted based on syntactic or functional markers or syntactic or functional attributes; and one or more groups, subgroups or assets are weighted based on non-syntactic, non-functional markers or non-syntactic or non-functional attributes.

Other embodiments include assigning a target weight to a group, subgroup, or asset; and periodically rebalancing the group, subgroup or security to the target weight.

In other embodiments, one or more portfolios, indexes, groups, subgroups, or securities, or the data entities that represent them, are presented in a graphical, sequential, clustered, or networked form.

Further embodiments include electronically using predictive analytics based on functional attributes to calculate performance, volatility, liquidity, variance, expected return, alpha, Jensen alpha, beta, variance, covariance, the Predict half-variance, half-deviation, correlation, auto-correlation, Sharpe ratio, Sortino ratio of one or more portfolios, groups, subgroups or securities, or the excess or remainder of any of these measures.

In further embodiments, one or more weights are assigned to an investment document based on a functional attribute of a corresponding economic entity or an electronic tag representing such an attribute.

Further embodiments include the transmission, sending or forwarding of information regarding one or more data units and one or more weights to an exchange, an index provider, an index calculator, a brokerage firm, an asset manager, an investment advisor, an investment manager, a specialist, a securities dealer, a authorized participants, one hand a financial professional, an investment professional, an investor, a general partner, a limited partner, a private equity investor, a venture capital investor, a hedge fund investor, a conglomerate manager, an executive, a pension fund advisor, an endowment trustee, a fund administrator, or a securities trading platform.

Other embodiments include using one or more weights to construct an index, buy, sell, place or transmit an order, or trade an asset, group, or portfolio.

In further embodiments, the functional attributes are associated with risk exposures, wherein at least two sets of assets are associated with different functional attributes and different risk exposures.

Other embodiments include associating two or more numeric values with two or more groups, labels, attributes, risk exposures, or relationships, where the numeric values relate to economic, financial, or capital market-based data; associating a statistical property selected from the mean, variance, standard deviation, skewness, kurtosis, correlation, half-variance and half-deviation with those groups, labels, attributes, risk exposures or relationships based on the numerical values; calculating two or more statistical values associated with the statistical property; determining the statistical significance of the calculated statistical values of each group, label, attribute, risk exposure or relationship; validating that the statistical values are significant at a predetermined level; and reassigning groups, labels, attributes, risk exposures, or relationships if the values are not significant.

In further embodiments, the number of securities in each group is chosen such that statistical power of the statistical analysis exceeds a predetermined level.

In other embodiments, the investments or groups are selected from stocks, bonds, derivatives, currencies, commodities, funds, bills, alternative investments, exchange-traded products, real estate, and structured products.

Other embodiments include choosing a financial or economic metric to take a measurement relative to one or more of the groups, indices, or portfolios, wherein the distribution of expected or achieved values of the metric is relatively more normal for the index, portfolio, or group than is the distribution of expected or achieved values of the measure for an alternative index, portfolio or group; or the value of the metric is more stable or more predictable for the index or portfolio than for an investment security as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or more predictable for the group than for an investment as measured by a mathematical test of stability or predictability. In some other embodiments, the normality of the distribution is determined using the Cramer-von-Mises criterion, the Kolmogorov-Smirnov test, the Shapiro-Wilk test, the Anderson-Darling test, the Jarque-Bera test, the Siegel -Tukey's test, the Kuiper test, the p-value test, a QQ chart, a skewness test, or a kurtosis test. As non-limiting examples, stability can be evaluated by a test of variance or a test of heteroscadasticity.

Further embodiments include electronically storing one or more data items, each of the data items representing the identity of a segmented group, the segmented group having one or more investments and being associated with one or more corresponding economic entities; and electronically tagging each group with one or more functional attributes of the corresponding economic entities.

Other embodiments include identifying an index or benchmark to track, select, group, or weight the investments in a manner that substantially tracks or replicates the performance of the identified index or benchmark.

In other embodiments, the portfolio or index comprises a synthetic conglomerate.

In further embodiments, one or more weights are assigned based on semantic, syntactic, morphological, morphosyntactic, anatomical, physiological, functional, graphical, or value chain proximity.

Further embodiments include electronically storing a computerized representation of an economic system syntax, where the economic system syntax can be applied by a computer processor to determine the validity of expressions of elements of the system based on one or more functional properties of the economic entities.

Other embodiments include recommending a portfolio, group, or investment security to the user based on functional attributes electronically identified by the system or a user.

Further embodiments include arranging the selected data items in a layered structure having at least two parent groups and at least two subgroups of each parent group such that one or more parent groups are defined by one or more functional attributes such that the data items of those parent groups have the attributes that these parent groups define, have in common, where at least two parent groups are associated with different risks; where the subgroups inherit one or more functional attributes and corresponding risks from the parent groups; and the subgroups are defined by one or more different functional attributes such that one or more subgroups are associated with risks that are different from the parent groups and other subgroups.

Further embodiments include calculating a measure of statistical dependency between each of two parent groups and between each of two subgroups; determining whether the parent groups and the subgroups have a relatively high intragroup statistical dependence; determining whether the mother groups and the subgroups have a relatively low intergroup statistical dependency; and reorganizing the groups or subgroups when the intra-group statistical dependency does not exceed the inter-group statistical dependency.

In further embodiments, one or more subgroups are assigned weights relative to one another according to a weighting scheme such that the weight of one or more mothers equals the sum of the products resulting from multiplying a subgroup by its weight assigned according to the weighting scheme is.

In other embodiments, the portfolio's realized returns exceed those of a commercially available index or benchmark for a given risk level over the past year, three and five years, or are equal to the index or benchmark for a lower risk level; wherein the securities in the portfolio are the same or substantially the same as the securities in the index or benchmark.

According to another aspect of the disclosure, there is provided a computer-implemented method for storing a database description of an index, portfolio, set, aggregate or composite of elements of a functional system, or a representation of those elements, the method comprising electronically storing a set of data entities in a database system, each of the data units corresponding to an element of a functional system, the functional system comprising a group of elements ordered according to their functional roles in converting inputs to outputs or as the inputs or as the outputs; electronically assigning one or more functional attributes, represented as an electronic tag, to each data entity associated with an element, the functional attributes describing the roles of each of the elements in a process of converting inputs to outputs; selecting multiple items, or a representation of those items, described by data items for inclusion in a portfolio, index, set, aggregate or composite; segmenting the selected elements or a representation of those elements based on the electronic tags representing the functional attributes associated with the corresponding elements into two or more defined groups, the first group sharing a first common functional attribute and the second group shares a second common attribute; electronically accessing the database representation of the segmented groups, electronically iterating through the representations accessed to generate one or more of the elements, or a representation of those elements based on the calculate a negative or positive weight for one or more segmented groups; and assigning the negative or positive weight to the one or more of the elements or a representation of those elements; and electronically storing the assigned weight in the database system.

In further embodiments, the functional system is economical; includes one or more inputs, outputs, resources, activities, functions, businesses, businesses, jobs, companies, projects, products, assets, equity, liabilities, commodities, currencies, imports, exports, communities, or economic interests in or collections of any of the above in the economic system; investments represent the elements of the economic system, choosing one or more investments or one or more groups from stocks, bonds, derivatives, currencies, commodities, funds, banknotes, alternative investments, exchange-traded products, real estate and structured products; and one or more entities identify one or more investment securities.

According to another aspect of the disclosure, there is provided a system for executing an instruction in a computing environment to construct a representation of an index or portfolio of investments in a database, the system including a computerized processor configured to process one or more data units electronically tagged with one or more functional attributes of the respective economic entities, the functional attributes describing the roles of each of the economic entities in one or more processes that convert inputs into outputs; selects multiple investment securities represented by the data entities for inclusion in an index or portfolio of investment securities; defines at least a first group and a second group of assets based on the electronic tags or the functional attributes associated with the respective economic entities, segments the selected assets into the two or more groups based on the electronic tags or the functional attributes wherein the assets in the first segmented group share a first common or close functional attribute and the assets in the second segmented group share a second common or close functional attribute; electronically access the database representation of the segmented groups; electronically traverses the accessed representations to calculate a negative or positive weight for one or more of the investments based on the one or more segmented groups into which the investments are segmented; and assigns the negative or positive weight to the one or more investments; and an electronic data store configured to electronically store the one or more data items in a database system, each of the data items identifying the identity of an investment document, the investment document being associated with a corresponding economic entity; and stores the assigned weight electronically in the database system.

In further embodiments, the computerized processor is further configured to select one of the segmented groups of assets that share a first common or proximate functional attribute; segments the selected group of investments into two or more subgroups, the subgroups being subsets of the segmented groups; weights the two or more segmented subgroups, where the assets in a first subgroup share a third common or close functional attribute and the assets in a second subgroup share a fourth common or close functional attribute.

In other embodiments, the computerized processor is further configured to select one of the segmented groups of elements, or representations of those elements, that share a first common or proximate functional attribute; the selected group of elements or representations of these elements is segmented into two or more subgroups, the subgroups being subsets of the segmented groups, the groups of elements or representations of these elements in a first subgroup having a third common or close functional attribute in common and the elements or representations of those elements in a second subset share a fourth common or close functional attribute.

character list

• 1 illustrates an example method for creating a stratified composite portfolio and weighting investments.
• 2 illustrates an example method for creating a stratified composite portfolio and weighting investments.
• 3 illustrates an example layering with three levels.
• 4 illustrates an example data set corresponding to the example stratification with three levels.
• 5 illustrates an example method for creating a stratified composite portfolio and weighting investments.
• 6 illustrates an example method for calculating weights for a stratified composite portfolio.
• 7 illustrates an example method for creating a layered composite portfolio with a target score.
• 8A until 8B illustrate an example architecture presented as statements defining an architecture and a barcode.
• 9 graphically illustrates exemplary relationships between syntax elements.
• 10 illustrates an example database implementation for the system.
• 11 Figure 12 illustrates an example computerized system for weighting a layered composite portfolio.

DETAILED DESCRIPTION

definitions

Investment Security: As used herein, an investment security is a financial instrument representing, as non-limiting examples, an ownership position in a company (a stock), a commodity, or a collection of assets; a certified creditor relationship with an entity, such as a corporation, multilateral entity or governmental body, that is directly or indirectly secured by the assets of the issuer (bond); potential rights to buy, sell or own represented by an option or other derivative instrument; a security interest in a commodity or real estate including, as non-limiting examples, energy, woodland and precious metals; a group of other securities bundled into one security, including, as non-limiting examples, a fund, exchange-traded fund, exchange-traded product, or structured product, or any collection thereof. A security can be a replaceable, transferable financial instrument that represents a type of financial value attached to an economic entity. The company or entity issuing the security is known as the issuer. The value of the security's value may be based on the type of security, the nature of the relationship with the issuer, and the nature of the assets and liabilities directly or indirectly related to the security.

Economic Entity: As used herein, an economic entity is involved in some capacity, either active or passive, in the production, distribution, commerce, or consumption of real or virtual goods or services. As non-limiting examples, an economic entity may include a society, firm, enterprise, plant, workgroup, department, worker, input, output, resource, activity, function, project, product, Asset, liability, commodity, currency, import, export, community, job, employee, individual, governmental body, intergovernmental organization, multilateral organization, non-governmental organization, social enterprise, foundation, non-profit organization, or any be a collection of it. As non-limiting examples, an economic entity may have financial, environmental, social, or governmental goals, or any combination thereof.

Functional Attributes: The economic entities represented by the investment document may have or be associated with attributes. Functional attributes describe the roles of economic entities in processes that transform inputs into outputs. The database system can operate on several types of attributes associated with an entity. As non-limiting examples, the database system may act on classes of attributes that are (a) relative and/or (b) functional and/or (c) contextual and/or (d) absolute. For example, relative attributes can be syntactic attributes, geographic, timewise, scoring systems, designations as high/low volume stocks or growth/value stocks.

In some cases, attributes can be defined to include attributes related to the entity associated with the security and correspondingly exclude attributes of the security itself. In these embodiments, the database system may be configured to define attributes such as attributes related to the type of investment, such as a stock, bond, or derivative, and characteristics of the investment, such as preference, maturity, term, or the exercise price can be specifically excluded. In these arrangements, these excluded attributes are not considered to be functional attributes because the included attributes relate to the economic entity to which the security is linked and not to the security itself.

In some embodiments, functional attributes can be applied to inputs, outputs, or functions that convert inputs to outputs. In other embodiments, functional attributes may apply to activities, resources, systems, subsystems, composites, or elements. As non-limiting examples, types of functional attributes can be syntactic attributes or semantic attributes. Examples of functional attributes may include, as non-limiting examples, (a) attributes related to investors (e.g. "institutional" versus "non-accredited"), (b) attributes of assets owned by the firm (e.g. "outsourced" versus "in-house" at a manufacturing company), (c) attributes related to the product (e.g. "raw material" versus "simple component"), (d) attributes related to customers (e.g. "store" versus "consumer" versus “government”) and (e) attributes related to suppliers (e.g. “wholesale” versus “retail”). The system can recognize any combination of different types of attributes. Some attributes may exhibit qualities that are both holistic and functional.

In some embodiments, attributes may be defined to exclude accounting and performance-based attributes. In some embodiments, the functional attributes may be qualities, characteristics, properties, or inherent characteristics of the underlying entity or assets to which an investment security is linked. Functional attributes can define relationships across the value chain and structure of an economic entity, including, as non-limiting examples, (a) what a firm does, such as manufacturing or transportation; (b) aspects of the company's product, such as a particular benefit provided by a vehicle, computer, or sofa; (c) what the Company's customer does, such as computer sales or business analytics; (d) what Customer's Customer is doing; (e) the products and materials a company uses to provide its product; (f) the multidimensional industries or industry segments in which a firm may operate; (g) the structure of a company's businesses, such as integrated, unintegrated, forward integrated, backward integrated or networked; (h) the risks inherent in the management of a company, including its decisions and strategies; (i) the risks inherent in a firm's internal operations.

Much of the interconnectedness in an economic system is attributable to non-systematic functional attributes associated with, as non-limiting examples, suppliers, products, industry, and operations, and a firm's geographic location. Without a full appreciation of such common attributes or interconnectedness, portfolios containing large numbers of securities can easily become over-concentrated in non-systematic risk categories.

At any given time, any of these attributes or an industry event related to these attributes may affect the risk associated with securities linked to entities exhibiting these attributes. Understanding different potential risk groups and managing them is difficult without a reliable and validated system of functional attributes as well as a layered or segmented composite architecture to manage the different attributes.

Functional attributes can be structured syntactically or semantically; they may be formulated in a natural or symbolic rational language using the methods described in US Patent Application Serial No. 14/216,936, the contents of which are hereby incorporated by reference. Any combination of multiple attributes can be formed as a composite attribute. Compound attributes can be defined as a new single attribute.

Layered Composite Entity: As used herein, a layered composite entity is a layered set of investments that has 1) a parent group defined by one or more attributes, with all members of the parent group having the attributes necessary to define the mother group used, have in common; and 2) at least two subgroups of the parent group that can be considered daughters of the parent group and/or siblings to each other. All members of a subgroup share the attributes used to define the subgroup and its parent group. Any layered composite unit and its component sub-units may include any number of other sub-units that follow the rules of their parent unit or sub-unit. In some cases, a layered composite unit may consist of only a parent group and two sub-units. In other cases, a layered composite unit may consist of as many parts as the size and variety of the original mother will support. With reference to 4 For example, a layered composite unit may include elements 1210, 1230, and 1235.

Segmented Composite Entity: As used herein, a segmented composite entity is a segment for securities that is 1) a segmented group defined by one or more common attributes; 2) at least two sub-segments of the larger segmented group, each containing component securities that share at least one attribute in common with each other and with the larger segment group. In some cases, a composite segmented unit may consist of just one larger segmented group and two sub-segments. In other cases, a segmented composite unit may consist of as many segments as the size and diversity of the larger segment will support. A security may be, in whole or in part, a constituent of one or more larger segmented composite entities. With reference to 4 a segmented composite unit may include elements 1205 and 1210.

Stratified Composite Portfolio: As used herein, a stratified composite portfolio is defined as comprising at least two stratified composite units, wherein the attributes of the mothers in the composite units represent risk groups such that 1) maternal risk groups have distinguishable risk profiles ; and 2) the sub-units comprising investments in risk groups are formed as layered composite units.

Segmented Composite Portfolio: As used herein, a segmented composite portfolio is defined as comprising at least two segmented composite entities, wherein the attributes of the larger composite entities represent risk groups such that 1) major risk groups have distinct risk profiles; 2) the sub-segments comprising securities in risk groups are formed as segmented composite entities.

Although there may be other qualifications that should be present in the parent or larger grouping of a layered or segmented composite unit, the composite unit mothers may require that they share a particular attribute or sets of attributes in common with the members to have in common. A parent grouping of the multiple layered composite entities may comprise a layered composite portfolio defined to create a portfolio of composite entities such that a distinct one defined by the composite entities making up the layered composite portfolio risk is addressed.

Portfolio: A portfolio, as used herein, can be any form or collection of investments held by an investment company, institution or individual.

Introduction to Risk

Investments are made with the expectation of appreciation or income and the potential risk or variability of those incomes. The two criteria are linked; at a given level of liquidity, the higher the expected risk, the higher the expected return. In other words, all else being equal, higher levels of risk should be offset by higher returns. The probability of the return is linked to the expected variance of the outcome for a given security. The actual expected return on a security may depend on many factors, including market conditions, a given supply of investment capital, or an expectation of inflation or deflation. For example, identifying that a company is in the semiconductor business represents a distinct risk. In addition, the Art of the semiconductor (eg, storage, processing, interconnection) are important, as are the raw materials needed and the identities of the customers.

Securities differ in their earnings characteristics and expectations. Certain types of securities represent a particular ownership position in a particular company. Each type, such as a bond, equity instrument, or derivative, has its own specific ownership and investment characteristics. The expected return from a security is based on the type of security and its characteristics and the underlying performance of the related entity with respect to the ownership represented by the security. For any security, the expected return and the actual return may differ materially. The theory, as well as empirical evidence, shows that the divergence at the security level is significantly higher for equities than for relatively safe fixed income instruments such as investment grade government bonds.

The expected return (and volatility) of an investment security depends on factors including both market forces and forces inherent in the particular investment security and its underlying characteristics. The former forces are systematic and affect broad classes of securities. The latter are specific and unique to each particular investment security and are tied to the attributes of each particular investment security or group of securities. The variance in investment security returns tied to the latter are tied to attributes of the particular security shared among various segments of heterogeneous populations.

Risk may be associated with the qualities or attributes of the entity to which the security is linked. The changes in assets or even the bankruptcy of a particular company are related to the functional attributes of the company itself. These include any number of factors including the company, its operations, its products, its customers, its customers' customers, the availability of resources, the strength of its suppliers, or the company's specific assets or liabilities. Events relating to any of these, or any combination thereof, may cause a change in a company's assets and thereby change the expected return of a company associated with a security.

In addition to an individual company, a portfolio of securities may be affected by these non-systematic risks if the portfolio is overly exposed to, or overly concentrated in, a particular non-systematic risk. One of the primary reasons for owning a portfolio is to reduce this exposure to non-systematic risk by spreading it across a number of investments with unique or different risks such that no single non-systematic risk will materially change the assets or expected return of the entire portfolio. This strategy is relatively easier for a single investor who can diversify a portfolio across a relatively small number of individual securities in relatively small amounts. But this strategy has proven elusive for large investors such as pension funds or endowments that have trillions of dollars (or dollar equivalents) to invest. At any given time, these large investors must invest in hundreds or thousands of securities representing trillions of dollars in value. For investors with this scale of investments, minimizing the impact of non-systematic risk factors in a portfolio has proven very difficult; they tend to be overweight in large industry bubbles and are adversely affected by repeated technology or commodity bubbles and a persistent overweight in large bankruptcies or large downgraded classes of financial instruments such as mortgage-backed securities or government bonds. The invention disclosed herein provides a method for portfolio managers to take systematic control of those non-systematic portfolio risks that disproportionately and negatively affect large-scale portfolios.

Functional Attributes

Functional attributes can be used in the multi-attribute weighting scheme described herein. The systems described in this document may operate by assigning one or more attributes to companies associated with an investment. The methods described in this document may be implemented at a computing device to group a portfolio of investments using the functional attributes associated with their related companies, commodities, assets, or liabilities. These attributes can be used as markers used for the specific risks associated with events such as bankruptcy or market downturns. These attributes allow a portfolio manager to stratify, segment, or subdivide a portfolio into groups by attribute, with each group representing a particular attribute-related risk. When constructed in a stratified fashion, the children of these mother groups exhibit both unique risks between the groups and common risks they share with their mother.

After stratifying or segmenting a portfolio, weights can be assigned to the units and a plan to restore the weights can be executed on a systematic basis. In this way, a portfolio manager can understand and manage the specific risks in the portfolio. In addition, levels of risk can be constructed by arbitrarily setting weights for the stratified units. In some embodiments, the manager can determine the desired risk at the beginning of the process and use this to form a multi-level hierarchy of distinct groups and sub-groups, and then weight the groups according to a desired risk score. In other embodiments, the groups are used to form non-hierarchical segments, clusters, or groupings and then weighted according to a desired risk outcome.

The methods described in this document enable the calculation and implementation of weighting schemes for portfolios and their component securities, each of which exhibits certain characteristics that differ from those of unmanaged portfolios of the same securities, based on security- or group-specific attributes. As described in more detail below, the invention uses a set of security-specific functional attributes that are syntactically and semantically related to components to reduce the impact of randomness on individual security returns at the portfolio level by constructing portfolios of securities that reflect the impact of the reduce risks associated with the functional attributes. It does this by layering and segmenting the attributes and their risks in a managed manner within a managed portfolio of population groupings, the groupings defined by common attributes, and groupings containing particular securities that share the attributes associated with the groupings , represent.

stratification and segmentation

In order to control for non-systematic risk, a portfolio manager must control for the particular set of business risks present in any given portfolio. These risks could be company-related, industry-related, customer-related or supplier-related, among other things. As a portfolio grows larger, it becomes more difficult for a portfolio manager to understand how certain non-systematic risks affect it. The risk group stratification techniques described in this document reduce the negative impact of attribute specific volatility on the portfolio as a whole.

The systems described in this document can be used to create a stratified architecture or segmented sets of specific risk groups, allocating securities across these stratified or segmented risk groups to a portfolio and applying calculated risk group exposures or user-supplied weights for identified non-systematic risks. Thus, stratification or segmentation can be used to systematically manage exposure to non-systematic risks. These exposures can then be managed over time by generating rebalancing rules that reset the exposure of these identified non-systematic risks to a portfolio with an appropriate periodic schedule. In this way, the exposure of a set of non-systematic risks to a large-scale portfolio of securities can be systematically determined and managed.

The systems may include a programmable coordinate guided system for creating computer generated risk groups and processing a programmable array of computer generated risk groups into computer generated portfolios of those risk groups each containing securities corresponding to the attributes of the particular group.

Economic entities that share one or more attributes correlate with events associated with that attribute or set of attributes. The degree of correlation will vary according to the level of importance of that attribute in a particular company. For example, if all network equipment companies have the same customers, the loss of a major customer such as Cisco, a giant network company, will affect all companies. However, the impact will be greater when Cisco is the company's sole customer than when Cisco accounts for less than 5% of a company's operations. In this way, grouping companies into risk groups defined by attributes provides a method for portfolio managers to organize, segment, or stratify securities into groups that correlate with specific attribute-related events. In addition, most attributes are in turn part of larger attribute groups. When the large telecommunications company Nortel went bankrupt, all of the companies they owned together were also part of a network equipment group, which in turn was part of a communications equipment group, which in turn was part of a larger digital technology group, all of which faced bankruptcy. In this way, using functional attributes allows a portfolio manager to group securities according to both broad and narrow categories and according to the importance of those categories in determining the performance of individual securities.

Endogenous economic models describe functional attributes that represent risk-related properties, qualities, or characteristics. Coding for these attributes in a coordinate-based or ordered tagging system allows a computer to associate tags with specific risks and create company groupings that share those attributes. The risk-based computer-generated groupings can be checked for correlations with their component companies, with other groups, with other labels, or with other individual companies or securities, as a non-limiting example. In an iterative process, a computer can use the markers in this manner to calculate the statistical importance of various computer-generated groupings or individual markers used to construct computer-generated risk-managed portfolios of securities with unique risk characteristics derived from the computer-generated groupings check and validate. Furthermore, the computerized system described herein can be used to generate an array of clusters including, by way of non-limiting example, a risk stratified portfolio composed of stratified clusters of statistical control groups.

The process of stratification or segmentation may involve dividing a population into subsets (referred to as strata or segments) into which one or more assets may be placed. Stratification and segmentation can be used in statistical portfolio management as they are used to divide a population into parts or subsets. The creation of defined subsets, which are assigned defined proportions, allows the creation of controls for population results by statistical methods.

A properly stratified or segmented population can be referred to as a control group because its components and subset weights are defined and can be examined. In any heterogeneous population, random variance tends to exist, with a subset of the population exhibiting different characteristics, properties, or qualities than the population as a whole. The influence of these divergent subpopulations can be mitigated by grouping the population into subpopulations that are expected to behave differently, and then ensuring that some of each subpopulation is used when examining the population as a whole. If one were to examine worker performance as an example, one might find that workers were less efficient on Monday morning than during the rest of the week. However, if one were to draw a random sample of twenty days worked during a year, one might randomly obtain a sample set that is abnormally biased toward Mondays. This would not be representative of workers as the dataset was skewed towards the one period when workers were least efficient. In an effort to eliminate this bias, one could stratify the population set over five subsets consisting of one subset for each day of the week. Random sampling would involve allocating an equal number of worker days to each subset, so that the total sample would consist of five subsets, each with an equal number of sample days. In this way, stratification can limit biases in a sample set and increase the likelihood of a representative result.

Stratification provides controls that can 1) provide an unbiased sample set representative of the entire population; or 2) can ensure that a particular exposure increases the likelihood of an outcome that is desirable but not necessary wisely representative of the underlying population. An example of the former can be found in clinical studies or experiments within the framework of the social sciences. In these cases, the experimenter attempts to create a representative sample set about which assumptions can be modified to study how they affect the controlled population. An example of the latter can be found in risk management, where different population subsets are designed to be relatively uncorrelated and to exhibit widely divergent occurrences or changes. In this case, the statistician may want to weight the sample set toward a particular subset, such as subsets that exhibit relatively higher or lower volatility. In either case, stratification allows the statistician to construct sample sets with relatively predictable results based on the type of stratification model implemented. The layers are generally formed based on common attributes or characteristics of the members. These attributes could be based on physically identifiable attributes such as color of hair, skin, or eyes, right-handedness, or left-handedness. Additionally, the attributes could be based on relative quantitative measures of a population, such as a population's size, speed, or age.

In the context of investment securities, the value of an investment security may be directly or indirectly related to 1) the types of assets, liabilities, or activities directly or indirectly related to the security; or 2) the specific functional attributes associated with the assets, liabilities, inputs, outputs, products or activities directly or indirectly related to the security.

The aggregate expected return of a composite portfolio constructed using the methods described herein can be determined from the expected returns of each individual security and its weight in the composite portfolio. The aggregated expected volatility of the composite portfolio can be determined from the expected volatility and weight of the individual securities and the pairwise correlations of these individual securities to each other. Therefore, overall volatility can be managed and reduced by stratifying or segmenting the portfolio into groups with relatively high intra-group correlation and relatively low inter-group correlation.

While quantitative values associated with securities are likely to exhibit significant non-stationarity, qualitative attributes are likely to persist over time, driving performance characteristics with consistency and facilitating large-scale portfolio management and index construction. The data systems described herein, which enable the syntactical and functional tagging of hundreds of thousands of securities and the dynamic segmentation and layering of larger sets of securities by their associated attributes, are essential to enabling this process. As a non-limiting example, by dividing the securities into correlation clusters, i.e. groupings formed on the basis of attributes corresponding to risks, it is possible to control volatility.

Syntactic Attributes

The attributes described above can be represented in a syntax that defines the structure of the composite entities and the composite portfolios. The structures may be defined through the use of syntax and architectural positions or coordinates, including identifying attributes associated with data entities associated with syntactic positions. Syntactic markers may have relational attributes that allow syntactic positions to be related to each other.

As used herein, in some embodiments, a syntax may include a set of rules. A syntactic position can be defined as a valid position based on the set of rules. As a non-limiting example, syntax can be represented in coordinate space in any number of dimensions.

A symbol in a database corresponds to a data unit. In some embodiments, a syntactical marker associates a symbol and a rule, where the symbols are components of a dictionary and the symbols can be combined to form valid expressions according to principles of syntax. A syntactic marker connects the data item marked by a symbol to the other data items based on the set of rules specified by the syntax. At some According to embodiments, the process of syntactical tagging provides a means to relate domain-specific information. It takes information in a domain and tags it with rules that relate it in the domain. Syntactic markers can be dynamic.

In some embodiments, syntax can be used to evaluate the validity of expressions in a system. A symbol in a database can be used to mark a piece of data. A syntactic marker can be used to mark the connection between a symbol and a mechanism for evaluating the validity of expressions. There can be several types of tags, including syntactic attribute tags that ascribe relationships between symbols and rules that describe attributes. In some embodiments, a syntactic marker associates the data item marked by a symbol with the other data items based on the syntax-based set of principles. As a non-limiting example, this syntactical tagging process provides a means of relating information to a domain or its subset, or across domains.

Syntactic markers can have some or all of the following properties.

The expressions can be combinations of labels for markers. In some embodiments, such expressions may conform to a syntax that can be expressed in BNF notation (Backus Normal Form or Backus Naur Form notation) or an equivalent meta notation.

Any valid expression or subexpression consisting of more than one element of the syntax can form a locus.

Any element of the syntax that has a range of potential values describes a dimension in a discrete multidimensional space composed of the dimensions associated with all such elements.

Any expression or sub-expression of the syntax containing elements that have a range of potential values can be layered, in which case that expression or sub-expression describes a dimension consisting of ranges and consecutive sub-ranges in the multidimensional space. As a standard, elements of the syntax designated as layered are interpreted according to their position in the expression from left to right and in the architecture as consecutive levels from top to bottom.

The syntax can represent coordinates that provide sequential specialization, with the level of specialization increasing with the depth of the architecture. The syntax can also provide stepwise serialization at each level, with the degree of serialization increasing with the number of elements in each level.

1. a) ihrer Mutter in der Architektur; und
2. b) ihren Geschwistern an analogen Positionen über verschiedene Architekturen in der gleichen Syntax gemeinsam.

In some embodiments, the syntax elements at each level of specialization and/or at each level of serialization have a close syntactical position with both

1. a) her mother in architecture; and
2. b) shared with its siblings at analogous positions across different architectures in the same syntax.

Syntax elements can be said to have a close syntactic position if they are relatively close to other elements based on either their specialization or their serial positions. These relationships allow comparison of values across syntactic positions. This property supports applications including, but not limited to, complex structures, population sorting, auto-classification, and integration with state-of-the-art temporal and spatial classification systems.

A functional information system (FIS) can be implemented as a database system that uses syntactic tagging and the related concept of a locus as a logical model for organizing data across a domain. A basic implementation of the FIS can be achieved by having an inventory of the syntactic expressions of the FIS to complement the inventory of data entities in the domain. Each data unit can contain a reference to its location in the exhibit FIS. These table references allow searching for all data items in a given location as well as searching for the location of any data item in the system.

Syntactic tags are assigned to structured or unstructured data either manually or through an automated process and can be associated with a unique identifier for each data entity. Optionally, when sets of data items are associated with a limited, well-known range of objects or items, a dictionary containing standardized identifiers can be used to facilitate the assignment of identifiers to data items.

As a non-limiting example, syntactic tags may be used to represent the syntactic components of a domain-specific data item. They can be used to record and store information that indicates to a user how particular data items are related to each other and/or to the domain. The markers can be used to determine which data items are the same and/or why they are different and/or to what degree they are different.

The domain-specific rules described herein can be used to describe the syntactic components of data units in a domain and population sets of domain-specific syntactic tags. They can be assigned to any domain-specific data unit associated with a domain-specific syntactic position. Once allocated, stored, and retrievable, the data item can now be associated with any other data item that shares any value in its syntactic tag. It can be used to group information based on, for example, broad values or very specific values. If the values are broad, it provides the ability to create even smaller subsets in the context of the broad sentence. When other domains share the same syntax, the markers can be used to compare data items in one domain to data items in other domains based on the shared syntax.

The rules of syntax can be based on any number of factors. As non-limiting examples, they could be based on a common temporal order, spatial order, anatomical, morphological, physiological, or mechanical order. The rules can be areas that are specialized for a certain domain, such as the order of their influences or their origins. The rules could be experimental and the validity of the rules could be checked using syntactic tags. In any case, the knowledge affected by any ordering principle has a syntax that provides the rules for ordering. Once recorded and stored and retrievable, the process of relating data items based on syntactic tags may be based on established rules that define how different data items are related. This system can be applied to any domain and any syntax. By doing so, it provides a tool to add dimensionality to information from any domain. It can also provide a process to convert a legacy system from any domain into this frame by applying syntactic tags to the legacy codes.

The syntactic positions in the system have certain attributes associated with the rules of syntax. For example, if a domain-specific syntax is a time-based syntax, the attributes will be related in time; if it is a space-based syntax, the attributes will be spatially related; or if the syntax is mechanically based, the attributes will be mechanically related. If the syntax is sequential, the attributes will be sequentially related. If the syntax is nested, the attributes will be related to the rules of the nesting.

In some embodiments, to generate syntactical markers, a domain is defined and then a domain-specific syntax is defined. In one embodiment, the system can be configured such that the determined rules of domain-specific syntax are represented entirely in domain-specific syntactic tags.

The syntactic tag associates data items with common attributes by associating each data item with an element in the set of common syntactic tags. The syntactic tags connect data units to each other according to their syntactic connections Units of data in a domain. By doing so, they inherently group and/or cluster all data units that share syntactic markers.

In some embodiments, syntactic tags may be assigned to data entities that have one or more attributes in common or have the same or similar meaning in a context of interest for the domain to which the FIS is applied. By tagging data items with data item type tags, the system can work on several different types of data within a domain or dataset. For example, data for products or markets can be added to company data. This feature can be used in conjunction with labeling features described below to indicate that certain labels may only be required for certain types of data.

1. (1) eine aufeinanderfolgende Spezialisierung, wobei alle Dateneinheiten, die auf einer höheren Ebene die gleiche Markierung gemeinsam haben, auch bestimmte gemeinsame Merkmale oder Bedeutungen in der Domäne gemeinsam haben; und das Ordnen derartiger Label innerhalb einer Ebene eine Frage der Markierungszuteilungskonvention ist oder willkürlich ist, und/oder
2. (2) einen sequentiellen Prozess, wobei alle Dateneinheiten, die auf der nächsthöheren Ebene die gleiche Markierung gemeinsam haben, auch das gemeinsame Merkmal aufweisen, dass sie auf der gleichen Ebene der Prozessgliederung aufeinanderfolgende Stufen des gleichen sequentiellen Prozesses der Domäne sind; und das Ordnen derartiger Labels innerhalb der Kategorie direkt die Reihenfolge der Schritte widerspiegelt, auszudrücken.

Syntactic markers can be used to

1. (1) a sequential specialization whereby all data items that share the same label at a higher level also share certain common features or meanings in the domain; and the ordering of such labels within a level is a matter of label allocation convention or is arbitrary, and/or
2. (2) a sequential process, wherein all data units that share the same label at the next higher level also share the common characteristic that they are, at the same level of the process organization, successive stages of the same sequential process of the domain; and the ordering of such labels within the category directly reflects the order of the steps.

In some embodiments, the full enumeration of valid syntactic tags, regardless of whether any data item is actually tagged with any given label, provides a complete pre-existing model for the structures of interest in the domain to which the FIS model is applied .

Syntactic markers for layered composite units can be combined to form expressions. Such expressions may conform to syntax that can be expressed in BNF notation or an equivalent meta-phrase. Any expression or sub-expression of the syntax that contains elements that have a range of potential values can be layered, in which case that expression or sub-expression describes a dimension that contains ranges and successive sub-ranges in the multidimensional space.

Syntactic elements can be considered close if they are relatively close to other elements based on either their symbolic representation or their serial or complementary positions. These relationships allow comparison of values across syntactic positions.

The syntactic tagging of attributes associates data items with common attributes by assigning data items to an element in the set of common syntactic tags. The syntactic tags connect data items according to their syntactic connections to the other data items. This allows them to group or cluster data units that share syntactic marks. In some cases, syntactic markers can be used to create a normative model for a portfolio, which is discussed in more detail below.

The systems described in this document can be used in combination with a barcode that identifies a variety of business attributes. The system can assign this standardized barcode to securities in a portfolio along with functional attributes and syntactic tags. Based on this barcode of attributes, certain non-systematic risk impacts present in a portfolio can be identified. Once identified, the process can be used to manage these non-systematic risks by limiting exposure of the portfolio to these risks.

An exemplary representation of an architecture developed by syntactic markers is given in 8A and 8B shown. A graphic representation is in 9 illustrated.

Creation of the portfolio architecture

Building a large scale portfolio of securities is challenging for a number of reasons. It is difficult to control without a reliable and validated system of attributes as well as a layering or segmentation system using a layered composite architecture or segmented composite units in view of the large number of functional attributes affecting performance at the security, group and portfolio level. Independently of each other, and together, the systems described in this document enable risk exposure to be engineered and managed on a large scale basis.

A constructed composite of securities is a group of securities constructed (or chosen) to have a different risk/reward profile than an unmanaged group of the population of underlying securities or underlying risk groupings used to create the build composite, own.

Layered or segmented composite portfolios comprising investment securities may be based on a dynamic combination of units of a related class to create a new unit consisting of a portion of each of the components combined to create a new unit create that has different properties than the separately consulted underlying components. Dynamic properties mean that the properties of investments vary and change over time. Investment composites can be configured to take this dynamic nature into account to create reliable composites that essentially retain their properties over time.

A method of constructing a layered composite portfolio using an asset syntax may include the following steps: 1) grouping assets with common risk attributes; 2) stratifying or segmenting the grouped investments into subgroups that a) carry different risks while b) still share the risk characteristics of the group in which they are included.

In one embodiment, a composite portfolio may include identification of multiple securities and their associated weights. As a non-limiting example, the identifications and weights may be generated using a computerized process according to the example method set forth in 1 is illustrated to be executed. As in 1 As illustrated, the method may first generate a layered portfolio architecture (1125) and then a resulting list of investments and weights (1150). In an initial step, a layering module (1105) may receive as inputs attributes (1120) related to assets and an architecture of attribute rules (1122), both of which may be stored in one or more computerized data storage devices. As non-limiting examples, the asset attributes can be chosen from the examples given above. Other attributes and types of attributes can be used.

The attribute rules can be provided by the portfolio architecture as described above. The architecture can be used to define or evaluate relationships between attributes, tags, values, and the assets associated with the attributes.

The layering module (1105) may also include a selection sub-module (1110) that receives a selection of attributes (120) as input from a user. In some embodiments, the functional attributes that describe the economic entities enable portfolios to be constructed from securities associated with those entities. As a non-limiting example, in 8A until 8B a syntax is presented that allows the evaluation of expressions describing economic entities. In other embodiments, the syntax can be customized to attributes chosen by the user. In other embodiments, the user may be provided with an interface to create new structures (1121) which are then input into the layering module (1105).

In some embodiments, a structure may be constructed from a Boolean statement in the form of "attribute", "operator", "value" that may return "true" or "false" for an entity or its associated asset based on its attribute . In other embodiments, a structure may be constructed from a Boolean expression that combines one or more statements (via Boolean operators). The lines in 9 represent examples.

In some embodiments, an architecture may be defined as a relationship in a set of structures that define the portfolio segments, with the constraint that any entity or asset that fails at a node in the structures is not defined by the Rules will be passed to any of that mother's daughters. The layering sub-module (1115) may be configured to run based on the set of structures (1122), asset attributes (1120) (optional at this stage), inputs regarding the creation and choice of structures (1121), or constructs a layered portfolio architecture (1125) from a listing or other identification of the investments (1131). The layered portfolio architecture (1125) can then be represented electronically and stored in a computerized data storage device.

A structure can be obtained from one or more statements that filter entities and assets based on attributes. As a non-limiting example, a layered structure can be used to define a relationship among structures. Any company that is excluded from a top level will also be excluded from lower groups. The multi-attribute system described herein can be configured by changing the population in each mother or daughter by changing one (or more) of the attributes that define that mother or daughter. The ordered rules can also be expressed as a chart or network that can be configured to allow dynamic ordering of the population based on functional attributes defined by the computerized system, the user, or a combination thereof.

Exemplary graphical and textual representations of a resulting layered portfolio architecture are in 3 and 4 illustrated. 3 illustrates example attributes and their syntax. The attribute-based rules contained in 3 are illustrated are in 4 represented graphically. In the 3 The rules illustrated describe a top level consisting of two groups with the business loci real estate (1; 1205) and furnishing material manufacturers (2; 1210). The rules in 3 also describe the company loci real estate developers (1.A; 1215), real estate operators (1.B; 1220), REITs (real estate investment trusts, real estate funds)/real estate landlords (1.C; 1225), manufacturers of materials for information processing equipment (2.A ; 1230) and manufacturers of materials for non-information processing equipment (2.B; 1235). These business loci are represented on the second level of the layered architecture. The rules in 4 contain some relationships at a third level. The third level defines relationships for consumer real estate developers (1A.i; 1240), commercial real estate developers (1B.ii; 1245) among real estate developers (1.A; 1215); North American Real Estate Operators (1stBi;1250), European Real Estate Operators (1stBi;1255), and Asian Real Estate Operators (1stBi;1260) under Real Estate Operators (1stB;1220); and low-leveraged REITs (1.Ci; 1265) and leveraged REITs (1.C.ii; 12710) under Real Estate Investment Trusts (REITs)/Landlords (1.C; 1225). Other relationships are shown under groups (2.A; 1230) and (2.B; 1235) but will not be described further here.

Numerous attributes can be used to create a portfolio architecture. The portfolio architecture can have a nested structure of groups. As a non-limiting example, these groups can in some cases be formed by reference to the attributes common to all entities in the universe, such that at each level every element of the universe is in exactly one group. In some embodiments, these groups can be subdivided into any number of child subgroups - although this number need not be the same for each of the original parent groups - and this subdivision process can be performed an arbitrary number of times, with the architecture each time being based on one of adding a layer in a top-to-bottom way. In some embodiments, layered composite units are used to build up larger layered composite units, creating a structure in a bottom-up fashion. In some embodiments, a combination of the top-down and bottom-up approaches may be used. In other embodiments, existing economic and financial classification schemes may be reconfigured using syntactic tagging to make them relational and dynamic, and in combination with any or all of the universe choice, weighting, rebalancing, and partial rebalancing schemes described above or used entirely in the portfolio architecture. Regardless of the method of construction, the resulting portfolio architecture (1125) may include an electronic representation of a set of attributes arranged in a graphical, segmented, layered, or a network form, as non-limiting examples, according to the defined attribute rules.

The weighting of investments

A stratified or segmented composite portfolio may be constructed from one or more stratified or segmented composites that maintain risk exposures defined by appropriately weighting the components of the stratified or segmented portfolio.

The stratification or segmentation described herein can be customized in a variety of ways to allow a user to control the population of assets and thereby the results arising from events associated with a population of assets. Based on changes made to any or all of 1) the population of the Investment Securities; 2) how the population of investments is stratified or segmented (the portfolio architecture); and 3) how the layering or segmented units are weighted within the architecture, diagram or network, portfolios can be adjusted and the resulting performance metrics constructed.

After the portfolio architecture has been determined, weights can be determined for the securities. As a non-limiting example, a weight function can be any function that, for a particular group in a layered portfolio architecture, returns a value between -1 and 1 that indicates the weight associated with the group relative to its siblings in the portfolio architecture. In some embodiments, the absolute value of the weight may exceed 1. As non-limiting examples, negative values can be implemented through short selling and weights whose absolute value exceeds 1 can be leveraged. In some embodiments, the sum of the weighting function may equal 1 for all siblings or composites at any level or unit.

In some embodiments, the weight of a security is only a function of its position in the architecture. As a non-limiting example, the weights in strata can be divided equally among all daughters of a given parent group. That is, if the first level contained ten groups, each would be given a weight of 10%. If any of these groups contained four subgroups, each would be given a weight of 25% of their parent group, resulting in a weight of 25% * 10% = 2.5%; whereas if another top-level group had five child groups, each child would weigh 20% * 10% = 2%. This process can be repeated for each level, eventually giving a weight for each group of the bottom group. A similar process can be applied to securities in each lowest level group, yielding weights for all securities in the universe.

 Klasse PortfolioGruppe
       # Gibt eine Liste der Portfoliogruppen
       # auf der gleichen Ebene wie diese Portfoliogruppe
           aus
       definiere Geschwister
           ···
       Ende
       # Gibt eine Mutter dieser Portfoliogruppe aus
       # Wenn diese Portfoliogruppe keine
       # Mutter aufweist, wird undefiniert ausgegeben
       definiere Mutter
           ···
       Ende
       # Gibt das Gewicht aus, das
       # dieser Portfoliogruppe zugeteilt werden soll
       definiere Gewicht
           Anzahl der Geschwister =
              Anzahl. eigene. Geschwister
           Wenn Mutter.definiert ist?
              Mutter_Gewicht = Gewicht.eigene.Mutter
           oder
              Mutter Gewicht = 100
           Ende
           Gibt 1/Anzahl der Geschwister*Mutter_Gewicht
              aus
       Ende
 Ende

In some embodiments, the weighting algorithm can be executed by a computer as follows:

 Class PortfolioGroup
       # Returns a list of portfolio groups
       # at the same level as this portfolio group
           out
       define siblings
           ···
       end
       # Returns a parent of this portfolio group
       # If this portfolio group does not have
       # Mother has is output undefined
       define mother
           ···
       end
       # Returns the weight that
       # should be allocated to this portfolio group
       define weight
           Number of siblings =
              Quantity. own. siblings
           If mother.is.defined?
              mother_weight = weight.own.mother
or
              mother weight = 100
           end
           Returns 1/number of siblings*mother_weight
              out
       end
 end

 Klasse PortfolioGruppe
       # Gibt eine Liste der Portfoliogruppen
       # auf der gleichen Ebene wie diese Portfoliogruppe
           aus
       definiere Geschwister
           ···
       Ende
       # Gibt eine Mutter dieser Portfoliogruppe aus
       # Wenn diese Portfoliogruppe keine
       # Mutter aufweist, wird undefiniert ausgegeben
       definiere Mutter
           ···
       Ende
       # Eine Funktion, die für eine bestimmte Gruppe in
       # einer geschichteten Portfolioarchitektur
       # einen Wert zwischen 0 und 1 ausgibt, der das 



           Gewicht,
       # das mit dieser Gruppe verbunden ist, in Bezug auf
           ihre
       # Geschwister in der Portfolioarchitektur angibt.
       #
       # Die Summe der Gewichtungsfunktion für die
       # Geschwister auf jeder Ebene ist gleich 1
       definiere Gewichtung
           ···
       Ende
       # Gibt das Gewicht, das
       # dieser Portfoliogruppe zugeteilt werden soll, aus
       definiere Gewicht ()
           Wenn Mutter.definiert ist?
              Mutter_Gewicht = Gewicht.eigene.Mutter
           oder
              Mutter Gewicht = 100
           Ende
       Gibt Gewichtung*Mutter _Gewicht aus
       Ende
 Ende

In other embodiments, the weight of any group can be obtained from the incidence attributes of the shapes in that group. As a non-limiting example, groups (formed using any of the attributes) may be weighted by a function of one or more of the attributes common to securities in the universe. As a non-limiting example, groups within their parent group may be weighted in proportion to the total debt of all securities in the group. In some embodiments, the function depends on a single attribute. In other embodiments, the function depends on multiple attributes. In some embodiments, the same function is used to weight each group in the architecture. In other embodiments, different functions can be used to weight different groups in the architecture. In some embodiments, the weighting can be performed by a computer as follows:

 Class PortfolioGroup
       # Returns a list of portfolio groups
       # at the same level as this portfolio group
           out
       define siblings
           ···
       end
       # Returns a parent of this portfolio group
       # If this portfolio group does not have
       # Mother has is output undefined
       define mother
           ···
       end
       # A function written for a specific group in
       # A layered portfolio architecture
       # returns a value between 0 and 1 representing the 



           Weight,
       # associated with this group, in relation to
           their
       # Indicates siblings in the portfolio architecture.
       #
       # The sum of the weighting function for the
       # Siblings at each level equals 1
       define weight
           ···
       end
       # Gives the weight that
       # should be allocated to this portfolio group
       define weight()
           If mother.is.defined?
              mother_weight = weight.own.mother
           or
              mother weight = 100
           end
       Returns weight*mother _weight
end
 end

Referring to the example of 1 the computerized weighting module (1130) obtains the portfolio architecture (1125). As in 2 As illustrated, the weighting module may also be configured to obtain identification of securities (1131) and identification of attributes (1132) associated with the securities. The weighting module can then generate a list of securities and their associated weights (1150). The weight module is in 6 shown in more detail. As in 6 As illustrated, the system may receive a selection and/or identification of the investments to be weighted (1305). The assets to be weighted could be positioned at any point or points in the architecture described above. Then weights for individual instruments and groups of instruments for the current level or segment can be calculated (1310). In some embodiments, the computation may start at the top layer. At the current level, the weighting scheme and rules for that level are identified (1315). By dividing the weight fraction outstanding by n, the number of securities or groups of securities, a weight coefficient can be calculated (1320). As a non-limiting example, reference can be made to FIG 4 the top-level weight can be calculated as 50% for group 1 and 50% for group 2. On the second level, groups 1A to 1C can each be weighted with 50 * 0.333 = 0.167 or 16.7%.

Any positive or negative weight bias can be applied before or after the weight calculation (1325). The alignments can be applied to the weights by arithmetic or other operations. In some embodiments, any alignments applied to a group or attachment require that a corresponding opposite alignment be applied anywhere in the same group or peer group at the same level. When the lowest level has been reached and completed, the weighting process can end. Otherwise, the process can continue to the next level.

The electronic representation of the weighted securities can then be entered as instructions into, as non-limiting examples, an exchange traded fund (ETF) or other financial instrument such as a hedge fund, mutual fund, limited partnership, or other investment vehicle.

In alternative embodiments, the steps of the layering, segmentation, and weighting method may be reordered. For example, the list of investments could be included somewhere in the portfolio construction process. Investments and/or a recovery process could be chosen prior to stratification or segmentation to create exposure through a particular universe. An architecture, weighting scheme, or rebalancing scheme could be chosen or selected before or after the investment choices.

Alternative orders and variations of the steps described above for creating the portfolio of investments are possible. For example, the identification of the investment securities with reference to 1 be provided to the stratification module (1105). With this arrangement, the stratification sub-module can generate the stratified portfolio architecture of investments (1125), which is then input to the weighting module (1130).

In some embodiments, universe identification, group selection, and features may be combined into one module. In other embodiments, frames representing queries, structures, and outputs can be combined into a module. The portfolio and its component groups, composite and/or securities may be presented in a layered, segmented, meshed or graphical format, or in a daisy chart, by way of non-limiting example. In some embodiments, the outputs may be selected from a chart, geographic map, tree diagram, microarray, or table.

recovery and rebalancing

Additionally, some embodiments may include restoring the designated segment or group weights on a periodic basis to maintain the desired risk impacts. A stratified or segmented portfolio can each consist of one or more stratified or segmented composite units, which are determined by weighting the Components and restoring the designated weights on a periodic basis to maintain the desired exposures, maintaining defined exposures. With reference to the embodiments described in 1 , 2 and 5 are illustrated, the steps illustrated can be performed at any point to create a rebalanced portfolio based on modified inputs such as modified weighting rules. With reference to 5 For example, in other embodiments, the re-weighting may be provided by a separate re-weighting module (1155). The rebalance module (1155) obtains a list of target exposures associated with portfolio groups, composites, or components (1151). The rebalancing module then selects new investments for inclusion in the layered composite portfolio.

Scoring of the composite portfolio

A score can be calculated for a composite portfolio using the methods described herein. Scoring can be a feature of the portfolio and can be used in multiple contexts. In some embodiments, the target score may be a number that the portfolio is constructed to achieve. In other embodiments, the target score may be a set of attributes that an investor desires the portfolio to have. The portfolio's score may be a value calculated from the portfolio or a vector of values that can be compared to an investor's target score for the portfolio. The target score can be a theoretical or an estimated value.

A target score can be used as a means of optimizing a portfolio. The investor can choose the target score and then the system can be used to build a stratified composite portfolio optimized for that score. Alternatively, a target score can be used to construct a portfolio that reflects the performance of the underlying population. That is, the target score can measure expected population performance, and the stratified or segmented composite can be used to measure actual population performance. Given a weighted list of securities in a portfolio and a target score, the score for the portfolio can be calculated based on achieved attributes of a portfolio.

The target score may represent an estimate of an expected or targeted portfolio performance. The target score may be achieved by measuring the performance of, as non-limiting examples, individual companies, randomly sampled individual companies, stratification units, segments, and/or composites.

The target score can also be identified as the target score that the investor is seeking as part of the investment objective. Here the investor may wish to use a stratified or segmented composite to achieve a predetermined target score. Risk groups can be formed by building groups based on common attributes. These risk groups can then be weighted appropriately to achieve the target score, resulting in a portfolio with known biases.

In some embodiments, a layered or segmented portfolio may be constructed to meet a user-specified target score. As non-limiting examples, a target score could be any or all of (a) an absolute return target (eg, expected rolling interest rates), (b) a risk/reward measure (eg, the Sharpe ratio, the Sortino ratio, or alpha), or (c) the risk objective as measured by volatility (eg downside deviation or beta). In some embodiments, a target score may be a one- or multi-dimensional vector of values or elements, such as the examples provided above. As a non-limiting example, the target score could be |the actual return - the risk-free rate|/|the expected return - the risk-free rate|, where the target score is greater than or equal to 1.

A method of building a layered composite with a target scoring according to one embodiment is described below with reference to FIG 7 described. In a first step, the user creates a population to invest in by identifying a universe of investments (7005). For example, the population could be financial and energy companies in the United States. Next, the securities universe is filtered (7015). Then the population of firms is stratified (7020). Through this process, they are based on common functio nal or syntactic markers, values or attributes set in layering units or groupings.

After population stratification or segmentation, the metrics used to assess the portfolio are identified. The metrics used may depend on the population being stratified. For example, the metrics used for a portfolio of investment grade bonds may be expected return and expected volatility, while the metrics used for an equity portfolio are expected risk and expected reward. After the metrics are identified, a target score may be established (7010). The target score is the target that the user wants the portfolio to achieve, with the target being measured by the identified metrics. For example, the target score of a portfolio of investment grade bonds may be an expected return and an expected volatility that an investor desires the portfolio to achieve. Exemplary embodiments for target scoring are described below.

After the target score has been determined, a constructed composite portfolio can be created (7020). Composites can be combinations of two or more layering units that can be engineered to achieve the target score. Composites can be constructed by strategically weighting the layering units and the companies in the layering units (7025) and re-weighting the component companies (7030). The weighting and re-weighting process may involve changing the components of the population (adding or deleting from the portfolio components that meet the criteria of the population).

The composite can be checked against the target score (7035). If the target score is accepted, the process can reach its conclusion. If the target score is not met, some or all of various parameters including 1) the architecture rules, 2) the weighting rules, 3) the universe filtered and weighted by the structure, and 4) the rebalancing/rebuilding principles may be adjusted. The process can then be repeated until a portfolio with a satisfactory score is created.

A layered composite can be used to optimize a portfolio. As described above, a constructed composite can be built to meet a target score. Here, the target score can be regarded as the investment target. For example, the goal might be to build a composite whose yield, performance, variance, or other property, quality, or other characteristic matches what is outlined in the goal score.

Therefore, rather than constructing a portfolio that is most representative of the underlying population, a portfolio can be constructed that strategically weights the lower level groupings such that the portfolio will come closest to its target score. Here, stratifying or segmenting the portfolio and building composites allows for the identification of risk groups within a population. Weights can thus be strategically assigned to them to meet the target scoring.

When it comes to investment securities, investors' primary concerns are risk, expected return and liquidity. Therefore, in some embodiments, the target score may reflect the portfolio's investment objectives quantified in terms of the portfolio's risk, expected return, and liquidity characteristics. The goal in creating investment composites is to organize risk, reward and liquidity through the composite design and the weighting of the underlying components. The constructed investment composites can produce composite scores (calculated from combining individual security data affected by multiple attributes) that can reliably achieve theoretical estimates

Using the methods described herein, composites can be engineered to enhance their functional properties, which can be identified or designed for use in specific environments. When categorizing assets, composites can be formed to maintain composite scores. A layered or segmented composite can be used to achieve a target score. Stratification or segmentation allows identified risks to be grouped within a portfolio. Therefore, by creating a constructed portfolio that meets a target score, it is possible to better understand, qualitatively and quantitatively, risks to which the portfolio will be exposed.

Synthetic conglomerates

The techniques described in this document can be used, by way of non-limiting example, as a means of achieving a targeted point on the portfolio's risk-return-liquidity frontier through functional diversification. The data systems described in this document enable the synthesis of instruments that achieve diversification at the scale sought by conglomerate managers, holding companies or investors in private equity firms without incurring the high transaction costs associated with private markets transactions or significant operating expenses. In some embodiments, a synthetic conglomerate is an engineered composite that, by way of non-limiting example, can be configured to achieve a specific target score.

As a non-limiting example, the management of the synthetic conglomerate can be accomplished through the data systems described herein in real time, allowing for the dynamic aggregation of the financial reports of each of the components of large portfolios and the calculation and presentation of their consolidated balance sheets, income statements and cash flow statements. reports is permitted. The technologies described herein allow a tailored identification and choice of exposures within large-scale portfolios across functional, temporal and geographic space.

In some embodiments, the elaboration of earnings estimates and forecast financial reports at the portfolio, constructed composite, or synthetic conglomerate level allows for the creation of an actionable internal benchmark. This customized portfolio, constructed composite or synthetic conglomerate can be compared to these earnings estimates or financial reports to determine if they have met or exceeded projections. As a result, the tailored portfolio, constructed composite or synthetic conglomerate can be reliably compared to internal forecasts, rather than relying on external benchmarks as is necessary with other portfolios and indices.

In some embodiments, the data systems described in this document enable the generation of income streams, dividends, and cash flows at the portfolio level that are more stable, consistent, and predictable than those at the group level. In other embodiments, the flows at the group level will be more stable, consistent, and predictable than those at the security level. In other embodiments, flows at the portfolio level will be more stable, consistent, and predictable than those at the security level. In some embodiments, the engineered composite or synthetic conglomerate may be viewed as a benchmark that provides more consistent, stable, and predictable returns that match the risk and liquidity-adjusted return rates predicted by financial theory more reliably than other commercially available or widely used indices or benchmarks.

The portfolio chart

Based on an underlying functional syntax in conjunction with semantic tags and attributes, geographic and temporal data, and associated measures and metrics, a diagram of a heterogeneous population of securities and their associated functional attributes, tags, and/or values can be constructed. As a non-limiting example, a chart of data entities representing a population of investment securities or financial instruments is described below.

The assets or financial instruments are assigned nodes on the chart; as non-limiting examples, the data units may correspond to historical or current companies, sectors, products, securities, investments, credits, or components, aggregates, inputs, or outputs thereof.

The nodes are defined based on the relationships (delimited by edges) between the underlying economic entities including, but not limited to, those encoded in the functional syntax, geographic or temporal relationships, or those derived from close economic relationships in the framework including Supplier-Operation, Intermediary-Seller, Working Group-Department, Sector-Branch related. In some embodiments, the graph is a directed graph.

The nodes can be grouped visually based on proximity relationships obtained from the functional syntax, semantic markers and attributes, and associated metrics and metrics. As non-limiting examples, the relationships can be ordered and represented by spectral analysis, eigenvector clusters, and k-means clusters.

In some embodiments, the edges may be weighted or colored based on the degree of interdependence among the nodes or the categorical relationships they reflect; as non-limiting examples, this may be obtained from commercial, transactional, investment, or financial data among the entities and their economic reference points, the commonality of semantic marks or attributes, the proximity of geographic or temporal relationships, or from the underlying functional syntax.

In some embodiments, the nodes may be weighted or colored based on the size or scale of the entity or any of the categories associated with the reference data; as non-limiting examples, this may include commercial, transactional, investment, financial, or other capital markets or accounting-based data associated with the entity, semantic, syntactic, or functional tags or attributes associated with the entity , geographic or temporal data associated with the entity, or measures and indices associated with any of the foregoing.

The visual representation of the chart and its components may be derived from default preferences determined by the system, preferences expressed by one or more users, or a combination thereof.

The chart can be dynamically updated to reflect changes in relationships among entities, allowing a visual representation of an evolutionary model for a portfolio.

portfolio fields

The model of the system can be presented as a non-limiting example as a field on which mathematical operations can be carried out, which facilitates the study of the interactions between the economic entities or the issuers of the investment securities.

In some embodiments, a set of economic entities E and a structure for this set S consisting of subsets s _{1,2...n of} the economic entities can be stored. In some embodiments, the structure is a member of the power set of E, P(E). A set of attributes A = {a _1,2...n } can be assigned to these subsets, based on the set a set of values V = {v _1,2...n } such that every a ∈ A has a Assignment a:S → V _a . In other embodiments, the units may not be economical.

In some embodiments, the array will be an ordered tuple (E,S,A). In other embodiments, the field will be an ordered tuple (E,S,A,V). In some embodiments, the field will not be ordered. In some embodiments, the units can be combined to form expressions or ordered sets that can be evaluated by syntax. In other embodiments, the expressions will be one or more combinations of entities that lack order or that cannot be evaluated by syntax. In some embodiments, a portfolio, group, subgroup, layer, or segment may be identified as a field.

As non-limiting examples, the model of the system indicated by the field can be syntactic, semantic, visual, qualitative, or quantitative, or any combination thereof. As non-limiting examples, the array can be represented graphically, hierarchically, or in a clustered or meshed form. In some embodiments, the representation will satisfy the formal mathematical properties of a field. In other embodiments, the representation will not satisfy the formal mathematical properties of a field.

Investment returns on securities

beschrieben werden, wobei a_1,2...n die Attribute in einem gegebenen Zeitraum sind, die den Ertrag des Wertpapiers beeinflussen, w_1,2...n die Gewichte sind, die jedem dieser Attribute zugeteilt werden sollen, k eine Konstante ist, und n ein Satz von Gleichungen ist, der stochastische Komponenten modelliert.In some embodiments, for any given security s, its return r over time period t can be calculated as $$k{\displaystyle \int {\displaystyle \int {\displaystyle \int {ƒ}_j\left(a\right)\mathrm{i.e}w\mathrm{i.e}a\mathrm{i.e}t}}}+{\displaystyle \int n_m\left(t\right)\mathrm{i.e}t}$$

where a _{1,2...n are} the attributes in a given time period that affect the return of the security, w _{1,2...n are} the weights to be assigned to each of these attributes, k is a constant and n is a set of equations that models stochastic components.

0 sein; bei anderen Ausführungsformen wird es ungleich Null sein.In some embodiments, the model can be used in conjunction with the mathematical field representation to map the impact of the attributes on performance characteristics across groups of securities. In other embodiments, this yield formula can be used for predictive modeling, diagnostics, or recommendations. In some embodiments, $${\displaystyle \int n_m\left(t\right)\mathrm{i.e}t}$$

be 0; in other embodiments it will be non-zero.

Investment statistics for stratified composite portfolios

A portfolio generated according to the methods described herein may be scored using modified versions of known statistical indicators including, as non-limiting examples, alpha, beta, and Sharpe and Sortino ratios. A score can be generated based on a normative layered or composite model portfolio and changes to the normative portfolio. For example, a stratified or segmented alpha can be calculated as a risk-adjusted premium for a score on a normative portfolio. A stratified or segmented beta with respect to a normalized market can also be calculated for a stratified or segmented portfolio where the normalized market is defined as having a beta of 1.

In some embodiments, normative stratified or segmented betas may be related to any market portfolio, e.g. B. as non-limiting examples, stratified or segmented composite portfolios of the whole market or a subset thereof can be calculated. For example, as non-limiting examples, the contextual subset could be defined as a sector, industry, geographic area, time period, dictionary term, or lexicon term.

Financial recommendation engine

The process described herein can be used to recommend stocks, composites and portfolios to users. These recommendations are driven by the securities and the syntactical and empirical relationships of their economic entity; the functional, syntactic, semantic, temporal, geographic, financial, or economic markers, attributes, and values assigned to economic entities; the expressed and disclosed preferences of users of the database or software; and the relationships of the users in the network.

The relationships embodied in the syntactic tags, attributes, and values assigned to economic entities allow, by way of non-limiting example, an initial standard calculation of proximity among them. In some embodiments, entities that share common or proximate values or attributes associated with multiple markers, loci, or partial or complete sequences thereof may be proximate within one or more databases used to provide recommendations while Entities with multiple different or differing values or attributes associated with multiple markers, loci, or partial or full sequences thereof may be distinct within those databases.

The proximity can also be stored from the empirical relationships among the securities and economic entities that are aggregated and attributed to the data entities and their reference points can be obtained. In some embodiments, these may be [Supplier - Customer], [Investor - Entrepreneur], [Impact Investor - Social Enterprise], [Intermediary - Customer], [Customer - Customer's Customer], [Lender - Borrower], [Input - Output] , [employer - employee], [company - department], [general partner - limited partner], [service provider - client], [department - working group], [object - activity - direct object - indirect object], [parent company - subsidiary], [ Raw material - basic component], [basic component - complex component] or [complex component - end product].

These empirical relationships can be weighted, scored, time stamped, or geotagged and stored in one or more databases as the basis for proximity calculations. In some embodiments, economic entities that share numerous recent or high-weight relationships with each other or with a common third party will be close to each other in one or more databases used to provide recommendations, while economic entities with no common relationships, or their Relationships that are purely historical will be distant from each other in these databases.

Proximity relationships can also be obtained from non-syntactic proprietary tags, attributes, and values assigned to economic entities and securities; as non-limiting examples, these markers can be functional or semantic. In some embodiments, these tags, attributes, and values may be [raw material], [basic component], [complex component], [end product], [information output], [middleman], [department], [workgroup], [customer], [secondary customer] , [Customer's Customer], [Procurement], [Transportation], [Storage], [Design], [Manufacture], [Quality Control], [Sales], [Exchange], [Banking], [Investment Design], [Management] , [auditing], [capital], [energy], [information], [land], [tools] or [labour].

In addition, proximity relationships can be obtained from the non-proprietary, commonly available markers, attributes, and values assigned to the economic entities. As non-limiting examples, these marks, attributes and values may include [asset class], [listing], [earnings], [duration], [convexity], [date of incorporation], [location of headquarters], [location of incorporation], [market capitalization] , [Income], [Expenses], [Net Income], [Cash Flow from Operations], [Cash Flow from Financing] or [Cash Flow from Investments].

These markers, attributes, and values can be weighted, scored, time-stamped, or geotagged and stored in one or more databases as the basis for proximity calculations. In some embodiments, economic entities or securities that currently share numerous identical or similar markers, attributes, and values will be close to one another in one or more databases used to provide recommendations, while economic entities with few common markers therein Databases will be remote from each other.

The relationships, tags, attributes, and/or values obtained from one or more securities and economic entity databases allow for a standard calculation of proximity to a user. User preferences, the user's current shares, and network location make it easy to tailor financial recommendations to users based on dynamic proximity calculations.

In some embodiments, users may enter their expressed preferences, whether syntactical, functional, non-syntactical, non-functional, or any combination thereof, and associated values upon registration to gain access to the database into the system. In other embodiments, these preferences, or filters, may be entered or modified at any time either through a separate module or by specifying a preference for or against data entities associated with securities and economic entities. The filters can be absolute in that they will allow the user to exclude or include certain relationships, attributes, labels or values, or they can be relative in that they will allow the user to determine the degree of preference for or against certain relationships, attributes , marks or values to specify.

As non-limiting examples, these filters may allow the user to specify absolute or relative preferences for [market capitalization], [asset class], [asset allocation], [fund], [expected return], [risk], [geography], [supplier] [investor ], [Customer], [Lender-Borrower], [Issuer-Investor], [1.1], [1.2], [1.3], [2.1], [2.2], [2.3], [3.1], [3.2], [3.3], [4.1], [4.2], [4.3], [.1], [.2], [.3], [A], [B], [C], [D], [E] , [F], [1i], [1ii], [1iii], [2i], [2ii], [2iii], [3i], [3ii], [3iii], [4i], [4ii], [4iii], [Portfolio ], [composite], [layered structure], or one or more of any of the other relationships, labels, attributes, or values associated with the securities and economic entities.

Users can also reveal their preferences through their interactions with the data entities in the system. In some embodiments, preferences are revealed by tracking user accounts, monitoring mouse clicks, screen time, portfolio construction and/or transactions executed, and using a machine learning process to improve recommendations tailored to a user based on their preferences. In some embodiments, users can upload their portfolio to the system, whose components can also be used to drive tailored recommendations.

Network position can facilitate proximity calculations and dynamic tailored recommendations. The system can track connections between users and the extent of their interactions. In some embodiments, strong connections between users in the system will cause their recommendations to converge significantly, while weak connections will cause recommendations to converge slightly, and numerous degrees of separation will cause recommendations to diverge.

In some embodiments, similarities between users may cause the recommendations provided to them to converge, while differences between users will cause those recommendations to diverge. As a non-limiting example, the system may use machine learning techniques to dynamically improve the quality of the tailored recommendations based on changes in the network of users, their preferences, or the markings, attributes, values, or relationships assigned to the securities or economic entities .

The data analysis

The systems syntax described herein is well suited to representing very large data sets associated with domains that can be effectively explored through functional models including, as non-limiting examples, biology, physics, ecology, economics, computational science, genomics, bioinformatics, aeronautics, telecommunications, electrodynamics, space, finance, investment management, healthcare, medicine, epidemiology, chemistry, geology, transportation, mechanical engineering, legal systems, regulatory systems, legislative systems , organize and analyze political systems and economic development. As non-limiting examples, data sets describing these complex systems may be hundreds of terabytes or petabytes in size, contain hundreds of thousands or millions of elements, and contain thousands of variables that significantly affect the characteristics or particularities of these systems. Analysis of these complex systems is impracticable without an underlying functional model and advanced custom data systems.

The attribution of tags, meta-tags, attributes, and values derived from an underlying relational model of activities and resources in complex systems facilitates the development of real-time tools to enable diagnostics, tailored recommendations, and predictive analysis, enabling dynamic responses to rapidly changing events are allowed. These real-time tools can be used during periods when the system is chaotic or far from equilibrium, and which may include, as non-limiting examples, disturbances, shocks, natural disasters, bubbles, panics, mania, or crashes, periods when mechanical models of systems and standard -Database tools likely to prove ineffective or even harmful will be critical. As non-limiting examples, the tags, metatags, or attributes may be syntactic, semantic, morphosyntactic, morphological, physiological, anatomical, geographic, temporal, or demographic. In some embodiments, the assignment or identification of one or more markers, one or more attributes, or one or more values, or the proximity or similarity of markers, attributes, or values may imply the assignment, identification, prediction, or recommendation of one or more others Facilitate markers, one or more other attributes, or one or more other values. In other embodiments, the use of diagram structures or network models facilitates the development of these analytical tools.

Normative cases for stratified portfolios

Using the systems and methods described in this document, a normative layered or segmented portfolio can be defined. Layered or segmented units can be used as a tool for building normative models and developing normative target scores. Reliable and validated categories of investment securities can be used to subdivide populations of securities to validate normative studies. The user can develop normative scores to test a hypothesis and validate a baseline for use in comparative study of other stratified or segmented portfolios. The system can be configured so that a normative stratified portfolio can be used to arrive at a target score. A target score for a stratified portfolio, such as a target alpha score, may be defined in terms of a normative target score of a baseline.

A variety of statistical properties can be examined using empirical or simulated data on a stratified or segmented portfolio, group, or subgroup. As non-limiting examples, a statistical property may be chosen from the mean, variance, standard deviation, skewness, kurtosis, correlation, half-variance and half-deviation, or excess, or remainder of any of these.

Statistical tests may be used to determine that securities associated with attribute-defined functional groupings of firms, commodities, securities, funds, assets, loans, or liabilities: (a) exhibit higher intra-group correlation than inter-group correlation; b) this correlation is more consistent and predictive over time than covariance in quantified groups; c) those groupings can be stratified or segmented in a portfolio to target or manage specific exposures to volatility, variance or non-systematic risk; d) the variance, standard deviation, half-variance and/or half-variance at the portfolio level is lower than at the group level and is lower at the group level than at the security level; e) they show more normally distributed expected or actual values for a given performance measure than an alternative grouping, index or portfolio; or f) this methodology increases the predictability of outcomes and the extent to which returns from large portfolios consistently achieve those theoretically predicted.

In some embodiments, the performance metrics may be performance, volatility, liquidity, variance, expected return, alpha, Jensen alpha, beta, variance, covariance, half variance, half variance, correlation, autocorrelation , Sharpe ratio, Sortino ratio, revenue, expenditure, operating expenses, profit, net profit, gross profit, income, gross income, net income, cash flow, cash flow from operations , the cash flow from investments or the cash flow from financing. As non-limiting examples, normality can be determined using the Cramer-von-Mises criterion, the Kolmogorov-Smirnov test, the Shapiro-Wilk test, the Anderson-Darling test, the Jarque-Bera test, the Siegel-Tukey tests, the Kuiper test, the p-value test, a QQ chart, a skewness test, or a kurtosis test.

As non-limiting examples, the statistical methodology allows for the construction of large and mid-cap equity portfolios that yield a consistent risk premium over the bond over long periods of time, as predicted by the Capital Asset Pricing Model, and allows the development of indices that track rates of the Realize risk- and liquidity-adjusted returns that achieve the market performance posited by financial theory more predictably than indices such as the S&P 500™, which are often used as market proxies.

In a first step, one or more theoretical or estimated scores can be defined. Using adjustments based on changes made to at least one of the following - 1) changes in the population of the investment securities; 2) the stratification or segmentation methodology applied to the population of investment securities; and 3) the weighting applied to the stratified units or segments - are made, the portfolio can be constructed to 1) produce a representative result for a given population (referred to herein as the normative case); 2) constructing a result that is statistically aligned in a user-specified direction.

Depending on the customization methodology, the targeting may be toward a population subset, such as a geographic or temporal group, or a particular class of functional attributes (or a subset of an attribute class) within a particular population set of securities. Within a layered or segmented architecture for a given population, a particular exposure (or lack thereof) may be determined by the structure itself (either structural or attribute choice) or the weight assigned to particular units, groups, attributes, clusters, or segments , to get managed.

Non-normative composites are composites designed to differ from the normative case. The deviation from the normative case can be presented as a constructed or algorithmic portfolio performance indicator, e.g. B. Alpha, can be viewed. Using the invention, negative variance can be constructed as alpha for short-term investment positions. Constructing positive variance can be constructed as alpha for long-term investment positions. For example, distributions can be normal (based on the normative case) or non-normal. Nonnormal distributions can be positively skewed (to the right of normal), negatively skewed (to the left of normal), platykurtic (thick-tailed), or leptokurtic (thin-tailed). Adjustments to the weights as described above can be used to create portfolios with these types of distributions.

In some embodiments, groupings may be used to determine pairwise correlation coefficients for use in Markowitz mean-variance optimization. Instead of using a single correlation value for all pairs of firms, the method can be used to assign correlations to any segment created by this methodology by, as a non-limiting example, taking the average observed residual pairwise correlation of all segments in a stratum, as well as used as a measure of out-of-group correlations.

As a non-limiting example, estimating a correlation value for each segment in the third layer of a stratified heterogeneous portfolio or index of 900 securities and assigning the relevant value to each component of a group may reduce the number of correlations needed to obtain pairwise correlations estimate by a factor of more than 200, making it easier to construct an index or portfolio that will approach the efficient frontier more consistently and predictably than other portfolio or index construction techniques.

Data set normalization and probability shaping

Mathematical processing according to the methods described herein can be applied to large sets of economic and financial data to reduce the variability and randomness of the results including, by way of non-limiting example, those of investment returns. In some embodiments, they include multi-dimensional algorithms that can be used to organize larger data sets. The methods can be used to create or identify causal links and perform real-time analysis.

The system can be configured to normalize the records representing securities. The normalization process involves statistical categorization based on attributes of the entity associated with the security. The attributes used for normalization may be of the types of attributes described above or may be other attributes associated, as non-limiting examples, with the activities, assets, suppliers, customers, customers of customers, departments or employees of the with related to the entity related to the security.

Multiple investments can be organized into statistical categories. A user interface for selecting among attributes may be provided by the system, which may include a statistical category editor (called a dictionary editor in some embodiments). The statistical categories can be defined using the editor in the system. A statistical category can be defined to be any one or more of the attributes described above, used alone or in combination. The statistical categories can also be defined based on the syntax and coding systems described above. In some cases, a statistical category can also be a stratified or segmented unit.

portfolio strengthening

The adjustment or stratification also improves statistical power, particularly when the adjustment, segmentation or stratification is based on important prognostic variables. Such approaches, accompanied by predetermined stratified or segmented analysis and sensitivity analysis, can therefore be useful.

A future analysis can be used to determine a sample size needed to achieve a target statistical power. In general, the most important component affecting statistical power is the sample size, in the sense that the most frequently asked question in practice is how many observations to collect. As a non-limiting example, when assessing portfolio performance, the null hypothesis could be that a stratified grouping has a Sharpe ratio of 1. The alternative hypothesis could be that a stratified grouping has a Sharpe ratio other than 1.

Power refers to the probability that a test will find a statistically significant difference when such a difference actually exists. Power is the probability that the null hypothesis will be correctly rejected. In some embodiments, the power should be 0.8 or greater such that there is an 80% or greater probability of finding a statistically significant difference if one exists.

Example of bankruptcy

The following example illustrates a use case for a composite of fixed assets. This example creates a stratified or segmented portfolio of investment grade corporate bonds.

Investment grade bonds are a specific class of securities with a well-defined expected return and risk. Each bond is rated by a rating agency, which is a third party. This rating captures the estimated probability that the bond issuer will default on its debt. In the case of default risk, one of the most relevant risks when investing in such securities, corporate bonds of the same rating should have similar yields to maturity, with other variables such as maturity held constant. Yield to Maturity is the compound annual yield that the bondholder will earn by holding the bond to maturity given its current price, assuming all payments (coupon payments and par) are made as expected. In other words, the Yield to Maturity is the discount rate that makes the present value of the bond's future cash flows, assuming all payments are made, equal to the bond's current price. For all bonds that have a comparable rating from these agencies, the predicted returns for a given maturity date will be the same or within a very narrow range. That said, investment grade corporate bonds behave predictably.

1. 1) Funktionale betriebs- oder vermögenswertbasierte Attribute: Diese Attribute sind keine buchhalterischen oder Leistungsmaße und Indikatoren, sondern vielmehr, als nicht beschränkende Beispiele, Attribute, die definieren, was eine Firma tut, wie etwa Herstellung oder Transport; Attribute oder Markierungen im Zusammenhang mit dem Produkt der Firma, wie etwa Kraftfahrzeuge, Computer oder Sofas wie auch der Art des Kraftfahrzeug, des Computers oder des Sofas; Attribute im Zusammenhang mit den Kunden einer Firma wie etwa Verbraucher oder Unternehmen; Attribute im Zusammenhang mit den Kunden der Kunden; Attribute im Zusammenhang mit dem geographischen Standort eines Unternehmens oder seiner einzelnen Tätigkeiten; Attribute im Zusammenhang mit den Produkten und Materialien, die eine Firma verwendet, um ihr Produkt bereitzustellen; Attribute im Zusammenhang mit einer beliebigen der mehrdimensionalen Branchen oder Branchensegmente, in denen eine Firma tätig sein kann; Attribute im Zusammenhang mit der Struktur der Geschäfte einer Firma wie etwa integriert, nicht integriert, vorwärts integriert, rückwärts integriert oder vernetzt; Attribute im Zusammenhang mit einem beliebigen der mehrdimensionalen staatlichen oder makroökonomischen Risiken, die mit einem bestimmten Geschäft oder Land, in dem eine Firma geschäftlich tätig ist, verbunden sind; Attribute, die mit der Buchhaltung oder mit Geschäftsrisiken, die von einer Firma als Kern für ihr Geschäft aufgelistet sind, verbunden sind; Risiken, die mit der Kategorisierung, die durch die Investorengemeinschaft an ein bestimmtes Geschäft oder Segment gebunden ist, verbunden sind. Zu jedem beliebigen Zeitpunkt kann jeder beliebige dieser Faktoren oder können Branchenereignisse, die mit diesen Faktoren in Zusammenhang stehen, das Risiko eines Konkurses bei irgendeiner bestimmten Firma verursachen oder erhöhen.
2. 2) Die Führung oder die Strategie: Eine Firma weist auf Basis ihres Führungsteams, ihrer Entscheidungen und ihrer Strategien einzigartige Risiken auf.
3. 3) Firmenvermögenswerte: Ein Konkurs (der eine Art des Ausfalls ist), ändert die Bedingungen der Wertpapiere, die von einer einzelnen Firma ausgegeben wurden, grundlegend. Mit der Anmeldung verändert sich die Vermutung von Erträgen auf Basis laufender Betriebe so, dass sie ein Liquidationsszenario und die Analyse der Rechte jedes Wertpapierhalters enthält. In diesem Fall bewerten Investoren ihre Fähigkeit, eine Zahlung für ein gegebenes Wertpapier zu erhalten, auf Basis seines Standorts in der Kapitalstruktur des Unternehmens. Wertpapieren kann eine Priorität bei der Liquidierung zugeteilt sein. Wenn ein zugrundeliegender Vermögenswert der Firma verkauft oder veräußert wird, bestimmen diese Liquidationsprioritäten die Vorrangigkeit.
4. 4) Finanzielle Hebelwirkung: Einige Firmen sind mehr oder weniger hebelfinanziert als andere Firmen.

Although different investment grade debt securities may have the same estimated probability of default, the event or events that trigger a default may differ from issuer to issuer. That is, companies may face different risk factors related to the particular set of attributes associated with the company and its operations. Some of these factors may be unique to that company, while other factors may be common to groups of companies. Such risks may include, as non-limiting examples, industry risk, product risk, customer risk, interest rate sensitivity, geographic, political or economic factors beyond a company's control, or risks associated with the company's director or management generally. There are many company-specific attributes that can be linked to a company's risk of default. These may include, but are not limited to, the following.

1. 1) Functional operational or asset-based attributes: These attributes are not accounting or performance measures and indicators, but rather, as non-limiting examples, attributes that define what a company does, such as manufacturing or transportation; Attributes or marks associated with the company's product, such as automobiles, computers, or sofas, as well as the type of automobile, computer, or sofa; Attributes related to a company's customers, such as consumers or businesses; Attributes related to customers'customers; Attributes related to a company's geographic location mens or its individual activities; Attributes related to the products and materials a company uses to provide its product; Attributes related to any of the multidimensional industries or industry segments in which a firm may operate; attributes related to the structure of a firm's businesses, such as integrated, unintegrated, forward integrated, backward integrated, or networked; Attributes related to any of the multidimensional sovereign or macroeconomic risks associated with a particular business or country in which a firm does business; Attributes associated with accounting or business risks listed by a firm as core to its business; Risks associated with categorization tied to a particular business or segment by the investment community. At any given time, any of these factors, or industry events related to these factors, may cause or increase the risk of bankruptcy for any particular company.
2. 2) The leadership or the strategy: A company presents unique risks based on its leadership team, its decisions and its strategies.
3. 3) Company Assets: Bankruptcy (which is a type of default) fundamentally changes the terms of the securities issued by an individual company. With the filing, the assumption of income based on current operations changes to include a liquidation scenario and the analysis of the rights of each security holder. In this case, investors evaluate their ability to receive payment for a given security based on its location in the company's capital structure. Securities may be given priority in liquidation. If any of the Company's underlying assets are sold or disposed of, these liquidation priorities determine precedence.
4. 4) Financial Leverage: Some companies are more or less leveraged than other companies.

Each attribute is a potential source of default or bankruptcy risk for a bond investor. Some of these attributes may apply to groups of companies (e.g., companies that manufacture automobiles or companies that have operations located in New Orleans). Therefore, a portfolio that does not manage certain attributes may inadvertently be exposed to a concentration at a particular risk. If one member of a group fails or files for bankruptcy, other companies in that group can also be affected.

The invention includes methods for constructing a stratified or segmented composite portfolio of investment grade corporate bonds in a manner that will limit exposure to bankruptcy threats, corporate events and other such non-systematic risk factors by managing the portfolio's exposure to any particular company or industry. In cap-weighted bond portfolios, securities are weighted in proportion to their issuance size relative to the total size of all issues in the portfolio. With such an unmanaged weighting scheme, it is possible for companies or industries that issue large amounts of bonds to become overweight in the portfolio. If any of these companies or industries experience an adverse event such as bankruptcy, the portfolio itself will be affected dramatically. A stratified or segmented composite portfolio is a tool to cap the financial impact of risk related to attributes.

Applying the invention to managing the risk of default on a portfolio of investment grade corporate bonds provides an illustration of one embodiment. Each debt security has a level of risk that is directly related to the value in liquidation of the company's underlying assets. The risk is clearly separated from financial market risks associated with the supply and demand for the debt security itself, as well as financial market factors that can affect the rate of return required for a given investment security at a given time, such as the risk-free interest rate at that time .

The systems described herein protect against such non-systematic risks across the portfolio, that is, they can reduce or eliminate the material impact of an individual security or a group of securities. This can be achieved by organizing companies into groups (layers or segments) based on non-systematic attributes, e.g. B. by grouping together companies with similar products or similar customer bases. In some cases, stratification or segmentation ensures that no single non-systematic impact poses material risk to the portfolio as a whole. In such a composite, the bankruptcy exposi tion spread across enough unique groups to minimize the impact of bankruptcies in any group or firm.

As a non-limiting example, the invention can be used to create layers or segments as follows. For investment grade bonds, there may be several types of reasons for a downgrade or bankruptcy, including, as non-limiting examples, 1) firm involvement; 2) an industry specific impact; and 3) may include product specific exposure. Investment grade bonds with a given rating should, in theory, ex ante have an equal probability of being at risk of downgrade or bankruptcy, but this rating does not provide information as to the likely reasons for bankruptcy. Indeed, the factors that can cause issuer default can be radically different for bonds of the same rating. However, these bankruptcy factors are directly linked to the functional attributes of the issuing firm. Using these attributes, it is possible to group bonds into risk groups based on characteristics of their issuers that are related to issuer bankruptcy factors. This process can be repeated to form a nested architecture of groups, where each subgroup has its own risk, but also has risks associated with the parent group. It can also be repeated to form diagrams or networks of segmented groups where each segment and sub-segment share common risks. These risk groups are then the layers or segments that can be used to build a layered or segmented composite portfolio. These processes reduce or mitigate the likelihood that an adverse event, either in an individual company or in an industry, will have a significant impact on the portfolio.

Industry risk example

The example below illustrates an additional use case for stratified or segmented composites of investment securities. In this example, a composite of stocks from the S&P 900® index is created. This composite is a broad-based index that includes large and mid-cap stocks issued by US-based companies across a variety of industries. This universe is a combination of the S&P 500® and S&P MidCap 400® indices, which track large and mid-cap US companies, respectively. Over time, such a universe of stocks should show a consistent premium relative to a relatively risk-free investment such as US Treasury bills.

In this example, the returns of the cap-weighted S&P 900® are compared to the returns of the same universe of securities constructed as a layered composite constructed using the method of the invention. Attributes pertaining to the functional characteristics of these 900 companies are used to create nested layers that group functionally similar companies together. These layers are used to determine weights for each security according to the methods described herein. The portfolio is rebalanced quarterly, with each security restored to its original weight.

Stratification and segmentation provide material benefits in environments where certain industries experience large negative price shocks, colloquially referred to as "bursting" an industry bubble. As an industry bubble grows, the market capitalization of companies in the industry increases, increasing that industry's weight in the cap-weighted portfolio. In cap-weighted funds that lack attribute-based control over the weights of both individual companies and groups of similar companies, such bubbles can create unintended overexposure to certain risk groups, including those disproportionately affecting a particular industry. If the overweight industry bubble bursts, the portfolio will suffer disproportionately. Even if companies outside of the industry bubble are performing reasonably well, the negative returns of the overweight companies can result in negative returns for the portfolio as a whole.

However, for layered composite portfolios, the risk of industry bubbles can be significantly mitigated by layering the universe in such a way that the layers correspond to distinct industry risks. In this way, industry-specific risks are isolated and cannot incur a disproportionately negative performance in the portfolio.

The rise and fall of information technology stocks from 1997 to 2000 illustrates the benefits of layered composite portfolios. Using functional attributes, a Group of companies whose business function involves moving, storing or processing information. Companies in this group include Microsoft, Cisco, Intel, AOL, Qualcomm and other such information technology companies.

The weighting of the top twenty such information technology stocks in the S&P 900® grew through the late 1990s to dominate the portfolio by 2000. At year-end 1997, 1998, and 1999, these twenty stocks collectively represented 11.8%, 13.7%, and 20.4% of the S&P 900@, respectively. In 2000, when the bubble burst, these stocks fell 42.3% in value, while the S&P 900® as a whole returned -6.9%. Excluding these information firms, the rest of the S&P 900® returned 6.8%. That is, the "market-wide" downturn in 2000 was not a systematic failure, but was the result of uncontrolled interference by a single industry.

Such industry-specific risk can be managed in a layered composite portfolio. In the example stratified composite portfolio, the same twenty information firms were weighted at 2.9% and rebalanced to that weight quarterly. In 2000, this isolated group performed poorly (down 59.7% in value), but the exemplary layered composite portfolio outside of this group had good returns. Excluding these twenty companies, the exemplary layered composite portfolio returned 21.3%. Overall, the exemplary stratified composite portfolio returned 17.6% in 2000, outperforming the cap-weighted portfolio of the exact same universe by 24.5%.

The performance of the cap-weighted S&P 900® compared to the exemplary stratified composite portfolio of the same universe shows how stratification can prevent non-systematic industry risk from affecting an entire portfolio.

The system architecture

The systems and methods described in this document may be implemented in software or hardware, or any combination thereof. The systems and methods described herein may be implemented using one or more computing devices, which may or may not be physically or logically separate from one another. The methods may be performed by components arranged as either on-premises hardware, on-premises virtual systems, or hosted private instances. In addition, various aspects of the methods described herein may be combined or merged into other functions.

An exemplary logical implementation of the system is in 10 shown. The relationships between tables (1005, 1010, 1015, 1020, 1025 and 1030) are represented by arrows. As illustrated, table 1010 serves as a link table between the company table (1005) and the barcode table (1015).

An exemplary computerized system for implementing the invention is in 11 illustrated. A processor or computer system may be configured to specifically perform some or all of the method described herein. In some embodiments, the method may be partially or fully automated by one or more computers or processors. The invention can be implemented using any combination of hardware, firmware, and/or software. The present invention (or any part(s) or function(s) thereof) may be implemented using hardware, software, firmware, or a combination thereof, and may be implemented in one or more computer systems or other processing systems. In some embodiments, the illustrated system elements could be combined into a single hardware device or separated into multiple hardware devices. If multiple hardware devices are used, the hardware devices could be physically close to or distant from each other. The described and illustrated embodiments of the methods are intended to be illustrative and not restrictive. For example, some or all of the steps of the methods may be combined, rearranged, and/or omitted in different embodiments.

In an exemplary embodiment, the invention may be directed to one or more computer systems capable of performing the functionality described herein. Exemplary computer systems include, but are not limited to, a personal computer (PC) system running any operating system, such as, but not limited to, Microsoft™ Windows™. The invention however, may not be limited to these platforms. Rather, the invention may be implemented on any suitable computer system running any suitable operating system. Other components of the invention such as, but not limited to, a computing device, a communications device, a cellular phone, a telephony device, a telephone, a personal digital assistant (PDA), a personal computer (PC), a handheld PC, an interactive television (iTV), a digital video recorder (DVD), client workstations, thin clients, thick clients, proxy servers, network communication servers, remote access devices, client computers, server computers, routers, web servers, data, media, audio, video, telephony - or streaming technology servers etc. can also be implemented using a computing device. The Services may be provided upon request, e.g. B., but not limited to, using an interactive television (iTV), a video-on-demand (VOD) system and via a digital video recorder (DVR) or other on-demand display system.

The system can have one or more processors. The processor(s) may be connected to a communications infrastructure such as, but not limited to, a communications bus, crossbar or network, and so forth. The processes and processors do not need to be in the same physical locations. In other words, processes can be executed on one or more geographically distant processors over a LAN or WAN connection, for example. The computing devices may have a display interface that may pass graphics, text, and other data from the communications infrastructure for display on a display unit.

The computer system may also include, but is not limited to, main memory, random access memory (RAM), and secondary memory, and so on. The secondary storage may include, for example, a hard disk drive and/or a removable storage drive such as a compact disk drive, CD-ROM, etc. The removable storage drive can read from and/or write to a removable storage device. As can be appreciated, the removable storage device may comprise a computer-usable storage medium on which computer software and/or data is stored. In some embodiments, a machine-accessible medium may refer to any storage device used to store data that is accessible by a computer. Examples of a machine-accessible medium can e.g. B., but not limited to, a magnetic hard drive; a floppy disk; an optical disk such as a compact disk read only memory (CD-ROM) or a digital versatile disk (DVD), a magnetic tape; and/or a memory chip, etc.

The processor may also include, or be operatively coupled to communicate with, one or more data storage devices for storing data. Such data storage devices may include, as non-limiting examples, magnetic disks (including internal hard disks and removable disks), magneto-optical disks, optical disks, read-only memory, random access memory, and/or flash memory. Storage devices suitable for tangible embodiment of computer program instructions and data may also include all forms of non-volatile memory, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supported by or incorporated into ASICs (Application Specific Integrated Circuits).

The processing system can be in communication with a computerized data storage system. The data storage system may include a non-relational or relational data store, such as a MySQL™ or other relational database. Other physical and logical database types could be used. The data store can be a database server such as a Microsoft SQL Server™, Oracle™, IBD DB2™, SQLITE™ or any other database software, rational or not. The data store may store the information identifying syntactic tags and any other information required to operate on syntactic tags. In some embodiments, the processing system may use object-oriented programming and store data in objects. In these embodiments, the processing system may use an object-rational mapper (ORM) to store the data objects in a relational database. The systems and methods described in this document can be implemented using any number of physical data models. In an exemplary embodiment, an RDBMS may be used. In these embodiments, tables in the RBDMS may have columns representing coordinates. In the case of economic syste Data representing shapes, products, etc. can be stored in tables in the RBDMS. The tables can have predefined relationships with one another. The tables may also have supplements associated with the coordinates.

In alternative exemplary embodiments, secondary storage may include other similar devices to allow computer programs or other instructions to be loaded into the computer system. Such devices may include, for example, a removable storage unit and an interface. Examples may include a program cartridge and cartridge interface (such as those found in, for example but not limited to, video game machines), a removable memory chip (such as, for example but not limited to, an erasable programmable read only memory ( EPROM) or programmable read only memory (PROM) and associated slot, and other removable storage devices and interfaces that allow software and data to be transferred from the removable storage device to the computer system.

The computing device may also include an input device such as, but not limited to, a mouse or other pointing device such as a digitizer, and a keyboard or other data input device (not shown). The computing device may also include output devices such as, but not limited to, a display, and a display interface. The computer may have input/output (I/O) devices such as, but not limited to, a communication interface, cable and communication paths, and so on. These devices may include, but are not limited to, a network interface card and modems. The communication interface can allow software and data to be transferred between the computer system and external devices.

In one or more embodiments, the present embodiments are practiced in a computer network or networks environment. The network may comprise a private network or a public network (e.g. the Internet as described below) or a combination of both. The network comprises hardware, software, or a combination of both.

From a telecommunications oriented point of view, the network can be described as a set of hardware nodes interconnected by a communication facility, each such node having one or more processes (hardware, software or a combination thereof) operating. The processes can communicate and exchange information with each other via communication paths between them using inter-process communication paths. Appropriate communication protocols are used on these paths.

An exemplary computing and/or telecommunications network environment consistent with present embodiments may include nodes that may include hardware, software, or a combination of hardware and software. The nodes may be interconnected via a communications network. Each node may have one or more processes executable by processors resident in the nodes. For example, a single process can be executed by multiple processors, or multiple processes can be executed by a single processor. In addition, each of the nodes can provide an interface point between the network and the outside world and can accommodate a number of sub-networks.

In an example embodiment, the processes may communicate with each other through inter-process communication paths that support communication through any communication protocol. The paths can work sequentially or in parallel, continuously or intermittently. The paths may use any of the communication standards, protocols, or technologies described in this document in relation to a communication network, in addition to standard parallel instruction sets used by many computers.

The nodes can include any entity capable of performing processing functions. Examples of such nodes that can be used with the embodiments include computers (such as personal computers, workstations, servers, or mainframes), wireless and wired handheld devices (such as personal digital assistants (PDAs), modem cellular phones with processing capability, wireless email devices including BlackBerry™ devices), document processing devices (such as scanners, printers, facsimile machines, or multifunction document devices), or complex devices (such as local area networks or wide area networks). which is connected to a number of processors as described. For example, in the context of the present invention, a node itself may be a wide area network (WAN), a local area network (LAN), a private network (such as a Virtual Private Network (VPN)), or an internetwork.

Communications between the nodes can be facilitated by a communication network. A node can be connected to the communications network either continuously or intermittently. As an example, a communication network in the context of the present invention can be a digital communication infrastructure that offers adequate bandwidth and information security.

The communication network may have wired communication capability, wireless communication capability, or a combination of both at any frequencies using any type of standard, protocol, or technology. Also, in the present embodiments, the communication network may be a private network (e.g., a VPN) or a public network (e.g., the Internet).

A non-inclusive list of example wireless protocols and technologies used by a communications network may include BlueTooth™, General Packet Radio Service (GPRS), Cellular Digital Packed Data (CDPD), Mobile Solutions Platform (MSP), Multimedia Messaging (MMS) , Wireless Application Protocol (WAP), Code Division Multiple Access (CDMA), Short Message Service (SMS), Wireless Markup Language (WML), Handheld Device Markup Language (HDML), Binary Runtime Environment for Wireless (BREW), Radio Access Network (RAN) and Packet Switched Core Networks (PS-CN). Also included are wireless technologies of different generations. An exemplary non-inclusive list of primarily wired protocols and technologies used by a communications network includes Asynchronous Transfer Mode (ATM), Enhanced Interior Gateway Routing Protocol (EIGRP), Frame Relay (FR), High-Level Data Link Control (HDLC ), Internet Control Message Protocol (ICMP), Interior Gateway Routing Protocol (IGRP), Internetwork Packet Exchange (IPX), ISDN, Point-to-Point Protocol (PPP), Transmission Control Protocol/Internet Protocol (TCP/IP), routing Information Protocol (RIP) and User Datagram Protocol (UDP). As those skilled in the art will recognize, any other known or anticipated wireless or wired protocols and technologies may be used.

Embodiments of the present invention may include apparatus for performing the acts in this document. A device may be specifically constructed for the desired purposes, or may comprise a general purpose device that is selectively activated or reconfigured by a program stored in the device.

In one or more embodiments, the present embodiments are embodied by machine-executable instructions. The instructions may cause a processing device, such as a general purpose or special purpose processor, programmed with the instructions to perform the steps of the present invention. Alternatively, the steps of the present invention may be performed by specific hardware components having hardwired logic for performing the steps, or by any combination of programmed computer components and custom hardware components. For example, the present invention may be provided as a computer program product as outlined above. In this environment, embodiments may include a machine-readable medium on which instructions are stored. The instructions may be used to program any processor or processors (or other electronic device) to perform a process or method consistent with the present example embodiments. In addition, the present invention can also be downloaded and stored on a computer program product. Here the program can also be transmitted by data signals embedded in a carrier wave or other propagation medium over a communications link (e.g. modem or network connection) from a remote computer (e.g. server) to a requesting computer ( e.g., a client), which signals may ultimately be stored in the computer systems for subsequent execution.

The methods may be implemented in a computer program product available from a computer usable or computer readable storage medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer-readable storage medium may be any device that stores the program for use by or in connection with the computer or the may contain or store an instruction execution system, an instruction execution device or an instruction execution device.

A data processing system suitable for storing and/or executing the corresponding program code may include at least one processor coupled directly or indirectly to computerized data storage devices such as memory elements. Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system. Network adapters may also be coupled to the system to allow the data processing system to be coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. In order to provide interaction with a user, the features on a computer with a display device such as an LCD (liquid crystal display) or other type of monitor to display information to the user, and a keyboard and an input device such as a mouse or trackball , allowing the user to provide input to the computer.

A computer program can be a set of instructions that can be used directly or indirectly in a computer. The systems and methods described herein may be implemented using programming languages such as Flash™, JAVA™, C++, C, C#, Python, Visual Basic™, JavaScript™, PHP, XML, HTML, etc., or any combination of programming languages, including compiled or interpreted languages and may be provided in any form, including as a stand-alone program, or as a module, component, subroutine, or other entity suitable for use in a computing environment. The software may include, but is not limited to, firmware, resident software, microcode, and so on. Protocols such as SOAP/HTTP can be used in implementing interfaces between programming modules. The components and functionality described herein may be run on any desktop operating system, running in a virtualized or non-virtualized environment, using any programming language suitable for software development, including, but not limited to, various Microsoft Windows™, Apple™ MAC™, iOS™, Unix™/X-Windows™, Linux™, etc. versions can be implemented. The system could be implemented using a web application framework such as Ruby or Rails.

Suitable processors for executing a program of instructions include, but are not limited to, general purpose and special purpose microprocessors and the sole processor or one of multiple processors or cores of any type of computer. A processor may receive and store instructions and data from a computerized data storage device, such as read-only memory, random access memory, both, or any combination of the data storage devices described herein. A processor may include any processing circuitry or control circuitry operable to control the operations and performance of an electronic device.

The systems, modules, and methods described herein may be implemented using any combination of software or hardware elements. The systems, modules, and methods described herein may be implemented using one or more virtual machines operating alone or in combination. Any applicable virtualization solution can be used to encapsulate a physical computing machine platform in a virtual machine that executes under the control of virtualization software running on a hardware computing platform or host. The virtual machine can include both virtual system hardware and guest operating system software.

The systems and methods described in this document can be used in a computer system that has back-end components such as a data server, or that has a middleware component such as an application server or an Internet server, or that has a front-end component such as a client computer with a graphical user interface or an Internet browser, or any combination thereof. The components of the system may be connected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include e.g. B. a LAN, a WAN, and the computers and networks that make up the Internet.

One or more embodiments of the invention may be practiced with other computer system architectures, including handheld devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and so on. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a network.

The terms "computer program medium" and "computer-readable medium" may be used to refer generally to media such as, but not limited to, a removable storage drive, a hard drive installed in a hard drive. These computer program products can provide software to the computer system. The invention may be directed to such computer program products.

References to "a single embodiment," "an embodiment," or "an exemplary embodiment," "various embodiments," etc. may indicate that the embodiment(s) of the invention so described have a particular feature, structure, or technology have a particular feature, but not necessarily every embodiment has the particular characteristic, configuration, or feature. Furthermore, repeated use of the phrase "in one embodiment" or "in an exemplary embodiment" is not necessarily referring to the same embodiment, although it may be.

In the specification and claims, the terms "coupled" and "connected," along with their derivatives, may be used. It should be understood that these terms are not intended to be synonyms for each other. Rather, "connected" may be used in certain embodiments to indicate that two or more elements are in direct physical or electrical contact with one another. "Coupled" may mean that two or more elements are in direct physical or electrical contact. However, "coupled" can also mean that two or more elements are not in direct contact with each other, but still co-operate or interact with each other.

An algorithm can be viewed here, and generally, as a self-consistent sequence of acts or actions leading to a desired result. These include the physical manipulation of physical quantities. Usually, but not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transmitted, combined, compared, or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. However, it should be understood that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenience labels given to those quantities.

Unless expressly stated otherwise, it is to be understood that throughout the specification, expressions such as "processing", "calculating", "calculating", "determining" or the like refer to the operation and/or processes of a computer or computing system or the like electronic computing device whereby data represented in the registers and/or memories of the computing system as physical, such as electronic, quantities are manipulated and/or converted into other data contained in the memories, registers or other such information storage, transmission or Display devices are also represented as physical quantities, are converted, relate.

Likewise, the term "processor" may refer to any device or part of a device that processes electronic data from registers and/or memory to convert the electronic data into other electronic data stored in registers and/or a memory can be stored. A "computing platform" may include one or more processors. As used herein, "software" processes may include, for example, software and/or hardware entities that perform activities over time, such as tasks, threads, and intelligent agents. Also, each process can refer to multiple processes to execute instructions sequentially, in parallel, continuously, or intermittently. The terms "system" and "method" are used interchangeably herein in that the system may embody one or more methods and the methods may be considered a system.

While one or more embodiments of the invention have been described, various changes, additions, permutations, and equivalents thereof are included within the scope of the invention.

In describing the embodiments, reference is made to the accompanying drawings which form a part of this document and show by way of illustration specific embodiments of the claimed subject matter. It is understood that other embodiments may be used and that changes or modifications, such as structural changes, may be made. Such embodiments, changes, or modifications do not necessarily constitute a departure from the scope of the intended claimed subject matter. Although the steps herein may be presented in a particular order, in some cases the order may be altered so that particular inputs occur at different times or in a different order without changing the operation of the systems and methods described. The disclosed acts could also be performed in other orders. In addition, various calculations in this document need not be performed in the order disclosed, and other embodiments using alternative orders of calculations could easily be implemented. Besides rearranging them, the calculations could also be broken down into sub-calculations with the same results.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

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Claims (9)

A system for executing an instruction in a computing environment to build a representation of an index or portfolio of investments in a database, the system a computerized processor configured to one or more data entities electronically tagged with one or more functional attributes of corresponding economic entities, wherein the functional attributes describe the roles of the respective economic entities in one or more processes that convert inputs into outputs; selects multiple investment securities represented by the data entities for inclusion in an index or portfolio of investment securities; defines at least a first group and a second group of assets based on the electronic tags or the functional attributes associated with the respective economic entities; segmented the selected assets into the two or more groups based on the electronic tags or the functional attributes, wherein the assets in the first segmented group share a first common or close functional attribute and the assets in the second segmented group share a second common or close functional attribute; electronically access the database representation of the segmented groups; electronically traverses the accessed representations to calculate a negative or positive weight for one or more of the investments based on the one or more segmented groups into which the investments are segmented; and assigns the negative or positive weight to the one or more of the investment securities; and an electronic data memory which is designed in such a way that it storing one or more items of data electronically in a database system, the items of data identifying the identity of an investment security, the investment security being associated with a corresponding economic entity, and stores the assigned weight electronically in the database system. system after claim 1 , further comprising: selecting an metric to take a measurement related to one or more of the groups, subgroups, sets or aggregates, wherein the distribution of expected or achieved values of the metric for the index, portfolio or group is relatively more normal than is the distribution of expected or achieved values of the measure for an alternative index, portfolio or group; or the value of the metric is more stable or more predictable for the index or portfolio than for the group as measured by a mathematical test of stability or predictability; or the value of the metric is more stable or predictable for the group than for an investment instrument as measured by a mathematical test of stability or predictability, the number of items in each group being chosen to provide statistical power of statistical analysis at a predetermined level level exceeds. A system as claimed in any preceding claim, wherein one or more weights are assigned to an asset based on a functional attribute of a corresponding economic entity or an electronic tag representing such an attribute. A system according to any one of the preceding claims, wherein the achieved returns of the index portfolio exceed those of a commercially available index or benchmark for a given risk level over the past year and the past three and five years or those of the index or benchmark for a lower level of risk where the securities in the portfolio are the same or substantially the same as the securities in the index or benchmark. The system of any preceding claim, further comprising recommending to the user a portfolio, group, or investment based on functional attributes electronically identified by the system or a user. A system according to any one of the preceding claims, further comprising arranging the selected data units in a layered structure such that the at least two parent groups and at least has at least two subgroups of each parent group, such that one or more parent groups are defined by one or more functional attributes such that the data entities of those parent groups share the attributes that define those parent groups, with at least two parent groups being associated with different actions; where the subgroups inherit one or more functional attributes and corresponding actions from the parent groups; and the subgroups are defined by one or more different functional attributes such that one or more subgroups are associated with actions that differ from the parent groups and other subgroups. The system of any preceding claim, further comprising electronically using predictive analytics based on functional attributes to measure performance, volatility, liquidity, variance, covariance, half variance, skewness, kurtosis, half variance, correlation , predict the autocorrelation of one or more sets, aggregates, portfolios, groups, subgroups, indices or composites, or the excess or remainder of any of these measures. The system of any preceding claim, further comprising electronically storing a computerized representation of an economic system syntax, the economic system syntax being operable by a computer processor to determine the validity of expressions of elements of the system based on one or more functional properties of the economic entities. A computer-readable medium comprising computer-executable instructions adapted to cause the computer system to perform storing of a database description of a set, aggregate or composite of elements of a functional system or a representation of those elements, the method further the electronic storage of a set of data units in a database system, the data units corresponding to elements of a functional system, wherein the functional system comprises a group of elements ordered according to their functional roles in converting inputs to outputs or as the inputs or as the outputs; electronically assigning one or more functional attributes, represented as an electronic tag, to the data entity associated with an item, wherein the functional attributes describe the roles of the elements in a process of converting inputs into outputs; selecting multiple items, or a representation of those items, described by data units for inclusion in a set, aggregate, or composite; segmenting the selected elements or a representation of those elements into two or more defined groups based on the electronic tags representing the functional attributes associated with the corresponding elements, wherein the first group shares a first common functional attribute and the second group shares a second common functional attribute; electronically accessing the database representation of the segmented groups, electronically iterating through the accessed representation to calculate a negative or positive weight for one or more of the items or a representation of those items based on the one or more segmented groups; and assigning the negative or positive weight to the one or more of the elements or a representation of those elements; and electronically storing the assigned weight in the database system.

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