KR101993038B1 - Stratified composite portfolios of investment securities - Google Patents

Abstract

A hierarchical mixed portfolio can be formed by selecting a group of investment securities, layering them according to attributes that correlate to specific asset risk, and assigning relative portfolio weights to the components based on their layered location . The attribute is selected from universal possible values. Other positive and negative biases can be applied to individual assets at random points or layered locations, including arbitrarily selected assets or optionally groups of layered locations.

Description

STRATIFIED COMPOSITE PORTFOLIOS OF INVESTMENT SECURITIES FOR INVESTMENT SECURITIES

The present application claims priority to U.S. Application Serial No. 61 / 930,807 filed January 23, 2014. This application is related to Serial No. 14 / 216,936, filed March 17, 2014, relating to Serial No. 14 / 216,390, filed March 17, 2014, which is incorporated herein by reference.

The present invention relates to computerized techniques using a logical data model to construct a layered mixed portfolio of investment securities.

Investment portfolio management has been the subject of substantial theory and research. Portfolio theory is a way to minimize the risk of a certain level of expected return by carefully choosing how funds should be invested and how to maximize the portfolio's expected return on a given amount of portfolio risk, . While a certain percentage of returns can be expected, the assessment of individual stakes in the portfolio can lead to upward or downward expected returns. From this expectation, upward or downward variability is known as variance or variation. Over time, securities should, in theory, have an efficient frontier of expected volatility and return. According to the theory, securities with high expected risk should have higher expected returns.

The S & P 500 is the world's largest stock benchmark. Tons of dollars are invested in benchmarks like these, or invested in benchmarked funds. Since 1999, market indices such as the S & P 500 have experienced the longest period in which the stock index returns failed. For example, at the end of 1999, S & P 500 investors saw a loss of about 20% at the end of 2010, ten years later. By the second half of 2012, the S & P 500 brought positive returns to investors at the end of 1999, including many large pension funds and donations.

During this period, broad-based government or corporate debt saw positive returns with more corporate debt than government debt during this period. These premiums were attributable to the risk of corporate bonds versus government expenditures. These markets had upward and downward trends for their years, but for a reasonable time, these securities had both the expected positive return and the difference based on risk. None of these work orders were against the stock index, such as the S & P 500, which lost absolute value over time and had virtually no performance in relation to relatively low risk indexes holding company or government bonds.

The S & P 500, like most market indices, is capital-weighted. This is because the weight of individual companies in the index is proportional to the total market capitalization in relation to other company indices. Within the S & P 500, a single security or group of securities with no control and common risk does not become too much of the portfolio. That is, the scientific fields controlled by the group and the control types used within the designed process are used to limit the impact of a portion of the population on the entire population being measured, and are not used in the broad market index.

These controls limit both positive and negative effects. In group studies, control is used to create a normative model of the important group. Since there are no controls on the benchmarks currently used to invest in equity securities, there is no guarantee that these earnings history will represent equity securities from the end of 1999 to the present. All that is known is that the weighting strategy (capital weight without control - weighting) yielded below average returns for a long time.

Since 1999, the results of major indices have been found to be inconsistent with investment securities and efficient market theories. Much of the work in the efficient marketplace has been achieved with the pioneering work of Markowitz and Sharp and the notable additional work of others such as Merton. These theories are based on the assumption that individual securities are priced at the expected level to produce risk-adjusted returns related to other investment securities, and that, according to certain rules, the portfolio of securities achieves a risk- The higher the probability. The rules proposed by Markowitz and others were designed to support investors and managers in choosing the most efficient portfolio design by analyzing the various possible portfolios of fixed securities.

By selecting securities that do not 'move' accurately together, models show how investors can reduce their risk. In this area, the basic model is known as the Mean-Variance Model because it is based on expected returns (mean) and standard deviation (variance) of the various portfolios. When developing the original mean-variance model, Makowitz has made the assumption that a portfolio that provides the maximum risk for a given risk or a minimum risk for a given profit is an effective portfolio.

Thus, a portfolio is selected using the following rules: (a) from a portfolio with the same profit, the investor prefers a portfolio with low risk, and (b) from a portfolio with the same risk level, Of the portfolio. Even if individual securities do not achieve long-term performance, the rules developed for effective portfolio composition are designed to reduce the likelihood that they will not achieve this performance on the portfolio of securities.

An explanation of the discrepancy between the modern portfolio and the theoretical portfolio for which effective market assumptions were developed has been developed and the modern portfolio is much larger and more complex than the theoretical portfolio. The theoretical model tends to use individual securities and describes diversification within a portfolio consisting of a number one and a low number two securities. Many of the basic literature, beginning with the creation of an individual retirement account (IRA) by the 1974 Employee Retirement Income Security Act (ERISA) and the introduction of the first index fund in 1976, began in the 1980s and expanded in the 1990s Was created before the Mutual Fund boom.

The Markowitz literature on the selection of portfolios published as financial journals was written in 1952. In 1952, according to a first-time proprietor survey by the New York Stock Exchange (NYSE), only 6.5 million Americans owned stocks (about 4.2 percent of the US population). Sharp's book, "A Simplified Model for Portfolio Analysis," was recorded in 1963, and Sharp's book, "Portfolio Theory and Capital Markets," was recorded in 1970, and a mutual fund boom created by ERISA, A long time ago, and long before investors began to recognize the inherent problem of managing large funds.

Modern portfolios manage trillions of dollars, and to reduce exposure to non-systemic risks, portfolios require thousands of securities in various risk groups. On this scale, building an effective portfolio is challenging. The absolute size of investment today has grown exponentially by the Averaging agency. In addition, important groups of securities have grown in complexity. Total investment in US mutual funds was $ 13 trillion in 2012. US public securities are the number of companies, which is less than 20% of total global securities. Also, most US companies rely heavily on the global economy. This diversity and interconnectivity is increasing every year. The need to control such non-systemic risks contained within the company's portfolio increases yearly.

This raises several questions: 1) the period in which the capital aggregate model is tested is too short and more time is allowed; 2) There is a misconception in risk and profit is not correlated; 3) Total Capital - A weighted portfolio of securities is an inefficient design, and another example of having control to compete with this model is needed. In other words, by applying Markowitz and Sharp's methodology and basic principles to the complexities of today's large-scale funds as a new normative case for building a portfolio of investment securities, the complexity and increasing size of today's companies, There is a need for a case to address diversity.

The current system of classification creates difficulties in building new models of a potentially efficient portfolio of such large-scale modern investment instruments. This system, similar to the basic literature on finance, was created before the advent of large-scale digital databases and models a database of time, such as the Dewey Decimal System and the standard industry classification system. These systems categorize entities that are classified. Each entity is a fixed hierarchy with one parent; Such parents are single parents. Each parent is described, but it is not a specific attribute that allows an entity under one parent to be associated with another entity under another parent.

Without this explanation, it is difficult to understand the multivariate risk that a company is exposed to and, therefore, how many companies in a large portfolio share similar or related risks. This type of difficulty in classification is becoming increasingly evident in view of the complexity of today's companies and the increasing size and diversity of today's funds. Total Capital - One of the biggest risks in a weighted strategy is that there are limited tools to handle these problems systematically, even though there is no single risk exposure, no bubbles or control over massive non-systematic price correction . Thus, there is a need for a multivariate statistical classification system capable of providing these tools and capable of presenting the ability to test each efficiency and create a number of different portfolios to test for normative cases do.

Volatility

Price volatility continues to occur when price fluctuations occur. Volatility is an important factor in portfolio performance, and these price fluctuations lead to drag on portfolio growth. For example, daily volatility has been found to be fatal to the Leverage Exchange Traded Fund (ETFS). (See Tony Cooper, Alpha Generation and Risk Smoothing Using Managed Volatility (2010)).

In an effort to reduce the impact of volatility in the portfolio, various weighting methods have been proposed. For example, one of the methods described in U.S. Patent No. 8,306,892 operates by calculating a weight based on market capitalization, gross domestic product and geographical area. As another example described in U.S. Patent No. 8,131,620, the portfolio weighting of securities is based on total capital and positive dividend yield. There are many different portfolio weighting methods. None of these weighting methods fully realizes the Markowitz model, which is a statistical process that particularly requires normalizing the risk / return to match the correctness with the number of securities between certain securities.

An example as described in U.S. Patent No. 8,005,740 uses the North American Industry Classification System (NAICS) sector for weighting. Weighted methods based on NAICS or the Global Industry Classification Standard (GICS) relate companies by their location in a fixed hierarchy. There are two important limitations on the fixed NAICS and GICS layers: 1) Items that are not common are not relevant and can not be compared; 2) Items in the same parent are only compared according to a matrix using NAICS and GICS to specify the same group (as long as the names of the groups indicate a matrix separating them, for example, "customer" versus "commercial" ≪ / RTI >

Without controls, random groups of securities can have significant valuation swing periods up and down from one period to another. These large changes in value may not be caused by variables such as the nature of accounting or their designation as "growth" or "value" shares. Rather, it can be caused by the unique characteristics of the individual company, including the group. An evaluation swing can be caused by a particular product when the product suddenly loses its value; Demand for a company or company's products is expected to be too high, which does not meet demand; They have long fixed cost contracts and the actual costs that can be used to change competitors; Or they overload a particular asset in the product mix and the class loses its value; There are other reasons.

There are many reasons for the random bubble. In certain cases, these are broad market (also referred to as systematic) bubbles; In other cases, they are limited to configuration groups (such as asset classifications or industry). There are systematic schedule events because they generate too many biases, such as Internet bubbles, but non-systematic. In any case, the impact on investor returns is extremely negative.

The random work theory represents a failure to coordinate the apparent randomization of liquidity and profit in equity - based investment securities. This random work theory is that no one knows that the stock price is moving at will, and that it can not be judged that any stock is better because the stock price already reflects all relevant information. Random work theory is the central reason for index funds and broad support for passive index funds in the academic world. Random Walk Theory, "Even if you invest in monkeys with blindfolded eyes, you can make as much profit as investors" (B. Malkiel, A Random Walk Down Wall Street, 10th ed., 2012)

Many different weighting strategies have been proposed to address this same problem of random liquidity in stock-based investment securities. The recent low performance of these indices has emphasized that such passive indices continue to be harassed by the same random hypothesis.

Historically, medical care has suffered from similar randomness issues. In medical practice, this hypothesis has been expressed as any patient going to any doctor who has the potential to receive any answer. Prior to modern medicine and modern statistical control, many people believed the probability of many random responses was very high. The healthcare industry has addressed this problem by developing statistical methods using information from these profiles to generate detailed patient information and to control the critical characteristics of a given population. This work progressed gradually in each disease area and government agencies studied and understood a series of natural biases until well-developed field-specific frameworks evolved.

The systems and methods described above can be used in investment management by controlling certain kinds of random events that affect the overall randomness of risk and returns in investment securities. Random movements in investment securities generate drag on earnings, and in particular have caused large downward shifts caused by events such as bankruptcy or non-systematic bubble emergence. In all of these cases, there is no expectation that investment securities will return to the previous level. In all of these cases, the securities that are affected are priced again because of a sudden market perception that their prices are too high.

Non-systematic bubbles and bankruptcies relate to non-systemic factors such as the significant intrinsic properties of an industry, company, or asset associated with a particular investment security. An important issue in risk management for a large portfolio of securities is that they can not control the occurrence of these kinds of events. If a portfolio is excessively weighted in a group of securities or securities that accidentally has a common bubble or bankruptcy risk, the return may be severely affected by the relatively small number of securities in that portfolio.

In many cases, overweighting in certain non-systematic variables has led to a systematic-like impact on one portfolio. In 2000, the total capital-weighted S & P 500 fell 9.09%. It was the worst year in the S & P 500 history. In that year, 16 shares fell to 49.8%, and the rest of the market rose 4.28%. Unfortunately for investors, these 16 companies were all doing business of moving, storing or processing information, accounting for 24.8% of the total portfolio.

Previous efforts to improve portfolio returns have at least two problems: 1) optimal number of subgroups, and 2) variation or interrelationships between groups or within each group in a group specific manner Can not be adjusted to do so. Large-scale portfolios of existing securities did not adequately adjust the capital weight weight for these composition groups and these weights did not solve the problem of collective adjustment.

Problems of Scale

For various reasons, the problem described above is particularly acute in the case of large portfolios of securities. Without a reliable and valid system of attributes and a hierarchical system using layered mixed layers, it is particularly difficult to adjust different attributes related to securities. A variety of reasons why large-scale management is difficult are provided below.

(a) Charter limits for ownership: In the case of many funds and fund managers, there are company percentage limits that they can own. For example, there is 5% possession, 13-D filling and supervision. Many funds can not exceed this limit.

(b) Liquidity constraints on ownership: The more the fund owns a large portion of the individual securities, the more difficult it is to sell depending on the liquidity of the shared equity. Also, because of its size, many funds have absolute dollar-equivalent limits on ownership. If the fund has $ 50 billion in funding, the $ 1 million investment is a small investment.

(c) Large funds require a large number of securities to cover a portfolio: Due to the factors described above and other practical issues, large funds need a large number of companies to invest due to liquidity and ownership issues. . In the economic system, there is much association, and the more companies there are, the more difficult it is to track and supervise the risk associations from these associations. An important part of these associations is attributed to the non-systematic nature of the company's suppliers, products, industries, work, geographical location, It is very easy for a portfolio with a large number of securities to concentrate too much in the non-systematic risk category.

Understanding and controlling other potential risk groups is difficult without a reliable and valid system for attributes as well as a layered mixed layer for controlling various attributes.

(d) Fact that investors do not select the entire company to invest: the above identified factors and many other practical issues, large funds need to invest in large companies. The companies available in these groups differ over time. Also, from time to time, these securities are variable and weighted differently depending on which company is in which category the company is at a given point in time. In addition to changes over time, these industry, sector, or company choices change geographically. In fact, sector differentiation can be a greater source of price fluctuations among regions than the key currency driving the product. For example, the US portfolio is much heavier in technology stocks than in Europe and Latin America. In Europe and Latin America, goods are relatively weighted. If the fund manager's goal is currency differentiation, it is important to control these sector fluctuations. It is difficult to understand the different temporal risk groups at a given point in time and in a particular geography or category, and then to adjust them using currently known techniques.

(e) Property risk is multidimensional because of the risk of concentration: single and multiple attributes help to differentiate risks within an individual company. For example, identifying a company engaged in the semiconductor business is a distinct risk. Also, the type of semiconductor (eg storage, processing, connection) is important because customer identification and raw materials are required. These are more important because they are concentrated into a single category of large funds. Existing categories in the current system tend to be standardized globally and can not be differentiated between these factors. The inability to represent linked multi-attribute risks is a serious limitation in large investment portfolios.

Unless a portfolio, especially a large portfolio, is better controlled, non-systematic events can have systematic effects. Examples of non-systematic events are provided below. The known and existing systems do not adjust critical statistical sources of systematic effects on random liquidity of large portfolio constructions of securities. However, with improved control, the impact of non-systematic events is limited.

A hierarchical mixed portfolio selects a group of investment securities, layers the securities into components according to attributes that correlate with one or more identified investment risk risks, and assigns relative portfolio weights to their layered locations To the components based on the < / RTI > The attribute may be selected from all possible values. Other positive and negative biases can be applied at any point or in a layered position and include arbitrarily selected investment securities or arbitrary layered positions as individual investment securities.

Certain attributes related to investment securities may be used to layer investment securities and may be used to hedge the holdings of investment securities in the portfolio by assigning certain weightings to the risk groups in which the underlying securities are held to meet the risk objectives of the entire portfolio Weight. For example, one purpose of layering risk groups is to reduce the impact of attribute-specific variability drag across the portfolio.

Multi-attribute risk mix can be used to increase or maximize potential returns from these risk types by reducing or minimizing potential risks from these attributes and / or by designing a mixed portfolio to take advantage of events that the manager expects to happen. To provide a tool for managing risk.

In certain embodiments, a layered mixed portfolio may be generated by applying a weighted method of tagging securities with risk attributes based on attributes unique to the investment securities and limiting exposure to individual attributes. The result of such a process is to layer the risks across the number of risk attribute categories, layer the risks within the individual groups, and group them into subgroups according to attribute categories within the group to achieve the required risk profile that can be represented by the target score Weighted portfolio.

Figure 1 is an illustration of an exemplary method for creating a layered mixed portfolio and weighting investment securities.
Figure 2 is an illustration of an exemplary method for creating a layered mixed portfolio and weighting investment securities.
Figure 3 is a diagram showing three layers of hierarchy.
4 is a diagram showing a data set having three layers of hierarchy.
Figure 5 is an illustration of an exemplary method for creating a layered mixed portfolio and weighting investment securities.
6 is a diagram illustrating a method for calculating weights for a layered mixed portfolio.
7 is a diagram illustrating a method for creating a layered mixed portfolio having a target score.
8A and 8B are diagrams showing a syntax displayed as a work instruction specifying a layer and a bar code.
9 is a graphical representation of the relationship between syntax elements.
10 is a diagram illustrating a computer system for a layered mixed portfolio weighting.

Introduction to Risk

Securities are securities representing the rights of ownership represented by ownership status (stock) at a listed company, creditor relationship (bond) with a government agency or company, or options. A security is an alternative and negotiable financial security that represents a class of financial assets. A company issuing securities is known as an issuer. The price of the security is based on the expected return on ownership time. These expected returns are again based on the expected quality and performance of entities related to securities.

Investments in investment property are made in anticipation of earnings and potential risks, or in the exchange of such returns. Investment securities have two key performance indicators: the rate of return and the probability or risk of achieving the expected rate of return within a specified period. These two indicators are interrelated: the greater the expected risk, the greater the expectation is. In other words, the higher the level of risk, the higher the rewards.

The probability of a return is related to expected fluctuations in a given investment security. The actual returns required for an investment property may be linked to a number of factors, including market conditions, the supply of investment capital, or expectations of inflation or deflation. However, at a fixed time, the relative return on investment securities will include the risk burden related to the type of investment securities and the underlying characteristics or attributes related to the investment property.

Securities will change their profit characteristics and expectations. Each type of security represents a specific ownership status of a particular company. Each type of bond, equity or derivative right has its own unique ownership and investment characteristics. Expected returns from securities are based on the type and nature of the securities and the underlying performance of the entities related to the ownership represented by the securities. For certain securities, expected returns and actual returns may differ substantially. The difference between expected and actual returns is securities risk.

Investment securities have two important types of risk. The first kind, systematic or market risk, is for overall results such as broad market returns, total economy-wide resource holding, or events that affect the overall outcome. In many cases, events such as earthquakes and major meteorological disasters give the total risk of affecting not only resource allocation but also the total amount of resources. The second type of non-systemic (also known as business risk) risk is the risk of changes or failures of assets experienced by securities-related entities due to a number of factors. Non-systemic risks relate to the entity-specific risks of entities that hold ownership of securities.

The expected return (and volatility) of the investment securities depends on factors including the market forces and the forces associated with the particular investment securities and their underlying characteristics. The power of the former is systematic and affects a wide range of securities. The latter force is unique to each investment security and is linked to a specific attribute of each investment security. Changes in the return on investment securities linked to the latter's ability to act are linked to certain attributes of a particular security.

Non-Systematic Risk

Non-systematic or business risk is specific to the quality or attributes of the particular entity to which the security relates. The fortune or bankruptcy of a particular business relates to the inherent attributes of the business itself. These include a number of factors including business, management / employee, operations, product, customer, customer's customer, availability of supply, robustness of the supplier or business specific asset or liability. Events associated with these or a combination of these will cause business luck to change, and in doing so, change the expected revenue of the business associated with the entity.

In addition to individual companies, a portfolio of securities can be impacted by such non-systemic risks if the portfolio is concentrated or exposed to certain non-systemic risks. One important reason for having a portfolio is to reduce exposure to non-systemic risks by spreading exposure to non-systemic risks with multiple investments with unique non-systemic risks, so that any non- The actual or expected return of the market. This strategy is relatively easy for individual investors who can diversify their portfolio into relatively small numbers of individual securities with relatively small amounts. However, such a strategy has proved difficult to explain to large investors such as pension funds or billions of dollars or equivalent.

These large investors have to invest hundreds of thousands of dollars worth of securities in a matter of hours. For investors of this size, it is very difficult to minimize the impact of non-systemic risk factors in the portfolio and tends to place an excessive weight on large industry bubbles, and the bankruptcy of the central bank, such as mortgage-backed securities, It is negatively affected by repeated bubbles and continuous overweighting. The present invention disclosed herein provides a method by which a portfolio manager can systematically control non-systematic portfolio risks that negatively affect such a large-scale portfolio.

Attributes

The system described herein may operate by assigning one or more attributes to a company associated with the investment securities. The method of the present invention may be realized in a computing device to divide the portfolio of investment securities using investment securities that relate to the underlying company or investment securities that use attributes associated with the associated company. These attributes can be used as markers for specific risks associated with events such as bankruptcy or bubbles. These attributes allow the portfolio manager to layer or divide the portfolio into groups according to specific attributes, each of which represents a particular attribute-related risk. The hierarchical groups of these parent groups have inherent risks among the groups and share common risks with these parent groups.

After layering the portfolios, the weights can be assigned to the layered units, and a plan for systematically reconfiguring the weights can be executed. In this way, the portfolio manager can understand and manage specific risks in that portfolio. In addition, certain risks can be designed by arbitrarily setting the weight for the layered unit. In some embodiments, the administrator can determine the required risk at the start of processing, use them to form multi-level hierarchies of multiple groups and subgroups, and weight the groups according to the next required risk result .

The method of the present invention calculates the weight of a portfolio of investment securities having a particular property different from the uncontrolled portfolio of the same investment portfolio based on the investment property-specific property. As will be explained in more detail below, the present invention utilizes investment property-specific attributes to reduce the randomness of individual investment returns by establishing a portfolio of investment securities, and controls the control portfolio for the group of groups Reduce the risk impact of certain investment property attributes by representing groupings defined by common attributes and groupings that include specific investment securities that layer certain attributes and their risks and share attributes related to grouping.

Stratification

To control non-systemic risk, you must control the business-specific risks that exist within the portfolio. These risks can be company-related, industry-related, product-related, customer-related or supplier-related. The larger the portfolio, the more difficult it becomes for the portfolio manager to understand how the portfolio is exposed to certain non-systemic risks. The method of risk group stratification described herein reduces the negative impact of attribute-specific volatility on such portfolios.

The system described herein can be used to generate standard bar codes that identify many business attributes. Such a system may assign standard bar codes with these standard attributes to securities in the portfolio. Based on these bar codes for attributes, certain non-systemic risk exposures that exist in a portfolio can be identified and controlled. Once identified, the inventive method can be used to control these non-systemic risks by limiting exposure to these risks in the portfolio.

The system can be used to generate a hierarchical structure of a particular risk group, assigning securities in the portfolio in this tiered risk group and applying a calculated or user-provided weight for the identified non-systemic risk , And selects the desired exposure for a layered risk group. Thus, tiering can be used to systematically control exposure to non-systemic risks. These exposures can then be managed over time by creating a rebalancing rule that resets the exposure of the portfolio to these identified non-systemic risks with an appropriate periodic schedule. In this way, the exposure of a large portfolio of securities to a number of non-systemic risks can be systematically determined and managed.

A business with a common set of unique attributes or attributes correlates with events associated with such attributes or attribute sets. Such a correlation decision can be changed by the importance of such attributes within a particular business. For example, if all network equipment companies share the same customers, the loss of key customers like Nortel (a huge network company) will affect every company. However, the impact will be greater if Nortel is the sole customer of the company rather than within 5% of the company's business.

Thus, the group companies in the risk group defined by the attribute provide a way for the portfolio manager to group securities that correlate to specific attribute related events. Also, most attributes are part of a larger group of attributes. All companies that have shared Nortel are part of a group of network equipment, which in turn are part of a group of communications equipment, and then part of a larger digital technology group. In this way, using specific attributes allows portfolio managers to group securities by broad and narrow categories and by the importance of these categories in individual securities.

The process of stratification involves dividing the population into independent subsets (called layers), and within that subset, independent samples from a particular population can be located. Layering is an important tool in statistics and is used to divide into a subset or a subset (known as a hierarchy) for the purpose of generating a particular set of sample sets, and the next assigned sample section is selected and comes from each of the hierarchies. Through the creation of the prescribed subset, the statistics are assigned the prescribed parts that can produce meaningful control over the group results.

The result of a layered population is called a control group because it can be configured and weighted and tested. In some populations, there is a tendency for random changes where the population subset has characteristics different from the population as a whole. The effects of these deviant subgroups can be mitigated by grouping the groups into sub-groups that are expected to behave differently and then using some of each of the sub-groups to study the group as a whole. As an example, if you study the work effect of a worker, you will find that Monday work efficiency is less efficient than other days.

However, if you run a random sample of 20 days in a year, you will have a sample set that is too biased against Monday. This is not representative of workers because the data set can be distorted by the operator for a period of lesser efficiency. To eliminate such bias, a population set can be layered for five subsets of one subset for each working day of the week. When applying a random sample, it is possible to assign an equal number of work days to each subset and the entire sample to be composed of five subsets each having the same number of days. In this way, stratification can limit the bias (bias) of the sample set and increase the probability of representative output.

The layering methodology is common to both cases mentioned above.

Layering can: 1) ensure a sample set with no bias (bias) representing the entire population; Or 2) that a particular bias (bias) produces a result that is not necessarily representative of a desirable but central group. An example of the former is a clinical trial or experiment in social sciences. In such a case, the experimenter tries to form a representative sample set, and for this representative set of samples, it can be modified to examine how they affect the adjusted population. An example of the latter is risk management, where different group subsets are not corrected and have high divergence occurrences or variations.

In such a case, the statistics may wish to bias the sample set toward a particular subclass such as a subset having a relatively high or low variability. In both cases, the layering may be able to create a set of samples with predictable results based on the layered model type on which the statistics are being performed. In layered sampling, the layer is formed based on the shared properties or characteristics of the members. These attributes may be based on relative quantification metrics such as size, speed, or age of the population. In addition, the attributes may be based on identifiable attributes such as head, skin or eye, right-handed or left-handed.

In relation to investment securities, the value of an investment security is determined either by 1) the type of asset or operation that is directly or indirectly related to the security, and / or 2) the particular attribute associated with the asset or operation that is directly or indirectly related to the security, Can be indirectly related.

The total expected return on the mixed portfolio can be determined from the expected return on that portion of the individual investment securities and within the mixed portfolio. The overall volatility of a mixed portfolio can be determined from the volatility and weighting of individual investment securities and the correlation of these individual investment securities to each other. Because of this fact, total volatility can be controlled and shrunk by layering the portfolio into groups with relatively high intra-group correlations and relatively low inter-group correlations. Volatility can be controlled by dividing the investment securities by the correlation classifier, ie by grouping them based on shared and unshared attributes corresponding to the risk.

Definitions

Investment securities: As used herein, investment securities are defined as financial securities that can represent any or all of the following: title of ownership in a company (stock) or a set of assets; The relationship between a company and a creditor; An individual or government entity that is directly or indirectly fixed by the assets of the issuer (bond); The right to ownership represented by options or derivatives. An investment security may be an alternative, negotiable financial security that represents the type of financial value associated with an entity. These values can be based on the type of securities, the types of relationships with issuers, and the types of assets and liabilities directly or indirectly related to the securities.

Attributes: Entities represented by investment securities can be related to attributes. The system can recognize multiple attribute types associated with an entity. As a non-exclusive example, the system may operate on the basis of the following class of attributes: (a) relative-versus-universal, or (b) inherent. Relative-to-universal attributes, for example, as a scoring system, designated as high / low volume securities or as growth / value securities. The system can be configured to recognize different kinds of unique attributes. As a non-limiting example, the types of unique attributes can be: syntax-specific attributes, context attributes, accounting attributes, market-based attributes.

The schedule specific attribute may also be considered absolute. An example accounting attribute can be a total liability, and an example market-based attribute can be the total market capitalization. Examples of contextual attributes include: (a) geographic attributes; (b) attributes of assets belonging to the company (eg, "large containers" versus "small containers" (Eg, "luxury" versus "non-luxury" clothing items); (d) attributes related to the customer (eg, a list of specific customers); Such a system can recognize different kinds of attribute combinations.

Any combination of multiple attributes can be formed as a mixed attribute. Any combination of intrinsic attributes may be considered to be a mixed intrinsic attribute, and any combination of relative attributes or intrinsic and relative attributes may be formed as mixed relative attributes. Mixed attributes can be defined as a new single attribute.

In certain cases, an attribute may be defined to include attributes related to the entity related to the investment securities and to exclude the attributes of the investment securities itself. In such cases, the system may be configured to define attributes to specifically exclude the following related attributes: the type of investment securities such as stocks, bonds, and the characteristics of the investment securities, such as preference, redemption period, or exercise price. In such a configuration, the attributes excluded are not considered attributes because the attributes included are related to the company or asset to which the investment securities are related and not to the investment securities itself.

In certain embodiments, the unique attributes can be defined as excluding accounting and performance attributes. An inherent attribute included may be an essential quality, feature, or inherent characteristic of a central entity or asset to which the investment security is related. For example, proprietary attributes define what a company does, such as production or transportation, and attributes relate to the type of car, computer, or bed, and the type of car, computer, or bed, ≪ / RTI > Attributes relate to the customer's customer; An attribute is associated with a business or individual operation geographic location; Attributes relate to the product and the materials the company uses to provide the product; Attributes relate to a diversified industry or industry segment that a company can operate on; Attributes relate to integrated, unintegrated, forward-integrated, backward-integrated or networked corporate business structures;

Attributes relate to management or strategy, such as unique risks, based on management, decisions and strategies of the company; Related to financial leverage; Attributes relate to any government or macro-economic risk associated with the country in which a particular business or company does business; An attribute is associated with an accounting or business risk identified by the business as a business core; Or the risk associated with a category connected to a particular business or segment by the investment community. At any time, any of these attribute factors or the industry events associated with these attribute factors affect the risk associated with the investment securities associated with entities with these attributes. Unique attributes can provide a relative order or location, but they do not have to be literal.

In certain embodiments, relative-to-universe attributes may be defined to include quality based on any of the following: a rating system; A point system that compares the performance characteristics of a rate-based accounting or entity or asset with the investment securities at a certain point in time and then groups the investment securities acquired by these relative points; Or an identification system, which identifies different identities at different times to the same entity, product or asset through a certain kind of scoring system; And rating system. In these systems, the same entities or groups of identical assets may be assigned different values at different points in time, since these are the decisions at the time of grouping investment securities based on decisions at a set time. Securities that meet the category are determined at a certain point in time by the group score of the company or asset and not necessarily the particular core business or score determined by the group of assets.

Layered Blending Unit: A layered Blending Unit, as described in this specification, is defined as a layered organization for investment securities, including: 1) All members of the parent group that are used to qualify the parent group A parent group defined by one or more attributes that the parent group has in common; And 2) two or more subgroups of the parent group, which can be considered as sibling to the child groups of the parent group and / or to each other. All members of a subgroup commonly have attributes used to define subgroups. In addition, all members of a subgroup commonly have attributes used to define the parent group of the subgroup. The sub-units in the layered mixing unit and the layered mixing unit may include any number of other sub-units that conform to their parent unit or sub-unit rules. In certain cases, a layered mixed unit may consist of only one parent group. In other cases, a layered blend unit may be composed of as many parts as the original blend unit size and variety supported by the parent group.

Layered Mixed Portfolio: As used herein, a layered mixed portfolio is defined to include at least two layered mixed units, and the attributes of the parent group in the mixed unit represent the risk group: 1) parent risk The group has a different risk profile than the other parent risk groups; And 2) all sub-units, including investment securities in the group, are formed as layered mixed units.

There may be other entitlements to be parent grouping of layered mixed units, but a parent group of mixed units may satisfy the condition of sharing certain common attributes or a common set of attributes with other members. Parent grouping of multi-layered mixed units allows for defined different risks to be addressed by a blending unit that includes a layered mixed portfolio, including a layered mixed portfolio defined to create a portfolio of mixed units.

Syntactically Structured Attributes

The attributes described above can be represented as a layered mixed unit and domain-specific syntax that defines the structure of a layered mixed portfolio. The structures may be defined by domain-specific syntax and domain-specific syntax location, including identification of attributes associated with domain-specific data entities associated with syntactic locations. Syntax tags can have associated attributes that allow them to relate to each other.

As used herein, a syntax may be considered to be a set of rules. The syntax position is a valid position based on such a rule set. Symbols in the database can be used to represent data entities. Syntactic tags can be used to indicate associations between symbols and rules. A syntactic tag associates a data entity represented by a symbol with another data entity in the domain based on a syntax-set of rules. Such syntactic tag processing provides a means for associating domain-specific information. It takes the information in the domain and tags it with rules that relate within the domain. Syntax tags are dynamic.

A syntactic tag for a layered mixed unit may be a representation that acts as a label for the tag. Such a representation may correspond to a syntax or equivalent meta-representation that may be displayed in a BNF representation. Representations or sub-representations of the syntax, which contain elements of tentative value, may be organized hierarchically, in which case the representation or sub-representation includes a multi-dimensional in-space region and a contiguous sub-region Describe the size that is. By default, the elements of a syntax specified as a hierarchy are consolidated from left to right according to their location in the same expression, and are consolidated as a continuous level from top to bottom in the hierarchy.

Syntax can represent hierarchical coordinates that provide continuous specialization, and the degree of specialization increases with the depth of the hierarchy. The syntax can also provide serialization in a stepwise manner in several steps; The degree of serialization increases with the number of elements in successive steps.

Also, in successive stages of specialization and / or in degree of serialization, syntax elements share the closest syntactic location to each other.

a) their parent in the hierarchy; And

b) sibilings of these at similar positions in different layers within the same syntax within the same domain.

Syntax elements may be considered to have a position in the closest statement if they are relatively close to other elements based on either their hierarchical specialization or serial position. These relationships allow a comparison of values across syntactic locations. By default, elements of the syntax specified as hierarchical are organized alphabetically and / or numerically within the level of a given hierarchy.

Syntax tagging of attributes associates data entities with shared attributes by assigning data entities as elements within a common set of syntactic tags. Syntactic tags associate data entities with other data entities in the domain according to their syntactic association. Thus, they uniquely group or cluster data entities that share syntactic tags. In certain cases, a syntactic tag may be used to generate a normative model for a portfolio, as described in more detail below.

An exemplary representation of the syntax is shown in Figures 8A and 8B. A graphical representation of the syntax is shown in Fig.

Portfolio Architecture Creation

Building a large portfolio of securities is problematic in many ways. This is difficult if you do not have a reliable and valid system for attributes, as well as layered systems that use layered mixed layers to control different attributes. Independently and together, the systems and methods described above enable engineering and management of risk exposure of large portfolios.

An engineered (blended) mix of investment securities is chosen to have a different risk / return profile from the centralized risk groupings used to build uncontrolled groupings or mixes from a group of central securities.

A layered mixed portfolio containing investment securities can be based on a dynamic combination of similar class entities and consists of a portion of each of the combined compositions to create a new entity with characteristics different from the central composition chosen separately. Dynamic characteristics mean that the characteristics of investment securities change over time. Investment mixes can be configured to account for these dynamic characteristics and create a reliable mix that retains their properties over time.

The method for building a layered mixed portfolio using domain-specific syntax for investment securities may include the following steps: 1) group investment securities with common risk attributes; 2) Grouped investment securities are stratified into sub-groups, a) associated with different risks, but b) still associated with the risk characteristics of the group in which they are included.

In one embodiment, the layered mixed portfolio may include multiple investment securities and an indication of an associated weighting. As a non-limiting example, identification and weighting may be performed using computerized processing in accordance with the exemplary method illustrated in FIG. As shown in FIG. 1, the method first generates a tiered portfolio architecture 1125, and then generates 1150 a list of investment securities and weights. In a first step, the layering module 1105 may receive the investment securities attribute 1120 and the attribute rules layer 1122 as inputs, all of which may be stored in one or more computerized data storage devices. As a non-limiting example, an investment securities attribute may be selected from the examples provided in the above definition. Other types of attributes and attributes may be used.

Attribute rules may be provided by the syntax for the portfolio architecture, as described above. Such a syntax rule can define the relationship between an attribute and the investment securities associated with such an attribute.

The layering module 1105 may also include a selection sub-module 1110 for receiving a selection 1121 from a user of attributes and / or rules as input. In certain embodiments, the structure of such rules and / or rules is redefined. For example, a syntax containing rules for describing a company is illustrated in Figures 8A-8B. In another embodiment, an already existing set of rules can be edited by the user or a set of rules can be defined by the user. The rules described in Figures 8A-8B define the relationship between the elements of the syntax. Next, the attributes selected by the user are applied to the syntax. In another embodiment, an interface for creating a new rule for the user is provided (1121), which is input to the layering module 1105.

In certain embodiments, a rule work order may be a form of an 'attribute', 'operator', or 'value' to return a truth or false statement about an entity or related investment securities based on an attribute. In certain embodiments, a rule may be a Boolean expression (via a Boolean operator) that combines one or more rule work instructions. The lines in FIG. 9 illustrate example rule.

In certain embodiments, entities or investment securities that do not follow a certain rule at a node in the hierarchy are subject to the order in which the rules are applied, provided that they do not pass through the rules of any of the parent's children A hierarchy of rules may be defined as the relationship between one or more sets of rules that specify. The layering submodule 1115 includes a hierarchy of rules 1122, an investment securities attribute 1120 (optional at this stage), an input 1121 relating to the creation and selection of a rule, or a list of other identifications of investment securities And to generate a layered portfolio architecture 1125 based on the portfolio architecture 1131.

A rule can be used as a work order to filter entities and investment securities based on attributes. A hierarchy is used to specify the relationship between rules specifying the order in which rules are applied. Companies that do not belong to the top level will also be excluded from the lower groups. The multiple attribute system described herein may be configured by changing a parent or group of children by changing one or more attributes that define the parent or child.

The resulting graphical and textual representations of the layered portfolio architecture are shown in FIGS. 3 and 4. FIG. Figure 3 illustrates exemplary attributes and their syntactic locations. The attribute-based rules shown in FIG. 3 are shown graphically in FIG. The rules shown in Figure 3 illustrate the top layer levels, which are comprised of two groups with a corporate locus (1) 1205 of real estate and a equipment material manufacturer (2; 1210). The rules in FIG. 3 are used by the real estate developer's enterprise locus (A) 1215, the real estate operator (IB) 1220, the REIT / real estate lesson (IC) 1225, 1230), and manufacturers of materials for non-information processing equipment (B, 1235).

These enterprise locuses are described at two levels of tiered architecture. The rules in FIG. 4 include several three level relationships. These three levels include customer real estate developers (l.A.i; 1240), industrial property developers (l.A.ii; 1245) under real estate developers (l. A North American Real Estate Operator (IB) 1250, a European Real Estate Operator (IB) 1255, and an Asian Real Estate Operator (IB) 1260 under the Balance Operator (LB) 1220; And REITs / Real Estate Lessons (l. C; 1225) and leverage REITs (l .C.ii; 1270).

 A number of attributes can be used to create the portfolio architecture. The portfolio architecture includes a nested hierarchy of groups. As an example, these groups can be formed with reference to properties common to all entities in the whole, and at each level, all elements of the whole are exactly in one group. In certain embodiments, these groups may be divided into any number of child sub-groups, such a number need not be the same for each parent group, and such sub-division processing may be performed over any number of times And add levels to the hierarchy in a "top-down" manner, respectively.

In certain embodiments, a layered blending unit is used to speak a larger layered blending unit and generates a layer in a " bottom-up " manner. In an embodiment, a combination approach of " top-down " and " bottom-up " Regardless of this configuration method, the resulting layered architecture 1125 may include an electronic representation of the set of attributes hierarchically arranged according to defined attribute rules.

Weighted investment securities

A layered mixed portfolio can consist of one or more layered mixes that maintain defined risk exposures by weighting the composition of the layered portfolio.

The layering described herein may be adjusted in a variety of ways in which the user can adjust the outcomes resulting from events associated with the group of investment securities and thus the group of investment securities. Bias in portfolio returns can be created and adjusted based on changes that occur in any of the following: 1) changes to the group of investment securities; 2) how a group of investment securities is stratified; And 3) the layered units are weighted at arbitrary locations within the layered hierarchy.

Once the portfolio architecture is determined, weights can be determined on the investment securities. In one embodiment, the weight function is a function that returns a value between 0 and 1 representing a weight associated with the group associated with siblings in the layered portfolio architecture, for a particular group in the layered portfolio architecture. . In certain embodiments, the sum of the weighting functions for all siblings at each level can be one.

In certain embodiments, the weighting of the security is only a function of the location within the hierarchy. In one embodiment, the weights may be evenly divided among all the children of a given parent group. That is, if the first level includes 10 groups, each will have a weight of 10%. If one of these groups contains 4 sub-groups, each will have a 25% weighting of the parent group, and the weight of the results is 25% * 10% = 2.5%; If the other top-level group has a five-character group, the weight of each one is 20% * 10% = 2%. This process can be repeated for each of the levels, resulting in a weight for each floor-level group. A similar process can be applied as a security within each floor-level group, creating a weight for each of the securities in the total.

In certain embodiments, the weighting algorithm may be executed by a computer as follows.

class PortfolioGroup (class PortfolioGroup)

# Returns a list of the portfolio groups

# at the same level as this portfolio group

def siblings

...

end

# returns a parent of this portfolio group.

# if this portfolio group does not have a

# parent, it returns undefined,

def parent

...

end

# returns the weight that should be associated

# with this portfolio group,

def weight

num of siblings = selfsiblings. count

if parent.is defined?

parent_weight = self.parent.weight

else

parent weight = 100

end

return l / num_of_sib lings * parent_weight

end

end

In another embodiment, the weighting of the group may be a function of the attributes of the company in the same group. In one embodiment, the group (formed using the attribute) may be weighted by a function of one or more attributes common to the security within the whole. In one embodiment, the groups may be weighted within their parent group that is proportional to the total liability of all securities in that group. In certain embodiments, the function depends on a single attribute. In another embodiment, the function depends on a number of attributes. In certain embodiments, the same function is used to weight all groups in the architecture. In another embodiment, another function may be executed by the computer as follows:

class PortfolioGroup (class PortfolioGroup)

# Returns a list of the portfolio groups

# at the same level as this portfolio group

def siblings

...

end

# returns a parent of this portfolio group.

# if this portfolio group does not have a

# parent, it returns undefined.

def parent

...

end

# A function that for a specific group in

# a stratified portfolio architecture returns

# a value between 0 and 1 indicating the weight

# associated with that group relative to its

# siblings in the stratified portfolio architecture.

#

# The sum of the weighting function for the

# siblings at each level equals 1.

def weighting

...

end

# returns the weight that should be associated

# with this portfolio group,

def weight ()

if parent.is defined?

parent_weight = self.parent.weight

else

parent weight = 100

end

return weighting * parent_weight

end

end

With respect to the embodiment of FIG. 1, the computerized weighting module 1130 receives the layered portfolio architecture 1125. As also shown in FIG. 2, the weighted module may also be configured to receive identification 1132 of the investment securities, and investment securities attribute identification 1132 associated with the investment securities. The weighted module then generates investment securities and a weighted list (1150). The weighted module is further illustrated in FIG. As shown, the system may choose 1305 to select and / or identify investment securities to be weighted.

The investment security to be weighted may be located at any point within the layered hierarchy described above. Weights for individual securities and groups of securities may be calculated for current level 1310. In certain embodiments, the computation may start at a top level of a layered hierarchy. At the current level, weighting techniques and rules 1315 for that same level are identified. A weighting factor can be calculated by dividing the weighted portion by n, where n is the number of investment securities or group of securities (1320). As an example, referring to FIG. 4, top level weighting can be calculated to be 50% for group 1 and 50% for group 2. At the second level, groups 1A-1C may be weighted 50 * .33 = 0.165 or 16.5%, respectively.

Before or after calculating the weight, a positive or negative weighted bias may be applied 1325. The bias may be applied by addition, subtraction, multiplication, division, or other operations on the weight. Any bias applied to a group or investment security requires that the corresponding opposite bias be applied to the same group or elsewhere within the same level of peer group. If the floor level has been reached and terminated, the weighting process can be terminated. Otherwise, such processing may continue at another level.

The electronic representation of the weighted investment incentive may then be entered as an exchange trade financing (ETF) or other financial instrument such as hedge financing, mutual financing, joint venture or other investment instrument.

In an alternative embodiment, the steps of layering and weighting may be reordered. For example, a list of investment securities can be entered anywhere in the portfolio design process. Investment securities and / or restructuring treatments may be selected prior to stratification to generate a specific overall exposure. The layering, architecture, weighting method and / or rebalancing method may be selected before or after selecting the investment securities.

The optional order and modification of the steps for creating the portfolio of investment securities described above is possible. For example, with reference to FIG. 1, an identification 1131 of investment securities may be provided 1105 to the layering module. In such an arrangement, a tiered sub-module may generate 1125 a layered portfolio architecture of investment securities 1125 and then input 1130 to a weighted module.

Reconstituting and Re-weighting

Additionally, certain embodiments may be able to maintain the required risk exposure, including reconstituting periodically assigned abstractions. A tiered portfolio may consist of one or more layered blending units that weigh the elements of the tiered portfolio and reconfigure periodically assigned weights to maintain the required risk exposures, thereby maintaining a defined risk exposure. With respect to the embodiment shown in Figures 1, 2 and 5, the depicted steps are performed at any point to generate an open weighted portfolio based on the modified input, such as a modified weighting rule. With respect to FIG. 5, in another embodiment, the re-weighting may be provided by a separate re-weighting module 1155. The re-weighting module 1155 receives the target exposure list assigned to the portfolio hierarchy location 1151. The re-weighting module then selects a new investment security to be included in the layered mixed portfolio.

Stratified Composite Portfolio Scoring

Using the method described above, a score (score) can be calculated for a tiered portfolio. Such a score can be a feature of the portfolio and can be used in conjunction with several cases. In certain embodiments, the objective score may be a quantifiable number that the portfolio would like to reach. In another embodiment, the goal score may be a set of attributes that the portfolio wishes to have. The portfolio score can be a vector of values or values calculated from a portfolio that can be compared to the target score the investor desires to have on the portfolio. The goal score can be a theoretical or an evaluation value.

The target score can be used as a method for optimizing the portfolio. The investor will set a target score and then let the system be used to create a layered mixed portfolio optimized for that same score. Optionally, target scores are used to create a portfolio that reflects the performance of key groups. That is, the target score can be a decision on how the group will perform, and so it can be used to determine the performance for the group. Once a weighted list of investment securities is established for the portfolio and objective score, the score for the portfolio can be calculated based on the attributes of the derived portfolio.

The goal score can form expectations on how the portfolio is expected to perform, or how the portfolio wants to perform based on the portfolio characteristics. A goal score can be achieved by determining the performance of all or some of the following: individual company, random sample individual company, layered unit, and / or hybrid.

The target score may also be identified as a target score that the investor requires as part of the investment objective. Here, the investor may want to use a layered blend to arrive at a predetermined target score. By forming a group based on the common attribute, a risk group can be formed. These risk groups are then appropriately weighted to achieve the target score, eventually allowing the portfolio to have a known bias.

In one embodiment, the layered mixed polio is designed to meet the target score set by the user. In one embodiment, the goal score may include all or a portion of: (a) an absolute profit goal (e.g., an expected rolling ratio), (b) a risk / benefit determination (e.g., Sharpe ratio) , Sortino ratio, or alpha), or (c) a risk objective (eg, downside deviation or beta) determined by volatility. In certain embodiments, the target score may be a primary or multi-dimensional vector, such as in the example provided above. For example, the target score can be [actual profit - risk free rate] / [expected return - risk free rate], and the target score is 1 or more.

According to one embodiment, a method for constructing a layered mixture having a target score is described with respect to FIG. As a first step, the user creates a group to be invested by identifying the entire investment securities (7005). For example, such a group could be a financial and energy company in the United States. Next, the entire securities are filtered (7015). Next, the group of companies is layered (7020). By such a process, they are applied in stratification units, hierarchical groupings based on common characteristics.

After the group stratification, the matrix to be used to evaluate the portfolio is identified. The matrix used can be subordinate to the group being layered. For example, the matrix used in an investment grade debt portfolio can be expected returns and volatility, and the matrix of stock portfolios can be expected to be risk and return. Once the matrix is identified, a target score may be set 7010. The goal score is the goal that the user wants to achieve, and such a goal is determined by the identified matrix. For example, the target score of an investment grade debt portfolio may be the expected return and expected volatility that the investor wants the portfolio to achieve.

An example of a target score is described next.

Once the target score is determined, a designed mixed portfolio is generated (7020). A mixed portfolio can be a combination of two or more layered units. Mixed port poles are designed to reach the target score. The mixed portfolio can be designed by strategically weighting (7025) the mixed portfolio in the layered unit and the layered unit and re-weighting the company in the layered unit (7030). This weighting and re-weighting process involves changing the composition of the group (adding or deleting configurations from a portfolio that meets the group criteria).

The mixed portfolio may be tested for a target score (7035). If the target score is accepted, then the process can be completed and terminated. If the target score is not satisfied, then some or all of the following various parameters can be adjusted; 1) hierarchical rules (eg, architectures), 2) weighted rules, 3) all filtered through hierarchy and weighting, and 4) rebalancing / reconfiguring policy. This process can be repeated until a portfolio with satisfactory scores is created.

Layered mixing can be used as a method to optimize the portfolio. As described above, the designed mix can be configured to meet the objective score. Here, the objective score can be considered for investment purposes. For example, such an objective could be to create a mixed portfolio, with its returns, performance, fluctuations, and / or other qualities matched to those outlined in the objective score.

Thus, instead of creating a portfolio that represents the most important groups, a portfolio that is strategically weighted for lower-level groupings is created, so that such a portfolio can best meet its target score. Here, layering the portfolio and creating a mixed portfolio allows you to identify distinct risk groups within a group. Because these risk groups are identified, the weighting is strategically assigned among them to achieve the target score.

In investment securities, the primary concern for investors is risk and return.

Thus, in certain embodiments, the target score may reflect the investment objective of the quantified portfolio in relation to the risk and return characteristics of the portfolio. The goal in generating an investment mix is to design risk and revenue through weighted and mixed design of critical components. This designed mix of investments can generate mixed scores (calculated from combining individual securities data affected by multiple attributes) that can reliably achieve the theoretical evaluation.

Using the methods described herein, a mixed portfolio is designed to improve these unique properties. Certain features may be generated for use in a particular environment. In investment securities, a mixed portfolio is formed to manage the composite score. A layered blend is used to achieve the target score. Tiering allows identified risks to be grouped into portfolios. Thus, when creating a designed portfolio that meets the target score, the risk that the portfolio is exposed can be better understood both quantitatively and qualitatively.

Investment Statistics for Stratified Composite Portfolios

Portfolios generated according to the methods described herein can be scored using a modified version of a known statistical analysis, including alpha, beta, and Sharp and Sorrento indices. A score can be generated based on a prescriptive stratified model portfolio and a change to the normative portfolio. For example, a layered alpha could be calculated as a score for a normative tiered portfolio as a risk-adjusted premium. Layered data relating to tiered and normed markets can be calculated for a tiered portfolio, and such tiered and normed market is defined as having a beta of one.

In certain embodiments, the normative and tiered beta may be defined in a context that the market is defined as a tiered portfolio of the context's subset of contexts. For example, the context-dependent subset may be defined as sector, industry, geography, time, dictionary terminology, and the like.

Normative Cases for Stratified Portfolios

Markowitz Markowitz posits creates multiple portfolios to determine which one represents the group most effectively. This process of creating individual models to test the most representative ones is how normative models are created. In creating a normative model, there is control over the central group and target scores that are assessed to be achieved from normative cases. In financial theory, such a portfolio development model is assumed using a portfolio of individual securities. There is a contradiction arising from the current normative case for investment securities, the fact that the total weighted portfolio weighted portfolio does not achieve the expected return or target score by theory.

Using the systems and methods described herein, a normative tiered portfolio can be defined. Layered units can be used as a tool to create normative models and open normative target scores. A reliable and valid category of investment securities is used to subdivide the group of investment securities to enable normative research. The user can develop normative scores to test the hypothesis and validate the baseline for use in comparative studies of different tiered portfolios. Such a system can be configured to use a normative, layered portfolio to derive target scores. A target score for a layered portfolio, such as a goal alpha score, can be defined in relation to a prescribed goal score.

At the initial stage, a theoretical or an evaluated score can be defined. Use adjustments based on the changes made against: 1) changes to the group of investment securities; 2) how the group of investment securities is stratified; And 3) how the layered units are weighted within the layered hierarchy. Portfolios are designed to: 1) generate representative results for a given group (called normative case); 2) generate a result biased in a first direction; Or 3) produce a biased result in the second direction.

Depending on how it is adjusted, the bias may be directed to a group of subsets, such as a geographic or temporal group or a specific inherent attribute class within a particular set of investment securities. Within a layered hierarchy for a given group, a particular bias (or a lack thereof) can be managed through the hierarchy itself (via structure or property selection) or by a weight assigned to a particular layer unit.

Non-normative mix is a mixed portfolio designed to change from normative cases. Variations from the normative case are considered to be designed or algorithmic. Using the present invention, negative changes can be designed as alpha for short investment assumptions. Designing for positive fluctuations can be designed as alpha for long investment assumptions. For example, the distribution may be normal (based on normative cases) or non-normative. Non-normative distributions can either be positive distortions (to the right of normal), or negative distortions (to the left of normal). As described above, adjustments to weighting may be used to generate a portfolio with this kind of distribution.

Data Set Normalization and Probability Shaping

Financial indices are often used to assess the performance of financial instruments (instruments). The S & P 500 index is an example of a benchmark index for stock-based funds and the Lehman Brothers aggregate bond index. The S & P 500 index is the weighted market cap and allows the market value of individual stocks to be used to weight the value of the stock in the index. As a result, a change in the market value of a relatively large company has an unbalanced impact on the index. Since the value of goods (certificates) representing a relatively large enterprise fluctuates, the funds that track these indices also experience corresponding changes.

The mathematical processing according to the method described in the present invention can be applied to a large set of economic data and reduce variation and randomization of results, such as investment returns. In certain embodiments, multivariate algorithms can be used to organize large data sets. This method can be used to generate causal connections and can be used to perform real-time analysis.

Such a system may be configured to normalize the data set representing the investment securities. This normalization process includes statistical taxonomy based on the attributes of entities related to investment securities. The attributes used for normalization may be the type of the attributes described above, or other properties associated with the operation and the entity's assets associated with the security.

A number of investment securities may be organized into statistical classifications. A user interface for selecting among the attributes may be provided by the system. Such a system may include a statistical classification editor (referred to in some embodiments as a thesaurus editor). The statistical classification may be defined to be one or more attributes described above, alone or in combination. The statistical classification may also be defined based on the syntax and coding system described above. In certain cases, the statistical classification is also a layered unit.

Bankruptcy Example

The following example illustrates a use case for a mix of investment securities. In this example, a layered mixed portfolio of investment grade corporate debt securities is created.

Investment grade debt is a specific class of securities with well-defined ratings of revenues and well-defined risks. Each bond is valued by a third party evaluation agency. This rating captures the probability that a bond issuer will default. In the case of the default risk, in the case of one of the most appropriate risks for investing in such securities, corporate bonds with the same rating should maintain other variables such as maturity and have similar returns to maturity.

The maturity rate is the annual rate of return earned by the bondholder when all payments (coupon payments and pace values) are expected to be paid as expected and the bond is held at maturity at the current price of the bond. In other words, the maturity yield is the expected rate of return, which is the amount of return per annum of the principal amount of the investment that will be received if the bond is held until maturity. The yield on a given maturity date will be within the same or very tight range for all bonds that will be subject to comparative evaluation by these institutions. In other words, investment grade corporate debt securities act in anticipation.

While different investment grade debt securities have the same probability of default, the event that triggers the default varies depending on the issuer. In other words, different companies may face different risk factors related to specific intrinsic value of the company and its operations. Some of these factors are unique to such companies, and others of these factors are common to company groups. These risks are sensitive to industry risk, product risk, customer risk, interest rate, geographical, political, or economic factors and are risks that the company can not control or that relate to the CEO or management of the company. There are many company-specific attributes associated with the company's default risk. These include:

1) Financial leverage: Some companies are more or less utilized than others;

2) Original sales or asset-based attributes: These attributes are not accounting or performance attributes, but attributes that the company performs, such as production or litigation; Property associated with a company product such as a car, computer or bed, as well as a type of car, computer or bed; Attributes related to the customer of the company, such as consumer or business; Customer's customer-related attributes; Attributes associated with a business or individual operation geographic location; Properties related to the product and the materials the company uses to provide the product; Attributes associated with diversified industries or industrial segments that the company may operate; Attributes related to integrated, unintegrated, forward integrated, backward integrated or networked corporate business structure;

The nature of the government or macro-economic risk associated with the country in which the particular business or company is doing business; Attributes related to accounting or business risk identified by the business as a business core; It is the risk associated with a particular business or category linked to the segment by the investment community.

At any time, any one of these attribute factors or an industry event related to these attribute factors can cause the risk of bankruptcy within a particular company and increase its risk.

3) Management or Strategy: The Company has inherent risks based on its management, its decisions and strategies.

4) Corporate Asset Value: Bankruptcy (a kind of default) basically changes the terms of investment securities issued by a single company. If there are submissions for bankruptcy, the assumption of revenue based on continued operation includes an analysis of the liquidation scenario and the rights of each individual investment security in the company. In such cases, the investor will assess their ability to receive payments on a given security based on the location of the investment securities in the issuing company's capital structure. Investment securities are assigned priorities in liquidation. If the company 's significant assets are disposed of, these liquidation priorities specify which securities are to be processed and when they are received.

Each of these attributes is a potential source of default or bankruptcy risk for bond investors. Some of these properties may be related to a group of companies (for example, a company that produces cars, or a company whose operations are located in New Orleans). Because of this, a portfolio that does not control certain attributes can inadvertently be exposed to certain risks. When a member of a group is defaulted or filed for bankruptcy, other companies in the group may also be affected.

The present invention relates to a method for establishing a layered mixed portfolio of debt securities of an investment grade company in a manner that limits exposure to bankruptcy risk, corporate events, and other non-systemic risk factors by managing exposure of a particular company or industry portfolio . In the capitalized weighted debt portfolio, securities are weighted in proportion to their issuing size based on the total size of all issues in the portfolio.

With this unmanaged weighting method, it is possible for the company or industry that generated a large amount of debt to be overweighted in the portfolio. If one of these companies or industry has a negative event, such as bankruptcy, then the portfolio itself will be dramatically affected. A layered mixed portfolio is a tool for limiting specific exposures to calculated quantities and for doing so for individual firms as well as for broad industry or macroeconomic shocks.

An application of the present invention for managing an investment grade corporate debt portfolio default risk provides an explanation of one embodiment. Each debt security holds a risk level that is directly linked to the liquidation value of the company's significant assets. These risks are clearly separated from the market risk associated with the supply and demand of the debt securities itself, and also the market factors that will affect the required return on investment securities at fixed times, such as zero risk interest rates Clearly separated.

The system described herein can protect against non-systemic risks in the portfolio; That is, they can reduce or eliminate the impact of a single security or group of securities. This can be accomplished by grouping together firms in a risk group (hierarchy) based on non-systematic attributes, for example, companies with similar products, or similar customer bases. When properly implemented, tiering ensures that no single non-systemic risk presents a substantial risk to the entire portfolio. In this tiered mix, bankruptcy risk spreads across sufficiently distinct groups, allowing the impact of bankruptcy in any group or company.

The present invention can be used to create hierarchies as follows. For investment grade bonds, there may be three downgrade or bankruptcy causes. 1) company-specific risks; 2) industry-specific risks; And 3) product-specific risks. Investment grade bonds with a given rating should have the same probability of down rating or bankruptcy risk, but such a rating does not provide any information about possible causes of bankruptcy. In fact, in the case of bonds of the same class, the factors that cause the issuer to default are very different.

However, these bankruptcy factors are closely linked to the proprietary nature of the issuer. Using these attributes, it is possible to group the bonds into risk groups according to the characteristics of the issuer on the issuer's bankruptcy factors. Such a process can be repeated to form a nested hierarchy of risk groups, each of which has its own risk, but also has a risk associated with the parent group. These risk groups are then the layers that can be used to construct a layered investment mix portfolio. By layering investments in these strata, the opportunity for a negative event to have a significant impact on the portfolio can be significantly mitigated in a single company or industry.

Industry Risk Example

The following examples illustrate additional use cases for layered mixing of investment securities. In this example, a mix of investment securities of the S & P 900 Index stock is created. This mixed portfolio is a broad-based index that includes large and medium equity securities issued by US companies in various industries. The entire portfolio is a combination of the S & P 500 and the S & P 400 index, tracking large and medium equity American firms, respectively. Over time, such equity capital as a whole must display certain premium income related to non-risk investments such as US bonds.

In this example, the revenues of total capital-weighted S & P 900 are compared to the revenues of the same securities designed in a layered mix constructed using the inventive method. The attributes of these 900 function and related attributes are used to create a hierarchically nested hierarchy that groups similar companies together. The portfolio is rebalanced and returns each of the securities to its initial weight.

Layering provides substantial benefits in the environment when a particular industry experiences a large negative price shock, referred to as an "industrial bubble" collapse. As the industry bubble grows, the total market capitalization of companies in the industry grows, and therefore the total capital - the weight of industry in the weighted portfolio grows. In aggregate capital that lacks property-based controls for the weighting of both individual companies and similar groups of companies - in weighted finance, such bubbles can cause unintended overexposure to specific risk groups, Including risks. When the excessively weighted industrial bubble bursts, the portfolio becomes unbalanced. Even though the above industry bubble company rationally performs, the negative earnings of overweight companies may generate negative returns on the entire portfolio.

However, in a tiered mixed portfolio, the risk of an industrial bubble can be considerably mitigated by layering the whole, ensuring that such tiers coincide with distinct industry risks. In this way, industry-specific risks are isolated and may not produce imbalanced negative performance in the portfolio.

The growth and collapse of information technology stocks between 1997 and 2000 illustrate the benefits of a layered mixed portfolio. Using a syntactic structure attribute, a business action defines a group of companies that contain move, store or process information. This group of companies includes Microsoft, Cisco, Intel, AOL, Qualcomm, and other information technology companies like these.

In S & P 900, the 20 largest information technology stocks grew in weight in the late 1990s, and in 2000, they dominated the portfolio. At the end of 1997, 1998, and 1999, these 20 stocks aggregated 11.8%, 13.7%, and 20.4% of the S & P 900. In 2000, when the bubble collapsed, these stocks fell 42.3% in value and the S & P 900 fell to -6.9%. Excluding these information companies, the rest of the S & P 900 returned to 6.8%. That is, the 2000 "market-full" down turn was not a systematic failure; This was an uncontrolled overexposure to a single industry.

In a tiered mixed portfolio, these industry-specific risks can be controlled. In the layered mixed portfolio embodiment, the same 20 information companies were weighted at 2.9% and rebalanced quarterly with this weighting. In 2000, such an isolated group failed to achieve (its value dropped by 59.7%), but out of such a group, the layered mixed portfolio achieved healthy returns. Excluding these 20 companies, the tiered mixed portfolio yielded 21.3%. Total, tiered mixed portfolio grew 17.6% in 2000 and exceeded exactly the same total capital-weighted portfolio by 24.5%.

Total capital-weighted S & P 900 performance for the same total layered mixed portfolio explains how stratification can prevent non-systemic industry risks from affecting the entire portfolio.

System Architectures

The systems and methods described herein may be implemented in software or hardware or a combination thereof. The systems and methods described herein may be implemented using one or more computing devices that are physically or logically separate from one another or away from one another. In addition, various features of the methods described herein may be combined or integrated with other functions. One example of a computerized system for realizing the present invention is described with reference to FIG.

In certain embodiments, the illustrated system elements may be combined into a single hardware device or may be separate into multiple hardware devices. If multiple hardware devices are used, the hardware devices may be located physically close to each other or remotely located apart.

The method may be embodied in a computer program product accessible from a computer usable or computer readable storage medium providing program code for use by or in connection with a computer or instruction execution system. The computer-usable or computer-readable storage medium may be any device capable of containing or storing a program for use by or in connection with a computer or instruction execution system, apparatus, or apparatus.

A suitable data processing system for storing and executing the corresponding program code may include one or more processors coupled directly or indirectly to a computerized data storage device, such as a memory element. Input / output (I / O) devices (keyboard, display, pointing device, etc.) can be connected to the system. The network adapter may also be coupled to the system so that the data processing system may be connected to another data network or remote printer or storage device via a private or public network intervening between the systems. To provide information to a user, to have a display device such as a liquid crystal display (LCD) or other type of monitor, and to allow the user to provide input to the computer A keyboard or an input device, such as a mouse or trackball.

A computer program can be a set of statements 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 #, Visual Basic ™, JavaScript ™, PHP, XML, HTML, Programming language combinations and may be deployed in any form including standalone programs or modules, components, subroutines, or other units suitable for use in a computer environment.

The software includes, but is not limited to, firmware, resident software, microcode. Protocols such as SOAP / HTTP can be used to implement interfaces between programming modules. The components and functionality described herein may be implemented in a desktop operating system running in a virtual or non-virtual environment and utilize a suitable programming language for software development and may be implemented on a variety of platforms, including Microsoft Windows ™, Apple ™, Mac ™, iOS ™, Unix ™ X-Windows ™, and Linux ™. Such a system can be realized using a web application framework such as Ruby on Rails.

The processing system may communicate with a computerized data storage system. The data storage system may include non-related or related data storage devices such as MySQL ™ or other related databases. Other physical and logical database types may be used. Such data storage devices may be Microsoft SQL Server ™, Oracle ™, IBM DB2 ™, SQLITE ™, or other database software, related or other database server. Such a data storage device may store information identifying a syntactic tag and information required to operate with a syntactic tag.

In certain embodiments, the processing system may use object oriented programming and may store data in a data storage device. In these embodiments, the processing system uses an object-relational mapper (ORM) to store data objects in the associated database. Such systems and methods described herein can be realized using a number of physical data models. In one embodiment, an RDBMS can be used. In such an embodiment, the table in the RDBMS may include a column representing the coordinates. In the case of an economic system, companies, products, etc. that provide data can be stored in a table within the RDBMS. The table may have a predetermined relationship between them. The table may also have an appendix associated with the coordinates.

Suitable processors for execution of a program of instructions include general and special purpose microprocessors and include either a single processor or multiple processors or cores of a computer. The processor receives and stores instructions and data from a computerized data storage device, such as a read-only memory, a random access memory, or a combination of data storage devices as described herein. The processor may include processing circuitry or control circuitry to coordinate the operation and performance of the electronic device.

The processor also includes and is connected to communicate with one or more data storage devices for storing data. Such data storage devices include magnetic disks (including internal hard disks and removable disks), magneto-optical disks, optical disks, read-only memories, random access memories, and / or flash memory devices. Appropriate storage devices for implementing computer program instructions and data also include 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 any type of non-volatile memory, including CD-ROM and DVD-ROM disks. The processors and memories can be supplemented or integrated by ASICs (application-specific integrated circuits).

The systems, modules, and methods described herein may be implemented using software or hardware element combinations. The systems, modules, and methods described herein may be implemented using one or more virtual machines operating alone or in combination with one another. An applicable virtualization solution may be used to encapsulate the physical computing system machine platform into a virtual computing machine running under the control of a hardware computing platform or virtualization software running on the host. A virtual machine may have both virtual system hardware and guest operating system software.

The systems and methods described herein may be implemented on a client computer that includes back-end components such as a data server, or that includes middleware components such as an application server or an Internet server, or a graphical user interface or Internet browser, , ≪ / RTI > such as a front-end component. The components of the system may be connected by any form or medium of digital data communication, such as a communications network. Examples of communication networks include LANs, WANs, and computers and networks that form the Internet.

One or more embodiments of the invention may be practiced in other computer system configurations including portable devices, microprocessor systems, microprocessor based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where operations are performed by remote processing devices connected via a network.

While one or more embodiments of the invention have been described, various modifications, additions, substitutions, and equivalents may be resorted to, falling within the scope of the invention.

In the following description of the embodiments, reference is made to the accompanying drawings which form a part of the description, and show specific examples of the claimed protection ranges. Other embodiments may be used and modifications or modifications, such as structural changes, may be made. Such an embodiment, modification, or alteration does not depart from the scope of protection in the claims.

The steps herein may be provided in a certain order, and in some cases such an order may be altered so as to be provided at different times or in different orders without altering the operation of the systems and methods described. The described process can be performed in a different order. In addition, various calculations in this specification need not be performed in the order described, and other embodiments using different orders of calculations may be practiced. In addition to the reordering, the calculation may be decomposed into sub-calculations to have the same result.

Claims (30)

A computer-implemented method for constructing a representation of investment securities in a database,
Storing a set of data entities electronically within a database system, each data entity representing an investment securities, each investment securities being an economic entity;
Electronically assigning tags representing the attributes of economic entities to a plurality of investment securities;
Selecting a plurality of investment securities represented by data entities for inclusion in an index, fund or portfolio of investment securities;
Layered investment securities selected in at least a first tiered group and a second tiered group based on a first common attribute and a second common attribute and the economic entity represented by the first tiered group investment securities is one Or economic entities represented by investment securities in a second tiered group share a second common attribute, identified by one or more tags, that share a first common attribute, identified by two or more tags , Select one layered group of investment securities;
Layered investment securities selected as at least a first layered group and a second layered group based on the third common attribute and the fourth common attribute and the economic entity represented by the first layered group investment securities is one Or an economic entity represented by an investment security in a second tiered group that shares a third common attribute, identified by two or more tags, shares a fourth common attribute, identified by one or more tags ;
Said common attributes being shared among each of these groups;
Assign a negative or positive weight to one or more investment securities based on groups and subgroups in which the investment securities are stratified;
Electronically store layered groups and subgroups in a database as a representation of an index, a fund or a portfolio;
Electronically connecting to a database representation of a layered group or subgroup;
Electronically repeat the connected representation to calculate a negative or positive weight for one or more investment securities based on one or more groups and subgroups in which the investment securities are stratified; And
And assigning and electronically storing the negative or positive weight as one or more investment securities in a database. 2. The method of claim 1, further comprising: assigning a negative or positive weight to one or more securities groups, the sum of the weights for one or more groups being 1;
Assigning a negative or positive weight to one or more subgroups, the sum of the weights for one or more subgroups of the group being 1; And
Negative or positive weighted to one or more investment securities and the sum of the weighted sum of one or more investment securities of the subgroup is one; And
Assigning a negative or positive weight to the investment risk weight by calculating the weight of the securities in the subgroup, the weight of the subgroup in the group, and the weight of the group in the portfolio. 3. The method of claim 2,
Set a target weight for a group, subgroup, or investment security; And
Balancing the target weighting on groups, subgroups, or investment securities periodically,
The target weight for the group, sub-group, or investment securities may be weighted for the group,
And further based on the properties of the investment securities. The method of claim 2, further comprising transmitting information about the one or more data entities and one or more of the weights to one or more of an exchange, an index provider, an index calculator, an intermediary, an asset manager, an investment adviser, a fund manager or a securities trading platform And a computer-implemented method. 5. The computer-implemented method of claim 4, further comprising using one or more weights to perform securities, composite groups or portfolio transactions. The computer-implemented method of claim 1, wherein the one or more securities, subgroups, groups, or portfolios are stocks, bonds, derivatives, commodities, funds or exchange-traded funds. 2. The method of claim 1, wherein the group, subgroup, and investment securities comprise a layered portfolio architecture of cluster investment securities; And wherein the database electronically displays the layered portfolio architecture so that a computer processor can cluster investment securities and assign a weight to the investment securities. The computer-implemented method of claim 1, wherein the investment securities are selected from stocks, bonds, derivatives, commodities, funds or exchange-traded funds. The computer realization method according to claim 1, wherein the economic entity represented by the investment securities is a company, an asset, a resource, a product, or a debt. 10. A computer realization method according to claim 9, characterized in that the common attribute is related to the input, output, operation, product, supplier, customer or customer of the company, asset, resource or liability represented by the investment securities. The method of claim 1, further comprising: receiving a target performance from a user; And evaluating the portfolio, group or subgroup to determine whether those portfolios, groups or subgroups meet the target performance. 12. The computer realization method according to claim 11, wherein the performance metric is a measure of an expected profit, a measure of risk, variability, alpha, beta, or Sharp exponent. The computer-implemented method according to claim 1, wherein the group and its constituent subgroups comprise layered mixing units. 14. The computer implemented method of claim 13, wherein the layered mixing unit is modified or selected to possess a risk / return profile that is different from an uncontrolled grouping from a population of base securities. The computer-implemented method of claim 1, wherein the index is an un-invested set of investment securities, represented by data entities, and the portfolio is an invested set of investment securities represented by data entities. The computer-implemented method according to claim 1, wherein the first property and the second property are different. 17. The computer realization method according to claim 16, wherein the third and fourth attributes are different from each other and different from the first and second attributes. The method of claim 1, further comprising identifying additional common attributes as a basis for further layering into any number of layers, and
Further comprising layering the subgroup based on the commonality of the attributes. The method of claim 1, wherein the group or subgroup is layered to generate distributed performance Characterized in that the computer realization method. 20. The method of claim 19, wherein the performance is measured by a return, an expected return, a risk, a variance, a variance, an expected variance, expected volatility, or liquidity. The computer-implemented method according to claim 1, characterized in that the attribute is unique to an economic entity. delete 22. The computer-implemented method of claim 21, wherein the inherent property assigned to the data entity is maintained at a constant value for any time period that is validated by statistical significance testing. The method of claim 1, characterized in that the unique attribute is associated with a company or an issuer, or is related to a company or issuer in its value chain. 26. The method of claim 24, wherein the operations are selected from the group consisting of: procurement, transportation, storage, research and development, manufacturing, quality control, sale, exchange, finance, investment research, asset management, It is the responsibility of the customer or any other party to make a claim to the customer or to the customer or to the customer, And a service provider. delete 2. The computer implemented method of claim 1, wherein the economic entity attribute identifies the role of each economic entity in the process of converting an operation input of a company or issuer into an output. 28. The computer-implemented method of claim 27, wherein the attribute is functional. A system for executing an order in a computer environment for building a representation of an investment security in a database, the system comprising:
Storing a set of data entities electronically within a database system, each data entity representing an investment security, each investment security corresponding to an economic entity;
Electronically assigning tags representing the attributes of economic entities to a plurality of investment securities;
Selecting a plurality of investment securities represented by data entities for inclusion in an index of investment securities, a fund or portfolio;
Layered investment securities selected in at least a first tiered group and a second tiered group based on a first common attribute and a second common attribute and the economic entity represented by the first tiered group investment securities is one Or economic entities represented by investment securities in a second tiered group share a second common attribute, identified by one or more tags, that share a first common attribute, identified by two or more tags , Select one layered group of investment securities;
Layered investment securities selected as at least a first layered group and a second layered group based on the third common attribute and the fourth common attribute and the economic entity represented by the first layered group investment securities is one Or an economic entity represented by an investment security in a second tiered group that shares a third common attribute, identified by two or more tags, shares a fourth common attribute, identified by one or more tags ;
Electronically connecting to a representation in a database of a layered group or subgroup; Electronically repeat the connected representation to calculate a negative or positive weight for one or more investment securities based on one or more groups or subgroups in which the investment securities are stratified;
An electronic data storage device is provided for electronically storing a set of data entities in the database, each data entity representing an investment securities, the investment securities corresponding to economic entities;
Electronically storing in the database a negative or positive weight for the one or more investment securities; And
An electronic data storage device configured to electronically store layered groups and subgroups in the database as a representation of an index, a fund or a portfolio. 30. The computer readable medium of claim 29, wherein the computer processor assigns negative or positive weightings to one or more groups of securities, wherein the sum of the weights for one or more groups is one;
Assigning a negative or positive weight to one or more subgroups, the sum of the weights for one or more subgroups of the group being 1; And
Negative or positive weighted to one or more investment securities and the sum of the weighted sum of one or more investment securities of the subgroup is one; And
Wherein the system is further configured to assign a negative or positive weight to the investment risk weight by calculating the weight of the securities in the subgroup, the weight of the subgroup in the group, and the weight of the group in the portfolio.

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