WO2016092434A2

WO2016092434A2 - System and method for securing variable fidelity in hybrid networks

Info

Publication number: WO2016092434A2
Application number: PCT/IB2015/059349
Authority: WO
Inventors: Asha PRASUNA
Original assignee: Prasuna Asha
Priority date: 2014-12-12
Filing date: 2015-12-04
Publication date: 2016-06-16
Also published as: WO2016092434A3

Abstract

A system and method for managing a stock portfolio is disclosed. In an embodiment, the method includes segregating a stock portfolio into a plurality of data packets, each data packet containing a subset of a set of securities comprising the stock portfolio, based on a plurality of macroeconomic themes. A proportional weight of the each data packet is modified, based on a sensitivity indicator of each macroeconomic theme over a financial performance of the stock portfolio.

Description

SYSTEM AND METHOD FORSECURING VARIABLE FIDELITY IN HYBRID

NETWORKS

FIELD OF THE INVENTION

[0001] The present invention relates to a hybrid data network for providing variable fidelity of big data. More specifically, the present invention related to methods and systems employed within the hybrid data network for obtaining an enhanced financial return on a stock portfolio.

BACKGROUND OF THE INVENTION

[0002] In the field of financial investments, institutional investors and individuals employ a number of complex optimization techniques for obtaining a higher return on a capital investment. Such optimization techniques involve frequent research on historical price data of individual stocks, fundamental analysis of related industries, correlations between economic factors responsible for price fluctuations in individual stocks, and the like. Due to enormous complex data involved in such research, information relevant to making an investment decision in a stock market cannot be accurately and readily interpreted.

[0003] In order to avoid aforementioned research involving complex processing of data, a technique involving an indexed stock fund is usually applied. The indexed stock fund usually includes stocks in a similar proportion as present within a benchmark index. The benchmark index is a market capitalization weighted index of stocks of a set of chosen companies that are indicative of a performance of an economy. For instance, CNX Nifty, also called as Nifty 50 or Nifty, is a benchmark index of National Stock Exchange (NSE) of India for an Indian equity market. The Nifty 50 covers a plurality of sectors of the Indian economy, and is derived from a weighted capitalization of stocks of 50 top performing companies that are listed on the NSE. As the indexed stock fund usually invests in stocks of the benchmark index, the aforementioned research involved is eliminated and transaction costs involved is minimized. However, a disadvantage of aforementioned technique is that the indexed stock fund merely tracks a performance of the benchmark index, but may not outperform the benchmark index. As a primary objective of financial investments is to attain superior returns, an alternative system for generating higher return as compared to the benchmark index is needed.

[0004] Further, current benchmarks usually rely on market cap, PE ratios, and industry representation in creating and churning of portfolio weights of the stocks that comprise the benchmark index. Such a representation does not reflect or segregate the macroeconomic risks, like growth, interest rate, exchange rate and government policy that generally influence prices of the stocks in the corresponding economy. While economic policies and factors change dynamically, the macroeconomic risks involved tend to create fluctuations in the prices of the stocks, which are usually not tracked by the indexed funds, as the portfolio weighting in the indexed fund follows that of the benchmark index. Additionally, the portfolio of stocks in the benchmark index are usually constant, and are changed over a larger span of time, as compared to the change in economic policies and factors.

[0005] Hence there is a need for an alternate system and method that quantifies the macroeconomic risks and creates a weighted portfolio of stocks that captures the performance of stocks impacted by said macroeconomic risks. The alternate system and method should provide a superior return on capital investment, as compared to the benchmark index. Thus an alternate solution for improving financial returns that takes into consideration dynamic changes of macroeconomic risks is proposed.

SUMMARY OF THE INVENTION [0006] A system for managing a stock portfolio in a data network is disclosed. According to one aspect of the present invention, the system includes a variable fidelity gate for segregating a stock portfolio data packet into a plurality of data packets, such that each data packet includes a subset of a set of securities comprising the stock portfolio. A subset of securities segregated, usually has a similar sensitivity to a macroeconomic theme. Further, a big data analyzer, is configured to process a time series data of a set of macroeconomic themes, and a time series data of a financial performance of the stock portfolio over a predefined time period. The processing of said time series data, yields a sensitivity indicator for each macroeconomic theme. A data portfolio engine is configured to modify a proportional weight of the each data packet based on the sensitivity indicator of the each macroeconomic theme.

[0007] According to another aspect of the present invention, a method for managing a stock portfolio is disclosed. The method includes segregating a stock portfolio data packet, including a set of securities comprising the stock portfolio into a plurality of data packets based on a set of macroeconomic themes, where each data packet comprises of a subset of the set of securities. A sensitivity indicator is identified for each macroeconomic theme and a proportional weight of each data packet is modified based on the sensitivity indicator of the each macroeconomic theme, corresponding to the each data packet.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The advantages and features of the present disclosure will become better understood with reference to the following detailed description and claims taken in conjunction with the accompanying drawings, wherein like elements are identified with like symbols, and in which:

[0009] FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention.

[00010] FIG. 2 is a flowchart illustrating a method for managing a stock portfolio, according to an embodiment of the present invention.

[00011] FIG. 3 is a flowchart illustrating a method for managing a stock portfolio, according to another embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[00012] The present invention provides systems, methods, and computer program product for securing variable fidelity of data packets transmitted through a hybrid data network. Exemplary embodiment referred to herein are for illustrative purposes only and can be subject to variations in mode of working and structure. The fidelity of data packets containing financial data of a portfolio of stocks, can refer to a financial return from the portfolio of stocks. The portfolio of stocks, hereinafter referred to as a stock portfolio, is usually a set of financial securities, viz. stocks of companies that are listed on a stock exchange. A financial system churning the stock portfolio shall be typically involved in generating a higher financial return for a financial investment on the portfolio of stocks over a predefined period of time. The variable fidelity viz. variable financial return of the data packet being transmitted through a hybrid network can be achieved by configuring a plurality of network elements and devices of the hybrid data network, as explained through the drawings and description below. [00013] A system for securing variable fidelity of a financial portfolio of stocks hereinafter referred to as stock portfolio, through a hybrid data network is now described. The variable fidelity of the stock portfolio can be interpreted to include altering, enhancing or modifying a financial return or performance of the stock portfolio. In a typical scenario, the financial return of the stock portfolio is required to be enhanced or increased, thereby requiring a high fidelity from the system. FIG. 1, depicts the system, comprising of an interface device 102, which is connected to a big data analyzer 104, and a variable fidelity gate 106. The system further includes a first synchronizer bridge 114, communicatively coupled to the big data analyzer 104, the variable fidelity gate 106, and a data portfolio engine 112. A second synchronizer bridge 116 connects the data portfolio engine 110 to a high fidelity server 118. The first synchronizer bridge 114, can be a communication pathway adopting communication protocols for transmission of a data packet. The second synchronizer bridge 114, can be a communication path, adapted to transfer information relating to the data packet, and the data packet to a particular network element, for processing of the data packet. In an instance, where the particular network element is the data portfolio engine 112, the second synchronizer bridge 114, shall be adapted to transfer the data packet and the information relating to the data packet to the high fidelity server 118. The interface device 102, is an electronic device suitably adapted to receive user defined inputs by a user, where the user can be a fund manager, a trader or any interested person involved in enhancing the financial return of the stock portfolio. The interface device 100 can include an input device such as keyboard, touch screen interfaces, electronic display screens and data transducers. The system further includes a network server 108 and a database server 110, accessible by via one or more communication paths, usually present in a hybrid data network. [00014] The variable fidelity gate 104, is a network element designed to segregate, a data input into a plurality of data packets based on predefined network policies, user defined standards, or dynamic classifiers dependent on varying metrics. In an embodiment, where the data input is unstructured financial data, such as the stock portfolio, the dynamic classifiers can be a set of macroeconomic themes such as domestic growth of an economy, interest rates, government policy, foreign exchange rate, and such economic variables that impact a price of a stock or security that compose the stock portfolio.

[00015] The big data analyzer 102, can be a digital processor, configured to handle high volume, heterogeneous and discrete ASCII and UNICODE data. The big data analyzer 102, can be configured to receive the data from the interface device 102. The interface device 102, can be configured to communicate with the network server 108 and the database server 110, to fetch data. The network server 108, can be a server storing data relating to one or more stock portfolio, over a predefined period of time. In the embodiment, the data may include a second time series data of a Net Asset Value (NAV) returns of the stock portfolio, over a ten year period, and a proportional weight of one or more stocks comprising the stock portfolio over the ten year period. In the embodiment, the database server 110, can store a first time series data of the economic metrics viz. economic variables over the ten year period. The network server 108 and the database server 110, shall be updated periodically so as to maintain latest changes in the second time series data of the NAV returns and the first time series data of the set of macroeconomic themes.

[00016] The data portfolio engine 112, is another network element, that can be arranged to modify a dimension of each data packet in response to one or more technical indicators, received from the big data analyzer 102. In the embodiment, a proportional weight of the each data packet, can be varied based on a sensitivity indicator as received from the big data analyzer 102. The sensitivity indicator may illustrate to which of macroeconomic theme, does the NAV return of the stock portfolio bear a highest sensitivity in comparison to other macroeconomic themes. The high fidelity server 118, is usually a server of the stock exchange, where the stock portfolio is churned on a regular basis, by executing one or more financial trades. The stock exchange usually includes one or more servers such as the high fidelity server 118, where buying and selling of stocks of companies, and other financial securities such as options, derivatives, forex and commodities takes place. Examples of stock exchanges include National Stock Exchange of India (NSE), the Nasdaq Canada, American Stock Exchange (AMEX), the Boston Stock Exchange, the Chicago Stock Exchange, the Cincinnati Stock Exchange, the NASDAQ, the Jamaica Stock Exchange, the Bolsa Mexicana de Valores (BMV), the Euronext, the Helsinki Stock Exchange HEX, the Paris Stock Exchange, the Frankfurt Stock Exchange, the Italy Stock Exchange, the Amsterdam Stock Exchange, the Oslo Stock Exchange, the Lisbon Stock Exchange, the Warsaw Stock Exchange, the New York Stock Exchange (NYSE), the Pacific Exchange, the Philadelphia Stock Exchange, the Toronto Stock Exchange (TSX), the Alberta Stock Exchange (ASE), the Canadian Venture Exchange (CDNX),, the Bucharest Stock Exchange (BVB), the Russia Stock Exchange, the Madrid Stock Exchange, the Stockholm Stock Exchange, the Swiss Stock Exchange, the London Stock Exchange (FTSE), the Tel Aviv Stock Exchange, the Tokyo Stock Exchange (TSE), the Nagoya Stock Exchange, the Nasdaq Japan Market (NJ), the Stock Exchange of Hong Kong (SEHK), the Taiwan Stock Exchange, the Thailand Stock Exchange, the Kuala Lumpur Stock Exchange, the Korea Stock Exchange, the Singapore Stock Exchange, the Buenos Aires Stock Exchange, the Sao Paulo Stock Exchange (BOVESPA), the Rio de Janeiro Stock Exchange, the Brazilian Mercantile and Futures Exchange (BM&F), the Maringa Mercantile and Futures Exchange, the Santiago Stock Exchange, the Australian Stock Exchange (ASX), the New Zealand Stock Exchange (NZSE), and the Johannesburg Stock Exchange.

[00017] In the embodiment, the user can select the stock portfolio from a plurality of portfolio of stocks that trade in a stock market. The user is usually a fund manager or a stock advisory firm involved in churning the plurality of portfolio of stocksfor generating higher financial returns for institutional and retail investors. The interface device 102 can be used as a medium for the user to select the stock portfolio. For instance, the interface device can be a keyboard, wherein the user shall input parameters of the stock portfolio. The parameters of the stock portfolio may be required to identify the stock portfolio from the plurality of portfolio of stocks stored in the network server 108. The stock portfolio may include a number of stocks listed on a stock exchange. Data of the stock portfolio may be fetched in a stock portfolio data packet from the network server 108 to the variable fidelity gate 104. The stock portfolio data packet shall usually contain a set of securities, and each security shall have a variable weight expressed as a percentage of the total weight of the stock portfolio. A summation of the variable weight of the each security shall be 100. Hence the variable weight is usually expressed as a percentage of a total summation of the variable weight.

[00018] Further, a value of each security shall bear sensitivity to a macroeconomic theme. The macroeconomic theme usually includes a set of related economic variables. The Bloomberg's widely used Global Industry Classification System (GICS) may be used for identifying the set of macroeconomic themes. Instances of the macroeconomic theme may include domestic growth rate, government policy, interest rate, foreign exchange rate and the like. For instance, a stock that derives a bulk of earnings due to domestic secular consumption growth can be termed to be sensitive to a Gross Domestic Product (GDP) growth and hence can be classified under the domestic growth rate macroeconomic theme. Instances of sectors falling under the domestic growth rate macroeconomic theme, may include Consumer Durables & Apparel, Consumer Services, Food & Staples Retailing, Food Beverage & Tobacco, Health Care Equipment & Service, Household & Personal Products, Materials, Media, Retailing, and Telecommunication Services as these sectors are dependent on consumption growth. Further, a company that derives revenues from exports can be said to have sensitivity to foreign exchange rate and hence classified under the foreign exchange rate macroeconomic theme. Instances of sectors belonging to the foreign exchange rate macroeconomic theme are energy, pharmaceuticals, biotechnology, software & services, technology hardware, equipment, and metals. Next, sectors that get impacted due to changes in a government policy such as stocks in an infrastructure or energy sector can be said to be policy sensitive and classified under the government policy macroeconomic theme, and a company whose earning is sensitive to an interest rate can be said to be interest rate sensitive and classified under the interest rate macroeconomic theme. Instances of sectors belonging to the interest rate macroeconomic theme include automobiles & components, banks, diversified financials, insurance, transportation and real estate. The variable fidelity gate 104, shall split the stock portfolio data packet into a plurality of data packets, where each data packet shall include a subset of securities of companies that can be classified under a single macroeconomic theme. Thus in the instance, the set of securities that are domestic growth rate sensitive shall be segregated into a data packet that corresponds to the domestic growth rate macroeconomic theme. As a result, in the instance, the stock data packet shall be segregated into a set of four data packets, corresponding to the domestic growth macroeconomic theme, the interest rate macroeconomic theme, the government policy macroeconomic theme and the foreign exchange rate macroeconomic theme.

[00019] The set of macroeconomic themes can be chosen by the user, or can be predicted by the big data analyzer 106, based on known predictive techniques. The big data analyzer 106 can be designed to fetch a first time series data of a the set of macroeconomic themes from a database server 110, and indicate the the set of macroeconomic themes to the variable fidelity gate 104. The first time series data shall include values of a plurality of economic variables representing the set of macroeconomic themes. Instances of the plurality of economic variables include GDP factor at cost, Index of Industrial Production (IIP), and Purchasing Managers Index (PMI) for the domestic growth macroeconomic theme, a Repo Rate, a reverse Repo Rate, Wholesale Price Index (WPI), and fiscal deficit for the interest rate macroeconomic theme, Exchange rate for the foreign exchange rate macroeconomic theme, and the like. The first time series data can be usually fetched for a predefined time period of ten years, as required for predictive analysis of the stock portfolio data packet. Further, a second time series data including a financial return viz. Net Asset Value (NAV) return of the stock portfolio over the predefined period of ten years. The big data analyzer 106 shall perform a linear regression of the second time series data over the first time series data, to determine a sensitivity indicator viz. macro beta value, of the NAV return over each macroeconomic theme. Further, a R square measure can be applied to the regression equation to determine to which macroeconomic theme of the set of the macroeconomic themes is the NAV return of the stock portfolio most sensitive. A threshold of 0.5 is usually chosen for the sensitivity indicator, to determine a high sensitivity. A macroeconomic theme of the stock portfolio is claimed to be a critical macroeconomic theme if the sensitivity indicator of the macroeconomic theme is greater than or equal to 0.5.

[00020] The sensitivity indicator of the stock portfolio is communicated via the first synchronizer bridge 114, to the data portfolio engine 112. The plurality of data packets of the stock portfolio is transmitted from the variable fidelity gate 106, through the first the synchronizer bridge 114, to the data portfolio engine 112. In an embodiment, the first synchronizer bridge 114 can be a network element or device, configured to transmit the sensitivity indicator of a particular stock portfolio and the plurality of data packets of the particular stock portfolio to a single data portfolio engine 112. Alternatively, in the embodiment, where more than one stock portfolio is managed, it is essential that the first synchronizer bridge 114, transmits the plurality of data packets of a stock portfolio to the data portfolio engine 112, to which the sensitivity indicator of the stock portfolio is transmitted. In the alternate embodiment, the system may include more than one data portfolio engine 112, to process a plurality of stock portfolio.

[00021] Further, based on the sensitivity indicator, the data portfolio engine

112 shall increase a proportional weight of a data packet corresponding to the critical macroeconomic theme. The proportional weight of the data packet is basically a summation of variable weights of the subset of securities comprising the data packet. Accordingly, the proportional weight of a data packet that corresponds to a macroeconomic theme other than the critical macroeconomic theme, of the stock portfolio viz. for which the sensitivity indicator is relatively low, shall be reduced. The proportional weight of the data packet is usually varied by altering the variable weight of one or more securities of the subset of the set of securities comprising the data packet. An output of the data portfolio engine 112 is basically a structured financial data, represented by the plurality of data packets of modified proportional weights based on the sensitivity indicator of the set of macroeconomic themes. The data portfolio engine 112 shall generate a set of trading instructions as required for buying or selling the set of securities present in the plurality of data packets. A plurality of electronic signals comprising the set of trading instructions shall be transmitted by the data portfolio engine 112, through the second synchronizer bridge 116, to the high fidelity server 118. Further, the plurality of modified data packets shall also be transmitted to the high fidelity server 118. Based on the plurality of signals, the high fidelity server 118 shall execute one or more financial trades, pertaining to the set of securities as represented by the plurality of data packets. As a resultof the variation in the proportional weight of the plurality of data packets of the stock portfolio data packet, and the financial trades executed on the modified plurality of data packets, the financial return of the stock portfolio for a particular time period is enhanced in comparison to a stock portfolio that imitates a composition of an existing benchmark index such as SENSEX of India. The enhanced financial return is achieved as the existing benchmark indexdoes not usually alter a weight of securities comprising the benchmark index when a macroeconomic theme that impacts a price of one or more securities, undergoes fluctuations. As the present invention takes into consideration the fluctuations in the set of macroeconomic themes while structuring the stock portfolio, the financial return is enhanced for a current time period.

[00022] FIG. 2 is a flowchart illustrating a method for managing a stock portfolio, according to an embodiment of the present invention. At step 202, a stock portfolio data packet is segregated into a plurality of data packets such that each data packet comprises of a subset of the set of securities of the stock portfolio. Further the subset of securities of the each data packet comprise of stocks of one or more companies that belong to sectors of the economy that sensitive in similar manner to a macroeconomic theme. The macroeconomic theme is usually a set of related economic variables. The Bloomberg's widely used Global Industry Classification System (GICS) may be used for identifying a set of macroeconomic themes. Instances of a macroeconomic theme include domestic growth rate, government policy, interest rate, foreign exchange rate and the like. For instance, a stock that derives a bulk of earnings due to domestic secular consumption growth can be termed to be sensitive to a Gross Domestic Product (GDP) growth and hence can be classified under the domestic growth rate macroeconomic theme. Instances of sectors falling under the domestic growth rate macroeconomic thememay include Consumer Durables & Apparel, Consumer Services, Food & Staples Retailing, Food Beverage & Tobacco, Health Care Equipment & Service, Household & Personal Products, Materials, Media, Retailing, and Telecommunication Services, as these sectors are dependent on consumption growth. Further, a company that derives revenues from exports can be said to have sensitivity to foreign exchange rate and hence classified under the foreign exchange rate macroeconomic theme. Instances of sectors belonging to the foreign exchange rate macroeconomic theme are, energy, pharmaceuticals, biotechnology, software & services, technology hardware, equipment, and metals. Next, sectors that get impacted due to changes in a government policy such as stocks in an infrastructure or energy sector can be said to be policy sensitive and classified under the government policy macroeconomic theme, and a company whose earning is sensitive to an interest rate can be said to be interest rate sensitive and classified under the interest rate macroeconomic theme. Instances of sectors belonging to the interest rate macroeconomic theme include, automobiles & components, banks, diversified financials, insurance, transportation and real estate.

[00023] Further at step 204, a first time series data relating to the set of macroeconomic themes, as identified in step 202, is processed over a second time series data including a financial return viz. performance of the stock portfolio. The first time series data shall usually include data of the set of related economic variables over a predefined time period such as ten years prior to a current year. Similarly the second time series data shall include the financial return such as a Net Asset Value (NAV) return of the stock portfolio over the past ten years. The NAV of the stock portfolio shall be regressed over each of the first time series data of the set of macroeconomic themes, to yield a sensitivity indicator viz. macro beta for each macroeconomic them. The macro beta, of a macroeconomic theme, shall indicate a measure of tendency to change of the NAV return of the stock portfolio with respect to fluctuations in the macroeconomic theme. The macroeconomic theme that attains a highest sensitivity indicator is determined as a critical macroeconomic theme. The critical macroeconomic theme is usually a set of economic factors to which the NAV return of the fund bears the highest sensitivity. Hence in order to attain higher financial return, it is pertinent for the data packet corresponding to the critical macroeconomic theme, to have a higher proportional weight.

[00024] As a result, at step 206, the proportional weight of the each data packet is modified based on the sensitivity indicator of the each macroeconomic theme, corresponding to the each data packet. In practice, a variable weight of one or more securities of the subset of the set of securities, comprising the data packet corresponding to the critical macroeconomic theme, is increased in a proportional manner, and a corresponding decrease in a variable weight of one or more securities of another data packet corresponding to a macroeconomic theme other than the critical macroeconomic theme is effected. Aforesaid adjustment of variable weights of the set of securities is performed such that the summation of variable weights of the set of securities is maintained at 100.

[00025] FIG. 3 is a flowchart illustrating a method of managing a stock portfolio according to another embodiment of the present invention. At step 302, an identifier of a stock portfolio data packet is received from an interface device. The interface device can be an input device such as a keyboard. A user managing the stock portfolio, such as a fund manager, can provide the identifier. The identifier shall usually be used to identify the stock portfolio as stored in a database server. The stock portfolio is usually a set of securities of companies listed on a stock exchange. The stock portfolio can be stored as a data packet, hereinafter referred to as the stock portfolio data packet, on the database server. At step 304, the stock data packet is retrieved from the database server, into a variable fidelity gate. The variable fidelity gate is usually a network deice, configured to segregate and classify data. At step 306, the stock data packet is segregated into a plurality of data packets by the variable fidelity gate. The segregation can be carried out on the basis of economic classifiers of a set of macroeconomic themes. The set of macroeconomic themes, can include one or more macroeconomic themes. The plurality of data packets shall include a subset of securities of companies that bear a similar sensitivity to a macroeconomic theme. Instances of macroeconomic themes include domestic growth, interest rate, and foreign exchange rate and government policies. It can be understood that each macroeconomic theme, involves economic variables that affect a segment of an economy, thereby impacting prices of stocks of companies operating in the segment of the economy. [00026] Further, at step 308, a first time series data relating to the set of macroeconomic themes, the macroeconomic themes corresponding to the plurality of data packets into which the stock portfolio data packet is segregated into in step 306, is retrieved from a network server. The first time series data can include values of a set of economic variables comprising the set of macroeconomic themes over a predefined time period. At step 310, a second time series data including a financial performance viz. Net Asset Value (NAV) return of the stock portfolio over the predefined time period is retrieved from the network server. At step 312, a linear regression analysis is performed over the first time series data and the second time series data, to yield a sensitivity indicator for each macroeconomic theme. At step 314, a proportional weight of each data packet is modified based on the sensitivity indicator of the each macroeconomic theme, corresponding to the each macroeconomic theme, by a data portfolio engine. As a result, a critical macroeconomic theme is identified for the sensitivity indicator of value greater than 0.5. The proportional weight of the data packet corresponding to the critical macroeconomic theme is usually increased, and the proportional weight of the data packet corresponding to the macroeconomic theme, other than the critical macroeconomic theme, is proportionally decreased. At step 316, the data portfolio engine generates a plurality of signals comprising a set of trading instructions, and at step 318, transmits the plurality of signals and the modified data packets to a high fidelity server. Further, at step 320, the high fidelity server, executes a number of financial trades on the modified data packets, in response to the received plurality of signals. The executed financial trades, on the modified data packets, shall enhance the financial return of the stock portfolio in a current time period. [00027] The present invention may be embodied as a system, a method, or a computer program product. The computer program product may include a computer program code embodied in one or more computer readable media for carrying out operations for various aspects of the present invention. The one or more computer readable media may include an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus, or device or a suitable combination of the foregoing. The computer readable media may also include a computestorage medium having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read-only memory (CD-ROM), an optical storage device, a magnetic storage device or a suitable combination of the foregoing. A computer program code embodied on the one or more computer readable media may be transmitted using any appropriate medium including but not limited to wireless, wired, optical fiber cable, RF, or a suitable combination of the foregoing.

[00028] The computer program code may be written in a combination of one or more programming languages. The computer program code may be configured to execute entirely on a user's computing device, entirely on a server device or a remote computer, or partly on the user's computing device and partly on the server device or the remote computer. A remote computer and the server device may communicate with the user's computing device through a communications network, such as a local area network, a wide area network or an Internet.

[00029] FIG. 4 illustrates a generalized example of a computing environment

400. The computing environment 400 is not illustrated to include any limitation within a scope of use or functionality of various embodiments described in the present invention. [00030] With reference to FIG. 4, the computing environment 400shallinclude preliminary components, such as at least one processor 402 and a memory 404. The processor 402 can be a real or a virtual processor that executes computer readable instructions. A multi-processing system shall include multiple processors that execute computer readable instructions, for increasing a processing power. The memory 404can be a volatile memory viz. registers, cache, or a RAM; or a non-volatile memory viz. ROM, EEPROM, flash memory, and the like, or a combination of the volatile memory and the non-volatile memory. In some embodiments, the memory 404shall storea software code 416 that embodies functionality and techniques of the present invention.

[00031] In an instance the computing environment 400, may include additional components, such as one or more storage device 406, one or more input device 410, one or more output device 408, and one or more communication channel412. In an embodiment, an interconnection mechanism such as a bus, controller, or a network, can interconnect the additional components of the computing environment 400. Typically, an operating system provides an operating environment for running the software code 416, within the computing environment 400, and for coordinating activities of the preliminary components and the additional components of the computing environment 400.

[00032] The storage device 406, can include one or more removable or nonremovable, electronic devices. Instances of the storage device 406 include a magnetic disk, a magnetic tape, a cassette, a CD-ROM, a CD-RW, a DVD, or any other medium which can be used to store information and which can be accessed within the computing environment 400. In some embodiments, the storage 416 stores instructions for the software code 416. [00033] The input device410 can be a touch input device such as a keyboard, mouse, pen, a trackball, a touch screen, a voice input device, a scanning device, a digital camera, or any other device that provides input to the computing environment 400. The output device408can be a video display, a printer, a speaker, or another device that provides output from the computing environment 400.

[00034] The communication channel412 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, audio or video information, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the communication medium can include wired or wireless techniques implemented with an electrical, optical, RF, infrared, acoustic, or other carrier.

[00035] Implementations can be described in the general context of computer- readable media. Computer-readable media are any available media that can be accessed within a computing environment. By way of example, and not limitation, within the computing environment 400, computer-readable media include the memory 404, the storage device 406, the communication media, and combinations of any of the above.

[00036] Having described and illustrated the principles of the present invention with reference to described embodiments, it shall be recognized that the described embodiments can be modified in arrangement and detail without departing from the principles. It should be understood that the system, processes, methods or computer program products, as described herein are not related or limited to any particular type of computing environment, unless indicated otherwise. A plurality of general purpose or specialized computing environments may be used with or perform operations in accordance with the techniques of the present invention. Elements of the described embodiments as included in the software code 416, shall be implemented in hardware and vice versa.As will be appreciated by those ordinary skilled in the art, the foregoing example, demonstrations, and method steps may be implemented by suitable code on a processor base system, such as general purpose or special purpose computer.

[00037] The techniques, computer program products, methods, processes, and system, as described in present description, herein include a preferred embodiment for carrying out the present invention. Various modifications to the preferred embodiment can be readily apparent to those skilled in the art and some features of the present invention may be used without the corresponding use of other features. Accordingly, the present invention is not intended to be limited to the embodiment shown but is to be accorded the widest scope consistent with the principles and features described herein. The present description has been intended for a purpose of obtaining a patent. It is further, intended by following claims to cover the various embodiments, modifications, and variations that may fall within a scope of subject matter described.

Claims

We claim:

1. A system for managing a stock portfolio in a data network, the system comprising: a variable fidelity gate, configured to segregate a stock portfolio data packet into a plurality of data packets based on a set of macroeconomic themes; a big data analyzer, configured to identify for each macroeconomic theme a sensitivity indicator of the stock portfolio; and a data portfolio engine, configured to modify a proportional weight of each data packet based on the identified sensitivity indicator of the macroeconomic theme corresponding to the each data packet.

2. The system of claim 1 , wherein the stock portfolio is stored as a stock portfolio data packet in a database server.

3. The system of claim 2, wherein: the stock portfolio data packet comprises of a set of securities, whereby a variable weight is assigned to each security; and a data packet comprises of a subset of the set of securities, whereby each security of the subset bears a similar sensitivity to a macroeconomic theme.

4. The system of claim 2, further comprising: an interface device, configured to receive an identifier of the stock portfolio data packet.

5. The system of claim 4, wherein the variable fidelity gate is further configured to: process a first time-series data relating to the set of macroeconomic themes, and a second time series data including a performance of the stock portfolio over a predefined time period, and retrieve the stock portfolio data packet from the database server, based on the identifier

6. The system of claim 2, wherein the big data analyzer is further configured to retrieve the first time series data from a network server and the second time series data from the database server.

7. The system of claim 1, further comprising: a first synchronizer bridge, configured to transmit the plurality of data packets, and the sensitivity indicator to the data portfolio engine; and a second synchronizer bridge to transmit a plurality of signals comprising a set of trading instructions and the modified data packet to a high fidelity server.

8. The system of claim 7, wherein the data portfolio engine is further configured to generate the plurality of signals comprising the set of trading instructions for processing the modified data packet.

9. The system of claim 7, wherein the high fidelity server is configured to execute one or more financial trade based on the received plurality of signals and the received modified data packet.

10. The system of claim 3, wherein the proportional weight of the each data packet is a summation of the variable weight of the each security in the subset.

11. The system of claim 3, wherein the proportional weight of the data packet is a value between 0.1 and 0.99.

12. The system of claim 1, wherein the sensitivity indicator of value greater than or equal to 0.5, indicates a critical macroeconomic theme.

13. The system of claim 10, wherein the proportional weight of a data packet of the critical macroeconomic theme is modified to a value greater than a previous proportional weight of the data packet.

14. The system of claim 1, wherein the macroeconomic theme includes one of a domestic growth, a foreign exchange rate, an interest rate, and a government policy.

15. The system of claim 1, wherein the big data analyzer is configured to apply a statistical regression technique for processing thefirst time-series data and the second time series data.

16. The system of claim 1, wherein the sensitivity indicator provides a measure of change of the performance of the stock portfolio in response to a change in the macroeconomic theme.

17. The system of claim 5, wherein the performance of the stock portfolio includes a Net Asset Value return of the stock portfolio over the predefined time period.

18. A method for managing a stock portfolio, the method comprising: segregating, by a network element, a stock portfolio data packet into a plurality of data packets, based on a set of macroeconomic themes; identifying, by a network element, a sensitivity indicator of the stock portfolio to each macroeconomic theme; and modifying, by a computing device, a proportional weight of each data packet based on the sensitivity indicator of the each macroeconomic theme corresponding to the each data packet.

19. The method of claim 18, wherein the network element includes a variable fidelity gate.

20. The method of claim 18, wherein the network element includes a big data analyzer.

21. The method of claim 18, wherein the computing device, includes a data portfolio engine.

22. The method of claim 18, wherein the stock portfolio is stored as a stock portfolio data packet in a database server.

23. The method of claim 18, wherein;

the stock portfolio data packet comprises of a set of securities, whereby a variable weight is assigned to each security; and the each data packet comprises of a subset of the set of securities, whereby each security of the subset bears a similar sensitivity to the each macroeconomic theme.

24. The method of claim 18, further comprising:

receiving an identifier of the stock portfolio data packet; retrieving, by the network element, the stock portfolio data packet from the database server based on the identifier; and retrievinga first time series data relating to the set of macroeconomic themes, from a network server, and a second time series data relating to a performance of the stock portfolio over a predefined time period, from the database server.

25. The method of claim 18, further comprising:

generating, by the computing device, a plurality of signals comprising a set of trading instructions for the modified data packets; and transmitting, by the computing device, the plurality of signals and the modified data packets, to a high fidelity server.

26. The method of claim 25, further comprising:

executing one or more financial trade, by the high fidelity server, in response to the received plurality of signals and the received modified data packets.

27. The method of claim 23, wherein the proportional weight of the each data packet is a summation of the variable weight of the each security in the subset of securities.

28. The method of claim 27, wherein the proportional weight of the data packet is a value between 0.1 and 0.99.

29. The method of claim 18, wherein the sensitivity indicator of value greater than or equal to 0.5 and less than 1.0, indicates a critical macroeconomic theme.

30. The method of claim 27, wherein the proportional weight of the data packet of the critical macroeconomic theme is modified to a value greater than a previous proportional weight of the data packet.

31. The method of claim 18, wherein the macroeconomic theme includes one of a domestic growth, a foreign exchange rate, an interest rate, and a government policy.

32. The method of claim 24, wherein the big data analyzer is configured to apply a statistical regression technique over the first time -series data and the second time series data, to identify the sensitivity indicator.

33. The method of claim 18, wherein the sensitivity indicator provides a measure of change of the performance of the stock portfolio in response to a change in the macroeconomic theme.

34. The method of claim 24, wherein the performance of the stock portfolio includes a Net Asset Value return of the stock portfolio over the predefined time period.

35. A non-transitory computer readable medium having stored thereon instructions for managing a stock portfolio which when executed by at least one processor, causes the processor to perform steps comprising: segregating a stock portfolio data packet into a plurality of data packets, based on a set of of macroeconomic themes; identifying a sensitivity indicator of the stock portfolio to each macroeconomic theme; and modifying a proportional weight of each data packet based on, the sensitivity indicator of the each macroeconomic theme corresponding to the each data packet.