US20200219195A1 - Fund of funds analysis tool - Google Patents
Fund of funds analysis tool Download PDFInfo
- Publication number
- US20200219195A1 US20200219195A1 US16/780,860 US202016780860A US2020219195A1 US 20200219195 A1 US20200219195 A1 US 20200219195A1 US 202016780860 A US202016780860 A US 202016780860A US 2020219195 A1 US2020219195 A1 US 2020219195A1
- Authority
- US
- United States
- Prior art keywords
- asset
- funds
- classification
- fund
- return
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
Definitions
- This disclosure relates to financial risk measurement, and more particularly to systems and methods for computing risk measures associated with fund of funds investments.
- TDF target date fund
- a TDF is a type of mutual fund structured by an entity (e.g., investment firm, mutual fund company, insurance company, and the like.) that automatically rebalances its portfolio to a more conservative asset allocation as a specific date target approaches (e.g., a retirement date).
- Entities typically create TDFs in a series, each TDF of the series having a different target date and portfolio mix selected from other funds provided by the entity.
- each TDF of the series shares a common glide path, which is a formula that describes how portfolio allocations for each TDF change over time.
- TDFs can improve overall investment and retirement planning, there is an increased need among plan sponsors, advisors, and investors for independent analysis and ratings of TDF series. As each TDF of a series shares the same glide path, there is a need to objectively quantify the risk associated with performance of these funds over the glide path to ensure consistency with investment objectives.
- the system is configured to provide at least one objective analytic that indicates the level of risk associated with a fund of funds investment strategy.
- the system provides both a quantitative and qualitative risk measurement value using actual portfolio holdings data of underlying funds that can be used to compare multi-faceted investment portfolios.
- Various aspects of the system relate to computing risk measurement values for an entity based on return volatility of fund assets.
- a computer-implemented method includes identifying a first fund, the first fund having a glide path and a first volatility of return value, identifying a second fund, the second fund having the glide path and a second volatility of return value, the first fund and the second fund being associated with an entity, and computing a risk score associated with the entity based upon the first volatility of return value and the second volatility of return value.
- the method also includes generating a signal associated with the risk score and transmitting the signal.
- the step of computing the risk score includes weighting the first volatility of return value by a corresponding expected account balance for the first fund, weighting the second volatility of return value by a corresponding expected account balance for the second fund, and summing the weighted first and second volatility of return values.
- the first and the second funds are target date funds, and each of the target date funds includes a plurality of mutual funds.
- the method also may include displaying graphically a plurality of computed risk scores associated with different entities on a display device.
- the method includes computing the first and the second volatility of return values based on historical rate of return values and expected rate of return values that are associated with asset classifications corresponding to assets underlying the glide path.
- the method can also include generating the historical rate of return values by computing a standard deviation of asset classification returns for each of the asset classifications over a time interval.
- the method can also include averaging the computed standard deviation of asset classification returns for each asset classification over the time interval, averaging asset classification returns for each asset classification over the time interval, and then computing a volatility premium and volatility free rate for each of the first and second funds using the averaged asset classification returns, averaged standard deviation of asset classification returns, and a data regression technique.
- Computing the expected rate of return values for each asset classification can include multiplying the computed volatility premium by the averaged standard deviation of asset classification returns and summing the volatility free rate to the multiplied amount.
- the method includes calculating a weighted average expected return along the time interval of the glide path by multiplying the calculated expected rate of return values of each asset classification by a proportion of the asset classification allocated in each fund over the time interval, and then summing the multiplied amounts.
- a system as well as articles that include a machine-readable medium storing machine-readable instructions for implementing the various techniques, are disclosed. Details of various implementations are discussed in greater detail below.
- the system can provide objective and independent analysis of a series of fund of funds investments. As each series of fund of funds is associated with a risk score, the system can provide a comparison of risk associated with series of fund of funds provided by different entities. This can be particularly advantageous when plan sponsors and/or advisors wish to ensure that risks undertaken by entities are consistent with plan and/or client demographics.
- Another advantage relates to scalability.
- the system can be utilized to analyze not only target date funds, but a wide array of fund of funds investments that may be suitable to investors.
- a further benefit of the system relates to accuracy: For example, the system relies on the long-term performance of asset classifications underlying funds, not short or mid-term performance of asset classifications, thereby minimizing the effect of asset classification return anomalies on computed risk scores.
- FIG. 1 is a schematic of an exemplary computer-based fund of funds analysis system.
- FIG. 2 illustrates an exemplary method for calculating a risk score.
- FIG. 3 illustrates an exemplary glide path shared for a series of target date funds.
- FIGS. 4A-4B illustrate asset allocations for two target date funds shown in FIG. 3 .
- FIG. 5 illustrates exemplary historical returns for asset classifications.
- FIG. 6 illustrates exemplary asset classification returns and risk levels.
- FIG. 7 illustrates an exemplary calculation of expected returns for asset classifications.
- FIGS. 8A-8B illustrate weighted average portfolio expected returns.
- FIG. 9 illustrates an exemplary account balance over a time interval.
- FIG. 10 illustrates a computed risk score for an example entity.
- FIG. 11 illustrates exemplary identifiers for association with a computed risk score.
- FIGS. 12A-12B illustrate rating scores for a plurality of entities.
- FIG. 1 shows a computer-based system for analyzing fund of funds investments.
- the system 10 is configured to calculate a risk level for a series of target date funds (TDFs) associated with an entity in response to a request.
- TDFs target date funds
- the phrase “series of target date funds” and “series of TDFs” refer to a plurality of target date funds that share a common glide path.
- TDFs target date funds
- Example fund of funds (FoF) investments that can be analyzed with the system 10 include, but are not limited to, mutual fund FoF, hedge fund FoF, private equity FoF, investment trust FoF, and combinations thereof.
- the system 10 is configured to include an access device 12 that is in communication with a server 14 over a network 16 .
- the access device 12 can include a personal computer, laptop computer, or other type of electronic device, such as a cellular phone or Personal Digital Assistant (PDA).
- PDA Personal Digital Assistant
- the access device 12 is coupled to I/O devices (not shown) that include a keyboard in combination with a pointing device such as a mouse for sending web page requests to the server 14 .
- memory of the access device 12 is configured to include a browser 12 A that is used to request and receive information from the server 14 .
- the system can support multiple access devices.
- the network 16 can include various devices such as routers, server, and switching elements connected in an Intranet, Extranet or Internet configuration.
- the network 16 uses wired communications to transfer information between the access device 12 and the server 14 .
- the network 16 employs wireless communication protocols.
- the network 16 employs a combination of wired and wireless technologies.
- the server device 14 preferably includes a processor 18 , such as a central processing unit (‘CPU’), random access memory (‘RAM’) 20 , input-output devices 22 , such as a display device (not shown) and keyboard (not shown), and non-volatile memory 24 , all of which are interconnected via a common bus 26 and controlled by the processor 18 .
- the non-volatile memory 24 is configured to include a web server 28 for processing requests from the access device.
- the web server 28 is configured to send requested web pages to the browser 12 A of the access device 12 in response to a web page request.
- the web server 28 communicates with the web browser 12 A using one or more communication protocols, such as HTTP (HyperText Transfer Protocol).
- HTTP HyperText Transfer Protocol
- the web server 28 is configured to include the Java 2 Platform, Enterprise Edition (‘J2EE’) for providing a plurality of screens included in a user interface displayed on the browser 12 A.
- J2EE Java 2 Platform, Enterprise Edition
- the web server 28 provides a run-time environment that includes software modules for computing risk levels associated with fund of funds (FoF) investments.
- the run-time environment includes a classification module 30 to categorize assets underlying each fund of the series of TDFs, a risk module 32 to compute a risk score for one or more series of TDFs, a participant module 34 to compute expected account balances for each fund of the series of TDFs, a portfolio module 36 to compute a portfolio expected return, a rating module 38 to associate computed risk scores with qualitative identifiers, and a display module 40 to display computed risk scores and qualitative identifiers associated with an entity. Details of the software modules 30 , 32 , 34 , 36 , 38 , 40 configured in the run-time environment are discussed in further detail below.
- a data store 42 is provided that is utilized by software modules 30 , 32 , 34 , 36 , 38 , 40 to access and store information relating to individual TDFs, as well as the series of TDFs.
- the data store 44 is a relational database.
- the data store 42 is a directory server, such as a Lightweight Directory Access Protocol (‘LDAP’) server.
- LDAP Lightweight Directory Access Protocol
- the data store 42 is a configured area in the non-volatile memory 24 of the device server 14 .
- the data store 42 can be distributed across various servers and be accessible to the server 14 over the network 16 , or alternatively, coupled directly to the server 14 , or be configured in an area of non-volatile memory 24 of the server 14 .
- system 10 shown in FIG. 1 is one implementation of the disclosure.
- Other system implementations of the disclosure may include additional structures that are not shown, such as secondary storage and additional computational devices.
- various other implementations of the disclosure include fewer structures than those shown in FIG. 1 .
- the disclosure is implemented on a single computing device in a non-networked standalone configuration. Data input is communicated to the computing device via an input device, such as a keyboard and/or mouse. Data output of the system is communicated from the computing device to a display device, such as a computer monitor.
- steps 50 , 52 , 56 , 58 , 60 and 64 - 69 of FIG. 2 are executed by the risk module 32 of FIG. 1 .
- Step 54 of the method is executed by the classification module 30 of FIG. 1
- step 62 is executed by the portfolio module 36 of FIG. 1 .
- Output from the participant module 34 of FIG. 1 is used by the risk module in step 64
- the signal generated by the risk module 32 in step 69 optionally includes output from the rating module 38 shown in FIG. 1 .
- the risk module 32 identifies a series of TDFs provided by an entity in response to a request 50 .
- entity refers to any investment firm, mutual fund company, insurance company, or the like, that provides a fund of funds (FoF) investment.
- the fund of funds investment is a target date fund.
- the request is sent from the browser 12 A and identifies the entity that provides the FoF investment.
- the request is received from one of the input/output devices 22 included in the server device 14 and identifies the entity that provides the FoF investment. Accordingly, both the network 16 and the access device 12 shown in FIG. 1 are not required structures in the non-networked stand-alone implementation.
- the request includes one or more entities that provide FoF investments.
- the risk module 32 determines a glide path for the series of funds 52 .
- each TDF of a series of TDFs shares a common glide path, which describes a portfolio allocation mix for each TDF of the series of TDFs at various time intervals.
- the risk module 32 accesses the glide path associated with a series of TDFs from the data store 42 .
- each TDF 70 A-F of the series of TDFs utilizes the glide path 70 to determine the percentage of underlying funds (e.g., equity, fixed income, etc.) to include in each TDF portfolio.
- underlying funds e.g., equity, fixed income, etc.
- the portfolio allocation mix of a first TDF having a later target date approaches that of a TDF in the same series having an earlier target date.
- the portfolio allocation mix of the 2040 TDF 70 B will approximate the portfolio allocation mix of the 2015 TDF 70 G over time.
- FIG. 4A an example portfolio allocation mix for the 2040 TDF 70 B at month one-hundred twenty-two (122) is illustrated.
- the glide path 70 defines that the 2040 TDF 70 B includes seven ( 7 ) different underlying funds 74 each weighted separately based on a point along the glide path 70 .
- the 2045 TDF 70 B portfolio includes a ‘Family 1 Large Capitalization fund’ 74 A that is approximately thirty percent (30%) 74 C of the total portfolio allocation, and a ‘Family 1 Government Bond Fund’ 74 B is approximately two percent (2%) 74 D of the total portfolio allocation.
- FIG. 4B an example portfolio allocation mix for the 2015 TDF 70 G is illustrated.
- a larger proportion of the 2015 TDF 70 G portfolio 74 is weighted in fixed income securities, rather than equity-based securities.
- the ‘Family 1 Large Capitalization fund’ 74 A is approximately fifteen percent (15%) 74 C of the total portfolio funds 74
- the ‘Family 1 Government Bond Fund’ 74 B is approximately fifteen percent (15%) 74 D of the total portfolio 74 .
- the risk module 40 provides glide path as well as underlying funds information, such as fund weighting information and asset classification information, to a user for further analysis of TDF dynamics.
- the classification module 30 categorizes the underlying funds of each of the series of TDFs 54 .
- the classification module 30 categorizes each of the underlying funds into one of several asset classifications based on characteristics of the assets comprising each underlying fund.
- the classification module 30 queries the data store 42 for asset information (e.g., holdings data) of each underlying fund and then associates characteristics of the holdings data with one of a plurality of pre-defined asset classification types.
- the risk module 32 calculates a historical risk profile for each of the identified asset classifications 56 .
- the risk module 32 generates historical rate of return values for each identified classification of each TDF in the series of TDFs.
- the risk module 32 generates historical rate of return values by computing a standard deviation of monthly asset classification returns 82 generated over a twenty-year (20) time interval 84 for identified asset classifications 80 .
- the risk module 32 determines the historical returns for each of the asset classifications over the time interval, the risk module 32 estimates the historical relationship between risk and return for each asset classification included in the series 56 . In one implementation, the risk module 32 averages the monthly returns 88 and standard deviation of monthly returns 86 , from FIG. 6 , for each of the asset classifications, and then determines the relationship between the averages.
- the risk module 32 determines the relationship between average returns and standard deviation of returns by regressing the averaged monthly asset classification returns 88 on the averaged standard deviation of monthly returns 86 using a regression technique.
- the risk module 32 uses a linear regression technique to determine the relationship.
- the risk module 32 depicts the risk and reward relationship in the form of a regression line 82 , which is displayed graphically to a user of the system 10 .
- the regression line 82 is displayed on the browser 12 A of the access device 12 shown in FIG. 1 . In a non-networked stand-alone configuration, the regression line 82 is displayed on a display device of the stand-alone computing device.
- the risk module 32 next computes an expected return for each asset classification 60 .
- the risk module 32 first computes a volatility premium 90 and a volatility free rate 92 for the series of TDFs.
- volatility premium refers to the amount of additional return expected for each additional unit of risk undertaken.
- volatility free rate refers to a level of return based on zero (0) volatility.
- the risk module 32 computes the volatility premium 90 from the slope of the regression line 84 and computes the volatility free rate 92 from an intercept of the regression line 84 .
- the risk module 32 computes the slope and intercept of the regression line 84 using the following formulas, respectively:
- a The intercept point of the regression line and the y axis.
- N Number of selected investment classifications
- the risk module 32 computes an expected return 91 for each asset classification by multiplying the computed volatility premium 90 for the series of TDFs by the averaged standard deviation of return for each asset classification, and then sums the volatility free rate 92 to the multiplied amount.
- FIG. 7 An example of computing a monthly expected asset classification return for one of a plurality of asset classifications is shown in connection with FIG. 7 .
- the risk module 32 accesses averaged standard deviation of return values 91 for each asset classification from the data store 42 .
- the ‘International Multi-Cap Core’ classification has an averaged standard deviation of return of ‘4.83’.
- the risk module 32 then computes the monthly expected return 96 for the ‘International Multi-Cap Core’ classification by multiplying the averaged standard deviation of return 91 value ‘4.83’ by the computed volatility premium value ‘0.084’ for the series 90 .
- the risk module 32 then adds the computed volatility free rate 92 value of ‘0.363’ to that sum, resulting in a computed expected monthly return 96 of ‘0.768’ for the ‘International Multi-Cap Core’ classification.
- the risk module 32 is also configured to compute expected annualized returns 98 based on the computed expected monthly returns 96 for each asset classification.
- the portfolio module 36 computes a total portfolio expected return for each time interval along the guide path using the computed expected return classifications 62 .
- the portfolio module 36 applies the computed expected returns generated from the risk module 32 to each interval of the glide path, and then calculates an expected total portfolio return for each time interval using asset classification weights defined by the glide path.
- FIG. 8A an example expected portfolio return for a series of funds provided by an entity at a first time-interval is shown.
- a particular asset allocation mix is defined for a series of TDFs.
- the glide path defines the asset allocation mix in terms of weights 104 .
- the classification module 30 identified an asset classification 102 and the risk module 32 computed both expected monthly returns 106 and expected annualized returns 108 for each asset classification.
- the portfolio module 36 uses the weights 104 and computed expected returns 106 , 108 to compute weighted expected portfolio returns 109 , which comprises a weighted expected monthly return 110 and a weighted expected annual return 112 , along the guide path. For example, as shown in the FIG. 8A example, in one implementation, at month one-hundred and twenty two (122), the portfolio module 36 computes the weighted expected monthly return 110 for the series of TDFs by multiplying the weight 104 associated with each asset classification at month (122) by the corresponding computed expected monthly return 106 for the asset classification at month (122) and then sums these products.
- the portfolio module 36 computes the weighted expected annualized return 112 at month (122) for the series of funds by multiplying the weight 104 associated with each asset classification at month (122) by the corresponding computed expected annualized return 108 for the asset classification at month (122) and then sums these products.
- FIG. 8B illustrates the same techniques executed by the portfolio module 36 to compute a total portfolio expected return at month three-hundred and sixty-eight (368) for the series of funds.
- the risk module 32 applies the total portfolio expected returns to estimated account balances along the guide path 64 .
- the risk module 32 weights the total portfolio expected returns by estimated account balances for each fund along the glide path.
- fund expected returns by estimated account balances the contribution of returns and actual contributions to account balances over time is obtained.
- the estimated fund account balance 120 is based at least in part on the amount of contribution 122 provided to the fund and the return of assets 124 underlying the fund.
- the amount of contribution 122 provides a much larger percentage of the estimated fund account balance 120 the earlier the fund is from the target date.
- the amount of contributions 122 provided to the fund typically contributes a lesser percentage of total account balance and the return of assets 124 underlying the fund provide a greater percentage of the estimated fund account balance 120 .
- the participant module 34 of the system 10 determines the amount of contributions 122 provided to the fund over time based on expected contributions to the fund. For example, in one implementation, the participant module 34 bases the amount of contributions 122 on at least one of a contributor salary, a contributor savings rate, a contributor salary increase(s), and/or a contribution schedule for contributors.
- the contributor salary, contributor salary increase(s), contributor savings rate, and/or contribution schedule can be dynamically defined by a user of the system and/or be included in the request. Alternatively, the contributor salary, contributor salary increase(s), contributor savings rate, and/or contribution schedule are predefined in the system 10 .
- the term ‘contributor’ refers to any company, partnership, sole proprietor, or individual that adds value to the fund.
- the risk module 32 computes classification return correlations and volatility of return values for each of the funds comprising the series of funds 66 .
- the risk module 32 computes historical correlations between asset classifications over a ten (10) year period and then computes an expected portfolio standard deviation for each of the finds in the target date series. Each of the computed portfolio standard deviations represents a volatility of return value for each fund in the series.
- the risk module 32 computes a risk score for the entity by weighting the volatility of return values for each of the funds of the series of funds by estimated account balances of each fund along the guide path, and then summing the weighted volatilities 68 .
- the risk score provides an indication of how aggressive or conservative the investment style of an entity is.
- An example risk score computation is illustrated in FIG. 10 .
- the risk module 132 computes a weighting 138 for each of the funds in the series by dividing the current account balance 136 of each fund by the estimated account balance corresponding to each fund. The risk module 32 then multiples each computed account balance weight 138 by a corresponding volatility of return 134 value (e.g., standard deviation) for each fund, and then sums the weighted volatility of return values for each fund in the series to compute a risk score 140 for the entity.
- a volatility of return 134 value e.g., standard deviation
- the rating module 38 associates the computed risk score with one of a plurality of qualitative identifiers describing an investment style for the entity.
- the rating module 38 compares the computed risk score to a plurality of pre-defined risk score range values associated with the identifiers, and then determines which of the identifiers to associate with the computed risk score based on the comparison.
- the plurality of TDF ratings 142 include identifiers entitled “Aggressive” 142 A, “Moderately Aggressive” 142 B, “Moderate” 142 C, “Moderately Conservative” 142 D, and “Conservative” 142 E, each have a corresponding risk score range value 144 A-E, respectively.
- the ratings module 38 compares the computed risk score to each of the risk score range values 144 A-E and then associates one of the plurality of identifiers with the computed risk score based on the comparison.
- the risk module 32 generates and transmits a signal associated with the risk score in response to the request 69 .
- the transmitted signal includes the computed risk score and corresponding qualitative identifier which are displayed to a user of the system 10 by the display module 40 .
- the signal includes a plurality of computed risk scores and corresponding qualitative identifiers for several different entities.
- the display module 40 of the web server 28 may implement various technologies to display contents of the signal depending on system 10 configuration.
- the display module 40 utilizes eXtensible Markup Language (XML) to display risk scores associated with different entities on the browser 12 A of the access device 12 .
- the display module 40 is formed from one or more enterprise java beans (EJBs) that execute and graphically display entity names in an order corresponding to computed risk scores for each entity. For example, as shown in FIG. 12A , in one implementation, the display module 40 plots each entity name 150 A- 150 H on a risk/return scale 152 in an order corresponding to each entity's computed risk score.
- EJBs enterprise java beans
- the display module 40 then displays the plot 150 to a user of the system 10 for comparison purposes.
- the display module 40 displays one or more risk scores 164 for entities 162 and corresponding qualitative identifiers 166 in a tabular text format 160 on a display device of the server 14 .
- the display module 40 displays both the plot of entity names 152 and the tabular text format 160 on a display device of the system 10 .
- Various features of the system may be implemented in hardware, software, or a combination of hardware and software.
- some features of the system may be implemented in one or more computer programs executing on programmable computers.
- Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system or other machine.
- each such computer program may be stored on a storage medium such as read-only-memory (ROM) readable by a general or special purpose programmable computer or processor, for configuring and operating the computer to perform the functions described above.
- ROM read-only-memory
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
Description
- The present application claims benefit of priority to U.S. Non-provisional application Ser. No. 12/765,365 filed Apr. 22, 2010, entitled Fund of Funds Analysis Tool, the entirety of which is hereby incorporated herein by reference.
- This disclosure relates to financial risk measurement, and more particularly to systems and methods for computing risk measures associated with fund of funds investments.
- Fund of funds (FoF) investments have become increasingly popular over the years. Companies and organizations that assume financial responsibility for individuals and groups, such as plan sponsors and advisers, use FoF investments to diversify risk. FoF investments hold a portfolio of other investment funds rather than investing directly in stocks, bonds, or other securities. One type of FoF investment that has garnered increased interest by plan sponsors, advisors, as well as individuals, is a target date fund (TDF). A TDF is a type of mutual fund structured by an entity (e.g., investment firm, mutual fund company, insurance company, and the like.) that automatically rebalances its portfolio to a more conservative asset allocation as a specific date target approaches (e.g., a retirement date).
- Entities typically create TDFs in a series, each TDF of the series having a different target date and portfolio mix selected from other funds provided by the entity. In addition, each TDF of the series shares a common glide path, which is a formula that describes how portfolio allocations for each TDF change over time.
- While TDFs can improve overall investment and retirement planning, there is an increased need among plan sponsors, advisors, and investors for independent analysis and ratings of TDF series. As each TDF of a series shares the same glide path, there is a need to objectively quantify the risk associated with performance of these funds over the glide path to ensure consistency with investment objectives.
- Further, there is a need to understand the risk levels of a series of target date funds on a relative basis, as the glide paths of TDFs having same target dates can vary greatly between investment firms. For example, some entities assume that participants desire a high degree of safety and liquidity, and therefore include more fixed income securities than other asset classes in their TDFs, while other entities assume that participants will hold onto the TDFs, and therefore include more equity securities in their TDFs, reflecting more potential for both risk and reward along a longer time horizon.
- Accordingly, there is a need for improved systems and techniques for analyzing and comparing fund of funds investments.
- Systems and techniques are disclosed to analyze fund of funds investments. The system is configured to provide at least one objective analytic that indicates the level of risk associated with a fund of funds investment strategy. The system provides both a quantitative and qualitative risk measurement value using actual portfolio holdings data of underlying funds that can be used to compare multi-faceted investment portfolios.
- Various aspects of the system relate to computing risk measurement values for an entity based on return volatility of fund assets.
- For example, according to one aspect, a computer-implemented method includes identifying a first fund, the first fund having a glide path and a first volatility of return value, identifying a second fund, the second fund having the glide path and a second volatility of return value, the first fund and the second fund being associated with an entity, and computing a risk score associated with the entity based upon the first volatility of return value and the second volatility of return value. The method also includes generating a signal associated with the risk score and transmitting the signal.
- In one implementation, the step of computing the risk score includes weighting the first volatility of return value by a corresponding expected account balance for the first fund, weighting the second volatility of return value by a corresponding expected account balance for the second fund, and summing the weighted first and second volatility of return values. In some implementations, the first and the second funds are target date funds, and each of the target date funds includes a plurality of mutual funds. The method also may include displaying graphically a plurality of computed risk scores associated with different entities on a display device.
- In another implementation, the method includes computing the first and the second volatility of return values based on historical rate of return values and expected rate of return values that are associated with asset classifications corresponding to assets underlying the glide path. The method can also include generating the historical rate of return values by computing a standard deviation of asset classification returns for each of the asset classifications over a time interval.
- The method can also include averaging the computed standard deviation of asset classification returns for each asset classification over the time interval, averaging asset classification returns for each asset classification over the time interval, and then computing a volatility premium and volatility free rate for each of the first and second funds using the averaged asset classification returns, averaged standard deviation of asset classification returns, and a data regression technique. Computing the expected rate of return values for each asset classification can include multiplying the computed volatility premium by the averaged standard deviation of asset classification returns and summing the volatility free rate to the multiplied amount.
- In yet another implementation, the method includes calculating a weighted average expected return along the time interval of the glide path by multiplying the calculated expected rate of return values of each asset classification by a proportion of the asset classification allocated in each fund over the time interval, and then summing the multiplied amounts.
- A system, as well as articles that include a machine-readable medium storing machine-readable instructions for implementing the various techniques, are disclosed. Details of various implementations are discussed in greater detail below.
- In some implementations, one or more of the following advantages may be present. For example, the system can provide objective and independent analysis of a series of fund of funds investments. As each series of fund of funds is associated with a risk score, the system can provide a comparison of risk associated with series of fund of funds provided by different entities. This can be particularly advantageous when plan sponsors and/or advisors wish to ensure that risks undertaken by entities are consistent with plan and/or client demographics.
- Another advantage relates to scalability. For example, the system can be utilized to analyze not only target date funds, but a wide array of fund of funds investments that may be suitable to investors.
- A further benefit of the system relates to accuracy: For example, the system relies on the long-term performance of asset classifications underlying funds, not short or mid-term performance of asset classifications, thereby minimizing the effect of asset classification return anomalies on computed risk scores.
- Additional features and advantages will be readily apparent from the following detailed description, the accompanying drawings and the claims.
-
FIG. 1 is a schematic of an exemplary computer-based fund of funds analysis system. -
FIG. 2 illustrates an exemplary method for calculating a risk score. -
FIG. 3 illustrates an exemplary glide path shared for a series of target date funds. -
FIGS. 4A-4B illustrate asset allocations for two target date funds shown inFIG. 3 . -
FIG. 5 illustrates exemplary historical returns for asset classifications. -
FIG. 6 illustrates exemplary asset classification returns and risk levels. -
FIG. 7 illustrates an exemplary calculation of expected returns for asset classifications. -
FIGS. 8A-8B illustrate weighted average portfolio expected returns. -
FIG. 9 illustrates an exemplary account balance over a time interval. -
FIG. 10 illustrates a computed risk score for an example entity. -
FIG. 11 illustrates exemplary identifiers for association with a computed risk score. -
FIGS. 12A-12B illustrate rating scores for a plurality of entities. - Like reference symbols in the various drawings indicate like elements.
-
FIG. 1 shows a computer-based system for analyzing fund of funds investments. Thesystem 10 is configured to calculate a risk level for a series of target date funds (TDFs) associated with an entity in response to a request. As used herein, the phrase “series of target date funds” and “series of TDFs” refer to a plurality of target date funds that share a common glide path. Although the example discussed below relates to TDFs, it will be appreciated by one skilled in the art that the systems and techniques disclosed herein can be utilized across various types of fund of funds investments. Example fund of funds (FoF) investments that can be analyzed with thesystem 10 include, but are not limited to, mutual fund FoF, hedge fund FoF, private equity FoF, investment trust FoF, and combinations thereof. - As shown in
FIG. 1 , in one implementation, thesystem 10 is configured to include anaccess device 12 that is in communication with aserver 14 over anetwork 16. Theaccess device 12 can include a personal computer, laptop computer, or other type of electronic device, such as a cellular phone or Personal Digital Assistant (PDA). In one embodiment, for example, theaccess device 12 is coupled to I/O devices (not shown) that include a keyboard in combination with a pointing device such as a mouse for sending web page requests to theserver 14. Preferably, memory of theaccess device 12 is configured to include a browser 12A that is used to request and receive information from theserver 14. Although only oneaccess device 12 is shown inFIG. 1 , the system can support multiple access devices. - The
network 16 can include various devices such as routers, server, and switching elements connected in an Intranet, Extranet or Internet configuration. In some implementations, thenetwork 16 uses wired communications to transfer information between theaccess device 12 and theserver 14. In another embodiment, thenetwork 16 employs wireless communication protocols. In yet other embodiments, thenetwork 16 employs a combination of wired and wireless technologies. - As shown in
FIG. 1 , in one implementation, theserver device 14 preferably includes aprocessor 18, such as a central processing unit (‘CPU’), random access memory (‘RAM’) 20, input-output devices 22, such as a display device (not shown) and keyboard (not shown), and non-volatile memory 24, all of which are interconnected via a common bus 26 and controlled by theprocessor 18. In one implementation, as shown in theFIG. 1 example, the non-volatile memory 24 is configured to include aweb server 28 for processing requests from the access device. - The
web server 28 is configured to send requested web pages to the browser 12A of theaccess device 12 in response to a web page request. Theweb server 28 communicates with the web browser 12A using one or more communication protocols, such as HTTP (HyperText Transfer Protocol). In one embodiment, theweb server 28 is configured to include theJava 2 Platform, Enterprise Edition (‘J2EE’) for providing a plurality of screens included in a user interface displayed on the browser 12A. - The
web server 28 provides a run-time environment that includes software modules for computing risk levels associated with fund of funds (FoF) investments. As shown inFIG. 1 , in one implementation, the run-time environment includes aclassification module 30 to categorize assets underlying each fund of the series of TDFs, arisk module 32 to compute a risk score for one or more series of TDFs, aparticipant module 34 to compute expected account balances for each fund of the series of TDFs, aportfolio module 36 to compute a portfolio expected return, arating module 38 to associate computed risk scores with qualitative identifiers, and adisplay module 40 to display computed risk scores and qualitative identifiers associated with an entity. Details of thesoftware modules - In one implementation, as shown in
FIG. 1 , a data store 42 is provided that is utilized bysoftware modules device server 14. Although the data store 42 shown inFIG. 1 is connected to thenetwork 16, it will be appreciated by one skilled in the art that the data store 42 can be distributed across various servers and be accessible to theserver 14 over thenetwork 16, or alternatively, coupled directly to theserver 14, or be configured in an area of non-volatile memory 24 of theserver 14. - It should be noted that the
system 10 shown inFIG. 1 is one implementation of the disclosure. Other system implementations of the disclosure may include additional structures that are not shown, such as secondary storage and additional computational devices. In addition, various other implementations of the disclosure include fewer structures than those shown inFIG. 1 . For example, in one implementation, the disclosure is implemented on a single computing device in a non-networked standalone configuration. Data input is communicated to the computing device via an input device, such as a keyboard and/or mouse. Data output of the system is communicated from the computing device to a display device, such as a computer monitor. - Turning now to
FIG. 2 , a method of calculating a risk score associated with an entity is disclosed. In one implementation, for example, steps 50, 52, 56, 58, 60 and 64-69 ofFIG. 2 are executed by therisk module 32 ofFIG. 1 .Step 54 of the method is executed by theclassification module 30 ofFIG. 1 , and step 62 is executed by theportfolio module 36 ofFIG. 1 . Output from theparticipant module 34 ofFIG. 1 is used by the risk module instep 64, and the signal generated by therisk module 32 instep 69 optionally includes output from therating module 38 shown inFIG. 1 . - As shown in
FIG. 2 , in one implementation, therisk module 32 identifies a series of TDFs provided by an entity in response to arequest 50. As used herein, the term ‘entity’ refers to any investment firm, mutual fund company, insurance company, or the like, that provides a fund of funds (FoF) investment. In one implementation, the fund of funds investment is a target date fund. - Various techniques may be employed by the
system 10 to receive requests. For example, in one implementation, the request is sent from the browser 12A and identifies the entity that provides the FoF investment. In the non-networked stand-alone configuration described previously, the request is received from one of the input/output devices 22 included in theserver device 14 and identifies the entity that provides the FoF investment. Accordingly, both thenetwork 16 and theaccess device 12 shown inFIG. 1 are not required structures in the non-networked stand-alone implementation. In yet other implementations, the request includes one or more entities that provide FoF investments. - Next, as shown in
FIG. 2 , therisk module 32 determines a glide path for the series offunds 52. As described previously, each TDF of a series of TDFs shares a common glide path, which describes a portfolio allocation mix for each TDF of the series of TDFs at various time intervals. In one implementation, therisk module 32 accesses the glide path associated with a series of TDFs from the data store 42. - In appreciation of the present invention, an
example glide path 70 for a series of TDFs is shown in connection withFIG. 3 . EachTDF 70A-F of the series of TDFs utilizes theglide path 70 to determine the percentage of underlying funds (e.g., equity, fixed income, etc.) to include in each TDF portfolio. Notably, as time proceeds forward, the portfolio allocation mix of a first TDF having a later target date approaches that of a TDF in the same series having an earlier target date. For example, in the example shown inFIG. 3 , the portfolio allocation mix of the 2040TDF 70B will approximate the portfolio allocation mix of the 2015TDF 70G over time. - Turning now to
FIG. 4A , an example portfolio allocation mix for the 2040TDF 70B at month one-hundred twenty-two (122) is illustrated. As shown in theFIG. 4A example, theglide path 70 defines that the 2040TDF 70B includes seven (7) differentunderlying funds 74 each weighted separately based on a point along theglide path 70. For example, as shown in theFIG. 4A , at month one-hundred twenty-two (122), the 2045TDF 70B portfolio includes a ‘Family 1 Large Capitalization fund’ 74A that is approximately thirty percent (30%) 74C of the total portfolio allocation, and a ‘Family 1 Government Bond Fund’ 74B is approximately two percent (2%) 74D of the total portfolio allocation. - Referring now to
FIG. 4B , an example portfolio allocation mix for the 2015TDF 70G is illustrated. As shown in theFIG. 4B example, a larger proportion of the 2015TDF 70G portfolioFIG. 4B , at month three hundred and sixty eight (368), the ‘Family 1 Large Capitalization fund’ 74A is approximately fifteen percent (15%) 74C of thetotal portfolio funds 74 and the ‘Family 1 Government Bond Fund’ 74B is approximately fifteen percent (15%) 74D of thetotal portfolio 74. - Advantageously, in several implementations, the
risk module 40 provides glide path as well as underlying funds information, such as fund weighting information and asset classification information, to a user for further analysis of TDF dynamics. - Referring back to
FIG. 2 , once therisk module 32 determines the glide path for the series, theclassification module 30 categorizes the underlying funds of each of the series ofTDFs 54. Theclassification module 30 categorizes each of the underlying funds into one of several asset classifications based on characteristics of the assets comprising each underlying fund. In one implementation, for example, theclassification module 30 queries the data store 42 for asset information (e.g., holdings data) of each underlying fund and then associates characteristics of the holdings data with one of a plurality of pre-defined asset classification types. - Next, once the
classification module 30 determines asset classifications, therisk module 32 calculates a historical risk profile for each of the identified asset classifications 56. In some implementations, for example, therisk module 32 generates historical rate of return values for each identified classification of each TDF in the series of TDFs. For example, in one implementation, as shown inFIG. 5 , therisk module 32 generates historical rate of return values by computing a standard deviation of monthly asset classification returns 82 generated over a twenty-year (20)time interval 84 for identifiedasset classifications 80. - Once the
risk module 32 determines the historical returns for each of the asset classifications over the time interval, therisk module 32 estimates the historical relationship between risk and return for each asset classification included in the series 56. In one implementation, therisk module 32 averages themonthly returns 88 and standard deviation ofmonthly returns 86, fromFIG. 6 , for each of the asset classifications, and then determines the relationship between the averages. - For example, in some implementations, turning now to
FIG. 6 , therisk module 32 determines the relationship between average returns and standard deviation of returns by regressing the averaged monthly asset classification returns 88 on the averaged standard deviation ofmonthly returns 86 using a regression technique. In one implementation, for example, therisk module 32 uses a linear regression technique to determine the relationship. In one implementation, as shown inFIG. 6 , therisk module 32 depicts the risk and reward relationship in the form of aregression line 82, which is displayed graphically to a user of thesystem 10. For example, in one implementation, theregression line 82 is displayed on the browser 12A of theaccess device 12 shown inFIG. 1 . In a non-networked stand-alone configuration, theregression line 82 is displayed on a display device of the stand-alone computing device. - Referring back to
FIG. 2 , based on the historical relationship of risk and return, therisk module 32 next computes an expected return for eachasset classification 60. As shown inFIG. 7 , in one implementation, for example, therisk module 32 first computes avolatility premium 90 and a volatilityfree rate 92 for the series of TDFs. As used herein the phrase “volatility premium” 90 refers to the amount of additional return expected for each additional unit of risk undertaken. The phrase “volatility free rate” 92 refers to a level of return based on zero (0) volatility. In one implementation, for example, therisk module 32 computes thevolatility premium 90 from the slope of theregression line 84 and computes the volatilityfree rate 92 from an intercept of theregression line 84. - In one implementation, the
risk module 32 computes the slope and intercept of theregression line 84 using the following formulas, respectively: -
Slope of regression line(b)=(ΣXY−(ΣXΣY)/N)/(ΣX2−(ΣX)2/N) -
Intercept of regression line(a)=(ΣY−b(ΣX))/N) - b=The slope of the regression line
- a=The intercept point of the regression line and the y axis.
- N=Number of selected investment classifications
- X=Standard Deviation of Monthly Returns for investment classifications
- Y=Average monthly historical returns for investment classifications
- ΣXY=Sum of the product of Standard Deviations and Average Monthly Returns
- ΣX=Sum of Standard Deviations
- ΣY=Sum of Average Monthly Returns
- ΣX2=Sum of squared Standard Deviations
- Once the
volatility premium 90 and volatilityfree rate 92 are computed for the series of TDFs, therisk module 32 computes an expectedreturn 91 for each asset classification by multiplying the computedvolatility premium 90 for the series of TDFs by the averaged standard deviation of return for each asset classification, and then sums the volatilityfree rate 92 to the multiplied amount. - An example of computing a monthly expected asset classification return for one of a plurality of asset classifications is shown in connection with
FIG. 7 . In one implementation, for example, therisk module 32 accesses averaged standard deviation of return values 91 for each asset classification from the data store 42. As shown in theFIG. 7 example, the ‘International Multi-Cap Core’ classification has an averaged standard deviation of return of ‘4.83’. Therisk module 32 then computes the monthly expectedreturn 96 for the ‘International Multi-Cap Core’ classification by multiplying the averaged standard deviation ofreturn 91 value ‘4.83’ by the computed volatility premium value ‘0.084’ for theseries 90. Therisk module 32 then adds the computed volatilityfree rate 92 value of ‘0.363’ to that sum, resulting in a computed expectedmonthly return 96 of ‘0.768’ for the ‘International Multi-Cap Core’ classification. In some implementations, as shown in theFIG. 7 example, therisk module 32 is also configured to compute expected annualized returns 98 based on the computed expected monthly returns 96 for each asset classification. - Referring back to
FIG. 2 , once therisk module 32 computes expected returns for the asset classifications, theportfolio module 36 computes a total portfolio expected return for each time interval along the guide path using the computed expected return classifications 62. In one implementation, theportfolio module 36 applies the computed expected returns generated from therisk module 32 to each interval of the glide path, and then calculates an expected total portfolio return for each time interval using asset classification weights defined by the glide path. - For example, referring now to
FIG. 8A , an example expected portfolio return for a series of funds provided by an entity at a first time-interval is shown. As explained previously, along each point of a glide path a particular asset allocation mix is defined for a series of TDFs. Accordingly, as shown in theFIG. 8A example, at month one-hundred and twenty-two (122) 114, the glide path defines the asset allocation mix in terms ofweights 104. As explained previously, in one implementation for each underlying fund of a TDF, theclassification module 30 identified anasset classification 102 and therisk module 32 computed both expectedmonthly returns 106 and expected annualized returns 108 for each asset classification. - The
portfolio module 36 uses theweights 104 and computed expected returns 106, 108 to compute weighted expected portfolio returns 109, which comprises a weighted expectedmonthly return 110 and a weighted expectedannual return 112, along the guide path. For example, as shown in theFIG. 8A example, in one implementation, at month one-hundred and twenty two (122), theportfolio module 36 computes the weighted expectedmonthly return 110 for the series of TDFs by multiplying theweight 104 associated with each asset classification at month (122) by the corresponding computed expectedmonthly return 106 for the asset classification at month (122) and then sums these products. Using a similar technique, theportfolio module 36 computes the weighted expected annualizedreturn 112 at month (122) for the series of funds by multiplying theweight 104 associated with each asset classification at month (122) by the corresponding computed expected annualizedreturn 108 for the asset classification at month (122) and then sums these products.FIG. 8B illustrates the same techniques executed by theportfolio module 36 to compute a total portfolio expected return at month three-hundred and sixty-eight (368) for the series of funds. - Referring back to
FIG. 2 , once theportfolio module 36 computes the total portfolio expected returns, therisk module 32 applies the total portfolio expected returns to estimated account balances along theguide path 64. In one implementation, therisk module 32 weights the total portfolio expected returns by estimated account balances for each fund along the glide path. Advantageously, by weighting fund expected returns by estimated account balances, the contribution of returns and actual contributions to account balances over time is obtained. - An example of factors affecting an estimated fund account balance 120 over time is shown in
FIG. 9 . As shown in theFIG. 9 example, the estimated fund account balance 120 is based at least in part on the amount ofcontribution 122 provided to the fund and the return ofassets 124 underlying the fund. Typically, for a TDF, the amount ofcontribution 122 provides a much larger percentage of the estimated fund account balance 120 the earlier the fund is from the target date. As the target date approaches, the amount ofcontributions 122 provided to the fund typically contributes a lesser percentage of total account balance and the return ofassets 124 underlying the fund provide a greater percentage of the estimated fund account balance 120. - The
participant module 34 of thesystem 10 determines the amount ofcontributions 122 provided to the fund over time based on expected contributions to the fund. For example, in one implementation, theparticipant module 34 bases the amount ofcontributions 122 on at least one of a contributor salary, a contributor savings rate, a contributor salary increase(s), and/or a contribution schedule for contributors. The contributor salary, contributor salary increase(s), contributor savings rate, and/or contribution schedule can be dynamically defined by a user of the system and/or be included in the request. Alternatively, the contributor salary, contributor salary increase(s), contributor savings rate, and/or contribution schedule are predefined in thesystem 10. As used herein the term ‘contributor’ refers to any company, partnership, sole proprietor, or individual that adds value to the fund. - Referring back to
FIG. 2 , once estimated account balances are applied to total portfolio expected returns, in one implementation, therisk module 32 computes classification return correlations and volatility of return values for each of the funds comprising the series offunds 66. In one implementation, for example, therisk module 32 computes historical correlations between asset classifications over a ten (10) year period and then computes an expected portfolio standard deviation for each of the finds in the target date series. Each of the computed portfolio standard deviations represents a volatility of return value for each fund in the series. - Next, the
risk module 32 computes a risk score for the entity by weighting the volatility of return values for each of the funds of the series of funds by estimated account balances of each fund along the guide path, and then summing theweighted volatilities 68. The risk score provides an indication of how aggressive or conservative the investment style of an entity is. An example risk score computation is illustrated inFIG. 10 . - Turning now to the
FIG. 10 , a plurality ofTDFs 132A-132I of a series of TDFs are shown with associated volatility ofreturn values 134 andaccount balances 136 at a particular point in time. In one implementation, the risk module 132 computes aweighting 138 for each of the funds in the series by dividing thecurrent account balance 136 of each fund by the estimated account balance corresponding to each fund. Therisk module 32 then multiples each computedaccount balance weight 138 by a corresponding volatility ofreturn 134 value (e.g., standard deviation) for each fund, and then sums the weighted volatility of return values for each fund in the series to compute arisk score 140 for the entity. - Once the
risk module 32 computes the risk score, therating module 38 associates the computed risk score with one of a plurality of qualitative identifiers describing an investment style for the entity. In one implementation, for example, therating module 38 compares the computed risk score to a plurality of pre-defined risk score range values associated with the identifiers, and then determines which of the identifiers to associate with the computed risk score based on the comparison. - For example, referring now to
FIG. 11 , an example of a plurality ofTDF ratings 142 and pre-defined riskscore range values 144 are shown. As shown in theFIG. 11 example, in one implementation, the plurality ofTDF ratings 142 include identifiers entitled “Aggressive” 142A, “Moderately Aggressive” 142B, “Moderate” 142C, “Moderately Conservative” 142D, and “Conservative” 142E, each have a corresponding riskscore range value 144A-E, respectively. Theratings module 38 compares the computed risk score to each of the risk score range values 144A-E and then associates one of the plurality of identifiers with the computed risk score based on the comparison. - Referring back to
FIG. 2 , once the risk score is computed, therisk module 32 generates and transmits a signal associated with the risk score in response to therequest 69. In one implementation, the transmitted signal includes the computed risk score and corresponding qualitative identifier which are displayed to a user of thesystem 10 by thedisplay module 40. In some implementations, the signal includes a plurality of computed risk scores and corresponding qualitative identifiers for several different entities. - The
display module 40 of theweb server 28 may implement various technologies to display contents of the signal depending onsystem 10 configuration. For example, in one implementation, thedisplay module 40 utilizes eXtensible Markup Language (XML) to display risk scores associated with different entities on the browser 12A of theaccess device 12. In another implementation, thedisplay module 40 is formed from one or more enterprise java beans (EJBs) that execute and graphically display entity names in an order corresponding to computed risk scores for each entity. For example, as shown inFIG. 12A , in one implementation, thedisplay module 40 plots eachentity name 150A-150H on a risk/return scale 152 in an order corresponding to each entity's computed risk score. Thedisplay module 40 then displays theplot 150 to a user of thesystem 10 for comparison purposes. In some implementations, as shown inFIG. 12B , thedisplay module 40 displays one ormore risk scores 164 forentities 162 and correspondingqualitative identifiers 166 in atabular text format 160 on a display device of theserver 14. In yet other implementations, thedisplay module 40 displays both the plot ofentity names 152 and thetabular text format 160 on a display device of thesystem 10. - Various features of the system may be implemented in hardware, software, or a combination of hardware and software. For example, some features of the system may be implemented in one or more computer programs executing on programmable computers. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system or other machine. Furthermore, each such computer program may be stored on a storage medium such as read-only-memory (ROM) readable by a general or special purpose programmable computer or processor, for configuring and operating the computer to perform the functions described above.
Claims (16)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/780,860 US20200219195A1 (en) | 2010-04-22 | 2020-02-03 | Fund of funds analysis tool |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/765,365 US10552909B2 (en) | 2010-04-22 | 2010-04-22 | Fund of funds analysis tool |
US16/780,860 US20200219195A1 (en) | 2010-04-22 | 2020-02-03 | Fund of funds analysis tool |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/765,365 Continuation US10552909B2 (en) | 2010-04-22 | 2010-04-22 | Fund of funds analysis tool |
Publications (1)
Publication Number | Publication Date |
---|---|
US20200219195A1 true US20200219195A1 (en) | 2020-07-09 |
Family
ID=44816636
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/765,365 Active 2032-11-28 US10552909B2 (en) | 2010-04-22 | 2010-04-22 | Fund of funds analysis tool |
US16/780,860 Abandoned US20200219195A1 (en) | 2010-04-22 | 2020-02-03 | Fund of funds analysis tool |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/765,365 Active 2032-11-28 US10552909B2 (en) | 2010-04-22 | 2010-04-22 | Fund of funds analysis tool |
Country Status (1)
Country | Link |
---|---|
US (2) | US10552909B2 (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130282614A1 (en) * | 2012-04-20 | 2013-10-24 | Nisa Investment Advisors, L.L.C. | Method and Apparatus of Determining Funded Status Volatility |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001035311A2 (en) * | 1999-11-12 | 2001-05-17 | Fmr Corp. | Portfolio risk management |
US20020198821A1 (en) * | 2001-06-21 | 2002-12-26 | Rodrigo Munoz | Method and apparatus for matching risk to return |
US20030208427A1 (en) * | 2000-12-13 | 2003-11-06 | Dirk Peters | Automated investment advisory software and method |
US7031935B1 (en) * | 2000-07-31 | 2006-04-18 | J.P. Morgan Advisory Services Inc. | Method and system for computing path dependent probabilities of attaining financial goals |
US20090094069A1 (en) * | 2007-10-09 | 2009-04-09 | Barclays Global Investors N.A. | Investment fund for maximizing a risk adjusted expected return while providing a defined minimum income at maturity |
US20090281958A1 (en) * | 2008-05-07 | 2009-11-12 | Business Logic Corporation | Benchmark and evaluation of reference-date dependent investments |
US20090327155A1 (en) * | 2008-06-30 | 2009-12-31 | Jpmorgan Chase Bank, N.A. | Method and System for Evaluating Target Date Funds |
US8396775B1 (en) * | 2008-12-19 | 2013-03-12 | Dimitry Mindlin | Optimal glide path design for funding financial commitments |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100100502A1 (en) * | 2008-10-16 | 2010-04-22 | Gerber James G C T | System and methods to provide for and communicate about safer and better returning asset-liability investment programs |
US8447681B2 (en) * | 2008-11-21 | 2013-05-21 | Hartford Fire Insurance Company | System and method for administering a destination fund having an associated guarantee |
-
2010
- 2010-04-22 US US12/765,365 patent/US10552909B2/en active Active
-
2020
- 2020-02-03 US US16/780,860 patent/US20200219195A1/en not_active Abandoned
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001035311A2 (en) * | 1999-11-12 | 2001-05-17 | Fmr Corp. | Portfolio risk management |
US7031935B1 (en) * | 2000-07-31 | 2006-04-18 | J.P. Morgan Advisory Services Inc. | Method and system for computing path dependent probabilities of attaining financial goals |
US20030208427A1 (en) * | 2000-12-13 | 2003-11-06 | Dirk Peters | Automated investment advisory software and method |
US20020198821A1 (en) * | 2001-06-21 | 2002-12-26 | Rodrigo Munoz | Method and apparatus for matching risk to return |
US20090094069A1 (en) * | 2007-10-09 | 2009-04-09 | Barclays Global Investors N.A. | Investment fund for maximizing a risk adjusted expected return while providing a defined minimum income at maturity |
US20090281958A1 (en) * | 2008-05-07 | 2009-11-12 | Business Logic Corporation | Benchmark and evaluation of reference-date dependent investments |
US20090281959A1 (en) * | 2008-05-07 | 2009-11-12 | Business Logic Corporation | Personalized financial illustration, guidance and advisory system for reference-date dependent investments |
US20090327155A1 (en) * | 2008-06-30 | 2009-12-31 | Jpmorgan Chase Bank, N.A. | Method and System for Evaluating Target Date Funds |
US8396775B1 (en) * | 2008-12-19 | 2013-03-12 | Dimitry Mindlin | Optimal glide path design for funding financial commitments |
Non-Patent Citations (7)
Title |
---|
"Diversification and Risky Asset Allocation," Chapter 11, McGraw-Hill Companies, 2008 (Year: 2008) * |
Allocations at retirement, Looking to Target Date Funds to Determine Appropriate Equity/Fixed Income allocations atRetirement; January 2009; Jonathan Kreider (Year: 2009) * |
Cisneros-Molina, M., H. Huang, and T. Salisbury. "Evaluation of Target Date Funds." (2008). (Year: 2008) * |
Deloitte Target Date Funds; Historical Volatility/ Return Profiles; Michael Brien et al. (Year: 2009) * |
Glide Path and dynamic asset allocation of target date funds; 01/05/2010 by Youngjun Yoon (Year: 2010) * |
Kirchner, James, "Simple Linear Regression" 2001 (Year: 2001) * |
Life-Cycle Funds Mature: Plan Sponsor and Participant Adoption, Volume 20 November 2005: "Plan and participant adoption. Nearly two thirds of Vanguard® DC plans offered lifecycle funds in 2005 (Year: 2005) * |
Also Published As
Publication number | Publication date |
---|---|
US10552909B2 (en) | 2020-02-04 |
US20110264600A1 (en) | 2011-10-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Daglish et al. | Volatility surfaces: theory, rules of thumb, and empirical evidence | |
Buraschi et al. | When uncertainty blows in the orchard: Comovement and equilibrium volatility risk premia | |
Gillaizeau et al. | Giver and the receiver: Understanding spillover effects and predictive power in cross-market Bitcoin prices | |
Brav et al. | An empirical analysis of analysts' target prices: Short‐term informativeness and long‐term dynamics | |
Broadie et al. | Model specification and risk premia: Evidence from futures options | |
Bauer et al. | Forecasting multivariate realized stock market volatility | |
Bichsel et al. | The relationship between risk and capital in Swiss commercial banks: a panel study | |
Hafner et al. | A generalized dynamic conditional correlation model: simulation and application to many assets | |
Alexander et al. | Model-free hedge ratios and scale-invariant models | |
US20160110811A1 (en) | Methods and Apparatus for Implementing Improved Notional-free Asset Liquidity Rules | |
Fiorani et al. | Single and joint default in a structural model with purely discontinuous asset prices | |
US20120078811A1 (en) | Regime-based asset allocation via adaptive risk premium | |
US8468081B2 (en) | Private company valuation | |
Liu et al. | Hedging industrial metals with stochastic volatility models | |
Braun et al. | The impact of private equity on a life insurer's capital charges under solvency II and the Swiss solvency test | |
Jung et al. | An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast | |
Todorova | Volatility estimators based on daily price ranges versus the realized range | |
Sepp | An approximate distribution of delta-hedging errors in a jump-diffusion model with discrete trading and transaction costs | |
US20130097059A1 (en) | Predictive initial public offering analytics | |
US20200219195A1 (en) | Fund of funds analysis tool | |
US20120036086A1 (en) | System and method for management of investment funds | |
Schoene et al. | A four-factor stochastic volatility model of commodity prices | |
Jang et al. | Systemic risk in market microstructure of crude oil and gasoline futures prices: A Hawkes flocking model approach | |
Katscher et al. | Properly estimating risk in emerging markets: a comparison of beta adjustment techniques | |
Allen et al. | Thoughts on VaR and cVaR |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: FINAL REJECTION MAILED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER |
|
STCV | Information on status: appeal procedure |
Free format text: NOTICE OF APPEAL FILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |