Abstract:
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.

Description:
TECHNICAL FIELD 
       [0001]    This disclosure relates to financial risk measurement, and more particularly to systems and methods for computing risk measures associated with fund of funds investments. 
       BACKGROUND 
       [0002]    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). 
         [0003]    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. 
         [0004]    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. 
         [0005]    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. 
         [0006]    Accordingly, there is a need for improved systems and techniques for analyzing and comparing fund of funds investments. 
       SUMMARY 
       [0007]    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. 
         [0008]    Various aspects of the system relate to computing risk measurement values for an entity based on return volatility of fund assets. 
         [0009]    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. 
         [0010]    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. 
         [0011]    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. 
         [0012]    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. 
         [0013]    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. 
         [0014]    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. 
         [0015]    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. 
         [0016]    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. 
         [0017]    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. 
         [0018]    Additional features and advantages will be readily apparent from the following detailed description, the accompanying drawings and the claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0019]      FIG. 1  is a schematic of an exemplary computer-based fund of funds analysis system. 
           [0020]      FIG. 2  illustrates an exemplary method for calculating a risk score. 
           [0021]      FIG. 3  illustrates an exemplary glide path shared for a series of target date funds. 
           [0022]      FIGS. 4A-4B  illustrate asset allocations for two target date funds shown in  FIG. 3 . 
           [0023]      FIG. 5  illustrates exemplary historical returns for asset classifications. 
           [0024]      FIG. 6  illustrates exemplary asset classification returns and risk levels. 
           [0025]      FIG. 7  illustrates an exemplary calculation of expected returns for asset classifications. 
           [0026]      FIGS. 8A-8B  illustrate weighted average portfolio expected returns. 
           [0027]      FIG. 9  illustrates an exemplary account balance over a time interval. 
           [0028]      FIG. 10  illustrates a computed risk score for an example entity. 
           [0029]      FIG. 11  illustrates exemplary identifiers for association with a computed risk score. 
           [0030]      FIGS. 12A-12B  illustrate rating scores for a plurality of entities. 
       
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       [0031]      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. 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 the system  10  include, but are not limited to, mutual fund FoF, hedge fund FoF, private equity FoF, investment trust FoF, and combinations thereof. 
         [0032]    As shown in  FIG. 1 , in one implementation, 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). In one embodiment, for example, 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 . Preferably, 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 . Although only one access device  12  is shown in  FIG. 1 , the system can support multiple access devices. 
         [0033]    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, the network  16  uses wired communications to transfer information between the access device  12  and the server  14 . In another embodiment, the network  16  employs wireless communication protocols. In yet other embodiments, the network  16  employs a combination of wired and wireless technologies. 
         [0034]    As shown in  FIG. 1 , in one implementation, 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 . In one implementation, as shown in the  FIG. 1  example, the non-volatile memory  24  is configured to include a web server  28  for processing requests from the access device. 
         [0035]    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 (Hyper Text Markup Language). In one embodiment, 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. 
         [0036]    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 in  FIG. 1 , in one implementation, 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. 
         [0037]    In one implementation, as shown in  FIG. 1 , 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. In one implementation, the data store  44  is a relational database. In another implementation, the data store  42  is a directory server, such as a Lightweight Directory Access Protocol (‘LDAP’) server. In yet other implementations, the data store  42  is a configured area in the non-volatile memory  24  of the device server  14 . Although the data store  42  shown in  FIG. 1  is connected to the network  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 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 . 
         [0038]    It should be noted that the 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. In addition, various other implementations of the disclosure include fewer structures than those shown in  FIG. 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. 
         [0039]    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  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 , and 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 , and the signal generated by the risk module  32  in step  69  optionally includes output from the rating module  38  shown in  FIG. 1 . 
         [0040]    As shown in  FIG. 2 , in one implementation, the risk module  32  identifies a series of TDFs provided by an entity in response to a request  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. 
         [0041]    Various techniques may be employed by the system  10  to receive requests. For example, in one implementation, the request is sent from the browser  12 A 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 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. In yet other implementations, the request includes one or more entities that provide FoF investments. 
         [0042]    Next, as shown in  FIG. 2 , the risk module  32  determines a glide path for the series of funds  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, the risk module  32  accesses the glide path associated with a series of TDFs from the data store  42 . 
         [0043]    In appreciation of the present invention, an example glide path  70  for a series of TDFs is shown in connection with  FIG. 3 . 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. 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 in  FIG. 3 , the portfolio allocation mix of the 2040 TDF  70 B will approximate the portfolio allocation mix of the 2015 TDF  70 G over time. 
         [0044]    Turning now to  FIG. 4A , an example portfolio allocation mix for the 2040 TDF  70 B at month one-hundred twenty-two (122) is illustrated. As shown in the  FIG. 4A  example, 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 in along the glide path  70 . For example, as shown in the  FIG. 4A , at month one-hundred twenty-two (122), 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. 
         [0045]    Referring now to  FIG. 4B , an example portfolio allocation mix for the 2015 TDF  70 G is illustrated. As shown in the  FIG. 4B  example, a larger proportion of the 2015 TDF  70 G portfolio  74  is weighted in fixed income securities, rather than equity based securities. For example, as shown in  FIG. 4B , at month three hundred and sixty eight (368), the ‘Family 1 Large Capitalization fund’  74 A is approximately fifteen percent (15%)  74 C of the total portfolio funds  74  and the ‘Family 1 Government Bond Fund’  74 B is approximately fifteen percent (15%)  74 D of the total portfolio  74 . 
         [0046]    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. 
         [0047]    Referring back to  FIG. 2 , once the risk module  32  determines the glide path for the series, 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. In one implementation, for example, 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. 
         [0048]    Next, once the classification module  30  determines asset classifications, the risk module  32  calculates a historical risk profile for each of the identified asset classifications  56 . In some implementations, for example, the risk 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 in  FIG. 5 , 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 . 
         [0049]    Once 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. 
         [0050]    For example, in some implementations, turning now to  FIG. 6 , 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. In one implementation, for example, the risk module  32  uses a linear regression technique to determine the relationship. In one implementation, as shown in  FIG. 6 , 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 . For example, in one implementation, 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. 
         [0051]    Referring back to  FIG. 2 , based on the historical relationship of risk and return, the risk module  32  next computes an expected return for each asset classification  60 . As shown in  FIG. 7 , in one implementation, for example, the risk module  32  first computes a volatility premium  90  and a volatility free 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, 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 . 
         [0052]    In one implementation, the risk module  32  computes the slope and intercept of the regression line  84  using the following formulas, respectively: 
         [0000]      Slope of regression line( b )=(Σ XY −(Σ XΣY )/ N )/(Σ X 2−(Σ X )2 /N )
 
         [0000]      Intercept of regression line( a )=(Σ Y−b (Σ X ))/ N )
 
         [0053]    Where:
       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       
 
         [0063]    Once the volatility premium  90  and volatility free rate  92  are computed for the series of TDFs, 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. 
         [0064]    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, the risk module  32  accesses averaged standard deviation of return values  91  for each asset classification from the data store  42 . As shown in the  FIG. 7  example, 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. In some implementations, as shown in the  FIG. 7  example, 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. 
         [0065]    Referring back to  FIG. 2 , once the risk module  32  computes expected returns for the asset classifications, 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 . In one implementation, 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. 
         [0066]    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 the  FIG. 8A  example, at month one-hundred and twenty two (122)  114 , the glide path defines the asset allocation mix in terms of weights  104 . As explained previously, in one implementation for each underlying fund of a TDF, 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. 
         [0067]    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. Using a similar technique, 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. 
         [0068]    Referring back to  FIG. 2 , once the portfolio module  36  computes the total portfolio expected returns, the risk module  32  applies the total portfolio expected returns to estimated account balances along the guide path  64 . In one implementation, the risk 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. 
         [0069]    An example of factors affecting an estimated fund account balance  120  over time is shown in  FIG. 9 . As shown in the  FIG. 9  example, 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. Typically, for a TDF, 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. As the target date approaches, 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 . 
         [0070]    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 . As used herein the term ‘contributor’ refers to any company, partnership, sole proprietor, or individual that adds value to the fund. 
         [0071]    Referring back to  FIG. 2 , once estimated account balances are applied to total portfolio expected returns, in one implementation, the risk module  32  computes classification return correlations and volatility of return values for each of the funds comprising the series of funds  66 . In one implementation, for example, 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. 
         [0072]    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 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 . 
         [0073]    Turning now to the  FIG. 10 , a plurality of TDFs  132 A- 132 I of a series of TDFs are shown with associated volatility of return values  134  and account balances  136  at a particular point in time. In one implementation, 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. 
         [0074]    Once the risk module  32  computes the risk score, the rating 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, 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. 
         [0075]    For example, referring to now to  FIG. 11 , an example of a plurality of TDF ratings  142  and pre-defined risk score range values  144  are shown. As shown in the  FIG. 11  example, in one implementation, 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. 
         [0076]    Referring back to  FIG. 2 , once the risk score is computed, the risk module  32  generates and transmits a signal associated with the risk score in response to the request  69 . In one implementation, 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 . In some implementations, the signal includes a plurality of computed risk scores and corresponding qualitative identifiers for several different entities. 
         [0077]    The display module  40  of the web server  28  may implement various technologies to display contents of the signal depending on system  10  configuration. For example, in one implementation, 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 . In another implementation, 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&#39;s computed risk score. The display module  40  then displays the plot  150  to a user of the system  10  for comparison purposes. In some implementations, as shown in  FIG. 12B , 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 . In yet other implementations, 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 . 
         [0078]    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.