Abstract:
A method is disclosed for allocating a portfolio investment among a population of securities held in an investment portfolio, wherein each security of the population of securities is issued by a company of a plurality of companies, and each security has at least one corresponding data element. The method includes the steps of assigning each security to a corresponding industry group, summing the industry total of each of the plurality of industry groups to provide the portfolio investment. One investment portion of the portfolio investment is distributed to at least one or more of the plurality of industry groups. The investment portion of the corresponding industry group is equal to a proportion of the industry total of the corresponding industry group to the portfolio investment. The investment portion may be distributed among a selected one or more of the securities of the corresponding industry group.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 60/181,718 filed Feb. 11, 2000, entitled “APPARATUS AND METHOD FOR CREATING AND MANAGING A FINANCIAL INSTRUMENT.” 
    
    
     BACKGROUND OF THE INVENTION 
     Equity mutual funds of all shapes and sizes tend to have one thing in common. Greater than 95% of equity mutual funds are managed by an individual Portfolio Manager or Investment Committee and would be considered “actively” managed. The remaining majority of funds would be considered “passively” managed index funds. An index fund uses the same representative portfolio as the published index it seeks to replicate. The majority of equity indexes that are published are weighted by market capitalization (the market price of a stock times shares outstanding). Market capitalization weighted indexes differ only by their universe selection. By gate-keeping an index universe, committees responsible for an index exclude certain component equities from their sample to maintain a predetermined portfolio characteristic of price/earnings ratio and price to book ratio. Our invention, The Industry Leaders Strategy Model was developed to generate portfolios based on the same universe, but using different ingredients to determine the weightings. Our process creates portfolios that have different portfolio statistics that are determined by the weighting factor and not a predetermined outcome. We developed a unique methodology for weighting portfolios by different fundamental inputs. 
     There are a small number of proprietary “model” based mutual funds that because of their secretive nature are as variegated as the actively managed funds. This invention has the same goal as these proprietary models (to be differentiated from actively managed funds by association to a discipline), yet this invention attempts to use a rigid and unique methodology to achieve the creation of understandably allocated portfolios. 
     SUMMARY OF THE INVENTION 
     It is an object of this invention to provide a method for the creation of portfolios of equity securities that does not require active management. 
     It is an object of this invention to provide a method of investment allocation based upon the data elements of the securities included within the investment portfolio. 
     In accordance with these and other objects of this invention, there is disclosed a method of allocating a portfolio investment among a population of securities held in an investment portfolio, wherein each security of the population of securities is issued by a company of a plurality of companies, and each security has at least one corresponding data element. The method includes the steps of assigning each security to a corresponding industry group, summing one of the corresponding data elements of each of the securities assigned to said corresponding industry group to provide an industry total for the corresponding industry group, and summing the industry total for each of the plurality of industry groups to provide the portfolio investment. Finally, a one investment portion of the portfolio investment is distributed to at least one or more of the plurality of industry groups. 
     In a further aspect of this invention, at least some of the population of securities is updated on a periodic cycle. Further, the plurality of securities are subdivided into a plurality of editions, wherein each edition is updated on a cycle that is staggered from the cycles of the other editions. 
     In a still further feature of this invention, the investment portion of the corresponding industry group is equal to a proportion of the industry total of the corresponding industry group to the portfolio investment. Further, the investment portion is distributed among a selected one or more of the securities of the corresponding industry group. In one embodiment of this invention, the investment portion is distributed to at least that security of the corresponding industry group that has the largest data element of the securities assigned to the corresponding industry group. In a further embodiment, two or more parts of the investment portion are allocated to two or more of the securities of the corresponding industry group that have the largest data elements. 
     In a still further aspect of the invention, the part of the investment portion allocated to a single security is set to not exceed a predetermined amount. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates how the universe of equities is determined for all applications of this invention. There are common exclusions to the chosen universes that are predetermined.  FIG. 1  builds a frame broken down by industry that includes all companies to be aggregated by the invention. 
         FIG. 2  illustrates how different data elements are used to create a universe aggregation that generates the portfolio allocation for a given industry. As different data elements are intruded into the process, different investment allocations by industry are created. 
         FIGS. 3–8  illustrate how an industry is represented by a unique set of leaders. This process has 6 steps of iteration available per industry. A fixed monthly allocation is created for each company that represents its industry. 
         FIG. 9  illustrates the model mechanics in an algebraic expression. 
         FIGS. 10   a  and  10   b  illustrate an example of this invention&#39;s portfolio for the data element of common shareholders equity. 
         FIG. 11  illustrates the allocation of an investment share to each security of an industry. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following example describes an illustrative embodiment of this invention with common shareholders equity as a selected data element input. Each application of the invention (using different data element inputs) creates a different investment strategy. 
     This illustrative embodiment produces a principal investment strategy that invests in a broad number of industries and companies with the highest common stockholders&#39; equity in their respective industries and produces a portfolio of approximately 95 to 110 companies that can be systematically managed to replicate the specified investment allocations. 
     Referring now to  FIG. 1 , there is shown data that is brought into the data processing system of this invention. Utilizing a public, published universe of equities, we sort the equities into their primary industries and prepare the system to incorporate data. Imported data can be incorporated from any known source including, among others, STANDARD &amp; POORS COMPUSTAT®, THE VALUE LINE INVESTMENT SURVEY® and BLOOMBERG®. For this illustrative embodiment of the data processing system, we have chosen to illustrate our data processing system using the VALUE LINE INVESTMENT SURVEY® (“VALUE LINE®”) found in step  2 . VALUE LINE® lists approximately the 1,700 of the largest publicly traded companies and classifies each company into an industry category, and is a good source to provide the contents of industries and representative companies for the previous 14 years. This established a fluid universe of equities to which we apply the data processing system. Step  4  sorts the industries and companies within each industry and formats them in a way that allows the data processing system to allow the universe to be refined. 
     To differentiate portfolios into international or domestic the invention using step  6  may exclude any population of equities or industries that an investment manager may choose in order to create a desired portfolio. Step  8  embodies an example of exclusions that are used for this illustration. The invention excludes from this illustrative example companies that are in the following foreign industries: Canadian Banks, Canadian Energy, Foreign Electronics/Entertainment and Foreign Telecommunications. VALUE LINE® publishes some data on investment companies which are excluded (closed-end domestic, foreign models, and income funds). We exclude from the universe companies whose shares are not directly traded in the United States (e.g., American Depository Receipts, commonly referred to as “ADRs”). Finally, the present system excludes from the universe companies included in VALUE LINE® as “miscellaneous” but which have not yet been assigned an Industry category because the invention does not assign industry categorization. The portfolio created from steps  6  and  8  will include domestic multinational corporations, but a smaller number of foreign companies, which do not have the same data reporting requirements as domestic corporations. 
     Step  10  uses the universe “update cycle” to determine how often changes are made to a given industry. An update cycle is the frequency to which the universe is modified by the publisher. Value Line changes their industry compositions every 3 months (1 quarter) and the cycle is set to 1 quarter. STANDARD and POORS® and BLOOMBERG® have different update cycles so step  10  would be different for these universes. In establishing this example universe of stocks, the invention also adjusts the Industry category of “Banks” to include “Banks Midwest” so as to unify the banking Industry analysis. Step  12  sorts the companies into the editions (weekly updates, numbering 13) found in Value Line which allows for an organized presentation of data from this data processing system. Step  14  highlights the update cycle found in the universe and this illustrative example describes the weekly update found in Value Line&#39;s quarterly update cycle. Industries and companies are included in this invention only for the periods during which they are published in the chosen universe by step  12 . 
       FIG. 2  illustrates how a chosen data element is incorporated into the refined universe found in step  12 . The invention has the ability to use any published data element for a public corporation. A data element is an input to which the data processing system is applied. Step  22  illustrates potential data elements such as market capitalization and net income, but is not a complete list of potential data inputs. Each data element that is applied to the invention produces a different investment style and therefore a different portfolio. Publicly available data is acquired, for example, electronically from the EDGAR database of the SEC for fundamental data elements like common shareholders equity, net income, net revenue, net earnings and total assets. A market data source such as Bloomberg is used to provide market capitalization data. The illustrative embodiment presented here uses common shareholders equity to produce a “Large Capitalized Value Styled Portfolio.” Step  24  acquires the chosen data element and imports the data into this data processing system. 
     Step  26  totals the data elements for all companies included in an industry for each month and step  28  totals the data elements for the selected universe. Finally, step  30  allocates an industry investment, which is calculated from the industry total divided by the universe total as determined in step  28 . This investment allocation is created on a systematic basis, e.g., monthly, and is denoted by variable I N . 
     There are many ways to assign an investment allocation to an individual equity and create a unique portfolio. With the industry previously defined and a data element chosen, the individual investment allocation process can use one of 2 allocation options. An investment manager may choose to maintain a portfolio with a manageable number of equities (less than 200), or he can choose to have all industry members represented by their prominence with regard to the total industry amount (individual percent of data element with regard to the specific industry). The first method is illustrated in  FIGS. 3 through 8  and the second method is illustrated in  FIG. 11 . 
     The number of securities to which an industry investment or allocation is made depends on the size of the industry allocation. The size of the industry&#39;s investment allocation determines how many representatives are used. Therefore to create a portfolio, the data processing system applies a redundant iteration for each included industry of the defined universe. In choosing this allocation method, the investment manager would determine the maximum limit for the portfolio.  FIGS. 3 through 8  illustrate the individual allocation limit to any one security is 2.25%. The example of 2.25% would limit an individual equity&#39;s portfolio representation to 2.25% of the total portfolio. By definition, the company with the largest data element for the given month would receive all of the industry&#39;s allocation determined by step  30 . 
     As shown in  FIG. 3 , the process looks to determine the size of the industry in step  40 . When the industry&#39;s allocation amount is below the 2.25% value, the process continues to step  44 . If the industry is larger than 2.25% then step  42  directs the process to step  60 . To determine the way a statistical tie would be broken, the data processing system allows for a significance test between the company with the largest data element and the next largest company. Step  44  illustrates a 2% value to determine if a statistical tie would be present and if so step  48  would split the allocation between the first 2 representatives of the industry. Step  46  would be used if no defined statistical tie is present, and the largest representative would be allocated the entire amount of the industry allocation. Step  50  takes the next industry back to step  40 . 
       FIG. 4 , step  60  would capture industries greater than or equal to 2.25% and less than 4.5%. If the industry is greater than 4.5% the test in step  62  would send the process to step  80 , as more fully shown in  FIG. 5 . Step  64  tests the significance of the leader by the previously defined 2.0%, and if there is no tie the data processing system goes to step  66  and the leader is assigned 2.25% and the next closest company is assigned (I n —2.25%). Step  68  would split the total amount of the industry between the two largest companies in the industry if the 2% significance test is failed and a tie is determined. Step  70  takes the next industry back to step  40 . 
       FIG. 5 , step  80  captures industries greater than or equal to 4.5% and less than 6.75% of the total portfolio allocation. If the industry is greater than 6.75%, step  80  would send the process to step  82  and be forwarded to step  120 . Step  84  tests the significance of the leader (F 1 ) by the previously defined 2.0%. If there is no tie and the 2% significance test is passed, the data processing system goes to step  86  and the leader (F 1 ) is assigned 2.25% and forwarded to step  88  for the 2% significance test between the second (F 2 ) and third (F 3 ) largest companies. Step  90  has the second company (F 2 ) clearing the 2% significance test and gaining the 2.25% limit. Step  92  tests for the 2% significance test between the third (F 3 ) and fourth (F 4 ) largest companies. Step  94  captures a 2% significance test tie and would split the remaining amount of the industry (I n —4.5%) between (F 3 ) and (F 4 ) and forwarded to step  108  and forwarded back to step  40 . Step  96  assigns 2.25% to (F 3 ) if the significance test in step  92  is passed and F 3  gained the remaining amount of the industry (I n 4.5%). Step  98  captures a tie of the step  84  significance test, and assigns F 1  and F 2  2.25%. Step  100  is a significance test with step  102  having the third leader F 3  capturing the remaining balance of the industry (I n —4.5%). From step  102  the data processing system forwards to step  108  and to be sent back to step  40 . Step  104  represents a tie between F 3  and F 4  and allocates a split of the remaining balance of the industry (I n —4.5%) and forwarded to step  108 . If step  106  determines a tie between F2 and F3, the second and third securities with the largest equity, the remaining balance of the industry allocation minus the 2.25% limit (I n —2.25%) is, split equally between F2 and F3, before the process moves to step  108 . 
       FIG. 6 , step  120  captures industries greater than or equal to 6.75% and less than 9.0% of the total portfolio allocation. If the industry is greater than 9.0%, step  120  would send the process to step  122  and be forwarded to step  160 . Step  124  tests the significance of the leader (F 1 ) by the previously defined 2.0%. If there is no tie and the 2% significance test is passed, the data processing system goes to step  126  and the leader (F 1 ) is assigned 2.25% and forwarded to step  128  for the 2% significance test between the second (F 2 ) and third (F 3 ) largest companies. Step  130  has the second company (F 2 ) clearing the 2% significance test and gaining the 2.25% limit and forwarded to step  132  and on to step  138 . Step  134  assigns the tie between F 2  and F 3  2.25%, and forwarded to step  142 . Step  136  captures the tie between F 1  and F 2  and assigns a value of 2.25%, and forwards to step  138 . Step  138  tests for the 2% significance test between the third (F 3 ) and fourth (F 4 ) largest companies. Step  148  captures a 2% significance test tie and would split the remaining amount of the industry (I n —4.5%) between (F 3 ) and (F 4 ) and forwarded to step  150  and forwarded back to step  40 . Step  140  assigns 2.25% to (F 3 ) if the significance test in step  138  is passed. Step  142  is a significance test with step  144  having the fourth leader F 4  being assigned the remaining balance of the industry (I n —6.75%). From step  144  the data processing system forwards to step  150  to be sent back to step  40 . Step  146  represents a tie between F 4  and F 5  and allocates a split of the remaining balance of the industry (I n —6.75%) and forwarded to step  150 . 
       FIG. 7 , step  160  captures industries greater than or equal to 9.0% and less than 11.25% of the total portfolio allocation. If the industry is greater than 11.25%, step  160  would send the process to step  162  and be forwarded to step  200 . Step  164  tests the significance of the leader (F 1 ) by the previously defined 2.0%. If there is no tie and the 2% significance test is passed, the data processing system goes to step  166  and the leader (F 1 ) is assigned 2.25% and forwarded to step  168  for the 2% significance test between the second (F 2 ) and third (F 3 ) largest companies. Step  170  has the second company (F 2 ) clearing the 2% significance test and gaining the 2.25% limit and forwarded to step  172  and on to step  178 . Step  174  assigns the tie between F 2  and F 3  2.25%, and forwarded to step  182 . Step  176  captures the tie between F 1  and F 2  and assigns a value of 2.25%, and forwards to step  178 . Step  178  tests for the 2% significance test between the third (F 3 ) and fourth (F 4 ) largest companies. Step  192  captures a 2% significance test tie and would assign 2.25% to both (F 3 ) and (F 4 ) and forwarded to step  194 . Step  180  assigns 2.25% to (F 3  if the significance test in step  178  is passed. Step  182  is a significance test with step  184  having the fourth leader F 4  being assigned 2.25%. From step  184  the data processing system forwards to step  186  to apply the significance test to F 5  and F 6 . Step  190  represents a tie between F 5  and F 6 , and allocates a split of the remaining balance of the industry (I n —9.0%) and forwarded to step  198 . Step  196  identifies a tie between F4 and F5, the fourth and fifth largest companies, and allocates a split of the remaining balance of the industry (I n —6.75%) there between, before the process moves to Step  198 . Step  188  captures a clearance of the significance test and assigns F 5  the balance of the industry allocation (I n —9.0%). Step  198  takes the process back to step  40 . 
       FIG. 8 , step  200  captures industries greater than or equal to 11.25% and less than 13.00% of the total portfolio allocation. If the industry is greater than 13.00%, step  202  would assign a limit on 13% to the industry and be returned back to step  200  with I n =13.00% (this size limit is included in this illustrative embodiment, but may be removed for other applications), Step  204  tests the significance of the leader (F 1 ) by the previously defined 2.0%. If there is no tie and the 2% significance test is passed, the data processing system goes to step  206  and the leader (F 1 ) is assigned 2.25% and forwarded to step  208  for the 2% significance test between the second (F 2 ) and third (F 3 ) largest companies. Step  210  has the second company (F 2 ) clearing the 2% significance test and gaining the 2.25% limit and forwarded to step  212  and on to step  218 . Step  214  assigns the tie between F 2  and F 3  2.25%, and forwarded to step  226 . Step  216  captures the tie between F 1  and F 2  and assigns a value of 2.25% to each company, and forwards to step  218 . Step  218  tests for the 2% significance test between the third (F 3 ) and fourth (F 4 ) largest companies. Step  222  captures a 2% significance test tie and would assign 2.25% to both (F 3 ) and (F 4 ) and forwarded to step  224  and be forwarded to step  234 . Step  220  assigns 2.25% to (F 3 ) if the significance test in step  218  is passed. Step  226  is a significance test between F 4  and F 5  with step  232  having the fourth leader F 4  clearing the significance test and being assigned 2.25%. Step  228  assigns F 4  and F 5  2.25% and is forwarded to step  230  and on to step  238 . Step  234  applies the significance test to F 5  and F 6 . Step  244  represents a tie between F 5  and F 6 , and allocates a split of the remaining balance of the industry (I n —9.0%) and forwarded to step  246 . Step  236  captures a clearance of the significance test of step  234  and assigns F 5 —2.25% and forwards the process to step  238  for a significance test between F 6  and F 7 . If F 6  clears the significance test of step  238 , it is assigned the balance of the industry (I n —11.25%) in step  240  and sent go step  246 . Step  242  allocates the step  238  significance tie to F 6  and F 7  with a split of the remaining balance (I n —11.15%). Step  246  takes the process back to step  40 . 
       FIG. 9  illustrates an algorithmic example of the illustrative embodiment, with an algorithmic example of the industries of the embodiment found in  FIGS. 10   a  and  10   b . When the data processing system is run, the following allocations of the illustrative embodiment are made to the respective companies as shown in  FIG. 10 . 
       FIG. 11  illustrates the simple process of assigning each company of the chosen universe. If the more detailed portfolio is chosen by the investment manager, the data processing system would assign in step  300  the individual company&#39;s relative percent to the entire universe. Step  302  would include all members of the defined universe, and a large portfolio would be created. 
     HISTORICAL PERFORMANCE OF THE INVENTION 
     Using the Illustrative Embodiment 
     The following table compares the actual performance of the STANDARD and POOR&#39;S® BARRA VALUE INDEX® (D(“S&amp;P Barra Value”) and the RUSSELL 1000 VALUE INDEX® (“Russell 1000 Value”), with the hypothetical results of the illustrative embodiment of the invention (common shareholders equity) for various historical periods. Total returns of the Strategy Model are returns on a hypothetical portfolio whose results have been approved by the SEC that are included in a Prospectus for a mutual fund composed of stocks selected by the Strategy Model (common shareholders equity) and re-balanced monthly. 
     The S&amp;P Barra Value and the Russell 1000 Value are indexes that have no costs or expenses of operation, however, its total return amounts reflect reinvestment of dividends for purposes of general comparison to this invention. 
     COMPARATIVE HISTORICAL TOTAL RETURN PERFORMANCE OF THIS INVENTION 
     Please note that past results of this embodiment do not necessarily indicate future performance or earnings of the invention 
     
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                   
               
               
                   
                 Industry 
                   
                   
               
               
                   
                 Leaders 
                 S&amp;P Barra 
                 Russell 1000 
               
               
                 Period 
                 Strategy 
                 Value Index ® 
                 Value Index ® 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                  1 year 
                 10.89% 
                 12.69% 
                 7.66% 
               
               
                 Dec. 31, 1998–Dec. 31, 1999 
               
               
                  3 years 
                 22.33% 
                 18.87% 
                 18.94% 
               
               
                 Dec. 31, 1996–Dec. 31, 1999 
               
               
                  5 years 
                 26.34% 
                 22.93% 
                 23.15% 
               
               
                 Dec. 31, 1994–Dec. 31, 1999 
               
               
                 10 years 
                 17.26% 
                 15.36% 
                 15/63% 
               
               
                 Dec. 31, 1999–Dec. 31, 1999 
               
               
                 13 Years 
                 16.94% 
                 15.90% 
                 15.87% 
               
               
                 Dec. 31, 1986–Dec. 31, 1999