WO2000079433A1 - System, method and computer readable medium containing instructions for evaluating and disseminating investor performance information - Google Patents

Abstract

An investor's performance is evaluated by utilizing information pertaining to at least one transaction made by the investor, such as an individual or entity, involving at least one investment (35). This evaluation includes determining a performance score indicative of the investor's performance relative to other investors (37). The performance score is determined at least in party by considering an average historical performance of the investment, following the transaction (32). In addition, the performance score is also determined by a historical consistency of the investor's performances with respect to transactions involving the investment (35a), and the number of transactions made by the investor. Then, the performance score of the investor may be compared against the performance scores of other investors (31). Advantageously, this performance score may be used to produce a ranked list of investors in a particular industry (32a), as well as with investors in different industries (31a).

Description

SYSTEM. METHOD AND COMPUTER READABLE MEDIUM CONTAINING INSTRUCTIONS FOR EVALUATING AND DISSEMINATING INVESTOR PERFORMANCE INFORMATION-

RELATED APPLICATION

This application claims priority from U.S. Provisional Application 60/139.771. filed June 18. 1999. to Bettis et al.. which is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates generally to business investment systems, and more particularly, to a system, method, and computer readable medium storing computer- executable instructions for evaluating the performance of corporate insiders and other investment traders or investors.

BACKGROUND ART

In the context of securities investments, tracking insider trading has always been considered a prudent exercise. Typically defined as company directors, officers, and other individuals owning more than 10% of its stock, insiders are people who know more about a company^'s earnings estimates and events affecting it than anyone else. Obviously, by investing their own money, insiders clearly expect to make an above average return when they acquire their own stock in a concerted manner.

Generally speaking, trading securities, or communicating such information to others who trade, based on material non-public information is illegal. Thus, the Securities and Exchange Commission requires insiders to file records of their trading in the company^'s stock by the tenth day of the month following the trade, at the very latest.

This "Statement of Changes of Beneficial Ownership^" or Form 4 for short. lists the number of shares boucht or sold, the nature of the transaction, whether direct market or otherw ise, and the current holding Similarly , a Form 3 is filed by an indiv idual establishing an insider position in the company s stock for the first time, and a Form 13- D is required to be filed bv in estors within 10 day s of their establishing a 5% stock position in anv companv Companv directors are required to report their intention to sell unregistered stock under SEC Rule 144

Clearlv . monitoring these SEC filings and information pertaining to the actions of insiders and other in estment traders can prov e rewarding under certain circumstances Accordingly , a need exists for a sy stem, method, and computer readable medium containing instructions utihzable for efficiently disseminating such insider and/or investment trader information to the public With access to this information, the public can make their own assessments and invest accordinglv

Several sy stems exist in the prior art lor prov iding such information One example includes on-line databases which track insider holdings, trades, outsider interests, and other investment trader actions These databases typically list information prov ided by government agencies such as the SEC ithin a day or two after publication, and can be a helpful guide to a stock^'s or other investment^'s price movement

While adequately prov iding access to SEC filings, these on-line databases nevertheless tail to prov ide any additional insight on the insider^'s or trader^'s actions For instance, the actions of certain insiders or traders may prove to be more reliable in predicting an inv estment s price mov ement than other insiders or traders Thus, a need exists lor a s stem. method, and computer readable medium containing instructions utihzable lor pro iding not only information concerning an insider^'s or trader^'s actions, but also lniormation pertaining to the reliability of a particular insider^'s or trader s actions

Sev eral prior art techniques have not adequatelv addressed these needs For example. L S Patent 5.132.899 to Fo\ discloses a stock and cash portfolio dev elopment sy stem Λs depicted in prior an FIG 1 of the present inv ention (FIG 1 of Fox), the sy stem of Fox uses data gathering and processing methodology to produce a sy stem where a list ot stocks and a cash position is generated and purchased for inv estment and operating accounts ( steps 1 -5 ) Specifically , the sy stem integrates three areas ot data investment performance for investment managers. Federal Securities and Exchange Commission reports filed quarterly by inv estment managers, and financial characteristics for a number ot stocks, to produce a stock portfolio

Similarly . L S Patent 4.566.066 to Towers relates to a securities valuation system As shown in prior art FIG 2 of the present application (FIG 1 of Towers), the system of Towers, comprised of components 10-23. produces securities portfolio aluation schedules for multiple simultaneous users In Towers, a customer communicates with the sy stem through terminal 10 to access and edit accounts in user file 12 By using a CUSIP routine 18 and an AMOUNT routine to reflect stock splits and dividends since the last stock pricing, the system of Towers produces and display s 23 a selected stock portfolio v aluation

In U S Patent 5.812.987 to Luskin et al . an investment fund management system manages assets in one or more inv estment funds over a specified period of time This system, comprised of components 31 -39. determines a strategic investment mix of assets in a particular fund periodically as a function of changing risk Each fund is managed by manipulating the inv estment mix of the fund in accordance with criteria related to a diminishing length of time to a horizon where cash will be w ithdrawn therefrom In prior art FIG 3 of the present inv ention (FIG 6 of Luskin et al ). the inv estment mix is adjusted by first obtaining investor portlo o lnlormation 35 and market data 36 This data is used to torecast market risks and returns 37. and to determine the portfolio risk 38 Then, the anticipated cash flow stream 39. the discount function 34. and present v alue of future cash flow 33 are calculated before producing an optimized portfolio 32 of assets

In U S Patent 5.761.442 to Barr et al . a data processing s stem selects securities and constructs an inv estment portto o based on a set of artificial neural networks (prior art FIG 4 of the present application FIG 2 of Barr et al ) The system comprises components 41 -70 and is designed to model and track the performance of each security in a giv en capital market and output a parameter which is related to the expected risk adjusted return for the secuπty Each artificial neural network is trained using a number of fundamental and price and v olume historv input parameters 10. 20 30 about the security and the underlv ing index The sy stem combines the expected return appreciation potential data 50 for each security v ιa an optimization process 60 to construct an investment portfolio which satisfies aggregate statistics The data processing sy stem receives input from the capital market and periodically ev aluates the performance of the investment portfolio, rebalancing whenev er necessary to correct performance degradations 70

Howev er while many ot these prior art references disclose adequate methods of managing investment funds and portfolios, none of the above make any mention of evaluating an insider^'s or other inv estment trader^'s performance Hence, what is lacking in the prior art is a technique directed not only toward an investment fund, but rather a technique for e aluating insiders and other traders based on their actions as well Accordingly , a need exists for a sy stem, method, and computer readable medium containing instructions utihzable for not only disseminating information concerning an insider^'s or trader^'s actions, but also for evaluating the insider^'s or trader^'s performance

In line with the above, a need exists for a system, method, and computer readable medium containing instructions utihzable not only for providing raw information and data but also utihzable for e aluating performance based on returns observed after decisions concerning buy ing and selling activity , historical consistency at picking good entry and exit points, and/or the number of buy ing or selling decisions made by the insider or investing entity

Furthermore, a need also exists for a sy stem, method, and computer readable medium containing instructions utihzable for not only ev aluating performance with respect to substantially all other insiders and traders but also w ith respect to substantially all other insiders and traders in a particular industry

SUMMARY OF THE INVENTION It is a teature and an adv antage of the present inv ention to prov ide a sy stem method, and computer readable medium containing instructions utihzable for efficiently disseminating insider and/or inv estment trader information

It is another feature and advantage of the present invention to prov ide a sy stem, method, and computer readable medium containing instructions utihzable for prov iding not only information concerning an insider or trader s actions, but also information pertaining to the reliability of a particular insider s or trader^'s actions

It is another feature and advantage of the present invention to prov ide a system, method, and computer readable medium containing instructions utihzable for not only disseminating information concerning an insider^'s or trader^'s actions, but also for evaluating the insider^'s or trader s performance

It is another feature and advantage of the present inv ention to prov ide a system, method, and computer readable medium containing instructions utihzable not only for providing raw intormation and data but also utihzable for evaluating performance based on returns observed after decisions concerning buying and selling activ ity , histoπcal consistency at picking good entry and exit points, and/or the number of buying or selling decisions made by the insider or investing entity It is another feature and adv antage of the present in ention to prov ide a s stem method, and computer readable medium containing instructions utihzable for not onlv ev aluating performance ith respect to substantially all other insiders and traders but also with respect to substantially all other insiders and traders in a particular industry

In accordance with the principles of the present inv ention, an inv estor s performance is ev aluated by utilizing information pertaining to at least one transaction made by the inv estor inv olv ing at least one in estment This ev aluation includes determining a performance score indicativ e of the inv estor s performance relative to other investors The performance score is determined at least in part by considering an average historical performance of the inv estment following the transaction In addition. the performance score is also determined by a histoπcal consistency of the inv estor s performances with respect to transactions involv ing the inv estment, and the number of transactions made by the inv estor Then, the performance score of the inv estor may be compared against the performance scores of other inv estors Advantageously , this performance score may be used to produce a ranked list of investors in a particular industry , as well as w ith inv estors in different industries

One embodiment of the present invention, particularly applicable to corporate insiders, is now summarized Although this example discusses inv estors that are people and information that is public, other ty pes alternativ es are possible For instance, non- person indiv iduals or entities that have similar characteristics as corporate insiders may also be included within the present invention As an example, entities that have a substantial inv estment in other entities may also be tracked for their investment strategies Alternativ ely, confidential and other nonpubhc data may also be used to rank the investing indiv iduals and/or entities, prov ided that the confidential data is prov ided on a timely basis For example, indiv iduals/entities may provide all confidential data to the administrator of the scoring/ranking system of the present invention, which will maintain the specific confidentiality of the present invention, but will also publicize the aggregate score and/or rank of the insiders/entities

INSIDER SCORING AND REPORTS

Functional Description

The Insider Scoring is an insider performance measurement In this example, all of the insiders listed in. tor example, a database, in this case, the Insider Universe, are evaluated All insiders to be ev aluated are ranked based on the returns that are observed after their decisions tor buying and selling activ ity (a decision is a cluster of buying, selling or other type of transaction ) All insiders are gi en a performance score for Buv ing relativ e to the Insider L niv erse. Selling relative to the Insider Lmv erse. Buy ing relativ e to the Insider's Respectiv e Industry and Selling relativ e to the Insider s Respectiv e Industry This produces a total of four alternativ e Insider Scores, which when optionally combined provide a unique set of information Prov en Buy Score - [PBS] represents Buy ing relative to the Insider univ erse

Proven Sell Score - [PSS] represents Selling relativ e to the Insider niverse

Proven Buy Industry Score - [PBIS] represents Buv ing relative to the Insider's Respective

Industry

Proven Sell Industrv Score - [PSIS] represents Selling relativ e to the Insider's Respective

Industry

These scores are calculated on a monthly or other basis Once the scores have been calculated and inserted into the Database, four reports are produced

1 Weekly or daily updates of transactions by proven buy ers and proven sellers for the Insider Univ erse (using PBS and PSS to determine the report contents)

2 Weekly or dailv updates of transactions by proven buyers and proven sellers divided into Industries (using PBIS and PSIS to determine the report contents)

3 Monthly summary report of newly calculated PBS and PSS scores

4 Monthly summary report of newly calculated PBIS and PSIS scores

Data Preparation Steps

As part of the process, the following steps are initially completed

1 Interpreter Backend [IBE] - an optional process which is used to supply and generate the data utilized by the present inv ention, such as. for instance, indiv idual returns, av erage returns statistical values such as standard dev iations. variances and the like, and/or counts ot the number of decisions, any of which may be used for the calculations (in other embodiments, the above data are gathered by the sy stem of the present inv ention itself)

2 IBE data relating to Trusts and Companies or other non-person entities are optionally , in this example, removed from the database (dB) In other embodiments, non-person entities may also be tracked For example, a closely - held company may optionally be considered as a person or an insider

Proven Insider Scoring Logic - Raw Buv Score The following logic is used to calculate the raw buy and sell scores which are the preprocesse scores that will optionally undergo statistical processing/weighing and/or filtering for all person/ security combinations in the dB with at least 2 buy or sell decisions that are at least 26 weeks old These raw scores are later ranked to formulate the final scores

Raw Buy Score - all inputs are based on calculated v alues related to buy decisions

For every person_ιd and security _ιd combination in the person_secuπty_ total table with 2 or more buy decisions, a raw buy score is calculated using the following logic (the logic is first summarized and then described in detail below)

Step 1 Calculate the average 13 week and 26 week (or other time intervals) returns for buy decisions [13/26 buy decision returns] for each person/security combination Step 2 Calculate the t-statistic [t-stat] values for 13/26 buy decision returns in absolute terms Step 3 Calculate the degrees of freedom [DF] values for 13/26 buy decision returns Step 4 Look up probability value using DF and |t-stat| for 13/26 buy decision returns Step 5 Calculate raw buy score using conditional logic Step 6 Calculate adjustment to raw buy score Step 7 Calculate adjusted raw buy score Step 8 Sort and rank the raw buy scores to produce the final Prov en Buy Scores. PBS and PBIS will rely on the same raw score, but are differentiated by the manner in which they are ranked (see following section)

For ev ery person_ιd and security _ιd combination in the person_secuπty_total table in the dB with person security total num_6mo_dec_buys >= 2. the following logic is performed

Step 1

The 13 week and 26 week return values are calculated by IBE for each person/security combination which generallv relate to histoπcal transactions Av erage 13week return = person_secuπty_total.av _3mo_return_buy s Average 26vveek return = person_secuπty_total.av g_6mo_return_buys

Step 2

The t-stat. a standard measure of confidence, is calculated in absolute terms rf e.. a non-negative number) for both the 13 week and 26 week returns using the following formulas

The π ^όwee^abs^sta^buy values should be rounded to 2 decimal places

To ensure that division by zero does not occur, if the standard deviation is zero, the final raw score is set to zero.

IF person_secuπty_total.sd_3mo_buys = 0. THEN raw_score_buy = 0 And start over ith the next person/security buy combination IF person_secuπty_total.sd_6mo_buys = 0. THEN raw_score_buy = 0 And start over with the next person/security buy combination

person jsecurity Jotal avg^mo retur Jbuys

13week abs tstat buv person _

_ totaled _ 3mo _ bins sJ person _ sec unn _ total num _ 3mo _ dec _ bin's

person _ seturιn Jotal

s

Jόu eek abs tstat bin = person _ sec unn _ total. d _ 6mo _ bu\s sJ person _ sec unn _ total num _ 6mo _ dec _ buys J

Other time inter als or similar algorithms may be used that are indicative of the measure of confidence of the insider

Step 3

The degrees of freedom are simply calculated by subtracting 1 from the number of obser ations for each return value 13week_df_buy = person_secuπty_total num_3mo_dec_buys- l

26week_df_buy = person_secuπty_total num_6mo_dec_buys- 1

Step 4

For each return period, using the absolute t-stat and DF for each respectiv e calculation as inputs to a lookup table the probabihtv is determined

IF 13 26week_df_buy > 100. THEN set 13/26\veek_df_bu> = 100

IF 13/26week_abs_tstat_buy > 20. THEN set 13/26week_abs_tstat_buy = 20

IF π ^όwee^abs^sta^buy < 01. THEN set 13/26week_prob_buy = 0

13week_prob_buy = lookup v alue indicated by using 13week_abs_tstat_buy and 13week_df_buy

26week_prob_buy = lookup value indicated by using 26week_abs_tstat_buy and 26week_df_buy

Other standard or similar probability determination processes or predetermined cπteπa may also be used Other threshold values may also be used In addition, the specific time periods may be altered The above modifications and all other modifications/alternativ es are relev ant to all embodiments of the present inv ention

Step 5

The conditional raw score is now calculated The conditional raw score is based on the av erage of the conditional raw scores for the 13 week period and for the 26 week period. which are then multiplied by 100 The condition to determine whether the probability is added or subtracted is based on w hether the av erage return is positiv e or negati e the lormula is as follow s cond raw score bin =

Logic:

13week portion of calculation

IF person _secuπty_total.a g_3mo_retum_buys > 0

THEN subtract 13week_prob_buy from .5 in numerator

— This next case means that a negative average return for the buy decisions exists and that the probability must be added —

ELSE add 13week_prob_buy to .5 in numerator

26week portion of calculation

IF person _ secuπty_total.av g_6mo_return_buys > 0

THEN subtract 26week_prob_buy from .5 in numerator

—This next case means that a negative average return for the buy decisions exists and that the probability must be added —

ELSE add 26week_prob_buy to .5 in numerator

Alternatively , the conditional raw score may be the median raw score, a subset grouping of the av erage'median. weighted average or median, or some other selection based on predetermined criteria consistent therewith.Final step

The two abov e calculated v alues are averaged and multiplied by 100. Other methods of statistically weighing or assessing the conditional raw score may be used, such as median, standard deviation, and other standard statistical methods. Other time durations may also be selected

Step 6 The adjustment to the conditional raw buy score is calculated using the following formula (the adjustment will always be negativ e)

adj raw buy = (-20)

— num_3mo_dec_buys and num_6mo_dec_buys are both taken from the person_secuπty_total table —

Other standard adjustment methods may also be used that prov ide similar or analogous results.

Step 7

The raw buy score is calculated by adding the adjustment to the conditional raw score

raw_score_buy = (cond_raw_score_buy) + (adj_raw_buy)

— here a negative number is added, therefore the adjustment should always be negative -

Other standard processes may be used to arrive at a consistent raw buy score For example, the raw buy score may be scaled or undergo mathematical conv ersion

Step 8 - Ranked Score

The follo ing ranking system is used to determine the final scores for the proven insider scores

PBS - Prov en Buy Score

Once all of the raw buy scores have been calculated, the raw buy scores are ranked by sorting the results in descending order and separating this list into 100 equal groups The group with the highest set of scores is given the ranking of 100 (signify ing the best proven buy performance), and the group with the lowest set of scores is giv en the ranking of 1 (signify ing the worst buy performance) The insiders in eac group are then assigned a PBS score equal to the group that they are in

Step 1 Sort raw_buy_score descending for all person_ιd/secuπty_ιd w here there is a score Step 2 Divide all scores into 100 equal groups, or into 100 groups based on distributional properties Step 3 Assign PBS score of 100 to highest ranking group and record the PBS score for the associated person-id and secunn id combinations in the group Step 4 Repeat for the remaining groups, in all there should be scores ranging between 100 - 1 for an equal amount of insiders in each group

Of course additional or alternativ e statistical processes and weights may be applied in a standard manner for performing the ranking These alternativ es apply to all scores described herein

PBIS - Proven Buy Industry Score

The PBIS is very similar to the PBS score except that all insiders are divided into their respective industries and then separated into 100 equal groups and assigned their PBIS scores

Step 1 Group each insider into their respectiv e industry

Step 2 Sort raw_buy_score descending tor all person_ιd/secuπty_ιd where there is a score separately for each industry

Step 3 Divide all scores in each industry into 100 equal groups, or into 100 groups based on distributional properties, if there are not 100 insiders in an industry then consider each insider sign rankings using the following increments. rounding to the nearest whole number [100/number of insiders in industry] Step 4 Assign PBIS score of 100 to highest ranking group and record the PBIS score for the associated person-id and secunn /-/ combinations in the group, see above if there are not 100 insiders in an industry Step 5 Repeat for the remaining groups, in all there should be scores ranging between

100 - 1 for an equal amount of insiders in each group Raw Sell Score - All inputs are based on calculated v alues related to sell decisions

For ev ery person-id and secuπty -id combination in the person_secuπty_total table with 2 or more sell decisions, a raw sell score using the following logic is calculated (the logic is first summarized and then explained in detail)

Step 1 Calculate the av erage 13 week and 26 week (or other time interv als) returns for sell decisions [13/26 sell decision returns] for each person/secuπty combination (done by IBE) Step 2 Calculate the t-statistic [t-stat] v alues for 13/26 sell decision returns in absolute terms

Step 3 Calculate the degrees of freedom [DF] values for 13/26 sell decision returns

Step 4 Look up probability v lue using DF and |t-stat| tor 13/26 sell decision returns

Step 5 Calculate raw sell score using conditional logic

Step 6 Calculate adjustment to raw sell score

Step 7 Calculate adjusted raw sell score

Step 8 Sort and rank the raw sell scores to produce the final Proven Sell Scores. PSS and PSIS rely on the same raw score, but are differentiated by the manner in which they are ranked (see following section)

For ev ery person_ιd and security _ιd combination in the person_secuπty_total table in the dB ith person_secuπty _total num_6mo_dec_sells >= 2. the following logic is performed

Step 1

The 13 week and 26 week return v alues are calculated by IBE for each person/ security combination

Av erage 13week return = person_secuπty_total av g_3mo_ return_sells Average 26week return = person_secuπty_total avg_6mo_return_sells

Step 2 The t-stat is calculated in absolute terms ( 1 e . a non-negativ e number) tor both the 13 week and 26 week returns using the following formulas

The

v alues should be rounded to 2 decimal places

To ensure that div ision by zero does not occur, if the standard deviation is zero, then the final raw score is set to zero

IF person_secuπtv_total sd_3mo_seIls = 0. THEN raw_score_sell = 0 And start over with the next person secunty buy combination

IF person_secuπty_total sd_6mo_sells = 0. THEN raw_score_sell = 0 And start o er with the next person/secunty buy combination

person secunn total a\g 3mo_return_seIls abs tstat sell person _ sec unn _ total sd _ 3 mo _ sells ] person _ sec unn _ total num _ 3mo _ dec _ sells

person _securιty_total avg όmo _return_sells

26M eek abs tstat sell person _ securιn _ total sd _ 6mo_ sells

4J person __ sec unn _ total num _ βmo _ dec _ sells

Other time interv als or similar algorithms may be used that are indicative of the measure ot confidence ot the insider

Step

The degrees of freedom are simply calculated by subtracting 1 from the number of obser ations for each return v alue

= person_secuπty_total num_3mo_dec_sells- l

26week_df_sell = person_secuπty _total num_6mo_dec_sells- l

Step 4 For each return period, lookup the probability using the absolute t-stat and DF for each respectiv e calculation as inputs to a lookup table

IF 13/26week_df_sell > 100. THEN set 13 '26week_df_sell = 100

IF 13/26week_abs_tstat_sell > 20. THEN set 13/26week_abs_tstat_sell = 20

IF 13/26week_abs_tstat_sell < 01. THEN set 13/26week_prob_sell = 0

13week_prob_seil = lookup value indicated by using 13week_abs_tstat_sell and 13week_df_sell

26week_prob_sell = lookup value indicated by using 26week_abs_tstat_sell and 26week_df_sell

Other standard or similar probability determination processes or predetermined criteria may also be used Other threshold v alues may also be used In addition, the specific time peπods may be altered The above modifications and all other modifications/alternatives are relevant to all embodiments of the present invention

Step 5

The conditional raw score is now calculated The conditional raw score is based on the average of the conditional raw scores for the 13 week period and for the 26 week period. which are then multiplied by 100 The condition to determine whether the probability is added or subtracted is based on whether the av erage return is positive or negative, the formula is as follows

con

Logic- (Note We are now using less than zero, which is opposite of the Buy logic) 13 w eek portion of calculation

IF person_security_total.avg_3mo_return-sells < 0

THEN subtract 13 eek_prob_sell from .5 in numerator

— This next case means that we have a positive average return for the sell decisions and we need to add the probability --

ELSE add 13week_prob_sell to .5 in numerator

26week portion of calculation IF person _ security_total.avg_6mo_return-sells < 0 THEN subtract 26week_prob_sell from .5 in numerator

— This next case means that we have a positive average return for the sell decisions and we need to add the probability -

ELSE add 26week_prob_sell to .5 in numerator

Alternatively, the conditional raw score may be the median raw score, a subset grouping of the average/median, a weighted average or median, or other selection based on predetermined criteria consistent therewith.E α/ step The two above calculated values are arranged and multiplied by 100. Other methods of statistically weighing or assessing the conditional raw score may be used, such as median, standard deviation, and other standard statistical methods. Other time durations mav also be selected.

Step 6

The adjustment to the conditional raw sell score is calculated using the following formula

( the adjustment will always be negative):

adj_raw_sell = (-20) _. mum _ =3mo _ =acc = sells - .. num _ = bmo _ _ a c _ = id Is ~ num_3mo_dec_sells and num_6mo_dec_sells are both taken from the person_secuπty_total table —

Step 7

The raw sell score is calculated by adding the adjustment to the conditional raw score-

raw_score_sell = (cond_raw_score_sell) + (adj_raw_sell)

-- here a negative number is added, therefore the adjustment should always be negative

Step 8 - Ranked Score

The following ranking system is used to determine the final scores for the proven insider scores.

PSS - Proven Sell Score

Once all of the raw sell scores have been calculated, the raw sell scores are ranked by sorting the results in descending order and separating this list into 100 equal groups The group with the highest set of scores is given the ranking of 100 (signifying the best proven sell performance ), and the group with the lowest set of scores is given the ranking of 1 (signifying the worst sell performance). The insiders in each group are then assigned a PSS score equal to the group that they are in

Step 1 Sort raw_sell_score descending for all person_ιd/secunty_ιd where there is a score Step 2 Divide all scores into 100 equal groups, or into 100 groups based on distributional properties Step 3 Assign PSS score of 100 to highest ranking group and record the PSS score for the associated person id and secunn id combinations in the group Step 4 Repeat for the remaining groups, in all there should be scores ranging between

100 - 1 for an equal amount of insiders in each group

PSIS - Prov en Sell Industrv Score The PSIS is ery similar to the PSS score except that all insiders are div ided into their respectiv e industπes and then separated into 100 equal groups and assigned their PSIS scores

Step 1 Group each insider into their respectiv e industry

Step 2 Sort raw_sell_score descending for all person_ιd/secuπtv_ιd where there is a score separatelv for each industry Step 3 Div ide all scores in each industry into 100 equal groups, or into 100 groups based on distributional properties, if there are not 100 insiders in an industry then consider each insider as a group and assign rankings using the following increments, rounding to the nearest whole number [100/number of insiders in industry ] Step 4 Assign PSIS score of 100 to highest ranking group and record the PSIS score for the associated person id and secunn d combinations in the group, see above if there are not 100 insiders in an industry Step 5 Repeat for the remaining groups, in all there should be scores ranging between

100 - 1 for an equal amount of insiders in each group

As mentioned above, the above summarized embodiment is particularly useful in evaluating corporate insiders It should, however, also be noted that the concepts of the present inv ention are equallv applicable for ev aluating other ty pes of investors For instance, bond and mutual fund investors just to name a few. mav also be evaluated in accordance w ith the concepts of the present invention

Hence to achiev e the these goals and to address the above and other problems of the prior art. the present invention prov ides a method, sy stem, and computer readable medium storing computer executable instructions for ev aluating an inv estor s performance bv utilizing information pertaining to at least one transaction made by the investor inv olving at least one inv estment In one embodiment, the present inv ention includes determining a performance score indicati e of the investor s performance relativ e to other inv estors The performance score is determined at least in part by considering an av erage historical performance of the at least one inv estment following the at least one transaction, a histoπcal consistency of the inv estor s performances with respect to transactions involv ing the at least one investment, and the number of transactions made by the inv estor This embodiment also includes compaπng the performance score of the investor against performance scores of other inv estors

In another embodiment, the present invention prov ides a method, sy stem, and computer readable medium storing computer executable instructions for use in producing a ranked list of investors according to an ev aluation of the inv estors^' performances relating to at least one transaction made by the investors inv ol ing investments associated with the investors In one embodiment, the inv ention includes retrieving a list of investors and generating an evaluation list by removing investors failing to meet predetermined criteria from the list This embodiment also includes calculating a performance score for each investor listed on the ev aluation list indicativ e of the investor s performance by considering an av erage historical performance of an investment following a transaction by the investor, a historical consistency of the investor s performances with respect to transactions inv olving the at least one investment, and the number of transactions made by the investor Also included in this embodiment is the step of calculating, for each investor using the performance scores, a final transaction score indicative of the investor^'s relative performance with respect to all investors on the evaluation list

There has thus been outlined, rather broadly , the more important features of the invention in order that the detailed description thereof that follows may be better understood, and in order that the present contribution to the art may be better appreciated There are of course, additional features of the invention that will be described hereinafter and which w ill form the subject matter of the claims appended hereto

In this respect, before explaining at least one embodiment of the inv ention in detail, it is to be understood that the inv ention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following descπption or illustrated in the drawings The inv ention is capable of other embodiments and of being practiced and carried out in v arious way s Also, it is to be understood that the phraseology and terminology employed herein are tor the puφose ot description and should not be regarded as limiting

As such, those skilled in the art will appreciate that the conception, upon which this disclosure is based, may readily be utilized as a basis for the designing of other structures, methods and systems for carry ing out the several puφoses of the present invention It is important, therefore, that the claims be regarded as including such equiv alent constructions insofar as they do not depart from the spirit and scope of the present invention

Further, the puφose of the foregoing abstract is to enable the U S Patent and Trademark Office and the public generally , and especially the scientists, engineers and practitioners in the art who are not familiar with patent or legal terms or phraseology , to determine quickly from a cursory inspection the nature and essence of the technical disclosure of the application The abstract is neither intended to define the invention of the application, which is measured by the claims, nor is it intended to be limiting as to the scope of the invention in any way

These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the claims annexed to and forming a part of this disclosure For a better understanding ot the invention, us operating adv antages and the specific objects attained by its uses, reference should be had to the accompany ing drawings and descriptive matter in which there is illustrated preferred embodiments of the inv ention

Other objects of the present inv ention will be ev ident to those of ordinary skill, particularly upon consideration of the following detailed description of the preferred embodiments

NOTATIONS AND NOMENCLATURE The detailed descπptions which follow mav be presented in terms of program procedures executed on computing or processing sy stems such as. for example, a standalone gaming machine, a computer or network of computers These procedural descriptions and representations are the means used by those skilled in the art to most effectively conv ey the substance of their work to others skilled in the art

A procedure is here, and generally, concei ed to be a self-consistent sequence of steps leading to a desired result These steps are those requiring phy sical manipulations of phy sical quantities sually , though not necessarily , these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated It proves convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, sy mbols, characters, terms, numbers, or the like It should be noted, howev er, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities

Further, the manipulations performed are often referred to in terms, such as adding or compaπng. which are commonly associated with mental operations performed by a human operator No such capability of a human operator is necessary . or desirable in most cases, in any of the operations descnbed herein which form part of the present invention, the operations are machine operations Useful machines for performing the operation of the present inv ention include general puφose digital computers or similar devices

BRIEF DESCRIPTION OF THE DRAWINGS

FIG 1 illustrates a prior art stock and cash portfolio dev elopment sy stem FIG 2 illustrates a pπor art securities valuation svstem. FIG 3 illustrates a prior art inv estment fund management sy stem FIG 4 illustrates a prior art data processing sy stem for selecting securities and constructing investment portfolios based on a set of artificial neural networks.

->-> FIG 5 depicts one example of a user interface utihzable for displav ing an inv estor evaluation according to the principles of the present invention.

FIGS 6a-6b depict another example of a user interface utihzable for display ing an investor ev aluation according to the principles of the present invention.

FIG 7 is a flow diagram illustrating a process for evaluating inv estors according to the principles of the present invention.

FIG 8 is a flow diagram illustrating a high lev el process for generating a buy performance measure utihzable in the process of FIG 7,

FIG 9 is a flow diagram illustrating a high lev el process for generating a sell performance measure utihzable in the process of FIG 7.

FIG 10 is a flow diagram illustrating a lower level process for generating a buy performance measure utihzable in the processes of FIGS 7-8,

FIG 1 1 is a flow diagram illustrating a lower lev el process for generating a sell performance measure utihzable in the processes of FIGS 7 and 9.

FIG 12 is a flow diagram illustrating a process for ranking investors according to buy performance utihzable in the process of FIG 7.

FIG 13 is a flow diagram illustrating a process for ranking investors according to sell performance utihzable in the process of FIG 7.

FIG 14 is a flow diagram illustrating a process for ranking investors in a particular industry sector according to buy performance utihzable in the process of FIG 7.

FIG 15 is a flow diagram illustrating a process for ranking investors in a particular industrv sector according to sell performance utihzable in the process of FIG 7.

FIG 16 illustrates one example of a central processing unit for implementing a computer process in accordance with a computer implemented embodiment of the present invention

FIG 17 illustrates one example of a block diagram of internal hardware of the central processing unit of FIG 16 FIG 18 illustrates another example of a block diagram of internal hardware of the central processing unit of FIG 16 FIG 19 illustrates one example of a memory medium which may be used tor stoπng a computer implemented process of the present inv ention.

FIG 20 illustrates an example of a combined Internet. POTS, and ADSL architecture which may be used with the present invention, and

FIG 21 shows a set of tabulated results of an evaluation performed according to the principles of the present inv ention

BEST MODE FOR C ARRYING OUT THE INVENTION Reference now will be made in detail to the presently preferred embodiments of the invention Such embodiments are provided by way of explanation of the invention, which is not intended to be limited thereto In fact, those of ordinary skill in the art may appreciate upon reading the present specification and viewing the present drawings that vaπous modifications and vanations can be made

For example, features illustrated or descnbed as part of one embodiment can be used on other embodiments to yield a still further embodiment Additionally, certain features may be interchanged with similar devices or features not mentioned yet which perform the same or similar functions It is therefore intended that such modifications and vanations are included within the totality of the present invention

In accordance with the principles of the present invention, an inv estor^'s performance is ev aluated by utilizing information pertaining to at least one transaction made by the investor involving at least one investment This evaluation includes determining a performance score indicative of the investor^'s performance relative to other investors The performance score is determined at least in part by considering an average historical performance of the investment, following the transaction In addition, the performance score is also determined by a historical consistency of the inv estor^'s performances w ith respect to transactions involving the inv estment, and the number of transactions made by the investor Then, the performance score of the inv estor may be compared against the performance scores of other investors Adv antageously , this performance score may be used to produce a ranked list of inv estors in a particular industry , as well as w ith inv estors in different industries

Refemng first to FIG 5. one example of a user interface 500 utihzable for displaying, for example, investor performance scores is illustrated In FIG 5. the user interface displays a wide v aπety of information including the performance scores of a number of investors 510 In this example, a predetermined number of high performing investors with respect to both buying and selling transactions for a particular date are listed Altemativelv . the user mav lust as easilv elect to view a list of onlv the investors having the highest selling transaction performance scores or of only the highest buying transaction performance scores

Advantageously . these performance scores are a measure of the predictive nature of the investor^'s trading decisions The higher the score (with one-hundred, for example, being the highest), the more predictive on a historical basis Furthermore, the performance score increases with the number of beneficial transactions For instance, the performance score increases when a rate of return of an investment increases and the transaction is a buy transaction, or when a rate of return of the investment decreases and the transaction is a sell transaction Likewise, the performance score decreases when a rate of return of the inv estment decreases and the transaction is a buy transaction, or when a rate of return of the inv estment increases and the transaction is a sell transaction Thus, a high buy score conesponds to historically high positiv e returns d e . the investor purchased a stock, and the stock went up in v alue) Similarly , a high sell score corresponds to high negative returns ( I e . they sold the stock, and the stock went down in value) In addition, it should be noted that although in this particular embodiment coφorate securities are mentioned as being one example of the investor^'s trading decision or investment, the instant invention is also applicable to and should be construed as encompassing any other form of similar investment Some examples include bonds, cunencies. commodities, paper, precious metals, minerals, natural resources, mutual funds, and the like or anv other analogous inv estments and/or investment entities As will be discussed in greater detail below, several factors are incoφorated into these performance scores For instance, an inv estor^'s av erage return history for both buys and sells may be considered Indeed in many embodiments this factor is accorded the greatest amount of weight Second, the inv estor^'s histoncal consistency at picking good entry and exit points in their own company s stock, as measured by . for example, standard dev tion. is also considered Finally . the number of decisions that make up an investor s track record is factored into the performance score for the simple reason that the more times an inv estor has demonstrated good timing the better Other factors are optionally used as well

In addition to listing the highest performing investors, interface 500 may also be linked to or used to display information concerning each investor and/or the corresponding secunty Hence, in FIG 5. the investor Nardelli is listed as being a senior v ice president of General Electric and as having a share volume of 25.000 Furthermore, the interface sponsor may optionally include commentary concerning, for example, any securities or investors, advertisements utihzable as a source of revenue 550. instructions or help information for beginning users, and information concerning or tracking the activities of exceptionally high performing investors, such as. for example the total number of transactions made by a list of "All Star^" investors (determined in this embodiment by hav ing a performance score abov e a predetermined level) 540. or other similar features

In FIG 6. a second example of a user interface is depicted Whereas the interface in FIG 5 depicts a tabulation of investors with high buying and selling performance scores listed together here the listing of investors is separated into separate lists of high performing buv inv estors 610 and high performing sell inv estors 620 Like the example in FIG 5 interface 601 also lists the total number of transactions made by a list of "All Star^" inv estors during the cuπent day . quarter and/or y ear

The embodiments of FIGS 5 and 6 depict interfaces that advantageously score insiders entities in a manner consistent with sporting events For example, the user interfaces in these examples simulate a baseball scoreboard Adv antageouslv . these scoreboards prov ide and it is customarv to also include advertisements that can be sponsored by actual sponsors Thus, mis ty pe of user interface provides a framework for which the public is accustomed to rev iewing and understanding information, in a context of ranking insiders/entities that the public may use when investing and/or tracking market trends Of course, other ty pes of scoreboards may also be use. as appropπate For example, football, basketball, soccer, and/or hockey scoreboards, and the like may also be used

In use. a user may advantageously view the information provided by the interfaces depicted in FIGS 5 and 6. in this case, the highest performing investors with respect to either buy and or sell transactions Alternatively , a user may select a particular investor or investing entity for review Similarly . a user may select any combination of entities or sets/groupings of data including, for example, all investors dealing with or investing in a particular coφoration or investment, or other analogous listings of information

A broad ov erview of the process of one embodiment of the present invention is now illustrated in FIG 7 First, a list of investors is retriev ed from one or any number of databases 710 In this particular embodiment, the database includes a list of all of the insiders filing insider documents with the SEC In other embodiments, other similar or analogous techniques mav be utilized to generate a list of inv estors For example, anv one of a v ariety of databases tracking industry leaders may just as easily be used

After retriev ing the list of investors, a smaller list of investors to be evaluated is optionallv generated according to predetermined criteria 715 In one example, three optional criteria are used to generate this list First, each inv estor may be required to be an actual liv ing person Thus, using the insider example mentioned abov e, any entities filing insider documents that are not people, such as companies, partnerships, or trusts. are remov ed from the evaluation list Second, the market capitalization of the company at which the insider is affiliated may be required to be greater than a predetermined amount, such as. for instance. S50 million Third, each inv estor mav be required to hav e made a minimum number of trades, such as ha ing traded at least two times in the past ten years and or once in the last fiv e y ears Thus, in this example, inv estors may optionally include living people inv esting in companies ha ing a market capitalization of at least S50 million and have traded at least two times in the past ten y ears or once in the last five years These requirements are disclosed for exemplary reasons only and it is to be understood that other ty pes of inv estors are possible within the scope of the inv ention For instance, non-people investors such as partnerships, coφorations. and other joint ventures or entities are also possible Similarly , entities not hav ing market capitalization or trading limits are also possible Furthermore, the concepts of the present invention are applicable to entities investing in investments other than securities, such as bonds, mutual funds, or any other similar inv estments

After generating the list of in estors to be evaluated, processing continues with the actual ranking of the inv estors 720 This ranking, as will be discussed in greater detail below, includes the determination, of one or more of a number of performance scores for each investor listed, including, for example, raw buy and sell performance scores, buy and sell performance scores relative to all investors listed in the evaluation list (prov en buy and sell scores), and industry wide buy and sell performance scores (or proven buy and sell industry scores) As will be discussed below, the inv ention is not limited to just the above proven buy and sell scores and prov en buy and sell industry scores To the contrary , other performance scores and rankings are also possible including, for example, rankings based on other groupings From there, the rankings produced in 720 may optionally be disseminated v ia. for example, one of the user interfaces depicted in FIGS 5 or 6 Alternatively, the rankings may be disseminated in any other similar or analogous medium, such as in a magazine or newspaper, a trade journal, telev ision. and radio or the like

Although in this embodiment a list of investors to be ev aluated is determined by the present invention, it is to be understood that other alternati es are also possible For instance, the present inv ention is to be construed as including scenarios where a user enters or inputs a predetermined listing of investors for ev aluation In such a case, only the particular entities entered by the user would be evaluated Likewise, it is also possible that a mixed grouping of investment entities may be ev aluated For instance, it is possible within the scope of the present invention to evaluate a listing of inv estors which includes coφorate insiders, nonperson joint ventures, and/or coφorations. and any other tv pes of inv estment entities or combinations thereof

In accordance with the principles of the present invention, an ov erview of the calculation of buy performance scores is illustrated with reference to FIG 8 Initially, a raw buy score, which as mentioned above is indicative of an investor^'s buy performance, is calculated 810 This raw buy score, then, is optionally manipulated and utilized in the generation of an optional proven buy score 850 and in the generation of an optional proven buv industry score 870 The proven buy score, as mentioned briefly abov e, is a ranking or an indication of the inv estor s buy performance as measured with respect to all other evaluated investors On the other hand, the proven buy industry score is a ranking or an indication of the investor^'s buy performance as measured with respect to all other evaluated investors in the investor s own industry Of course, other scores are also possible For instance, it is possible that rankings or proven scores may be calculated reflecting relative performance based on geographical regions, between only particular tv pes of entities such as only non-profit organizations or entities, or investors involved with a particular coφoration. and the like

A similar procedure is performed for calculation of sell performance scores Referring to FIG 9. a raw sell score which in contrast to the raw buy score is indicativ e ot an inv estor s sell performance is calculated 910 Like the above procedure, the raw sell score is optionally utilized in the generation of an optional proven sell score 950 and in the generation of an optional prov en sell industrv score 970 As may be ev ident from the discussion above, the proven sell scores rank the indiv idual investors sell performance and are used to ev aluate the inv estor s performance with respect all other investors in the evaluation list Likewise, the proven sell industrv scores rank the indiv idual inv estor s sell performance in the inv estors industry and are used to ev aluate the inv estor^'s performance with respect all other inv estors in the inv estor s own industry

In accordance with the principles of the present in ention and as discussed bπefly abov e, these scores or measures reflect an investor^'s or entity ^"s performance and are generated by considering a number of performance factors For instance, generation of the scores may include a measure of the inv estor^'s historical performance One method used to measure such performance includes, for example, the return v alue of the investor^'s investment as determined at certain time intervals after the investment Another example may include consideπng the investors^" success at selecting entry and exit points in the investor^'s investment Additionally, the investor^'s performance score or measure may be modified or weighted based on the amount of transactions made by the investor or other similar factors Of course, the above factors are listed for exemplary reason only and it is to be understood that other similar and analogous factors may also be considered so long as they prov ide an accurate measurement of the inv estor^'s success or lack thereof

As one example, the calculation of one performance measure or raw buy score is now described ith reference to FIG 10 In this example, the rate of return on the investor s investment is utilized in the generation of the performance score More specifically , one or more return values for one or more return inter als are. initially , calculated or gathered for each investor 1012 by anv suitable means In this particular embodiment, two return v alues, one at 3 months (I e . 13 weeks) and a second at 6 months d e . 26 weeks), are utilized for exemplary puφoses In actual practice however any number of return values, at any number of time inter als, are allowable so long as they are suitable proxies for short and long-term performance

Subsequently , a statistical measure of confidence in the inv estor s performance may be calculatedfor each return value 1014 As one example, a t-statistic or t-stat. taken in absolute terms, is utilized as this measure of confidence The t-stat is a measure on a random sample (or pair of samples) in which a mean (or pair of means) appears in the numerator and an estimate of the numerator^'s standard dev iation appears in the denominator The latter estimate is based on the calculated sample v ariance estimates of the samples Alternativ ely , other standard v ariance methods or some other similar index of variability may optionally be used in place of standard dev iation Significantly , if these calculations yield a value of (t) that is sufficiently different from zero, the test is considered to be statisticallv significant

In this embodiment, the t-stats for each return period may be determined by buv return buv t st at buv s tan dard deviation sJ total _ number _ of _ buv __ decisions

Furthermore, to prevent div ision bv zero, if the standard dev iations for either of the time intervals is zero, then the raw buy score for that investor is set to zero In addition, although the t-stat is used in this example, other measures of confidence may just as easily be incoφorated in the present invention As another example, the z-statistic or other similar measurement may also be used Furthermore, regardless of what method is used to produce the measure of confidence, the measure of confidence may also be manipulated or weighted to consider other factors, so long as it accurately reflects, for instance, the inv estor^'s return history and historical consistency at picking entry and exit points in the inv estment

After calculating the measures of confidence, in this embodiment the t-stats for each time interv al or return period, one or more degrees of freedom are determined by subtracting, for example, one from the number of obser ations or decisions for each return v alue 1016 The degree of freedom is used to describe the number of values in a final calculation of a statistic that are free to vary The observ ations or decisions, on the other hand, is a consistent pattern of behav ior by a given inv estor ov er a period of time Hence, the degree of freedom is determined by

buy degree of freedom = total number of buy decisions - 1 Next, a probability indicating, tor example, the likelihood that an insider s and or investing entity ^'s actions will actual produce the expected result, is determined for the measure of performance 1018 In this embodiment, the degree of freedom v alues, along with the absolute t-stat values, are used in determining a probability for each return penod 1018 Specifically , subject to the following optional conditions, the probabilities are looked-up in a common statistics lookup table using each return interval^'s degree ot freedom and t-stat If any of the degrees of freedom are greater than 100. then that degree of freedom is set to 100 before looking up the probability If any of the absolute t- stats are greater than 20. then that absolute t-stat is set to 20 before looking up the probability Finally, if either t-stat is less than 01. then the corresponding probability is simply set to 0 In addition, the probabilities may be calculated utilizing other methods For instance, the t-stat and the degree of freedom may undergo further manipulation before looking up the probability Alternatively, other analogous methods may be utilized in place of the look-up table to determine the probabilities

Subsequent to determining the probabilities for each return penod. a conditional raw score is generated 1020 The conditional raw score represents an initial inv estor performance measure and may undergo one of any number of manipulations to result in vaπous other performance measures For instance, the conditional raw score may be weighted to place additional emphasis on the maturitv of the coφoration Alternatively investments in conservative sectors may also receiv e a conesponding modification to reflect the same These modifications are introduced for exemplary reasons only and it is to be understood that other manipulations are also possible within the scope of the present invention

In this embodiment, the conditional raw score is determined by

, , \ [l - ( 5 J- or - \ 3week prob bin ) | + [l - ( "> + or - 26week prob bin )] υnd ruxx score bm = < - — = - — — ^J * 100 Whether the probabilities are added or subtracted is determined by the follow ing conditions If either probability is greater than zero, then that probability is subtracted from 0 5 Otherwise. 0 5 is added to the probability

Then, any number of optional adjustments to the conditional raw score are determined 1022 In one example, an adjustment reflects and emphasizes the number of transactions made by the investing entity

In this example, the adjustment is determined by

adjustment = (-20) *

As will be noted from the above formula, two separate decision amounts are considered in this embodiment However, as discussed above, any other modifications are also possible For instance, modifications may be made placing a much greater emphasis on short term investments to produce an adjustment aimed at producing scores for use in formulating short term investment strategies. Or. in contrast, greater weight may be placed on returns over longer periods of time to reflect, for example, a more conservative investment strategy

From there, the raw buy score is calculated by adding any adjustments to the conditional raw score 1024 Once again, ev en though only one adjustment is made to the conditional score in this example, it is to be understood that any number of adjustments or modifications may be made within the scope of the invention

Generation of a raw sell score is similar to generation of a raw buy score and is depicted in FIG 1 1 Like the raw buy score, the raw sell score is indicativ e of an investor^'s sell performance, and may be manipulated in any number of way s to produce other investor performance scores Similar to the generation of the raw buy scores. . one or more return values are initially calculated or gathered for each investor 1 1 12 by any suitable means Again, two return v alues, one at 3 months (I e . 13 weeks) and a second at 6 months (i e . 26 weeks), are utilized for exemplary puφoses

JJ Subsequently , a statistical measure of confidence in the inv estor s performance is calculated 1 1 14 In this case, a t-statistic (t-stat). in absolute terms, is again utilized for exemplary reasons, and is likewise calculated for each return value 1 1 14

For sell transactions, the t-stats for each return penod is determined by

sell return sell t star sell _ s tan dard _ deviation ψotal _ number _of _ sell _ decisions

Like with the buy performance score, to prevent div ision by zero, if the standard deviations for either of the time intervals is zero, then the raw sell score for that investor

Again, as with the above example, it is to be understood that other measure of confidence may be substituted for the t-stat Also, any number of modifications are also possible As indicated above, as long as an accurate reflection of the investor^'s return history and/or historical consistency at picking entry and exit points is considered, any measure of confidence, for example a z-stat. may be used

After calculating the measures of confidence, in this case t-stats. for each time interv al or return period, one or more degrees of freedom are determined by subtracting, for example, one from the number of observ ations or decisions for each return value 1 1 16 Accordingly , the degree of freedom is determined by

sell degree of freedom = total number of sell decisions - 1

Subsequently , a probability is determined for the measure of performance 1 1 18 In this example, the degree of freedom v alues, along with the absolute t-stat values, are used in determining a probability for each return period 1 1 18 Specifically . subject to the following optional conditions, the probabilities are looked-up in a common statistics lookup table using each return interv al^'s degree of freedom and t-stat If any of the degrees of freedom are greater than 100. then that degree of freedom is set to 100 before looking up the probability If any of the absolute t-stats are greater than 20. then that absolute t-stat is set to 20 before looking up the probability Finally . if either t-stat is less than 01. then the conesponding probability is simply set to 0 Alternatively , the probabilities may be calculated based on other the methods For example, the measures of confidence and degrees of freedom mav be modified or manipulated before looking up the probability in the lookup table Also, any other similar methods may be used in place of the look-up table to generate a probability

Subsequent to determining the probabilities for each return penod. a conditional raw score is generated 1 120 This conditional raw score pro ides an initial investor performance measure and may be modified to produce various other performance measures Although the below example depicts only one modification, namely to emphasis the number of transactions made by the investor, any number of other modifications are possible

In this example, the conditional raw score is determined by l \. - ( 5 + or - \ 3week prob sell)] + [l - ( 5 + or - 26week prob sell)] ] _ „ <- ""d _ raw _ score _ sell = ' - — ' — ^l- — -^J 1 * 100

Whether the sell probabilities are added or subtracted is determined by the following condition In contrast to the buy logic which tests for probabilities that are greater than zero, if either sell probability is less than zero, then that probability is subtracted from 0 5 Otherwise. 0 5 is added to the probability

Then, any optional adjustments to the conditional raw score are determined 1 122 In this example, an adjustment is made to reflect and emphasize the number of transactions made by the investor In this embodiment, the adjustment is determined by adjustment — (-20 ) | imtm mo dec_sells _^ num bmo _at c _setιs ι \ ^{= =} 2

I

From there, the raw sell score is calculated bv adding any adjustments to the conditional raw score 1 124

In accordance with the pπncφles of the present invention, one example of prov en buy score generation is depicted with reference to FIG 12 In particular, buy score generation commences with sorting of the investors into descending (or ascending) raw buy score order 1252 Then, if there are one-hundred or more investors, the inv estors are divided or separated into 100 substantially equally sized groups 1254. according to the descending (or ascending) order If there are less than one-hundred inv estors, each investor constitutes an entire group Each group is subsequently assigned a rank 1256. with the highest ranking group receiv ing the highest rank and with the next highest ranking group receiving the next highest rank This procedure is repeated until each group has been ranked 1258 If there are less than one-hundred groups, the highest ranking investor receives the highest rank, with the next highest performing investor being assigned a rank reduced from the highest rank by an amount equal to one-hundred divided by the number of investors Again, this procedure is repeated until all of the investors have been ranked

A similar procedure is utilized to rank the inv estors ith respect to prov en sell scores, as depicted in FIG 13 Thus this process is only briefly summarized here Specifically , the investors are sorted into descending (or ascending) prov en sell score order 1352. separated into equally sized groups 1354. and then assigned a rank 1356 and 1358 in a similar manner as discussed above

In accordance with the principles of the present inv ention, one example of proven buy industry score generation is depicted with reference to FIG 14 In particular, buy industry score generation commences with the grouping of inv estors into industry sectors 1472 After grouping into industries, processing continues with sorting of the inv estors in the industry sector into descending (or ascending) raw buv score order 1474 Then, if there are one-hundred or more investors in the particular industry, the investors are divided or separated into 100 substantially equally sized groups 1476. according to the descending (or ascending) order. If there are less than one-hundred investors in the industry, each investor constitutes an entire group. Each group is subsequently assigned a rank 1478. with the highest ranking group in the industry receiving the highest rank and with the next highest ranking group receiving the next highest rank. This procedure is repeated until each group in the industry has been ranked. If there are less than one- hundred groups, the highest ranking investor receives the highest rank, with the next highest performing investor receiving a rank reduced from the highest rank by an amount equal to one-hundred divided by the number of investors in the industry at issue. Again, this procedure is repeated until all of the investors in the industry sector have been ranked. Optionally, this procedure may be repeated until any or all additional industries have been ranked 1480.

A similar procedure is utilized to rank the investors with respect to proven sell industry scores, as depicted in FIG. 15. Thus, this procedure is only briefly summarized here. Specifically, the investors are grouped into industry sectors 1572. sorted into descending (or ascending) proven sell industry score order 1574, separated into equally sized groups 1576. and then assigned a rank 1578. This procedure may optionally be repeated for each industry 1580. in a similar manner as discussed above.

In addition, as mentioned above, any other similar and analogous groups/rankings or performance measures may be generated. For instance, instead of grouping investors into industries, the present invention is to be construed as allowing the grouping and the subsequent generation of performance score based on. for example, the size or value of a non-person investment entity. Thus, using this example, the present invention may be used to rank coφorations having a predetermined value. Similarly, as another example, a user may only wish to rank or evaluate the performance of nonprofit organizations investing only in currencies. Or perhaps the user may wish to evaluate only organizations headquartered in Midwestern United States, perhaps to be supplied by the user In any ot these cases, the present invention would allow any such or similar designation by the user before processing

One example depicting the tabulated results of processing in accordance with the abov e techniques is shown in FIG 21

Viewed externally in FIG 16. a computer system designated by reference numeral 140 has a computer 142 having disk drives 144 and 146 Disk dπv e indications 144 and 146 are merely sy mbolic of a number of disk driv es which might be accommodated by the computer system Typically, these would include a floppy disk dπve 144. a hard disk drive (not shown externally) and a CD ROM indicated by slot 146 The number and type of drives vary, typically with different computer configurations Disk drives 144 and 146 are in fact optional, and for space considerations, are easily omitted from the computer system used in conjunction with the production process/apparatus described herein

The computer system also has an optional display upon which information, such as the screens illustrated in FIGS 5-6 may be displayed In some situations, a keyboard 150 and a mouse 152 are provided as input devices through which a user^'s actions may be inputted, thus allowing input to interface with the central processing unit 142 Then again, for enhanced portability , the key board 150 is either a limited function key board or omitted in its entirety In addition, mouse 152 optionally is a touch pad control device, or a track ball device, or even omitted in its entirety as well, and similarly may be used to input a user^'s selections In addition, the computer system also optionally includes at least one infrared transmitter and/or infrared received for either transmitting and/or receiv ing infrared signals, as described below

FIG 17 illustrates a block diagram of the internal hardware of the computer sy stem 140 of FIG 16 A bus 156 serves as the main information highway interconnecting the other components of the computer system 140 CPU 158 is the central processing unit of the system, performing calculations and logic operations required to execute the processes of the instant invention as well as other programs Read only memory (ROM) 160 and random access memory (RAM) 162 constitute the main memory of the computer Disk controller 164 interfaces one or more disk drives to the svstem bus 156 These disk drives are. for example, floppy disk drives such as 170. or CD ROM or DVD (digital video disks) dπve such as 166. or internal or external hard drives 168 As indicated prev iously , these vanous disk drives and disk controllers are optional dev ices

A display interface 172 interfaces display 148 and permits information from the bus 156 to be displayed on the display 148 Again as indicated, display 148 is also an optional accessory For example, display 148 could be substituted or omitted Communications with external devices, for example, the other components of the system described herein, occur utilizing communication port 174 For example, optical fibers and/or electrical cables and/or conductors and/or optical communication (e g . infrared, and the like) and or wireless communication (e g . radio frequency (RE), and the like) can be used as the transport medium between the external devices and communication port 174 Peripheral interface 154 interfaces the keyboard 150 and the mouse 152. permitting input data to be transmitted to the bus 156 In addition to the standard components of the computer, the computer also optionally includes an infrared transmitter 178 and/or infrared receiv er 176 Infrared transmitters are optionally utilized when the computer sy stem is used in conjunction ith one or more of the processing components/stations that transmits/receiv es data v ia infrared signal transmission Instead of utilizing an infrared transmitter or infrared receiv er, the computer sy stem may also optionally use a low power radio transmitter 180 and/or a low power radio receiv er 182 as shown in the alternate embodiment of FIG 18 The low power radio transmitter transmits the signal for reception bv components of the production process, and receiv es signals from the components v ia the low power radio receiver The low power radio transmitter and/or recei er are standard dev ices in industry

FIGS 19 is an illustration of an exemplary memory medium 1 84 which can be used w ith disk drives illustrated in FIGS 16- 18 Typically , memory media such as floppy disks, or a CD ROM. or a digital v ideo disk will contain, for example, a multi-byte locale for a single bvte language and the program information for controlling the computer to enable the computer to perform the functions descnbed herein Alternatively . ROM 160 and or RAM 162 illustrated in FIGS 17 and 18 can also be used to store the program information that is used to instruct the central processing unit 158 to perform the operations associated with the instant processes

Although computer sy stem 140 is illustrated having a single processor, a single hard disk drive and a single local memory , the system 140 is optionally suitably equipped with any multitude or combination of processors or storage devices Computer system 140 is. in point of fact, able to be replaced by. or combined with, any suitable processing system operative in accordance w ith the principles of the present invention, including sophisticated calculators, and hand-held, laptop/notebook, mini, mainframe and super computers, as well as processing sy stem network combinations of the same

Conventional processing sy stem architecture is more fully discussed in Computer Organization and Architecture, by William Stalhngs. MacMillan Publishing Co (3rd ed 1993). conventional processing sy stem network design is more fully discussed in Data Network Design, by Darren L Spohn. McGraw-Hill. Inc (1993), and conventional data communications are more fully discussed in Data Communications Principles, by R D Githn. J F Hayes and S B Weinstain. Plenum Press ( 1992) and in The Irvvin Handbook of Telecommunications, by James Harry Green. Ir in Professional Publishing (2nd ed 1992) Each of the foregoing publications is incoφorated herein by reference Alternatively , the hardware configuration is. for example, ananged according to the multiple instruction multiple data (MIMD) multiprocessor format for additional computing efficiency The details of this form of computer architecture are disclosed in greater detail in. for example. U S Patent No 5.163.131. Boxer. A . Where Buses Cannot Go. IEEE Spectrum. February 1995. pp 41 -45. and Baπoso. L A et al . RPM A Rapid Prototy ping Engine tor Multiprocessor Systems. IEEE Computer February 1995. pp 26- 34. all of which are incoφorated herein by reference In alternate preferred embodiments, the above-identified processor, and. in particular. CPU 158, may be replaced by or combined with any other suitable processing circuits, including programmable logic devices, such as PALs (programmable arcay logic) and PLAs (programmable logic arrays). DSPs (digital signal processors). FPGAs (field programmable gate arrays). ASICs (application specific integrated circuits). VLSIs (very large scale integrated circuits) or the like.

FIG. 20 is an illustration of the architecture of the combined Internet. POTS (plain, old. telephone service), and ADSL (asymmetric, digital, subscriber line) for use in accordance with the principles of the present invention. Furthermore, it is to be understood that the use of the Internet. ADSL, and POTS are for exemplary reasons only and that any suitable communications network may be substituted without departing from the principles of the present invention. This particular example is briefly discussed below .

In FIG. 20. to preserve POTS and to prevent a fault in the ADSL equipment 254, 256 from compromising analog voice traffic 226. 296 the voice part of the spectrum (the lowest 4 kHz) is separated from the rest by a passive filter, called a POTS splitter 258, 260. The rest of the available bandwidth - - from about 10 kHz to 1 MHz - - carries data at rates up to 6 bits per second for every hertz of bandwidth from data equipment 262. 264. and 294. The ADSL equipment 256 then has access to a number of destinations including significantly the Internet 220 or other data communications networks, and other destinations 270. 272.

To exploit the higher frequencies. ADSL makes use of advanced modulation techniques, of which the best known is the discrete multitone (DMT) technology. As its name implies. ADSL transmits data asymmetrically - - at different rates upstream toward the central office 252 and downstream toward the subscriber 250.

Cable television providers are providing analogous Internet service to PC users over their TV cable systems by means of special cable modems. Such modems are capable of transmitting up to 30 Mb/s over hybrid fiber/coax system, which use fiber to bring signals to a neighborhood and coax to distribute it to individual subscribers.

Cable modems come in many forms. Most create a downstream data stream out of one of the 6-MHz TV channels that occupy spectrum above 50 MHz (and more likely 550 MHz) and carve an upstream channel out of the 5-50-MHz band, which is cunently unused. Using 64-state quadrature amplitude modulation (64 QAM), a downstream channel can realistically transmit about 30 Mb/s (the oft-quoted lower speed of 10 Mb/s refers to PC rates associated with Ethernet connections). Upstream rates differ considerably from vendor to vendor, but good hybrid fiber/coax systems can deliver upstream speeds of a few megabits per second. Thus, like ADSL, cable modems transmit much more information downstream than upstream. Then Internet architecture 220 and ADSL architecture 254. 256 may also be combined with, for example, user networks 222. 224. and 228.

In accordance with the principles of the present invention, in one example, a main computing server implementing the process of the invention may be located on one computing node or terminal (e.g., on user network 222. or system 240). Then, various users may interface with the main server via. for instance, the ADSL equipment discussed above, and access the information and processes of the present invention from remotely located PCs. More specifically, a process 242 capable of performing the investor evaluating of the instant invention may advantageously implemented in system 240. As depicted in FIG. 20. process 242 may access a list of investors stored, for instance in data structure 294. Furthermore, although data structure 294 is shown as being stored in system 240. it may just as easily be located in a remote and distinct computer system. For example, process 242 may be implemented in such a manner as to have access to one of any number of databases 281. 282. 283. 285. 286. 287 or 288.

Furthermore, the investor evaluating process of the present invention may also be implemented manually. For instance, it is possible to evaluate investors by hand without the assistance of computing systems. The many features and advantages of the invention are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention. Further, since numerous modifications and variations will readily occur to those skilled in the art. it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to. falling within the scope of the invention.

Claims

CLAIMS What is claimed is

1 A method of evaluating an investor^'s performance by utilizing information pertaining to at least one transaction made by the investor involv ing at least one investment, said method compπsing the steps of determining a performance score indicative of the investor^'s performance relativ e to other investors, said performance score determined at least in part by considering an average historical performance of the at least one investment following the at least one transaction, a histoπcal consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor, and comparing the performance score of the investor against performance scores of other investors

2 The method of claim 1. wherein the average histoπcal performance of the at least one investment is considered over a first period of time and a second penod of time

3 The method of claim 2. wherein said first period of time is approximately three months and said second period of time is approximately six months

4 The method of claim 1. w herein said performance score is determined at least in part utilizing confidential data

5 The method of claim 1. wherein the performance score increases w ith the number of beneficial transactions

6 The method of claim 1. w herein the performance score increases hen a rate of return of the at least one investment increases and the transaction is a buy transaction, or when a rate of return of the at least one investment decreases and the transaction is a sell transaction 7 The method of claim 1. wherein the performance score decreases when a rate of return of the at least one inv estment decreases and the transaction is a buy transaction, or when a rate of return of the at least one investment increases and the transaction is a sell transaction

8 The method of claim 1. wherein said determining a performance score comprises determining a first av erage return value (av e return] ) and a second average return value (ave return₂) for the at least one transaction, said first and said second average return values conesponding respectively to an average rate of return on the at least one transaction involving the investment for a first penod of time (timei ) and an average rate of return on the at least one transaction involving the investment for a second period of

calculating, in absolute terms, a first t-stat v alue (t-stat] ) for said first average return value and a second t-stat value (t-stat₂) for said second av erage return value, said first and second t-stat v alues calculated by utilizing said first and second average return values, a first standard deviation (SDi) and a second standard deviation (SD₂) , a total number of transactions (decisions] ) occurπng more than or equal to the first period of time before said evaluating, and a total number of transactions (decisions?) occurring more than or equal to the second period of time before said evaluating, calculating a first degree of freedom ( DF| ) for said first a erage return value and a second degree of freedom ( DF₂ ) for said second av erage return value. determining a first probability (prob] ) for said first period of time by utilizing said first t-stat value and said first degree of freedom, and a second probability (prob₂) for said second period of time by utilizing said second t-stat value and said second degree of freedom. generating a conditional raw score (cond raw score) for the investor by utilizing said first and second probabilities. generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occumng more than or equal to the first period of time before said evaluating, and said total number of transactions occurring more than or equal to the second penod of time before said ev aluating, and generating a raw score (raw score) indicativ e of said inv estor^'s performance by adding said adjustment to said conditional raw score

The method of claim 8. wherein if SD] = 0. then raw score = 0 otherwise ave return 1 t stat_\

SD\

ΛJ decisions]

wherein if SD? = 0. then raw score = 0 otherwise

return2 t stah

SD2

~] decisions!

wherein DF] = decisions] - 1. and wherein DF? = decisions? - 1

10 The method of claim 8 wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of DF, DF₂ t-stat]. and t- stat?. unless DF] ^ 100. in which case DF| is set to 100. DF? > 100. in which case DF? is set to 100. t-stat i > 20. in which case t-stat] is set to 20. t-stat? > 20. in which case t-stat? is set to 20. t-stat i < 0 01 , in which case prob] is set to 0, or t-stat₂ < 0 01. in which case prob? is set to 0

1 1 The method of claim 8. wherein | . t ond _ *

¹⁰⁰ wherein for buv transactions

prob] is subtracted from 0 5 if av e return] > 0. else probi is added to 0 5. wherein for buy transactions prob? is subtracted from 0 5 if av e return? > 0. else prob: is added to 0 5. wherein for sell transactions prob] is subtracted from 0 5 if ave retumi < 0. else prob] is added to 0 5. wherein for sell transactions prob: is subtracted from 0 5 if av e return: < 0. else prob: is added to 0 5

12 The method of claim 8. herein

adjustment = (-20) decisions] + decisions!

13 The method of claim 8, wherein raw score = adjustment + cond raw score

14 The method of claim 1 wherein the at least one transaction compnses a buy transaction, said method thereby providing an ev aluation of the investor^'s buy performance

15 The method of claim 1. wherein the at least one transaction comprises a sell transaction, said method thereby providing an ev aluation of the investor^'s sell performance

16 The method of claim 1. further compπsing posting said measure of performance on a computer sy stem accessible v ia one or more data communications networks by other remotelv located computer sy stems

1 7 The method of claim 1. further comprising posting or providing access to inv estor and inv estment information on a computer system accessible v ia one or more data communications networks by other remotely located computer sy stems

18 The method of claim 1. wherein said investor is at least one of a coφorate insider, an indiv idual, a joint v enture, a coφoration. a trust, a partnership, or other inv estment entitv . and the like 19 The method of claim 1. wherein the inv estment is at least one of a security , bond, currency . commodity , paper, precious metal, mineral, mutual fund, natural resource, or

20 The method of claim 1. wherein said inv estor is listed in a database containing substantially all entities filing insider documents with the Secunties and Exchange Commission

21 A method for use in producing a ranked list of investors according to an evaluation of the investors^" performances relating to at least one transaction made by the investors involving investments associated with the investors, said method comprising the steps of retrieving a list of investors. generating an evaluation list by removing investors failing to meet predetermined criteria from said list, calculating a performance score for each investor listed on said evaluation list indicative of the investor^'s performance by consideπng an average historical performance of an investment following a transaction by the investor, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the inv estor, and calculating, for each inv estor using said performance scores, a final transaction score indicative of the investor^'s relative performance with respect to all investors on said evaluation list

22 The method of claim 21. further comprising calculating a final transaction industry score indicativ e of the investor^'s relati e performance with respect to other investors on said ev aluation list participating in a same industry as the investor

23 The method of claim 22. further comprising producing said ranked list of investors according to said final transaction industrv scores 24 The method of claim 23. further comprising posting said ranked list on a computer sy stem accessible v ia one or more data communications networks by other remotely located computer sy stems

25 The method of claim 22. wherein said calculating said final transaction industry score compπses separating said inv estors into their respectiv e industπes. ordering said investors in each industry into a performance score order, dividing, for each industry having at least one-hundred investors, said investors into one-hundred substantially equally sized groups. assigning a rank, ranging from one-hundred to one. to each group in each industry having at least one hundred investors, wherein a highest performing group in each industry is assigned a highest rank and the next highest performing group in each industry is assigned the next highest available rank and repeating until each group in each industry has been ranked, assigning, in each industry having less than one-hundred investors, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred divided by the number of investors in the industry, wherein a highest performing investor is assigned said highest rank and the next highest performing investor is assigned the next highest available rank equal to the previous rank minus the decremental amount and repeating until each investor has been ranked, and wherein said rank constitutes the final transaction industry score of each inv estor

26 The method of claim 21. further comprising producing said ranked list of investors according to said final transaction scores

27 The method of claim 26. further comprising posting said ranked list on a computer system accessible v ia one or more data communications networks by other remotely located computer sy stems 28 The method of claim 21. further compnsmg posting or prov iding access to investor and investment information on a computer system accessible v ia one or more data communications networks by other remotely located computer sy stems

29 The method of claim 21. wherein said removing compnses at least one of remov ing investors that are not people, remov ing investors affiliated with companies having a market capitalization of less than approximately fift -million dollars, and removing inv estors that have entered into less than two transactions within approximately ten y ears of the ev aluation or investors that have no transactions within approximately five years of the evaluation

30 The method of claim 21, wherein said calculating said final transaction score comprises ordering said investors into a performance score order. dividing said investors into one-hundred substantially equally sized groups if there are at least one-hundred investors on said evaluation list, assigning to each group, if there are at least one-hundred investors on said evaluation list, a rank, ranging from one-hundred to one. herein a highest performing group is assigned a highest rank and the next highest performing group is assigned the next highest available rank and repeating until each group has been ranked. assigning, if there are less than one-hundred investors on said evaluation list, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred div ided by the number of investors, wherein a highest performing investor is assigned a highest rank and the next highest performing investor is assigned a rank equal to the prev IOUS rank minus the decremental amount and repeating until each investor has been ranked, and wherein said rank constitutes the final transaction score of each investor

31 The method of claim 21. wherein said investor is at least one of a coφorate insider, an indiv idual, a joint venture, a coφoration. a trust, a partnership, or other inv estment entitv . and the like 32 The method of claim 21. wherein said calculating a performance score for each investor on said ev aluation list compπses determining a first av erage return v alue (av e return i ) and a second av erage return value (av e return?) for the at least one transaction, said first and said second av erage return values conesponding respectively to an average rate of return on the at least one transaction involving the inv estment for a first period of time (timei ) and an av erage rate of return on the at least one transaction invol ing the investment for a second period of

calculating, in absolute terms, a first t-stat value (t-stat] ) for said first av erage return v alue and a second t-stat v alue (t-stat:) for said second average return value, said first and second t-stat v alues calculated by utilizing said first and second average return values, a first standard dev iation ( SDi) and a second standard deviation (SD:) a total number of transactions (decisions] ) occurring more than or equal to the first period of time before said ev aluating, and a total number of transactions (decisions?) occurring more than or equal to the second period of time before said evaluating, calculating a first degree of freedom (DFi ) for said first average return v alue and a second degree of freedom (DF:) for said second average return value, determining a first probability (probi ) for said first period of time by utilizing said first t-stat value and said first degree of freedom, and a second probability (prob:) for said second period of time by utilizing said second t-stat v alue and said second degree of freedom generating a conditional raw score (cond raw score) for the investor by utilizing said first and second probabilities. generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occumng more than or equal to the first period of time before said ev aluating, and said total number of transactions occumng more than or equal to the second period of time before said ev aluating, and generating a raw score (raw score) indicativ e of said inv estor s performance by adding said adjustment to said conditional raw score JJ . The method of claim 32. wherein if SD| = 0. then raw score=0 otherwise ave. return I t stati

SDI J ecisionsl

; wherein if SD?. = 0. then raw score = 0 otherwise

ave. return t sta

SD! easions!

wherein DFi = decisions] - 1 ; and wherein DF? = decisions? - 1.

34. The method of claim 32. wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of: DFi. DF?_. t-stat ι . and t- stat₂, unless: DFi > 100. in which case DFi is set to 100; DF: > 100. in which case DF? is set to 100; t-stat ι > 20. in which case t-stat] is set to 20: t-stat? > 20. in which case t-stat? is set to 20; t-stat] < 0.01. in which case prob] is set to 0: or t-stat? < 0.01. in which case prob: is set to 0.

35. The method of claim 32. wherein f [l - ( .5 4- or - prob\ )] + \\ - ( .5 * or - probi)] 1 cond ran score = ' - — - ^! * 00 wherein prob] is subtracted

from 0.5 if ave. return] > 0. else probi is added to 0.5: and wherein prob: is subtracted from 0.5 if ave. return: > 0. else prob: is added to 0.5.

36. The method of claim 32. w herein

32 adjustment = (-20) * [decisions] -_r decisions!

V

37 The method of claim 32. wherein raw score = adjustment -+^■ cond raw score

38 The method of claim 21. wherein said list is generated from a database containing substantially all entities filing insider documents ith the Securities and Exchange Commission

39 The method of claim 21. wherein the average historical performance of the at least one investment is considered over two periods of time

40 A system for evaluating an investor^'s performance by utilizing information pertaining to at least one transaction made by the investor involving at least one investment, compπsing a processor. a memory storing a computer program controlling operation of said processor, said program including instructions for causing the processor to effect. determining a performance score indicative of the investor^'s performance relative to other investors, said performance score determined at least in part by considering an average historical performance of the at least one inv estment following the at least one transaction, a historical consistency of the inv estor s performances w ith respect to transactions involv ing the at least one investment, and the number of transactions made by the inv estor, and comparing the performance score of the investor against performance scores of other inv estors

1 The sy stem of claim 40. wherein the av erage historical performance of the at least one inv estment is considered ov er a first period of time and a second period of time

_>i 42 The sy stem of claim 41. wherein said first period of time is approximately three months and said second period of time is approximately six months

43 The s stem of claim 40. wherein said performance score is determined at least in part utilizing confidential data

44 The system of claim 40. wherein the performance score increases with the number of beneficial transactions

45 The sy stem of claim 40. wherein the performance score increases when a rate of return of the at least one investment increases and the transaction is a buy transaction, or when a rate of return of the at least one investment decreases and the transaction is a sell transaction

46 The sy stem of claim 40. wherein the performance score decreases when a rate of return of the at least one investment decreases and the transaction is a buy transaction, or when a rate of return of the at least one investment increases and the transaction is a sell transaction

47 The system of claim 40. wherein said determining a performance score comprises determining a first av erage return value (ave return ι ) and a second av erage return v alue (ave return:) for the at least one transaction, said first and said second average return v alues conesponding respectively to an average rate of return on the at least one transaction inv olv ing the investment for a first period of time (timei ) and an average rate of return on the at least one transaction involv ing the investment for a second period of

calculating, in absolute terms, a first t-stat value (t-stat] ) for said first average return v alue and a second t-stat value (t-stat?) for said second av erage return v alue, said first and second t-stat v alues calculated by utilizing said first and second average return v alues, a first standard deviation (SD] ) and a second standard deviation (SD?) . a total number ot transactions (decisions] ) occumng more than or equal to the first period of time before said ev aluating, and a total number of transactions ( decisions?) occumng more than or equal to the second penod of time before said ev aluating, calculating a first degree of freedom ( DF i ) for said first average return value and a second degree of freedom (DF:) for said second average return v alue. determining a first probability (prob] ) for said first penod of time by utilizing said first t-stat v alue and said first degree of freedom, and a second probability (prob:) for said second period of time by utilizing said second t-stat value and said second degree of freedom. generating a conditional raw score (cond raw score) for the investor by utilizing said first and second probabilities. generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occurring more than or equal to the first period of time before said ev aluating, and said total number of transactions occumng more than or equal to the second period of time before said evaluating, and generating a raw score (raw score) indicative of said investor^'s performance by adding said adjustment to said conditional raw score

48 The sy stem of claim 47. w herein if SD] = 0. then raw score = 0 otherwise t-stati =

ave return 1

SD\

^■Jdi ecisionsi

wherein if SD:. = 0. then raw score = 0 otherw ise t-stat? =

a\ e return!

.. SD! lώ ecisions.

: wherein DF] = decisionsi - 1 : and wherein DF? = decisions? -

49. The system of claim 47. wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of: DF], DF: t-stat |. and t- stat₂. unless: DF, > 100. in which case DF] is set to 100; DF: > 100. in which case DF? is set to 100: t-stat, > 20. in which case t-stat| is set to 20; t-stat > 20. in which case t-stab is set to 20: t-stat] < 0.01. in which case probi is set to 0; or t-stat: < 0.01. in which case prob: is set to 0.

50. The svstem of claim 47. wherein

[l - (.5 or - prob\ )] + [\ - { .5 + or - prob2)] 1 cond raw score = ^{' l 0}° , wherein for buv transactions

probi is subtracted from 0.5 if ave. return] > 0. else prob] is added to 0.5; wherein for buy transactions prob₂ is subtracted from 0.5 if ave. return: > 0. else prob: is added to 0.5; wherein for sell transactions probi is subtracted from 0.5 if ave. return] < 0. else prob] is added to 0.5: wherein for sell transactions prob: is subtracted from 0.5 if ave. return? < 0. else prob; is added to 0.5.

The svstem of claim 47. wherein

adjustment = (-20) decisions ] + decisions!

The system of claim 47. wherein raw score = adjustment + cond. raw score. 53 The sy stem of claim 40 wherein the at least one transaction comprises a buy transaction, said method thereby providing an ev aluation of the inv estor^'s buv performance

54 The sy stem of claim 40. wherein the at least one transaction comprises a sell transaction, said method thereby prov iding an ev aluation of the inv estor s sell performance

55 The sy stem of claim 40. further including instructions for posting said measure of performance on a computer system accessible v la one or more data communications networks by other remotely located computer sy stems

56 The system of claim 40. further including instructions for posting or providing access to investor and investment information on a computer system accessible via one or more data communications networks by other remotely located computer systems

57 The system of claim 40. wherein said investor is at least one of a coφorate insider, an individual, a joint venture, a coφoration. a trust, a partnership, or other investment entity . and the like

58 The sv stem of claim 40 wherein the inv estment is at least one of a securitv . bond, cuπency commodity , paper, precious metal, mineral, mutual fund, natural resource, or

59 The system of claim 40. wherein said investor is listed in a database containing substantially all entities filing insider documents with the Securities and Exchange Commission

60 A sv stem for use in producing a ranked list of inv estors according to an ev aluation of the inv estors performances relating to at least one transaction made by the inv estors involv ing inv estments associated with the investors, comprising a processor a memory storing a computer program controlling operation of said processor, said program including instructions for causing the processor to effect retπev ing a list of investors. generating an evaluation list by removing investors failing to meet predetermined criteπa from said list. calculating a performance score for each investor listed on said ev aluation list indicative of the investor^'s performance by consideπng an average historical performance of an investment following a transaction by the investor, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor, and calculating, for each investor using said performance scores, a final transaction score indicative of the investor s relative performance with respect to all investors on said evaluation list

61 The sy stem of claim 60. further including instructions for calculating a final transaction industry score indicative of the investor^'s relative performance with respect to other investors on said evaluation list participating in a same industry as the investor

62 The sy stem of claim 61. further including instructions for producing said ranked list of investors according to said final transaction industry scores

63 The sy stem of claim 62. further including instructions for posting said ranked list on a computer sy stem accessible v ia one or more data communications networks by other remotely located computer systems

64 The sy stem of claim 61. wherein said calculating said final transaction industry score comprises separating said inv estors into their respective industries. ordering said investors in each industry into a performance score order. div iding, for each industry having at least one-hundred inv estors, said inv estors into one-hundred substantially equally sized groups. assigning a rank, ranging trom one-hundred to one. to each group in each industry having at least one hundred investors, wherein a highest performing group in each industry is assigned a highest rank and the next highest performing group in each industry is assigned the next highest av ailable rank and repeating until each group in each industry has been ranked. assigning, in each industry having less than one-hundred investors, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred divided by the number of investors in the industry, wherein a highest performing investor is assigned said highest rank and the next highest performing investor is assigned the next highest available rank equal to the previous rank minus the decremental amount and repeating until each investor has been ranked, and wherein said rank constitutes the final transaction industry score of each investor

65 The sy stem of claim 60. further including instructions for producing said ranked list of investors according to said final transaction scores

66 The system of claim 65. further including instructions for posting said ranked list on a computer system accessible v la one or more data communications networks by other remotely located computer systems

67 The sy stem of claim 60. further including instructions for posting or prov iding access to inv estor and investment information on a computer system accessible via one or more data communications networks by other remotely located computer systems

68 The sy stem of claim 60. wherein said removing comprises at least one of remov ing investors that are not people, remov ing investors affiliated with companies having a market capitalization of less than approximately fifty -million dollars, and remo ing inv estors that have entered into less than two transactions within approximately ten years of the ev aluation or investors that hav e no transactions within approximately fiv e y ears of the evaluation

69 The sy stem of claim 60. wherein said calculating said final transaction score comprises ordering said investors into a performance score order. dividing said investors into one-hundred substantially equally sized groups if there are at least one-hundred investors on said ev aluation list. assigning to each group, if there are at least one-hundred investors on said evaluation list, a rank, ranging from one-hundred to one. wherein a highest performing group is assigned a highest rank and the next highest performing group is assigned the next highest available rank and repeating until each group has been ranked. assigning, if there are less than one-hundred investors on said evaluation list, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred divided by the number of investors, wherein a highest performing investor is assigned a highest rank and the next highest performing investor is assigned a rank equal to the previous rank minus the decremental amount and repeating until each investor has been ranked, and wherein said rank constitutes the final transaction score of each investor

70 The system of claim 60. wherein said investor is at least one of a coφorate insider, an individual, a joint v enture, a coφoration. a trust, a partnership, or other investment entity, and the like

71 The system of claim 60. wherein said calculating a performance score for each investor on said ev aluation list comprises determining a first av erage return value (ave return ι ) and a second average return value (av e return:) for the at least one transaction, said first and said second av erage return v alues corresponding respectiv ely to an average rate ot return on the at least one transaction involving the inv estment for a first period of time (timei ) and an average rate of return on the at least one transaction involving the investment for a second period of time (time?); calculating, in absolute terms, a first t-stat value (t-stat| ) for said first average return value and a second t-stat value (t-stat:) f°^r said second average return value, said first and second t-stat values calculated by utilizing said first and second average return values, a first standard deviation (SD]) and a second standard deviation (SD:) • a total number of transactions (decisions] ) occurring more than or equal to the first period of time before said evaluating, and a total number of transactions (decisions:) occumng more than or equal to the second period of time before said evaluating; calculating a first degree of freedom (DFi) for said first average return value and a second degree of freedom (DF:) for said second average return value; determining a first probability (probi) for said first period of time by utilizing said first t-stat value and said first degree of freedom, and a second probability (prob:) for said second period of time by utilizing said second t-stat value and said second degree of freedom; generating a conditional raw score (cond. raw score) for the investor by utilizing said first and second probabilities; generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occuning more than or equal to the first period of time before said evaluating, and said total number of transactions occuning more than or equal to the second period of time before said evaluating; and generating a raw score (raw score) indicative of said investor^'s performance by adding said adjustment to said conditional raw score.

72. The system of claim 71. wherein if SD] = 0. then raw score = 0 otherwise ave. return! t stat

wherein if SD?. = 0. then raw score = 0 otherwise ave. return! SE2 decisions!

; wherein DF] = decisions ι - 1 : and wherein DF: = decisions? - 1.

73. The system of claim 71. wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of: DFj, DF?. t-stat]. and t- stat₂. unless: DF] > 100. in which case DFi is set to 100: DF_: > 100. in which case DF? is set to 100; t-stat] > 20. in which case t-stat] is set to 20; t-stat? > 20. in which case t-stab is set to 20: t-stati < 0.01. in which case prob] is set to 0: or t-stat? < 0.01. in which case prob? is set to 0.

74. The system of claim 71. wherein ] _ , -_ ond _ raw _ score 100 _ wherein prob, is subtracted

from 0.5 if ave. return] > 0. else probi is added to 0.5; and wherein prob: is subtracted from 0.5 if ave. return: ^> 0. else prob: is added to 0.5.

75. The svstem of claim 71. wherein

adjustment = (-20) *

76. The system of claim 71. wherein raw score = adjustment + cond. raw score.

77. The system of claim 60. wherein said list is generated from a database containing substantially all entities filing insider documents with the Securities and Exchange Commission.

78. The system of claim 60. wherein the average historical performance of the at least one investment is considered over two periods of time. 79 A computer readable medium stonng instructions executable by a computer, the instructions instructing the computer to effect evaluating an inv estor^'s performance by utilizing information pertaming to at least one transaction made by the investor inv olving at least one investment, said instructions comprising determining a performance score indicative of the inv estor^'s performance relative to other investors, said performance score determined at least in part by considering an average historical performance of the at least one investment following the at least one transaction, a histoπcal consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the inv estor. and comparing the performance score of the investor against performance scores of other inv estors

80 The computer readable medium of claim 79, wherein the average historical performance of the at least one investment is considered over a first penod of time and a second period of time

81 The computer readable medium of claim 80, wherein said first period of time is approximately three months and said second period of time is approximately six months

82 The computer readable medium of claim 79. wherein said performance score is determined at least in part utilizing confidential data

83 The computer readable medium of claim 79. wherein the performance score increases with the number of beneficial transactions

84 The computer readable medium of claim 79. wherein the performance score increases when a rate ot return of the at least one investment increases and the transaction is a buy transaction, or when a rate of return of the at least one investment decreases and the transaction is a sell transaction

85. The computer readable medium of claim 79. wherein the performance score decreases when a rate of return of the at least one investment decreases and the transaction is a buy transaction, or when a rate of return of the at least one investment increases and the transaction is a sell transaction.

86. The computer readable medium of claim 79. wherein said determining a performance score comprises: determining a first average return value (ave. return i ) and a second average return value (ave. return?) for the at least one transaction, said first and said second average return values conesponding respectively to an average rate of return on the at least one transaction involving the investment for a first period of time (timei ) and an average rate of return on the at least one transaction involving the investment for a second period of time (time:); calculating, in absolute terms, a first t-stat value (t-stati) for said first average return value and a second t-stat value (t-stat?) for said second average return value, said first and second t-stat values calculated by utilizing said first and second average return values, a first standard deviation (SDi) and a second standard deviation (SD?) . a total number of transactions (decisionsi ) occumng more than or equal to the first period of time before said evaluating, and a total number of transactions (decisions?) occurring more than or equal to the second period of time before said evaluating: calculating a first degree of freedom ( DF] ) for said first average return value and a second degree of freedom (DF:) for said second average return value; determining a first probability (probi ) for said first period of time by utilizing said first t-stat value and said first degree of freedom, and a second probability (prob?) for said second period of time by utilizing said second t-stat value and said second degree of freedom: generating a conditional raw score (cond. raw score) for the investor by utilizing said first and second probabilities: generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occuning more than or equal to the first period of time before said ev aluating, and said total number of transactions occumng more than or equal to the second penod of time before said evaluating, and generating a raw score (raw score) indicative of said inv estor^'s performance by adding said adjustment to said conditional raw score

87 The computer readable medium of claim 86. wherein if SD] - 0. then raw score 0 otherwise ave return] t stat]

SD\

V decisions]

wherein if SD?. = 0. then raw score = 0 otherwise

ave return2 t stah

SD! eciswns.

wherein DFi = decisionsi - 1. and wherein DF? = decisions? - 1

88 The computer readable medium of claim 86. wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of DF] . DF_; t-stat i . and t-stat?. unless DFi > 100. in which case DF, is set to 100. DF: > 100. in which case DF? is set to 100. t-stat] > 20. in which case t-stat] is set to 20. t-stat? > 20. in w hich case t-stat? is set to 20. t-stat ι < 0 01. in which case prob] is set to 0. or t-stab < 0 01. in which case prob: is set to 0

89 The computer readable medium of claim 86. wherein ^co wherein for buv transactions

probi is subtracted from 0 5 if av e return] > 0. else prob] is added to 0 5. wherein for buy transactions prob; is subtracted from 0 5 if ave return: > 0. else prob; is added to 0 5. wherein for sell transactions prob] is subtracted from 0 5 if av e return] < 0. else prob] is added to 0 5. wherein for sell transactions prob; is subtracted from 0 5 if av e return; < 0. else prob; is added to 0 5

90 The computer readable medium of claim 86. wherein

1 adjustment = (-20) * decisions] + decisions!

91 The computer readable medium of claim 86. wherein raw score = adjustment + cond raw score

92 The computer readable medium of claim 79. wherein the at least one transaction comprises a buy transaction, said method thereby providing an evaluation of the investor^'s buy performance

93 The computer readable medium of claim 79. wherein the at least one transaction comprises a sell transaction, said method thereby prov iding an ev aluation of the investor^'s sell performance

94 The computer readable medium of claim 79. further compπsing instructions for posting said measure of performance on a computer sy stem accessible v ia one or more data communications networks by other remotely located computer systems

95 The computer readable medium of claim 79. further comprising instructions for posting or prov iding access to inv estor and investment information on a computer system accessible v ia one or more data communications networks by other remotely located computer sy stems

96 The computer readable medium of claim 79. wherein said investor is at least one of a coφorate insider, an individual, a joint venture, a coφoration. a trust, a partnership, or other investment entity . and the like

97 The computer readable medium of claim 79. wherein the investment is at least one of a secunty . bond, cunency . commodity , paper, precious metal, mineral, mutual fund, natural resource, or the like

98 The computer readable medium of claim 79. wherein said investor is listed in a database containing substantially all entities filing insider documents with the Securities and Exchange Commission

99 A computer readable medium stoπng instructions executable by a computer, the instructions instructing the computer to effect producing a ranked list of investors according to an evaluation of the investors^" performances relating to at least one transaction made by the investors involving investments associated with the investors. said instructions comprising retrieving a list of investors. generating an ev aluation list by removing investors failing to meet predetermined criteria from said list. calculating a performance score for each investor listed on said evaluation list indicative of the investor s performance by considering an average histoπcal performance of an investment following a transaction by the investor, a historical consistency of the investor^'s performances with respect to transactions involving the at least one inv estment, and the number of transactions made by the investor, and calculating, for each investor using said performance scores, a final transaction score indicativ e of the investor s relative performance with respect to all investors on said ev aluation list 100 The computer readable medium of claim 99. further compπsing instructions for calculating a final transaction industry score indicative of the investor s relative performance with respect to other investors on said evaluation list participating in a same industry as the investor

101 The computer readable medium of claim 100. further comprising instructions for producing said ranked list of investors according to said final transaction industry scores

102 The computer readable medium of claim 101. further compπsing instructions for posting said ranked list on a computer system accessible via one or more data communications networks by other remotely located computer systems

103 The computer readable medium method of claim 100. wherein said calculating said final transaction industry score comprises separating said investors into their respective industries, ordenng said investors in each industry into a performance score order. dividing, for each industry having at least one-hundred investors, said investors into one-hundred substantially equally sized groups, assigning a rank, ranging from one-hundred to one. to each group in each industry having at least one hundred investors, wherein a highest performing group in each industry is assigned a highest rank and the next highest performing group in each industry is assigned the next highest available rank and repeating until each group in each industry has been ranked. assigning, in each industry having less than one-hundred investors, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred divided by the number of investors in the industry , wherein a highest performing investor is assigned said highest rank and the next highest performing investor is assigned the next highest available rank equal to the prev ious rank minus the decremental amount and repeating until each investor has been ranked, and wherein said rank constitutes the final transaction industrv score of each investor 104 The computer readable medium of claim 99. further compπsing instructions for producing said ranked list of investors according to said final transaction scores

105 The computer readable medium of claim 104. further comprising instructions for posting said ranked list on a computer sy stem accessible \ la one or more data communications networks by other remotely located computer systems

106 The computer readable medium of claim 99. further comprising instructions for posting or providing access to investor and investment information on a computer sy stem accessible via one or more data communications networks by other remotely located computer svstems

107 The computer readable medium of claim 99. wherein said remov ing comprises at least one of removing investors that are not people, removing investors affiliated with companies having a market capitalization of less than approximately fifty-million dollars, and removing investors that have entered into less than two transactions within approximately ten years of the evaluation or investors that have no transactions within approximately five years of the evaluation

108 The computer readable medium of claim 99. wherein said calculating said final transaction score comprises ordering said investors into a performance score order. dividing said investors into one-hundred substantially equallv sized groups if there are at least one-hundred inv estors on said evaluation list, assigning to each group, if there are at least one-hundred investors on said evaluation list, a rank, ranging from one-hundred to one. wherein a highest performing group is assigned a highest rank and the next highest performing group is assigned the next highest available rank and repeating until each group has been ranked. assigning, if there are less than one-hundred investors on said evaluation list, a rank, starting at a highest rank and decrementing by amounts equal to one-hundred divided by the number of investors, wherein a highest performing investor is assigned a highest rank and the next highest performing investor is assigned a rank equal to the previous rank minus the decremental amount and repeating until each investor has been ranked: and wherein said rank constitutes the final transaction score of each investor.

109. The computer readable medium of claim 99. wherein said investor is at least one of a coφorate insider, an individual, a joint venture, a coφoration. a trust, a partnership, or other investment entitv. and the like.

1 10. The computer readable medium of claim 99. wherein said calculating a performance score for each investor on said evaluation list comprises: determining a first average return value (ave. returni ) and a second average return value (ave. return?) for the at least one transaction, said first and said second average return values conesponding respectively to an average rate of return on the at least one transaction involving the investment for a first period of time (timei ) and an average rate of return on the at least one transaction involving the investment for a second period of time (time;); calculating, in absolute terms, a first t-stat value (t-stati) for said first average return value and a second t-stat value (t-stat;) for said second average return value, said first and second t-stat values calculated by utilizing said first and second average return values, a first standard deviation (SDi) and a second standard deviation (SD?) . a total number of transactions (decisions] ) occumng more than or equal to the first period of time before said evaluating, and a total number of transactions (decisions?) occuning more than or equal to the second period of time before said evaluating; calculating a first degree of freedom (DFi) for said first average return value and a second degree of freedom (DF?) for said second average return value: determining a first probability (probi) for said first period of time by utilizing said first t-stat value and said first degree of freedom, and a second probability (prob;) for said second period of time by utilizing said second t-stat value and said second degree of freedom; generating a conditional raw score (cond raw score) for the investor by utilizing said first and second probabilities. generating an adjustment to the conditional raw score (adjustment) by utilizing said total number of transactions occumng more than or equal to the first penod of time before said evaluating, and said total number of transactions occurring more than or equal to the second penod of time before said evaluating, and generating a raw score (raw score) indicative of said investor^'s performance by adding said adjustment to said conditional raw score

1 1 1 The computer readable medium of claim 1 10, wherein if SDi = 0. then raw score=0 otherwise ave return!

SD] decisions] wherein if SD?, = 0. then raw score = 0 otherwise

ave return2 l stah

SD!

wherein DF| = decisions] - 1. and wherein DF? = decisions? - 1

1 12 The computer readable medium of claim 1 10. wherein said probabilities are determined according to a predetermined look-up table using actual calculated values of DF,. DF; t-stat!, and t-stat?. unless DF| > 100. in which case DF| is set to 100. DF; > 100. in which case DF? is set to 100. t-stat| > 20. in which case t-stat) is set to 20. t-stat? > 20. in which case t-stat? is set to 20. t-stat ι < 0 01. in which case probi is set to 0. or t-stat; < 0 01. in which case prob? is set to 0 1 13 The computer readable medium of claim 1 10. wherein

con _ wherein prob, is subtracted

from 0 5 if ave return i > 0. else probi is added to 0 5. and wherein prob? is subtracted from 0 5 if ave return; > 0. else prob; is added to 0 5

1 14 The computer readable medium of claim 1 10. wherein

1 adjustment = (-20) decisions] + decisions!

1 15 The computer readable medium of claim 1 10. wherein raw score = adjustment + cond raw score

1 16 The computer readable medium of claim 99. wherein said list is generated from a database containing substantially all entities filing insider documents with the Securities and Exchange Commission

1 17 The computer readable medium of claim 99, wherein the av erage historical performance of the at least one investment is considered over two periods of time

1 18 A method for evaluating an investor s performance by utilizing information pertaining to at least one transaction made by the investor involving at least one investment, said method comprising determining a probability that a predetermined investor^'s return will occur, generating a conditional performance measure indicative of the investor s performance by utilizing said probability , adjusting the conditional performance measure utilizing at least one factor relating to and further characterizing the investor s performance to result in a final performance score, and ranking the investor using the performance score relative to other investors.

1 19. The method of claim 1 18. wherein said conditional performance measure is adjusted to reflect a number of transactions made by the investor.

120. The method of claim 1 18. wherein said conditional performance measure is generated utilizing at least one of an average historical performance of the at least one investment following the at least one transaction, or a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment.

121. An interactive computer-implemented interface for displaying a listing of investors ranked according to a performance order by utilizing information pertaining to at least one transaction made by each investor involving at least one investment, said interactive interface comprising: a computer-driven display; a listing of investors displayed on said computer-driven display ranked by utilizing at least one of an average historical performance of the at least one investment following the at least one transaction, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and a number of transactions made by the investor, said listing providing names used to identify the investors, and a performance score used to rank the investors; and a sporting event scoreboard including a template displayed on said computer- driven display and incoφorating said listing of investors ranked according to the performance order, facilitating a user^'s review of said listing.

122. The interactive interface of claim 121. further comprising at least one of one or more advertisements, or one or more commentaries relating to the investors, and information associated with investments or companies, displayed on said computer- driven display. 123 The interactive interface of claim 121. wherein only a predetermined number of highest performing investors are listed

124 The interactive interface of claim 121. wherein said investors are ranked by determining a probability that a predetermined investor^'s return will occur, generating a conditional performance measure indicative of the investor^'s performance by utilizing said probability, adjusting the conditional performance measure utilizing at least one factor relating to and further characterizing the investor^'s performance to result in a final performance score, and ranking the investor using the performance score relati e to other investors

125 The interactive interface of claim 121, wherein said listing, responsive to the user^'s commands, is capable of displaying at least one of an investor buy ranking, an investor sell ranking, an investor buy industry ranking, and an investor sell industry ranking, or the like

126 The interactive interface of claim 121, wherein said interface, responsive to the user^'s commands, is capable of reevaluating and displaying said listing daily , weekly, monthly or yearly

127 A system for evaluating an investor^'s performance by utilizing information pertaining to at least one transaction made by the investor involv ing at least one investment comprising means for determining a performance score indicative of the investor^'s performance relative to other investors, said performance score determined at least in part by considering an average histoπcal performance of the at least one investment following the at least one transaction, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor, and means for comparing the performance score of the investor against performance scores of other investors

128. A system for use in producing a ranked list of investors according to an evaluation of the investors^' performances relating to at least one transaction made by the investors involving investments associated with the investors, said system comprising: means for retrieving a list of investors; means for generating an evaluation list by removing investors failing to meet predetermined criteria from said list; means for calculating a performance score for each investor listed on said evaluation list indicative of the investor^'s performance by considering an average historical performance of an investment following a transaction by the investor, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor; and means for calculating, for each investor using said performance scores, a final transaction score indicative of the investor^'s relative performance with respect to all investors on said evaluation list.

129. A method for evaluating an investor^'s performance by utilizing information pertaining to at least one transaction made by the investor involving at least one investment comprising: a step for determining a performance score indicative of the investor^'s performance relative to other investors, said performance score determined at least in part by considering an average historical performance of the at least one investment following the at least one transaction, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor: and a step for comparing the performance score of the investor against performance scores of other investors.

130. A method for use in producing a ranked list of investors according to an evaluation of the investors^' performances relating to at least one transaction made by the investors involving investments associated with the investors, said method comprising: a step for retrieving a list of investors; a step for generating an evaluation list by removing investors failing to meet predetermined criteria from said list; a step for calculating a performance score for each investor listed on said evaluation list indicative of the investor^'s performance by considering an average historical performance of an investment following a transaction by the investor, a historical consistency of the investor^'s performances with respect to transactions involving the at least one investment, and the number of transactions made by the investor; and a step for calculating, for each investor using said performance scores, a final transaction score indicative of the investor's relative performance with respect to all investors on said evaluation list.