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
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)
— 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
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.
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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.