Patent Application
20140279684
Making successful decisions that involve a future performance of an item or entity often requires significant insight and/or predictive tools. This type of insight may come naturally to some individuals, but for most, it is acquired as a result of a significant amount of experience. However, capturing, and benefiting from, an individual's insight over an extended period of time has been difficult to accomplish.
Embodiments of the present invention facilitate enhanced assessments of the future performance of an item or entity through the actions and analysis of multiple decision-makers particularly in view of their individual successes. For example, embodiments of the present invention relate to mining messages provided over networks for references to successful risk/return-related decisions such as indications of successful securities trades. The risk/return-related messages are analyzed to identify messaging users that regularly or semi-regularly generate messages having references to risk/return-related decisions. These messaging users are referred to herein as return advisors. In embodiments, the return advisors are evaluated and ranked based on potential returns associated with their risk/return-related decisions, which may also be evaluated and/or ranked. These rankings may be used to provide risk/return-related services.
While the present invention is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The present invention, however, is not limited to the particular embodiments described. On the contrary, the present invention is intended to cover all modifications, equivalents, and alternatives falling within the ambit of the present invention as defined by the appended claims.
Although the term “block” may be used herein to connote different elements of illustrative methods employed, the term should not be interpreted as implying any requirement of, or particular order among or between, various steps disclosed herein unless and except when explicitly referring to the order of individual steps.
Users of communication platforms often generate messages about predictions of future performance of items or entities, including those concerning activities that involve risk and return (risk/return-related activities). Risk/return-related activities may include, for example, trading securities and other exchange traded products (ETPs), wagering money on outcomes of events such as sports games and horse races, and the like. Messaging users that regularly or semi-regularly provide information (generally, in the form of some type of prediction) about their risk/return-related activities, thoughts, opinions, and recommendations may be referred to as return advisors. Many of the messages generated by these users include references to risk/return-related decisions such as, for example, actions performed by the user, suggested by the user, and/or recommended by the user. In embodiments, acting in accordance with recommendations or actions of return advisors may result in potential financial returns. Return advisors often provide references to risk/return-related decisions through any number of a variety of types of messages such as, for example, social media messages, emails, short message service (SMS) messages, blog posts, web-published articles, and the like. According to embodiments, these messages may be analyzed to identify and evaluate return advisors and decisions referenced in their messages. A reference to a decision may be an explicit reference or an implicit reference (e.g., a decision may be implied from a prediction). These evaluations, as well as evaluations of the decisions themselves, may, for example, facilitate enhanced assessments of the future performance of an item or entity. These enhanced assessments may be used to provide recommendations to consumers, to generate risk/return-related products, or the like.
As shown in
For example, a return advisor may be a Twitter® user that tweets about stock markets and his/her trading activities. In embodiments, this type of return advisor may be referred to as a trader, though he or she may not actually be a professional securities trader. For example, a ten-year old child with insight into the securities market may be identified as a trader based on tweets having references to decisions about stocks that should be bought or sold at certain times, though the child may never actually buy or sell stock. Other examples of a return advisor may include a Facebook® user that “likes” certain sports teams contemporaneously with games in which the teams are involved; a Facebook® user that posts about particular racing horses; and the like. Although the term “return advisor” is intended to refer to a classification applied to a messaging user that may provide messages having references to decisions related to any activity, event, or market that effectively involves an assessment of future performance of an item or entity, the particular example of the trader (e.g., a return advisor that provides information associated with trading securities) will be used throughout this disclosure to illuminate various aspects of embodiments of the present invention. References to traders, in lieu of other types of return advisors, are not meant to imply any limitation of the scope of the term “return advisor,” but are used solely for purposes of clarity.
Still referring to
In operation, the advisor analyzer 108 accesses retrieved messages (e.g., from the memory 118, the message retriever 120, or the like) and analyzes the messages to identify a set of risk/return-related messages. For example, the advisor analyzer 108 may analyze message content to identify messages that are likely to relate to securities trading, wagering on sports outcomes, or the like. The set of risk/return-related messages are further analyzed to identify a set of return advisors. In embodiments, a representation of each of the identified return advisors may be stored in the database 122 and may serve, for example, as a representation identifying a record associated with the return advisor. The advisor analyzer 108 is further configured to analyze a performance of each of the return advisors and to rank the return advisors. This ranking may be based, at least in part, on the evaluations of the return advisors' respective performances. Additionally, the advisor analyzer 108 may evaluate and rank specific decisions referenced in predictive messages generated by return advisors.
The rankings may be used to facilitate one or more services. In embodiments, aspects of the services may be provided using the services component 112 which may include, for example, applications, service functions, or the like that provide enhanced assessments of the future performance of one or more items or entities, recommendations of return advisors, recommendations of risk/return-related decisions (which could be, e.g., based upon the cumulative recommendations of multiple highly-ranked return advisors), information for generating products, and the like. Additionally, the server 102 may facilitate (e.g., by providing information) generation of products based on decision rankings and/or may provide decision rankings to other entities for use in generating risk/return-related products and/or services. In embodiments, for example, securities trading recommendations may be used to generate securities portfolios, funds (e.g., exchange traded funds (ETFs)), and the like. In embodiments, the server 102 may provide wagering recommendations to booking agencies, or the like.
According to embodiments, various components of the operating environment 100, illustrated in
In embodiments, a computing device includes a bus that, directly and/or indirectly, couples the following devices: a processor, a memory, an input/output (I/O) port, an I/O component, and a power supply. Any number of additional components, different components, and/or combinations of components may also be included in the computing device. The bus represents what may be one or more busses (such as, for example, an address bus, data bus, or combination thereof). Similarly, in embodiments, the computing device may include a number of processors, a number of memory components, a number of I/O ports, a number of I/O components, and/or a number of power supplies. Additionally any number of these components, or combinations thereof, may be distributed and/or duplicated across a number of computing devices.
In embodiments, the memory 118 includes computer-readable media in the form of volatile and/or nonvolatile memory and may be removable, nonremovable, or a combination thereof. Media examples include Random Access Memory (RAM); Read Only Memory (ROM); Electronically Erasable Programmable Read Only Memory (EEPROM); flash memory; optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; data transmissions; or any other medium that can be used to encode information and can be accessed by a computing device such as, for example, quantum state memory, and the like. In embodiments, the memory 118 stores computer-executable instructions for causing the processor 116 to implement aspects of embodiments of system components discussed herein and/or to perform aspects of embodiments of methods and procedures discussed herein. Computer-executable instructions may include, for example, computer code, machine-useable instructions, and the like such as, for example, program components capable of being executed by one or more processors associated with a computing device. Examples of such program components include the advisor analyzer 108 (and the components thereof, illustrated in
The illustrative operating environment 100 shown in
According to embodiments, the message retriever 120 obtains, copies, or otherwise accesses messages and collects attributes associated with each message such as, for example, an identification of the messaging platform from which the message was accessed, an identification of the account identifier under which the message was created, an identification of the originating user of the message, an identification of a user or users to whom the message was addressed, sent, or otherwise provided, the date and time that the message was created, and content (e.g., text, hyperlinks, uniform resource locators (URLs), charts, graphs, images, and the like) included in the message. Additionally, in embodiments, other attributes specific to a messaging platform may be collected. For example, for messages sent via Twitter® (e.g., tweets), the message retriever 120 may collect properties such as hashtags, mentions, replies, indications of retweets, identifications of followers, and the like. As another example, for messages sent via Facebook®, the message retriever 120 may collect properties such as “likes,” posts, indications of “status,” links, friends, and the like.
The message filter 202 may identify, within the plurality of accessed messages, a set of risk/return-related messages. To identify risk/return-related messages, the message filter 202 may utilize, for example, one or more sets of rules, classifiers, and/or the like. According to embodiments, the message filter 202 utilizes a set of simple rules to maximize efficiency, thereby enabling expeditious identification of messages to facilitate services that “keep up with” the nearly real-time speed of messaging. For example, the message filter 202 may identify risk/return-related messages by identifying certain types of content in the messages such as, for example, content that appears to be associated with risk/return-related activities, character strings that are commonly associated with one or more risk/return-related activities, or the like.
For example, the message filter 202 may identify a message as a risk/return-related message if the content of the message includes one of a plurality of key words from a key word list, in combination with one or more words or phrases commonly associated with a particular risk/return-related activity and/or decision. The key word list may be created, for example, by determining the frequency with which certain words occur in messages of certain types. In embodiments, the frequency of occurrence of these words may be measured in the context of other words such as by determining the frequency of occurrence within a several word window. Words occurring with a frequency that exceeds a threshold may be included in the list. Words, character strings, and/or other text and associated context may also be analyzed in messages from known return advisors to identify key words to be included in the list. In embodiments, the list may be updated continuously, continually, or periodically to improve accuracy, account for changing trends in vocabulary, and the like.
For example, the message filter 202 may identify a tweet as a trading-related tweet if the tweet ends with “$$” and/or if the tweet contains one or more key words (e.g., buy, short, sell, add, locked, stopped out, etc.) in combination with at least one stock ticker symbol (e.g., GOOG, TRI, etc.), company name (e.g., “Google,” “Thomson Reuters,” etc.), fund name (e.g., “SPDR”), industry type (e.g., “energy,” “financial services,” “biotech,” etc.) and/or the like. In embodiments, abbreviations of, portions of, and/or approximate spellings of key words, company names, fund names, and the like may be identified. In addition, words corresponding to expressions of sentiment (for example, positive or negative “feelings”) associated with a risk/return-related activity, forum, or the like may be used, at least in part, to identify risk/return-related messages.
As indicated above, the set of risk/return-related messages identified by the message filter 202 originates from a set of messaging users. As shown in
As shown in
In embodiments, a reference to a risk/return-related decision may include an explicit reference to a decision to act upon an implied prediction, an implied reference to a decision to act upon an implied prediction, an implied reference to a decision to act upon an explicit prediction, or the like. An example of an explicit reference to a decision to act upon an implied prediction may be a message that says “Buy TRI.” In this example, the text of the message explicitly references a decision to buy stock in Thomson Reuters, Inc. (“TRI”) and this decision may be based, for example, upon an implied prediction that the current price (i.e., the price near the time that the message was generated) of TRI stock will be increasing at some point in the future. An example of an implied reference to a decision to act upon an implied prediction may be a message that says “TRI is hot.” In this example, the text of the message implicitly references a decision to buy TRI stock and this decision may be based, for example, upon a prediction (implied from the phrase “is hot”) that the current price (i.e., the price near the time that the message was generated) of TRI stock will be increasing at some point in the future, for example, as a result of a large number of purchases that may be made shortly after the message was created. An example of an implied reference to a decision to act upon an explicit prediction may be a message that says “TRI is on the rise.” In this example, the text of the message implicitly references a decision to buy TRI stock and this decision may be based, for example, upon the explicit prediction that the price of TRI stock will be increasing at some point shortly after the message was created.
In embodiments, the advisor evaluator 214 may use a supervised learning algorithm (e.g., a classifier) to identify references to risk/return-related decisions included in a particular return advisor's messages. For example, the advisor evaluator 214 may utilize a support vector machine (SVM) that analyzes features such as list-based features (e.g., whether a character sequence is included within an action list or whether a character sequence is included within a positive/negative sentiment word list), unigram and bigram word features, and the like. For example, the advisor evaluator 214 may identify a reference to a trading-related decision based on an identification of a key word in the message in combination with an object of a trading action such as, for example, a company name, a stock ticker symbol, industry type, or the like (e.g., “buy Thomson Reuters stock,” “sell TRI,” “buy energy,” etc.).
Although the supervised learning algorithm may be trained using human supervision, in embodiments, the supervised learning algorithm may be trained using automatically-generated training data, thereby facilitating an automatic implementation. For example, training data may be automatically generated by identifying a relatively significant risk/return-related event (e.g., a large movement in the price of a security, an important win for a sports team or a horse race) and analyzing a large number of messages generated around the time of the event. The messages may be associated with a particular type of decision based on the nature of the event and may be used as training data for use in training the algorithm.
For example, in the case of securities trading, a relatively large movement in the price of a security (e.g., an increase or decrease of more than 3%) may be identified and tweets occurring around the time of the price change may be accessed. If the stock price increased, the tweets may be labeled as “buy” tweets; and if the stock price decreased, the tweets may be labeled as “sell” tweets. This labeling scheme may be facilitated by, for example, an assumption that most tweets generated when a stock price increases will be related to selling the stock, and that most tweets generated when the stock price decreases will be related to buying the stock. That is, the labeling scheme may be facilitated by an assumption that these messages will be likely to cause the algorithm to generate accurate classifications. According to embodiments, the labels need not be completely accurate, but may be sufficient for algorithm training purposes if the tweets are more likely to correspond to a decision to buy or sell stock rather than, for example, simply an opinion of a company stock.
According to embodiments, the advisor evaluator 214 may store a representation of each identified risk/return-related decision in the database 122. According to embodiments, the advisor evaluator 214 may associate each risk/return-related decision with an intensity level such as, for example, “strong,” “ok,” “maybe,” or the like, to indicate a level of confidence/commitment of the return advisor with respect to the decision. The intensity level may be determined based on any number of different factors including, for example, a sentiment level expressed in the message, a number of users to whom the message was provided, and a number of times the message is forwarded or acknowledged (e.g., re-tweeted or “liked”) by others. A sentiment level may be determined based on, for example, the presence of certain expressive words, punctuation, and/or other semantic features.
Having identified a return advisor and identified predictive messages associated therewith, the advisor evaluator 214 may evaluate the return advisor by generating a score vector 216 corresponding to the return advisor. The score vector 216 may be stored in the database 122, and may be used to compute, or otherwise determine, a performance score corresponding to the return advisor. A performance score provides an indication of a level of performance of the return advisor with respect to one or more risk/return-related decisions, items, or entities. For example, a performance score may indicate a level of success (e.g., financial return) that one might experience as a result of acting in accordance with a risk/return-related decision referenced in a predictive message generated by the return advisor. The performance score also may be based on the intensity level of the specific risk/return-related decision, which also may be used, for example, to generate a recommendation of an amount of money to be allocated to the decision. In embodiments, a number of performance scores, each associated with a particular aspect (referred to herein as a “performance metric”) of the return advisor's performance, may be determined for a return advisor. That is, for example, performance scores corresponding to a trader may include a performance score associated with the trader's performance with respect to day-trading, long-term trading, trading within a particular industry sector, funds trading, and/or a combination of these.
The score vector 216 may be generated, for example, by evaluating each referenced risk/return-related decision to determine a decision score. In embodiments, a risk/return-related decision may be evaluated in the context of a variety of performance metrics, thereby resulting in a score vector 216 having a number of dimensions, each corresponding to a performance metric. In embodiments, a number of such score vectors may be organized within the performance matrix 206.
As shown in
For example, the score vector 302 includes a first decision score 340 (“S1(D)”) that represents a financial return corresponding to the associated decision 312 with respect to a trading time period of one day; a second decision score (“S1(W)”) 342 that represents a financial return corresponding to the associated decision 312 with respect to a trading time period of one week; a third decision score (“S1(M)”) 344 that represents a financial return corresponding to the associated decision 312 with respect to a trading time period of one month; and a fourth decision score S1(Y) 346 that represents a financial return corresponding to the associated decision 312 with respect to a trading time period of one year. In the illustrated example, S1(D) 340 may be determined based on a percent gain or loss in the value of TRI stock over the course of a day (e.g., the percent gain or loss of the value of the stock from the time at which the message was created, or some arbitrary time such as 11:00 a.m., to the time at which the market closed). Similarly, S1(W) 342 may be determined based on a percent gain or loss in the value of TRI stock over the course of a week, and so on.
In embodiments, score vectors may also include decision scores that are determined based on previous decisions, which may facilitate evaluation of strategic sets of decisions by a return advisor that, for example, result in returns achieved due to a relationship between two or more decisions. For example, while S1(D) 340 may be based on a gain of 0.07% for the specified day (i.e., the percent change in the value of TRI stock between the opening price and the closing price on Mar. 4, 2013), and S3(D) 348 may be based on a loss of 1.61% for the specified day (i.e., the percent change in the value of TRI stock between the opening price and the closing price on Mar. 6, 2013), the return advisor's decision to sell TRI stock on March 6, after having purchased the stock on March 4, may result in a net gain of 1.77% (i.e., the percent change in the value of TRI stock between the opening price on March 4 and the closing price on March 6), which would result in a larger financial return than buying and selling the stock within either of the specified days. Accordingly, the score vector 302 may include, for example, a score (not shown) that is based on this strategic pair of decisions.
Additionally, in embodiments, each of the decision scores 340, 342, 344, and 346 may include a weighting based on related historical decision scores (e.g., a decision score associated with each of a pair of decisions such as the pair of decisions discussed above may be weighted more heavily than decision scores associated with individual decisions that are not identified as components of such a “strategic pair”), a weighting based on a temporal attribute of the decision score (e.g., decision scores associated with recent decisions may be weighted more heavily than those associated with older decisions), and the like. Decision scores associated with strategic pairs of decisions may, themselves, also include weightings (e.g., a decision score associated with a strategic pair of decisions such as the decision score discussed above may be weighted more heavily than decision scores associated with each of the individual decisions of the pair). In embodiments, the score vectors 302, 304, 306, 308, and 310 may be selectively aggregated, and/or otherwise analyzed, to generate score vectors (not shown) corresponding to particular entities (e.g., “TRI,” “AFL,” and “HRB”), particular industry sectors, ETPs, and the like. According to embodiments, any number of different performance metrics and message attributes may be analyzed to determine decision scores that reflect potential returns associated with the identified decisions. Additionally, the score vectors 302, 304, 306, 308, and 310 may be updated each time a new message is received and analyzed, each time additional objective data is obtained, and/or the like.
Similarly, for example, a performance score associated with a wagering decision may be based on an amount of return that would have been earned had a wager been placed in accordance with the decision. That is, for example, if a message generated by a sports better just before a horse race in which a horse named “Lucky Day” raced says “Lucky Day by 2 lengths,” a first performance score may be generated based on an amount of return that would have been earned from placing a bet that Lucky Day would win the race; a second performance score may be generated based on an amount of return that would have been earned from placing a bet that Lucky Day would finish the race in either first place or second place; and a third performance score may be generated based on an amount of return that would have been earned from placing a bet that Lucky Day would finish the race in first place, second place, or third place.
Returning to
According to embodiments, the advisor evaluator 214 may also derive an importance score for each return advisor. An importance score may reflect, for example, that a large number of people appear to trust a return advisor. The importance score may be based, for example, on the return advisor's social media network. For example, in Twitter®, a return advisor may follow other return advisors, and/or be followed by others. Additionally, a return advisor's tweets may be re-tweeted. In embodiments, the advisor evaluator 214 may generate a graph to model relationships between one or more return advisors.
For example, as shown in
In embodiments, an importance score may be determined using any number of different graph-theory techniques. For example, an importance score associated with a return advisor may be determined based on a connectivity of the return advisor to a set of other return advisors such as, for example, those that are separated from the return advisor by no more than three nodes. That is, using the illustrative graph 400 shown in
Suppose, for example, that the weighting of each of edges 406, 410, 416, and 414 is 1, indicating that there has been only one interaction between each pair of respective return advisors linked by these edges, and that the weighting of edge 420 is 2, indicating that there have been two interactions between the fourth and fifth return advisors 412 and 418. Additionally, suppose that an importance score of a return advisor is determined based on the connectivity of the return advisor to the set of other return advisors that are linked to the return advisor by no more than two edges. The importance score of the first return advisor 402, then, may be determined to be Si(402)=Dw(402-404)+Dw(402-408)+Dw(402-412), where Si is the importance score of the return advisor associated with node 402 (i.e., the first return advisor) and Dw is the weighted distance between the identified nodes. Thus, in the example of
Increased numbers of interactions with, and/or connections to, a return advisor may suggest that the return advisor is a more trusted return advisor, a more active return advisor, or the like, as opposed to a return advisor with a lower importance score. For example, a celebrity that only occasionally tweets about his or her securities trades may, nevertheless, have a high importance score compared to other return advisors because the celebrity has a large number of followers and may actually influence a stock price by tweeting about a decision related to that stock (e.g., when large numbers of users following the celebrity's tweets buy shares of the stock because the celebrity recommended that they do so, the price of the stock may rise). According to embodiments, other techniques for utilizing graphs such as, for example, techniques that involve matrix analysis, Markov chains, game theory, and the like, may be used to determine importance scores associated with return advisors.
For a specified set of performance metrics, the advisor evaluator 214 may determine an advisor score based on one or more performance scores, importance scores, and the like. For example, for a specified trading time period and stock type, the advisor evaluator 214 may determine an advisor score based on a weighted combination (e.g., sum) of the corresponding performance score and one or more importance scores. Return advisors may be ranked based on the advisor scores. Additionally, in embodiments, the advisor evaluator 214 may identify the return advisors with the highest scores and label those return advisors as, for example, winning return advisors. For example, winning advisors may include advisors whose scores exceed a threshold. A list of winning return advisors may be dynamic, as an advisor score associated with a return advisor may be updated, for example, based on each new risk/return-related message that is accessed. Further, in embodiments, separate lists of winning return advisors may be generated for different performance metrics, sets of performance metrics, or the like. For example, some traders may have a high score if evaluated based on long-term stock performance, other traders may have a high score only in a technology domain, and the like. In this manner, lists of winning return advisors may be utilized to provide beneficial recommendations of return advisors and/or decisions for different purposes, decision-styles, and the like.
As indicated above, the advisor analyzer 108 also includes an advice module 208. According to embodiments, the advice module 208 is configured to evaluate decisions referenced in return advisors' messages. In embodiments, the advice module 208 may identify a first risk/return-related decision based on a recent decision referenced in a message generated by a first return advisor and may identify a second risk/return-related decision based on a recent decision referenced in a message generated by a second return advisor. The advice module 208 may utilize information resulting from the evaluation of the first and second return advisors, information from an objective information source (e.g., the objective information source 110 shown in
In embodiments, decision rankings may be calculated in the context of one or more attributes, performance metrics, or the like. Additionally, in embodiments, the advice module 208 may utilize sentiment associated with the relevant risk/return domain (e.g., market sentiment) as guidance to recommend other aspects of a decision (e.g., an amount of money to be used to purchase securities or wager on a sports team, odds to be used when wagering, and the like). For example, in embodiments, the advice module 208 may allocate a percentage of a total amount of money to each of a number of recommended decisions by incorporating market sentiment into a determination of a decision score and by dividing the decision score by a sum of all of the recommended decisions.
According to embodiments, the advice module 208 may employ one or more different strategies in identifying decisions to recommend. For example, the advice module may recommend all of the identified decisions generated by return advisors, the most commonly recommended decisions, the least commonly recommended (e.g., unique) decisions, decisions generated by winning return advisors, and/or the like. Additional strategies may be utilized as well. For example, in embodiments in which the advice module 208 evaluates securities trade decisions, the advice module 208 may compare the number of recent messages containing a reference to a decision associated with buying stock with the number of recent messages containing a reference to a decision associated with selling stock. If the buy decisions outnumber the sell decisions, for example, the advice module 208 may recommend buying shares in a particular exchange traded product (ETP) such as, for example, the SPDR® S&P 500 fund; whereas if the sell decisions outnumber the buy decisions, the advice module 208 may recommend selling shares of the SPDR® S&P 500 fund.
As described above, an advisor analyzer (e.g., the advisor analyzer 108 shown in
A message filter (e.g., the message filter 202 shown in
Embodiments of the method 500 further include assigning an advisor ranking to each return advisor (block 510). Each advisor ranking may, for example, be based on one or more performance scores corresponding to the return advisor. Embodiments of the method 500 further include identifying risk/return-related decisions based on references to recent risk/return-related decisions in messages generated by the return advisors (block 512), and evaluating the risk/return-related decisions (block 514). Decision rankings may be assigned to risk/return-related decisions (block 516) and a risk/return-related service may be provided based on the decision rankings (518).
An experiment relating to securities trading was conducted during the trading period of Mar. 28, 2012 through Jun. 20, 2012. Large samples of tweets generated during this time period were examined to identify a set of trading-related tweets that originated from a set of Twitter® users. Using the set of trading-related tweets, a binary classifier was used to classify each user as either a trader or not a trader. In this context, a user was classified as a trader if the user's tweets indicated that the user tweets about securities and their securities trading activities.
Tweets including references to trading decisions were identified for each of the traders and score vectors were determined for each of the traders by calculating, for each identified decision, an amount of return that would have been realized if that decision had been followed for one day. That is, for example, where a tweet included a reference to a decision to purchase stock in a certain company on a certain day, a decision score was calculated based on the amount of return that would have been earned (or lost) if a share of that stock had been purchased at the beginning of trading on that day and then sold at the close of the same trading day. Performance scores were determined based on aggregations of the score vectors, and the traders were ranked to identify 120 winning traders.
The one day time period returns corresponding to each of five different recommendation strategies were aggregated over the study period and the results were compared to the aggregated daily return values of the SPDR® S&P 500 fund. The five sets of decisions included: “All Trades” (the sum of returns associated with following all of the trading decisions); “Top Trades” (the sum of returns associated with following the most commonly recommended trades); “Unique Trades” (the sum of returns associated with following the least frequently recommended trades); “Majority” (the sum of returns associated with following the set of trading decisions that occurred most frequently among the identified traders); and “Winners” (the sum of returns associated with following the 120 highest ranked traders, where the ranking of the traders was updated daily).
While embodiments of the present invention are described with specificity, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed invention might also be embodied in other ways, to include different steps or features, or combinations of steps or features similar to the ones described in this document, in conjunction with other technologies.
