SYSTEM AND METHOD FOR DETERMINING AND UTILIZING SUCCESSFUL OBSERVED PERFORMANCE

• US 20140279684A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Active Application

• Current Assignee: Refinitiv US Organization, LLC (Refinitiv US Holdings, Inc.)
• Original Assignee: Thomson Reuters Global Resources
• Inventors: Liao, Wenhui, Makrehchi, Masoud, Shah, Sameena
• Granted Patent: US 10,290,058 B2
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

In embodiments, a message retriever accesses a plurality of messages and a message filter identifies, within the accessed messages, a set predictive messages. The risk/return-related messages are analyzed to identify associated return advisors. The return advisors are evaluated and ranked according to advisor scores, and risk/return-related decisions referenced in the messages are also identified, evaluated, and ranked. The ranked return advisors and decisions are used to facilitate assessment of future performance of an item or entity.

Claims

1. A computer-implemented method for facilitating an assessment of future performance of an item or entity, the method comprising:

accessing, using a computing device having a processor and a memory, a plurality of messages from a message source;

identifying, within the plurality of messages and using the processor, a plurality of predictive messages relating to the item or entity, wherein the plurality of predictive messages originates from a set of messaging users;

identifying, within the set of messaging users and using the processor, a first return advisor and a second return advisor;

evaluating, using the processor, a performance of the first return advisor, wherein evaluating the performance of the first return advisor comprises analyzing a first set of predictive messages generated by the first return advisor;

evaluating, using the processor, a performance of the second return advisor, wherein evaluating the performance of the second return advisor comprises analyzing a second set of predictive messages generated by the second return advisor;

assigning, using the processor, a ranking to the first return advisor and a ranking to the second return advisor, wherein the rankings are based on the performances of the first and second return advisors;

storing the rankings in the memory; and

facilitating an assessment of the future performance of the item or entity based on the rankings.

2. The method of claim 1, wherein at least one of the first and second sets of predictive messages comprises social media messages.

4. The method of claim 1, wherein the first and second return advisors comprise a first and second trader, respectively.

5. The method of claim 4, wherein each message of the plurality of predictive messages comprises at least one of a ticker symbol, a company name, a fund name, a “

$$”

symbol, and a key word.

6. The method of claim 5, wherein evaluating the performance of the first return advisor comprises identifying, in at least one of the first set of predictive messages, at least one reference to at least one trade-related decision and determining an amount of financial return that would have resulted from acting in accordance with the at least one trade-related decision.

7. The method of claim 6, wherein evaluating the performance of the first return advisor comprises:

determining a number of social media interactions between the first return advisor and one or more additional return advisors;

determining an importance score based on the number of social media interactions; and

determining a performance score based on the amount of financial return and the importance score.

8. The method of claim 1, wherein identifying the first return advisor and the second return advisor comprises using a classifier configured to classify a messaging user as a return advisor by analyzing content of a plurality of messages generated by the messaging user.

9. The method of claim 1, wherein evaluating the performance of the first return advisor comprises generating a first performance matrix corresponding to the first return advisor, and wherein evaluating the performance of the second return advisor comprises generating a second performance matrix corresponding to the second return advisor.

10. The method of claim 9, wherein each of the first and second performance matrices comprises a first plurality of score vectors and a second plurality of score vectors, respectively, each of the first and second pluralities of score vectors comprising at least one decision score corresponding to a performance metric.

11. The method of claim 10, wherein at least one of the plurality of decision scores corresponds to a pair of risk/return-related decisions referenced in at least one predictive message of the first set of predictive messages, and wherein the at least one of the plurality of decision scores is based on an amount of financial return that would have been earned as a result of acting in accordance with the pair of risk/return-related decisions.

3. (canceled)

12. (canceled)

13. A computer-implemented method for facilitating a securities trading related service, the method comprising:

accessing a plurality of social media messages;

identifying, within the set of social media messages and using a processor, a plurality of trading-related messages, wherein the plurality of trading-related messages originates from a set of social media users;

identifying, within the set of social media users and using the processor, a first trader and a second trader;

evaluating, using a processor, a performance of the first trader, wherein evaluating the performance of the first trader comprises analyzing a set of trading-related messages associated with the first trader;

evaluating, using the processor, a performance of the second trader, wherein evaluating the performance of the second trader comprises analyzing a set of trading-related messages associated with the second trader;

assigning, using the processor, a first ranking to the first trader and a second ranking to the second trader, wherein the first and second rankings are based on the performances of the first and second traders, respectively; and

facilitating a trade-related service based on at least one of the first and second rankings.

14. The method of claim 13, wherein each of the set of trading-related messages comprises at least one of a ticker symbol, a company name, a fund name, a “

$$”

symbol, and a key word.

15. The method of claim 13, wherein identifying the first trader and the second trader comprises using a classifier configured to classify a social media user as a trader by analyzing content of a plurality of social media messages generated by the social media user.

16. The method of claim 13, wherein evaluating the performances of the first and second traders comprises determining, for each of the first and second traders, a performance score associated with at least one performance metric, wherein the performance score is based on an amount of financial return that resulted, or would have resulted, from acting in accordance with a set of trade decisions associated with the respective trader, the performance metric comprising at least one of a specified company, a specified trading time period, and a specified industry type.

17. The method of claim 13, wherein facilitating the trade-related service comprises providing a trade decision recommendation.

18. The method of claim 17, wherein providing the trade decision recommendation comprises:

identifying a reference to a first trade decision in at least one of the first set of trading-related messages;

identifying a reference to a second trade decision in at least one of the second set of trading-related messages;

assigning a first decision ranking to the first trade decision;

assigning a second decision ranking to the second trade decision; and

recommending either the first trade decision or the second trade decision based on the first and second decision rankings.

19. A system for providing an assessment of future performance of an item or entity, the system comprising:

a server configured to receive, from a message source, a plurality of messages generated by a plurality of messaging users, the server comprising a processor that instantiates a plurality of software components stored in a memory, the plurality of software components comprising;

an advisor analyzer configured to;

(a) evaluate a performance of each of a first return advisor and a second return advisor, wherein the advisor analyzer is configured to evaluate the performances of the first and second return advisors by analyzing a first and second set of messages generated by the first and second return advisors, respectively; and

(b) assign a first ranking to the first return advisor and a second ranking to the second return advisor, wherein the first and second rankings are based on the performances of the first and second return advisors, respectively; and

a services component configured to facilitate a risk/return-related service based on at least one of the first and second rankings.

20. The system of claim 19, the advisor analyzer comprising an advice module configured to:

identify a reference to a first risk/return-related decision in at least one message of the first set of messages;

identify a reference to a second risk/return-related decision in at least one message of the second set of messages;

assign a first decision ranking to the first risk/return-related decision; and

assign a second decision ranking to the second risk/return-related decision, wherein the first decision ranking is greater than the second decision ranking when acting in accordance with the first risk/return-related decision is likely to result in a greater financial return than acting in accordance with the second risk/return-related decision.

21. The system of claim 19, the advisor analyzer comprising a message filter configured to identify, within the plurality of messages, a plurality of risk/return-related messages, wherein the plurality of risk/return-related messages is generated by a set of messaging users.

22. The system of claim 19, wherein the plurality of messages comprises a set of social media messages, the set of social media messages comprising at least one tweet.

23. (canceled)