Systems and methods for data mining and modeling

Abstract

Techniques for prediction of financial instrument returns, identifying statistical history, the discovery of pricing anomalies, and financial instrument visualization are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for identifying financial instrument returns and pricing anomalies including matching, using at least one computer processor one or more portions of current market data associated with a financial instrument with historical market data, averaging outcomes of matched historical market data, and providing a probabilistic outcome for financial instrument returns, pricing anomalies, or other metrics based on the matched historical market data and the current market data. Techniques for financial instrument analysis may also include processing event data, correlating the event data using a large volume of historical market data to identify a predicted impact on returns of a financial instrument and/or pricing anomalies, and presenting the predicted impact to a user (e.g., in near real time).

1 . A method for financial instrument return analysis comprising:
 processing event data using at least one computer processor;   correlating the event data using a large volume of historical market data to identify a predicted impact to a financial instrument; and   presenting the predicted impact to a user, wherein the predicted impact to the financial instrument is presented within a same day the event data was received.   
     
     
         2 . The method of  claim 1  wherein the predicted impact to a financial instrument comprises a change to a return of the financial instrument for an observation period. 
     
     
         3 . The method of  claim 1 , wherein the event data comprises at least one of: user entered event data to model an impact of a potential event on a financial instrument, an actual event received from a data feed, and an event generated by a system to model an impact of an upcoming potential event, and an actual event entered by a user. 
     
     
         4 . The method of  claim 1 , wherein events of the event data include at least one of geopolitical events, earnings events, weather events or other natural world events, news events, economic data surprises, central bank statements, central bank actions, product releases, earnings surprises, mergers and acquisitions, IPOs, corporate governance changes, regulatory approvals, regulatory denials, seasonality, and surprises relative to expectations for one or more events. 
     
     
         5 . The method of  claim 1  wherein the large volume of historical market data comprises time series financial data. 
     
     
         6 . The method of  claim 1  wherein the predicted impact is provided as a notification to a user. 
     
     
         7 . The method of  claim 6 , wherein the notification comprises at least one of an alert, an email, a text message, a blog post, a web based ticker, a web based animated banner, a transmitted recorded audio message, and an electronic notification. 
     
     
         8 . The method of  claim 6 , wherein the notification contains one or more of: a frequency of positive returns, a rank order of returns, a number of prior observations, and a confidence indicator. 
     
     
         9 . The method of  claim 8 , wherein the confidence indicator is derived from inputs comprising one or more of: a number of observations in the alert, a probability that a returns of assets for a period of time are statistically anomalous compared to all other days during the same period of time, a frequency distribution of returns, and other relevant factors 
     
     
         10 . The method of  claim 1 , further comprising:
 providing an interactive analysis environment allowing development of one or more queries.   
     
     
         11 . The method of  claim 10  wherein the interactive analysis environment includes a natural language based query interface for generating studies. 
     
     
         12 . The method of  claim 10  wherein the interactive analysis environment allows generation of queries using associations between near real time event data and historical financial data. 
     
     
         13 . The method of  claim 10 , wherein the interactive analysis environment comprises one or more templates for generating reports. 
     
     
         14 . The method of  claim 1  wherein the identification of a predicted impact allows a user to create and test optimal investment strategies without programming. 
     
     
         15 . The method of  claim 1 , further comprising:
 analyzing historical event data to generate a set of precedent events for the event data being processed.   
     
     
         16 . The method of  claim 15 , wherein generating a set of precedent events comprises ranking the magnitude of the event data being processed versus the magnitude of similar historical event data. 
     
     
         17 . The method of  claim 16 , wherein ranking the magnitude of the event data being processed comprises determining a standard deviation of the event data being processed with respect to similar historical event data. 
     
     
         18 . The method of  claim 15 , wherein the predicted impact to the financial instrument is determined using financial instrument pricing anomalies associated with the set of precedent events. 
     
     
         19 . The method of  claim 18 , wherein a statistical average of pricing anomalies associated with the set of precedent events is used to calculate the predicted impact to the financial instrument. 
     
     
         20 . A method for financial instrument return prediction comprising:
 determining a baseline probability for at least one financial instrument return of a financial instrument;   inputting current market data associated with the financial instrument;   matching, using at least one computer processor, one or more portions of the current market data with historical market data;   averaging outcomes of matched historical market data; and   providing a probabilistic outcome for the at least one financial instrument return based on the matched historical market data and the current market data.   
     
     
         21 . The method of  claim 20 , wherein the return is expressed as an overall market percentage change for the financial instrument since the opening of the trading day. 
     
     
         22 . The method of  claim 20 , wherein the current market data comprises an amount of time left in a current trading day. 
     
     
         23 . The method of  claim 20 , wherein the current market data comprises at least one of: an indication of market volume since the opening of the market for the financial instrument and an indication of volatility of the financial instrument. 
     
     
         24 . The method of  claim 23 , wherein the volatility comprises a standard deviation of recent daily returns for the financial instrument. 
     
     
         25 . The method of  claim 20 , wherein the historical market data includes at least one of: an average historical performance for a current month of a year, an average historical performance for a current calendar day, an average historical performance for a numerical trading day of a week, a number of positive closes for the financial instrument during previous trading days, and a number of positive closes of a financial market associated with the financial instrument during previous trading days. 
     
     
         26 . The method of  claim 20 , further comprising:
 increasing an amount of historical market data by identifying additional historical market data based on a correlation of the additional historical market data.   
     
     
         27 . The method of  claim 26 , wherein the financial instrument comprises a first financial instrument and the additional historical market data comprises historical market data of a second financial instrument and correlation is based upon price behavior. 
     
     
         28 . The method of  claim 26 , further comprising:
 setting a minimum level of correlation required for identification of additional historical market data.   
     
     
         29 . The method of  claim 28 , wherein the minimum level of correlation required is based at least in part on an amount available historical data for the financial instrument. 
     
     
         30 . The method of  claim 28 , wherein the minimum level of correlation required is set statically. 
     
     
         31 . The method of  claim 27 , wherein the historical market data of the second financial instrument is weighted based on a level of correlation to the first financial instrument. 
     
     
         32 . The method of  claim 20 , wherein matching, using at least one computer processor one or more portions of the current market data with historical market data comprises matching on one or more market data portions including at least one of price, minutes left in a trading day, volume, and volatility. 
     
     
         33 . The method of  claim 31 , wherein a strength of a match is weighted based on a number of market data portions matched. 
     
     
         34 . The method of  claim 31 , wherein the market data portions are weighted individually and a strength of a match is based on which market data portions match. 
     
     
         35 . An article of manufacture for financial instrument return analysis, the article of manufacture comprising:
 at least one non-transitory processor readable storage medium; and   instructions stored on the at least one medium;   wherein the instructions are configured to be readable from the at least one medium by at least one processor and thereby cause the at least one processor to operate so as to:
 process event data using at least one computer processor; 
 correlate the event data using a large volume of historical market data to identify a predicted impact to a financial instrument; and
 present the predicted impact to a user, wherein the predicted impact to the financial instrument is presented within a same day the event data was received. 
 
   
     
     
         36 . A system for financial instrument return analysis comprising:
 one or more processors communicatively coupled to a network; wherein the one or more processors are configured to:   
       process event data using at least one computer processor;
 correlate the event data using a large volume of historical market data to identify a predicted impact to a financial instrument; and
 present the predicted impact to a user, wherein the predicted impact to the financial instrument is presented within a same day the event data was received.