Method and system for analyzing investment information

Abstract

An information processing apparatus and associated method for analyzing investment information. An input unit receives a financial request and one or more investment variables relating to the financial indicator and a description vector unit generates at least two vectors containing the one or more investment variables based on the financial request. A description vector similarity unit then calculates a similarity value by comparing the at least two vectors and an investment analysis unit analyzes the similarity value and the financial request, and determines investment advice based on results of the analysis.

1 - 20 . (canceled) 
     
     
         21 . A method for performing computational analytics on financial data, comprising:
 receiving financial data corresponding to at least one category relating to an investment decision;   receiving at least one benchmark corresponding to the at least one category;   generating, via a processor, a vector between the financial data and the at least one benchmark;   calculating, via the processor, at least one similarity value based on the magnitude of the vector;   organizing the financial data into at least one category based on one or more corresponding similarity value.   
     
     
         22 . The method of  claim 21 , wherein the financial data and the at least one benchmark are normalized prior to generating the vector. 
     
     
         23 . The method of  claim 21 , wherein the at least one category includes at least one of net income, earnings before interest and taxes (EBIT), earnings before interest taxes, depreciation and amortization (EBITA), earnings per share (EPS), price to earnings ratio (P/E Ratio), return on investment (ROI), and return on equity (ROE). 
     
     
         24 . The method of  claim 21 , further comprising:
 calculating a difference between the at least one similarity value and at least one threshold value.   
     
     
         25 . The method of  claim 21 , further comprising:
 graphically displaying, via a graphical display, the at least one category.   
     
     
         26 . The method of  claim 25 , wherein the graphical display includes a multidimensional plot. 
     
     
         27 . The method of  claim 25 , further comprising:
 scaling the at least one similarity value for display.   
     
     
         28 . A method for performing computational data analytics for investment decision purposes, comprising:
 receiving at least one financial data vector corresponding to an investment decision, the at least one financial data vector including at least one investment variable and a first dimension corresponding to the at least one investment variable;   receiving at least one benchmark vector having the at least one investment variable and a second dimension greater than or equal to the first dimension;   generating, via a processor, at least one similarity vector corresponding to the financial data vectors and a benchmark vector as a function of a vector difference between each the at least one financial data vector and the at least one benchmark vector;   calculating, via the processor, at least one similarity value corresponding to a magnitude of the at least one similarity vector; and   processing, via the processor, the at least one similarity value for presentation.   
     
     
         29 . The method of  claim 28 , further comprising:
 generating a graphical representation of the at least one similarity value.   
     
     
         30 . The method of  claim 28 , further comprising:
 generating a graphical representation of the at least one investment variable for the at least one financial data vector.   
     
     
         31 . The method of  claim 29 , wherein the graphical representation includes at least one multidimensional plot. 
     
     
         32 . The method of  claim 29 , further comprising:
 scaling the at least one similarity value of the graphical representation for display.   
     
     
         33 . The method of  claim 28 , wherein the at least one financial data vector and the at least one benchmark are normalized before generating the at least one similarity vector. 
     
     
         34 . The method of  claim 28 , further comprising:
 calculating a difference between the at least one similarity value and at least one threshold value.   
     
     
         35 . A method for determining similarity in financial information, comprising:
 receiving at least one financial data vector, wherein the at least one financial data vector is defined as a benchmark vector;   generating, via the processor, at least one similarity vector as a function of a vector distance between the at least one financial data vector and the at least one benchmark vector;   calculating, via the processor, at least one similarity value based on the vector distance, the at least one similarity value corresponding to a magnitude of the at least one similarity vector; and   determining, via the processor, at least one similarity classification corresponding to a difference between the at least one similarity value and at least one threshold value.   
     
     
         36 . The method of  claim 35 , wherein the at least one financial data vector is normalized after being received. 
     
     
         37 . The method of  claim 35 , wherein the at least one similarity classification includes at least one of net income, earnings before interest and taxes (EBIT), earnings before interest taxes, depreciation and amortization (EBITA), earnings per share (EPS), price to earnings ratio (P/E Ratio), return on investment (ROI), and return on equity (ROE). 
     
     
         38 . The method of  claim 35 , wherein the at least one similarity classification is graphically displayed via a graphical display. 
     
     
         39 . The method of  claim 38 , wherein the graphical display includes at least one multidimensional plot. 
     
     
         40 . The method of  claim 38 , further comprising:
 scaling the at least one similarity value for display.