Method and system for analysis, display and dissemination of financial information using resampled statistical methods

Abstract

The present invention provides a method and system for the statistical analysis, display and dissemination of financial data over an information network such as the Internet and WWW. The present invention utilizes resampled statistical methods for the analysis of financial data. Resampled statistical analysis provides a meaningful and reasonable statistical description of financial information, which typically escapes modeling using parametric methods (i.e., assumptions of a Gaussian distribution). The present invention provides at least a GUI that provides functionality for user input of statistical queries, a statistical computation engine that performs statistical analysis of financial data and a graphical rendering engine that generates graphical display of statistical distributions generated by the statistical computation engine. According to one embodiment, the present invention employs a parallel processing architecture to speed generation of the resampled statistics.

Description

FIELD OF THE INVENTION[0001]The present invention relates to the area of electronic information systems. In particular, the present invention relates to a method and system for the delivery of financial information using resampled statistical methods over an information network.BACKGROUND OF THE INVENTION[0002]Investors and financial analysts rely upon electronic information systems for the delivery of accurate financial and investment data and analysis in order to devise meaningful investment strategies. The growth of the Internet and World Wide Web (“WWW”) highlights the potential for global distribution of “real time” or “near real time” financial information and analysis. For example, a number of WWW sites provide financial information to clients such as investors and financial analysts.[0003]However, conventional financial information sites do not provide meaningful analysis tools to accurately analyze, forecast and predict the behavior of financial markets. Conventional techno...

AI Technical Summary

Benefits of technology

The present invention is a financial information network node that allows clients to request resampled statistical analysis of financial investments and receive a graphical display of the results. The node includes a front end subsystem, a resampled statistical analysis engine (RSAE), and a graphics rendering engine (GRE). The RSAE performs resampled statistical analysis of financial data and incorporates routines to preserve temporal correlation in the data. The GRE generates a graphical display of statistical distributions generated by the RSAE. The invention employs a parallel processing architecture that speed generation of the resampled statistics, which allows for real-time or near-realtime statistical analysis and enables analysis on a large number of financial entities through a weighting process.

Problems solved by technology

However, conventional financial information sites do not provide meaningful analysis tools to accurately analyze, forecast and predict the behavior of financial markets.

However, these rudimentary statistical functions are not useful to investors in forecasting the behavior of financial markets because they rely upon assumptions that the underlying probability distribution function (“PDF”) for the financial data follows a normal or Gaussian distribution, which is generally false.

It is therefore incorrect to rely upon a statistical model based on assumptions of normality (e.g., standard deviation).

Thus, statistical measures such as the standard deviation provide no meaningful insight into the distribution of financial data.

However, conventional methods, which rely upon assumptions of a Gaussian distribution, are dangerous to investors because these analyses understate the true risk and overstate potential rewards for an investment or trading strategy.

Thus, this information is not generally useful and if relied upon promotes imprudent investment decisions.

In general, the heavy tailed nature of financial data presents significant challenges in providing meaningful statistical analysis.

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