Data compression techniques for stock market prediction

Published

Journal Article

This paper presents advanced data compression techniques for predicting stock markets behavior under widely accepted market models in finance. Our techniques are applicable to technical analysis, portfolio theory, and nonlinear market models. We find that lossy and lossless compression techniques are well suited for predicting stock prices as well as market modes such as strong trends and major adjustments. We also present novel applications of multispectral compression techniques to portfolio theory, correlation of similar stocks, effects of interest rates, transaction costs and taxes.

Duke Authors

Cited Authors

  • Azhar, S; Badros, GJ; Glodjo, A; Kao, MY; Reif, JH

Published Date

  • January 1, 1994

Published In

  • Proceedings of the Data Compression Conference

Start / End Page

  • 72 - 82

Citation Source

  • Scopus