Data compression techniques for stock market prediction

Salman Azhar*, Greg J. Badros, Arman Glodjo, Ming Yang Kao, John H. Reif

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of the Data Compression Conference
EditorsJames A. Storer, Martin Cohn
PublisherPubl by IEEE
Number of pages11
ISBN (Print)0818656379
StatePublished - Jan 1 1994
EventProceedings of the Data Compression Conference - Snowbird, UT, USA
Duration: Mar 29 1994Mar 31 1994


OtherProceedings of the Data Compression Conference
CitySnowbird, UT, USA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering


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