Data compression techniques for stock market prediction

Author

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

Author_Institution

Dept. Comput. Sci., North Carolina A&T State Univ., Greensboro, NC, USA

fYear

1994

fDate

29-31 Mar 1994

Firstpage

72

Lastpage

82

Abstract

Presents advanced data compression techniques for predicting stock markets behavior under widely accepted market models in finance. The techniques are applicable to technical analysis, portfolio theory, and nonlinear market models. The authors 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. They also present novel applications of multispectral compression techniques to portfolio theory, correlation of similar stocks, effects of interest rates, transaction costs and taxes

Keywords

data compression; filtering and prediction theory; financial data processing; stock markets; advanced data compression techniques; correlation; finance; interest rates; lossless compression techniques; lossy compression techniques; major adjustments; market modes; multispectral compression techniques; nonlinear market models; portfolio theory; stock market prediction; stock prices; stocks; taxes; technical analysis; transaction costs; trends; Contracts; Costs; Data compression; Data security; Economic indicators; Finance; Portfolios; Predictive models; Stock markets; Subcontracting;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Compression Conference, 1994. DCC '94. Proceedings

Conference_Location

Snowbird, UT

Print_ISBN

0-8186-5637-9

Type

conf

DOI

10.1109/DCC.1994.305914

Filename

305914