High frequency time series analysis and prediction using Markov models

Author

Papageorgiou, Constantine P.

Author_Institution

Center for Biol. & Comput. Learning, MIT, Cambridge, MA, USA

fYear

1997

fDate

23-25 Mar 1997

Firstpage

182

Lastpage

188

Abstract

There has been a surge in interest in the analysis and prediction of high frequency time series in recent years. We consider the problem of predicting the direction of change in tick data of the U.S. dollar/Swiss Franc exchange rate. To accomplish this, we show that a Markov model can find regularities in certain local regions of the data and can be used to predict the direction of the next tick. Predictability seems to decrease in more recent years. With transaction costs, the model is unlikely to be profitable

Keywords

Markov processes; costing; economic cybernetics; finance; foreign exchange trading; probability; time series; Markov models; dollar Franc exchange rate; foreign exchange rate; high frequency time series analysis; model; tick data change prediction; time series prediction; transaction costs; Artificial intelligence; Biological system modeling; Biology computing; Exchange rates; Frequency; Hidden Markov models; Laboratories; Learning; Predictive models; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE 1997

Conference_Location

New York City, NY

Print_ISBN

0-7803-4133-3

Type

conf

DOI

10.1109/CIFER.1997.618933

Filename

618933