Title :
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
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;
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
DOI :
10.1109/CIFER.1997.618933
