Forecasting Change Directions for Financial Time Series Using Hidden Markov Model

Authors:
Sang-Ho Park;Ju-Hong Lee;Jae-Won Song;Tae-Su Park
Affiliations:
Dept. of Computer& Information Engineering, Inha University, Nam-gu, South Korea 402-751;Dept. of Computer& Information Engineering, Inha University, Nam-gu, South Korea 402-751;Dept. of Computer& Information Engineering, Inha University, Nam-gu, South Korea 402-751;Dept. of Computer& Information Engineering, Inha University, Nam-gu, South Korea 402-751
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

Citing 4
Cited 1

Event Pattern Discovery from the Stock Market Bulletin

DS '02 Proceedings of the 5th International Conference on Discovery Science
StockMarket Forecasting Using Hidden Markov Model: A New Approach

ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Generalized hidden Markov models. I. Theoretical frameworks

IEEE Transactions on Fuzzy Systems
Support vector machine with adaptive parameters in financial time series forecasting

IEEE Transactions on Neural Networks

Can social microblogging be used to forecast intraday exchange rates?

Netnomics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Financial time series, i.e. stock prices, has the property of being noisy, volatile and non-stationary. It causes the uncertainty in the forecasting of the financial time series. To overcome this difficulty, we propose a new method that forecasts change direction (up ordown ) of next day's closing price of financial time series using the continuous HMM. It classifies sliding windowed stock prices to two categories (up ordown ) by their next day's price change directions, and then trains two HMMs for two categories. Experiments showed that our method forecasts the change directions of financial time series having dynamic characteristics effectively.