Forecasting Change Directions for Financial Time Series Using Hidden Markov Model

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 or down ) of next day's closing price of financial time series using the continuous HMM. It classifies sliding windowed stock prices to two categories ( up or down ) 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.