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forecasted Korean Stock Price Index (KOSPI) by three forecasting models including back-propagation neural network model (BPNN), Bayesian Chiao’s model (BC), and the seasonal autoregressive integrated moving average model (SARIMA).
FORECASTING USING HIDDEN MARKOV MODEL MATLAB SERIES
used a nonlinear time series model to forecast the tendency of the Bel 20 stock market index. Although faced with complicated challenges, the forecast of stock index has still attracted the attention of many industrial experts and scholars. However, the stock price index is influenced by many factors such as the economic situation, policy changes, and emergency. As a barometer of the stock market, the stock index is an important reference for investors to make investment strategies. The stock market is filled with the coexistence of high-risk and high-yield characteristics. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events.
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For a long time, there have been a lot of researches on the forecast of stock index. The stock index reflects the fluctuation of the stock market.