A Hybrid Model Based on Hidden Markov Model to Forecast Taiwan Stock Market

• Main Authors: Aminata Manneh, 梅米娜
• Other Authors: Tzong-Yi Lee
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/449tnh

碩士 === 元智大學 === 生物與醫學資訊碩士學位學程 === 106 === Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. This act has attracted much attention from academia. However, as a results of its non-linear, volatile and complex nature of the data, it is quite difficult to predict. Thus the motivation behind this research is to innovatively combine a novel deep learning and statistical framework where Discrete Wavelength Transform (DWT), Stacked Sparse Autoencoder (SSAE) and Hidden Markov Model (HMM) are combined for stocked price forecasting. Three refined processes have been propose in the hybrid model for forecasting: 1) Use DWT to eliminate noise from the data via decomposition; 2) Select highly relevant features via SSAE; 3) Employ a Hidden Markov Model to predict the stock market price trend. An eight year period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) historical data is employed as experimental database to test the prediction ability of the proposed model with a performance indicator; Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), R and Theil U. Algorithm trading has evolved exponentially in recent years, thus, this research offers evidence on the predictive ability of a new hybrid forecasting model. To evaluate the robustness of model, Nifty 50 stock index data was also used and when compared to other state-of-the-art models, it outperforms them based on the same time frame and market condition. This research has demonstrated that combination of deep learning and statistical method are effective in the prediction of financial time series data.