Apply Autoencoder and Principal Component Analysis with Decision Tree to Forecast TSMCs Stock Price

• Main Authors: LU, CHIA-YING, 呂佳穎
• Other Authors: CHOU, TSUNG-NAN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/7k4v4y

碩士 === 朝陽科技大學 === 財務金融系 === 107 === Nowadays, technology improves rapidly every day. AlphaGo defeated the world Go King in 2016. Artificial Intelligence (AI), which aroused the interest again. If AI can learn by itself without influencing by human, AI wont be biased with emotions and makes more objective decision. Therefore, both of Machine Learning and Deep Learning are extended from AI, which swept the world. TSMC accounts for 21.29% of Taiwan Stock Exchange Capitalization Weighted Stock Index. Therefore, this study intended to predict its stock price using three different methods. This experiment data was collected for a period of 10 years from 2009 to 2018, which included the training samples (from2009 to 2017) and testing samples (2018 only). In this study, the CART decision tree, autoencoder and the principal component analysis are conducted to evaluate their prediction performance based on confusion matrix. In the variables, the four general technical indicators, such as the Moving Average, the stochastic KD, the Relative Strength Index and the Moving Average Convergence and Divergence are used by the general public and extend Institutional investors net buy or net sell. The results show that among the three methods of the CART decision tree, AE-CART and PCA-CART, the best prediction accuracy of PCA-CART with 7 variables is 77.73%. It proves that proper dimensionality reduction can improve accuracy. The autoencoder is categorized into two methods, AE-CART and PCA-AE-CART for analyzing three different Activation Functions (Relu, Tanh and Sigmoid), which observes the prediction accuracy of the three functions. The results show that the accuracy of Tanh prediction by PCA-AE-CART is up to 66.80%. Compared to the use of the autoencoder combined with the decision tree, using the principal component analysis to perform the dimensionality reduction process improves the accuracy more significantly.