Using Fundamental, Technical and Chip Factors to Construct Taiwan Stock Deep Learning Trading Strategies

• Main Authors: Tzu-Hsuan Chen, 陳子軒
• Other Authors: Chou-Wen Wang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/3q4ttv

碩士 === 國立中山大學 === 金融創新產業碩士專班 === 106 === In Taiwan stock market, there are three major ways to analyze the stocks. The first is fundamental factors analysis, evaluating the value of a company by analyzing financial statements, the company’s pros and cons, industrial situation, and so on. The second is technical factors analysis, forecasting the stock price through patterns of the stock price or the candlestick, technical indicators calculated by price and volume, such as moving average(MA), relative strength index(RSI), and others. The last is “chip” factors analysis, predicting future performance of the stock price by tracking institutional investors’ behavior, like foreign investors, dealers, and investment trusts. The purpose of this study is to prove that combination of three major stock analyses could make trading strategies more profitable. In addition, this study also employs the deep learning technology to construct better trading strategies. This study constructs a composite trading strategy of Taiwan equity market which could be divided into two phases. The first phase is using the deep learning model trained by 129 indicators from financial statements to select strong stocks in next season, which form a stock pool. The second phrase is to buy and sell stocks from the stock pool according to signals consisting of technical indicators and a chip indicators. The results of this study reveal that the composite strategy has an annual return up to 26.86%, a max drawdown equal to 33.13%, and a Sharpe ratio equal to 1.13. All of them are better than the respective performances of the deep learning models and the timing strategies. There are two conclusions of this study: 1. Moderately integrating three major stock analyses could improve a trading strategy performance and enhance investors’ confidence. 2. A well-defined deep learning is useful to construct trading strategies in practice.