Application of deep learning and statistical methods in predicting Taiwanese stock trends

Abstract

Since its inception, the stock market has been a topic of considerable interest. Its variation and the complexity of integrating technology into the stock market have made it difficult for stock market trends to be fully understood. Various metrics and analytical approaches have been proposed in response to such changes, ranging from purely technical metrics to hardware upgrades. The widespread application of deep learning in the stock market, from basic metrics (opening price, closing price, highest price, lowest price, trading volume) to machine learning in sentiment analysis, further increases the possibility of increasing profits. Some front-end techniques, such as noise reduction through mathematical models, enhance the accuracy of deep learning models. However, few studies have centered on predicting long-term stock price changes. The traditional moving average (MA) cannot rapidly reflect drastic changes on its curve even though it can display trends; therefore, this study proposes an MA-based approach that improves the 200-day MA such that its delayed response to actual prices in real-time can be overcome. This deep learning model training was performed by combining 200-day MA data with two other types of MA data, thereby creating a new approach to metric analysis. The sample consisted of stocks of 13 Taiwanese companies with a high market cap: Taiwan Semiconductor Manufacturing Co., Ltd., MediaTek Inc., Chunghwa Telecom Co., Ltd., Fubon Financial Holding Co., Ltd., Cathay Financial Holding Co., Ltd., Nan Ya Plastics Corp., United Microelectronics Corp., Delta Electronics, Inc., CTBC Financial Holding Co., Ltd., Mega Financial Holding Co., Ltd., Formosa Chemicals & Fibre Corp., Hon Hai Precision Industry Co., Ltd., and Formosa Plastics Corp. Through multiple evaluation metrics, the experimental results revealed that the proposed model performed better in general than the traditional MA model for all stocks.