Constructing stock price forecast model with deep learning: Evidence from Taiwan Stock Market
碩士 === 國立臺中科技大學 === 財務金融研究所碩士班 === 107 === Machine learning and deep learning have achieved remarkable results in big data analysis and Fintech in recent years. For the era of collecting mass data and analyzing big data, using deep learning to process data and analyze data is an advantage.This paper...
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| Format: | Others |
| Language: | zh-TW |
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2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/625d39 |
| Summary: | 碩士 === 國立臺中科技大學 === 財務金融研究所碩士班 === 107 === Machine learning and deep learning have achieved remarkable results in big data analysis and Fintech in recent years. For the era of collecting mass data and analyzing big data, using deep learning to process data and analyze data is an advantage.This paper applies deep learning that can extract features from a large of raw data without relying on the predictive model''s pre-prediction mode to analyzes and forecasting in stock market. Such an architecture makes deep learning potentially predictive of stock market forecasts. This study used the Long Short Term Memory Network model (LSTM) to analyzing Taiwan stock price that data include 52 financial related indicators between 2000 and 2018.The biggest difference between this study and the past literature that most of the literature studies are aimed at forecasting the trend of stock market on the next day and this study further explores the forecast of the stock price index. The results of this paper show that the LSTM in deep learning can be effectively used in the forecast of Taiwan''s stock market.
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