| 摘要(英) |
In recent literature or journals, there is a large amount of Deep Learning used to predict what will happen at the next point in time, that is, there are many documents that predict the closing price or stock price at the next time, but Only researched whether it is possible to accurately predict the future stock price, but no one has studied whether such predictions can actually make a profit on actual transactions.
This study uses deep learning to use LSTM and TCN, which are better predictors of time series, as a stock price forecasting model. However, how to use it in trading after the forecast is relatively small, and it is used in trading if the program is used. It is easier to systematically understand its profit and risk. This study proposes that LSTM is traditionally used to predict stock prices, but it has not been tested with the fixed trading pattern to predict the closing price. For the sake of profitability, this study uses program trading and stretches the forecast time to weeks, because the stock price is more likely to fluctuate up and down in weeks, and the two are combined to explore the possibility of profit.
This study explores the fact that Taiwan′s 50 constituent stocks have existed for more than 30 years. It takes more than 30 years because the weekly units and forecasting models need to have a certain amount of data. Therefore, there are 11 companies in Taiwan with more than 30 years. Of the 11 companies, 9 of them have a good profit performance.
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| 參考文獻 |
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