| 摘要(英) |
FinTech is a popular application in artificial intelligence applications. Although many studies have been conducted to predict the movement of stock price based on historical data and technical indicators, how to com- bine the prediction with portfolio allocation remains a problem. In this re- search, we consider financial news and technical indexes in the stock price movement prediction to achieve better performance. To support adaptive portfolio optimization, this study further proposes a new task to predict the ranking of stocks return. With a simple strategy of investing top ranked stocks, the prediction model achieves a high return on investment (ROI) than Taiwan 50 ETF and Taiwan mid-cap 100 ETF. The thesis is divided into three parts: binary classification of stock movement on the next trad- ing day, the ranking prediction of stock returns in the next week/month and an application of portfolios trading strategy based on backtesting.
For stock trend prediction, we use stock technical indicators as the model’s input features, and compare the performance with or without news headlines or news sentiment on the next trading day stock movement pre- diction. Experiments show that adding news sentiment has better per- formance than adding news headline about 4%. Comparing it with the baseline (Random Forest), performance has been improved around 5%. Secondly, we formulate a new problem to predict stock rank for the next week/month based on return of investment to reduce frequent trading. Base on technical indicators, we compare the performance of stock rank prediction with or without news headlines and news sentiment. While the model with news headlines achieves lower loss, the model with news sen- timent has better balance in stock rank prediction. Finally, we use the next week/month stock rank prediction to select the top ranked k stocks for adaptive portfolio allocation. Experiments show that the portfolio al- location strategy achieve higher investment return than Taiwan 50ETF, especially when using the model with news sentiment, the average ROI performance can be improved around 23%. |
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