Formulate a Trading Strategy Using Sentiment Analysis

• Main Authors: Wu, Wen-Xuan, 吳文萱
• Other Authors: Hsieh, Ming-Hua
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/n6bv9n

碩士 === 國立政治大學 === 風險管理與保險學系 === 107 === Studies in the past provides evidence of confidence in connection with news sentiment and financial assets trend. Zhang and Skiena (2009) uses news data including the companys’ news release frequency and sentiment generated by a large-scale Natural Language Processing (NLP) news analysis system to predict or reflect on its stock return and trading volume. The study also formulate trading strategies based on the news market. Feuerriegel and Prendinger (2016) confirmed that news sentiment can explain stock price movements and construct trading strategies with news text mining. The empirical study is also based on the need to explore whether sentiment analysis has an impact and predictive power on financial asset trends. The empirical study uses several supervised machine learning classification methods, including logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine. I predict the future rise and fall of Taiwan index futures and look for the model with the best predictive power to construct the trading strategy. The empirical result shows that the addition of news sentiment variables can effectively improve the predictive power of all models. This empirical study selects the best model to construc trading strategy with the best forecast of the day and the next day. The performance of the test set can beat the buy-and-hold strategy and momentum investment strategy and the strategy predicts that the strategy of the next day's change is better than the forecast of the day's change. The news sentiment has the effect of delaying the emergence, and the strategy of using the gradient boosting model is outperforming.