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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/125540

    Title: 情緒分析交易策略設計
    Formulate a Trading Strategy Using Sentiment Analysis
    Authors: 吳文萱
    Wu, Wen-Xuan
    Contributors: 謝明華
    Hsieh, Ming-Hua
    Wu, Wen-Xuan
    Keywords: 機器學習
    Machine learning
    Sentiment analysis
    News sentiment
    Trading strategy
    Date: 2019
    Issue Date: 2019-09-05 15:48:06 (UTC+8)
    Abstract: 過去的文獻提供了財經新聞情緒、社群新聞情緒等對金融商品價格之間具有顯著相關性性的信心實證。Zhang and Skiena (2009) 中使用由大規模自然語言處理(Natural Language Processing , NLP)新聞分析系統生成的新聞數據,對公司的新聞發布頻率,情緒和主觀性如何預測或反應在其股票交易量和報酬上進行研究,並提供基於新聞市場的交易策略。Feuerriegel and Prendinger (2016) 中則肯定新聞情緒能解釋股價走勢並以新聞文本分析建構交易策略。本實證研究基於欲探討情緒分析對金融資產走勢是否具有影響性及預測力,利用數種監督式之機器學習分類方法,包括邏輯斯回歸 (Logistic Regression)、隨機森林 (Random Forest)、梯度提升 (Gradient Boosting)、自適應提升(Adaptive Boosting) 以及支持向量機 (Support Vector Machine),以台灣指數期貨的未來漲跌作為模型預測目標,尋找其中預測力最佳之模型並以此建構台指期交易策略。
    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.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106358006
    Data Type: thesis
    DOI: 10.6814/NCCU201900710
    Appears in Collections:[風險管理與保險學系 ] 學位論文

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