An Empirical Analysis of Risk Alert with the Text Mining of Internet Financial News

• Main Authors: Yeh, Hung-Ching, 葉鴻青
• Other Authors: Dai, Tian-Shyr
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/50713728929196548640

碩士 === 國立交通大學 === 管理學院財務金融學程 === 104 === With the increasing development of Chinese text analysis tools and machine learning methodologies, more researches analyze Madridan financial statements and financial news provided from internet media to predict the performance of stocks returns for risk management purpose. This study analyzes the excess return of the stocks with the nature language method and supervised machine learning. The Chinese financial mood dictionary provided by professor Chuan-Ju Wang makes the financial meanings of the news more accurate. By using technique of the Chinese word segmentation like Jieba, we can build a matrix with the excess return and the feature TF-IDF weightings. There are two types of models. The first type of models categorizes excess returns into positive/negative. These methods include the Logit regression model, Naïve bayes classifier and Support vector classifier. The second type of models estimates quantitatives of excess returns. These models include the linear regression model and Support vector machine. According to our empirical study, the average accuracies of 50-fold cross validation in the first type of models are about above 60%, and the AUC indicates the model has at least middle discremanatory power. The mean square errors and the coefficients of determination are passable. The predict excess return is worthy of our reference. It appears that my proposed five models can process Chineses financial news to predict the excess return of the stock. It confirms the hypothesis that Taiwan stock market does not meet the strong form of efficient market.