News semantic feature extraction process design and the correlation analysis between news and stock price

• Main Authors: Wen-Chiuan Chung, 鍾文荃
• Other Authors: Jorng-Tzong Horng
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/a8v4s6

碩士 === 國立中央大學 === 資訊工程學系 === 104 === In recent years, there are many studies try to predict the direction of stock price with available message on the market, such as financial statements and financial news. According to Fama's efficient market hypothesis[12], these public information will be reflected in the change of stock price. Therefore, how to retrieve the effective message from news to determine the stock price trend is the significant point of such research. However, in this aspect, past studies mostly established prediction model with bag of words, still further was the use of complex word such as n-gram, noun phrase, etc, few studies have further to search the text content associated with the stock price in the news. In this study, we used some text mining tools to find the more relevant content of specific company and analyze the relationship between these content and the company’s stock price. We hope to get effective features through applied the more mature text mining technology for part of speech of words, sentence structure and sentiment analysis, we can enhance the accuracy of the prediction model.