An analysis in the relations between the key phrases andthe stock performance in Taiwanese stock market(e.g., Technology stocks and food industry stocks)

• Main Authors: Hung-Chieh Lee, 李宏杰
• Other Authors: Mao-Wei Hung, Ph.D.
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/chetvs

碩士 === 國立臺灣大學 === 國際企業學研究所 === 106 === Nowadays, the fundamental and technical analysis are often used as analysis techniques by publics in Taiwanese stock market. Related research from many scholars present that both techniques take great part in the research progress, but utter difficulties and restrictions are coming up in the forecasting stock prices. For examples, scholars found it hard to solve the problems such as short analysis interval, short-term fluctuations or difficulties to know the detailed information from policies in companies. Also, there are more measuring problems about setting indexes. In practice, when it comes to forming up an investment strategy or entering certain conditions, both analysis prove themselves as valuable methods to forecast the market trend, but they are still insufficient to explain the trend. The Taiwanese stock market are often considered as a nearly-inefficient market. Many investors desire to gather first-hand information as soon as possible in order to help decision in the meantime. In this research, this hypothesis also exists. However, it is hard to forecast the market by the information-gathering approach due to the lack in time and several issues, like huge data range, the different choosing ways in the text. In short, many complicated factors needed to be concerned. In order to solve these problems, many scholars applied the text mining approach to help analyze the performance in the stock market and sample the needed text on the website. This research will be mainly focus on data mining by using ( Python 3.6 ) Pandas toolbox techniques to select the stocks in the same categories. For example, TSMC and HTC belongs to technology stocks. We will choose the Taiwanese market observation post system to capture daily historical message on the three year basis and compare with the counterpart daily stock price. Then, mark positive and minus on the selective nouns and verbs to help the latter analysis. We will use CKIP and Jeiba system in the progress of this research. By finding the key words and valuation, we can improve this way of forecasting stock price and help investors understand more details about text mining in forecasting stock prices.