جستجوی مقالات مرتبط با کلیدواژه « text mining » در نشریات گروه « مالی »

Due to the increasing growth of social networks in recent years, investors in various markets, in addition to reviewing and analyzing classic market information, also pay attention to news and information published on social networks. By examining and evaluating the relationship between news and information published on social networks and changes in stock prices, it is possible to understand the impact of the information on stock prices and predict the future trend.In this article, using the methods of sentiment analysis and text mining, the impact of public thoughts and feelings caused by news on the Internet and cyberspace on stock prices is examined. The information used in this research includes content published on the social network Twitter about stocks and real stock price data of the top 5 companies on the US stock exchange. Using the presented method, general feelings about a text are estimated and a general score is considered for it. Then, using back testing methods and adopting different trading strategies, the impact of these emotions on the share price trend will be examined and the obtained results will be compared with and without the effect of emotion analysis. According to the results of this study, the effectiveness of strategies based on sentiments analysis is significantly higher than technical analysis-based methods.

The purpose of this study is to investigate the effect of informed and noise investors' perceptions of financial statements on stock returns. In this regard, the qualitative data of the reports of the board of directors of 116 companies listed on the Tehran Stock Exchange during the period 2011-2019 have been used. To qualitative analyzing the reports of the board of directors and extracting the textual elements considered by the two groups of informed and noise traders, the text mining and Lasso regression approach was used and to separate the capital market traders into two groups of informed and noise traders, Kalman filter was used. Findings of the study show that both groups of informed and noise traders can achieve abnormal returns by using the information of board reports and basing words and separating them into words with real and fact basis. In the case of mix-meanning based words, the perception of informed traders can affect abnormal stock returns, while noise traders are unable to distinguish mix-meaning words and do not seem to pay much attention to them in their decisions. The results of the research are generally in line with the theory of noise traders and show the behavioral basis (imitative effect) in the Tehran Stock Exchange.