6,625 research outputs found
Web Mining For Financial Market Prediction Based On Online Sentiments
Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely considered to be useful in financial market prediction. In this paper, a combined web mining and sentiment analysis method is proposed to forecast financial markets using web information. In the proposed method, a spider is firstly employed to crawl tweets from Twitter. Secondly, Opinion Finder is offered to mining the online sentiments hidden in tweets. Thirdly, some new sentiment indicators are suggested and a stochastic time effective function (STEF) is introduced to integrate everyday sentiments. Fourthly, support vector regressions (SVRs) are used to model the relationship between online sentiments and financial market prices. Finally, the selective model can be serviced for financial market prediction. To validate the proposed method, Standard and Poor’s 500 Index (S&P 500) is used for evaluation. The empirical results show that our proposed forecasting method outperforms the traditional forecasting methods, and meanwhile, the proposed method can also capture individual behavior in financial market quickly and easily. These findings imply that the proposed method is a promising approach for financial market prediction
Predicting the Effects of News Sentiments on the Stock Market
Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.Comment: 4 page
Analyzing stock market movements using Twitter sentiment analysis
In this paper we investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed sentiments for more than 4 million tweets between June 2010 to July 2011 for DJIA, NASDAQ-100 and 13 other big cap technological stocks. Our results show high correlation (up to 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger's Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of Rsquare (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA)
- …