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)

On using Twitter to monitor political sentiment and predict election results

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content

The Information of Spam

This paper explores the value of information contained in spam tweets as it pertains to prediction accuracy. As a case study, tweets discussing Bitcoin were collected and used to predict the rise and fall of Bitcoin value. Precision of prediction both with and without spam tweets, as identified by a naive Bayesian spam filter, were measured. Results showed a minor increase in accuracy when spam tweets were included, indicating that spam messages likely contain information valuable for prediction of market fluctuations

Using Twitter to Predict Investor Decisions

Since the stock market’s inception in the 17th century, people’s thoughts and feelings have played a part in a stock’s success in trading. Obviously, company performance and an investor’s rigorous analysis of a stock drive most valuation, but it has been proven that, especially in the short term, investors’ cognitive biases drive some decisions as well. What if an investor knew how others felt about a company? What if they could see a facet of those biases? With this kind of information, investors and companies could make more informed and profitable decisions every day. With technology today, we may have a tool that shows how people feel in regards to a company: Twitter. I ask the question: can Twitter be used to predict how an individual stock will move on a given day? Using DiscoverText, an application that collects Tweets based on keywords, I collected data on Tweets about three major corporations: Home Depot, Starbucks, and Southwest Airlines. WordStat, an application that counts words in text data, was used to code positive and negative sentiment for Tweets. SPSS was then used to develop a Time Series regression model. Results indicate predictive relationships between the stock price of a company and positive Tweets, negative Tweets, and the number of words in each Tweet. The study finds a statistically significant relationship between the sentiments, volume of Tweets, and stock price, but the relationship differs between companies. Future research needs to determine if this is because of difference in product or some other factor. Going forward, my research has the ability to play a role in larger models and allow investors to make more educated and more profitable investing decisions.No embargoAcademic Major: Financ