Correlating Reddit Sentiment and Market Returns

Reddit is the most popular online forum on the internet, but its effects on the stock market remain relatively unstudied. While Reddit has proven to be an effective platform for influencing the price of individual stocks such as in the short-squeeze of GameStop, the influence of Reddit on broader macroeconomic trends is less clear. To answer this question, I will compare the “mood” of the top 1,000 daily Reddit posts to market index performance from the same day in order to find any potential correlation

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements

Recently, social media, particularly microblogs, have become highly valuableinformation resources for many investors. Previous studies examined general stockmarket movements, whereas in this paper, USD/TRY currency movements based on thechange in the number of positive, negative and neutral tweets are analyzed. Weinvestigate the relationship between Twitter content categorized as sentiments, such asBuy, Sell and Neutral, with USD/TRY currency movements. The results suggest thatthere exists a relationship between the number of tweets and the change in USD/TRYexchange rate

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Business Value of Enterprise Micro-Blogs: Empirical Study from weibo.com in Sina

The increasing use of micro-blogs as marketing tools has increased the research attention on the usage and performance of enterprise micro-blogs. Based on research on information system (IS) usage and the resource-based view (RBV) theory, this study develops a model to measure the business value of enterprise micro-blogs. The model consists of metrics on micro-blog usage, micro-blog operational performance, firm capability, and performance. Questionnaires were distributed to firms that use micro-blogs. This study collects 317 valid responses for empirical analysis. The result suggests that the extent of micro-blog usage improves the operational performance of enterprise micro-blogs directly and indirectly by increasing firm capability. The operational performance of enterprise micro-blogs significantly affects firm performance. This study reveals the mechanism of business value generation of enterprise micro-blogs and extends the stream of research that combines IS usage and the RBV theory

Big Data analysis and Finance: a literature review

In this paper, we discuss the exploitation of Big Data in finance, particularly; we discuss financial opportunities to better management and challenges related to the emergence of big data. We review various works putting big data at the service of finance using analytical or predictive techniques. Furthermore, we recall some methods suitable to handle and extract relevant information from big data

Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment

Correlating and Predicting Stock Prices with Twitter Sentiments

This paper presents an empirical study of correlating Twitter sentiments with individual stock price movements. We used an existing text-mining technique, OpinionFinder, to extract Twitter sentiment data from plaintext tweets. Different from prior researches, we explored a novel approach to aggregate Twitter sentiment features and Twitter metadata features associated with the tweets that mention a technology stock to construct a set of features, which was then correlated with the stock price movements of the respective stock prices. We thereby selected a subset of these features, which have positive correlation coefficients with the stock prices, to predict future stock price movements. The results of the prediction, however, are not as successful as expected. Although it is too early to conclude that Twitter sentiments cannot be used to predict an individual stock price, our results do provide one piece of negative evidence for such hypothesis.Master of Science in Information Scienc