What does investors’ online divergence of opinion tell us about stock returns and trading volume?

We analyse 289,443 online tweets from StockTwits and construct a divergence of opinion (disagreement) indicator for investigating the impact of disagreement on stock returns and trading volume. We find that the impact of disagreement on returns is asymmetric; it is negative (positive) during bull (bear) market periods. We also find that higher online disagreement increases trading volume; this effect is detected irrespective of whether the market is bullish or bearish. Moreover, portfolio strategies that are designed on the basis of our disagreement indicator are shown to generate abnormal profits. Overall, our results confirm the important role of belief dispersion in financial markets

The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOnline stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stock micro-blogging sentiments to forecast future stock price behavioural movements by investigating the various roles played by investor sentiments in determining asset pricing on the stock market. The empirical analysis in this thesis consists of four main parts based on the predictive power and the role of investor sentiment in the stock market. The first part discusses the findings of the text-mining procedure for extracting and predicting sentiments from stock-related micro-blogging data. The purpose is to provide a comparative textual analysis of different machine learning algorithms for the purpose of selecting the most accurate text-mining techniques for predicting sentiment analysis on StockTwits through the provision of two different applications of feature selection, namely filter and wrapper approaches. The second part of the analysis focuses on investigating the predictive correlations between StockTwits features and the stock market indicators. It aims to examine the explanatory power of StockTwits variables in explaining the dynamic nature of different financial market indicators. The third part of the analysis investigates the role played by noise traders in determining asset prices. The aim is to show that stock returns, volatility and trading volumes are affected by investor sentiment; it also seeks to investigate whether changes in sentiment (bullish or bearish) will have different effects on stock market prices. The fourth part offers an in-depth analysis of some tweet-market relationships which represent an open problem in the empirical literature (e.g. sentiment-return relations and volume-disagreement relations). The results suggest that StockTwits sentiments exhibit explanatory power in explaining the dynamics of stock prices in the U.S. market. Taking different approaches by combining text-mining techniques with feature selection methods has proved successful in predicting StockTwits sentiments. The applications of the approach presented in this thesis offer real-time investment ideas that may provide investors and their peers with a decision support mechanism. Investor sentiment plays a critical role in determining asset prices in capital markets. Overall, the findings suggest that investor sentiment among noise traders is a priced factor. The findings confirm the existence of asymmetric spillover effects of bullish and bearish sentiments on the stock market. They also suggest that sentiment is a significant factor in explaining stock price behaviour in the capital market and imply the positive role of the stock market in the formation of investor sentiment in stock markets. Furthermore, the research findings demonstrate that disagreement is not only an important factor in determining trading volumes but it is also considered a very significant factor in influencing asset prices and returns in capital markets. Overall, the findings of the thesis provide empirical evidence that failure to consider the role of investor sentiment in traditional finance theory could lead to an imperfect picture when explaining the behaviour of stock prices in stock market

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

There is a general consensus of the good sensing and novelty character- istics of Twitter as an information media for the complex fi nancial market. This paper investigates the permeability of Twitter sphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to fi nancial-specifi c events and establishes Twitter as a relevant feeder for taking decisions regarding the fi nancial market and event fraudulent activities in that market. However, the provenance of contributions, their diferent levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specifi c financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the fi nancial market. The experimental results con rm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specifi c fi nancial forum, it is permeable to financial events

Investor sentiment and the dispersion of stock returns: evidence based on the social network of investors

This paper extracts an investor sentiment indicator for the 30 DJIA stocks based on the textual classification of 289,024 online tweets posted on the so-called StockTwits, and examines its contemporaneous and predictability effects on the dispersion of stock returns using the quantile regression technique. We find that both contemporaneous and predictability effects of sentiment are heterogeneous throughout the return distribution. Specifically, sentiment is positively contemporaneously associated with stock returns at higher quantiles. However, it is a strong negative predictor of future returns at lower quantiles. Overall, our findings are broadly consistent with most behavioural theories and show that sentiment mainly affects the valuation of assets in extreme market conditions

Quantifying stocktwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithms

This article is available under the terms of the Creative Commons Attribution License (CC BY).Growing evidence is suggesting that postings on online stock forums affect stock prices, and alter investment decisions in capital markets, either because the postings contain new information or they might have predictive power to manipulate stock prices. In this paper, we propose a new intelligent trading support system based on sentiment prediction by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyse and extract semantic terms expressing a particular sentiment (sell, buy or hold) from stock-related micro-blogging messages called “StockTwits”. An attempt has been made to investigate whether the power of the collective sentiments of StockTwits might be predicted and how the changes in these predicted sentiments inform decisions on whether to sell, buy or hold the Dow Jones Industrial Average (DJIA) Index. In this paper, a filter approach of feature selection is first employed to identify the most relevant terms in tweet postings. The decision tree (DT) model is then built to determine the trading decisions of those terms or, more importantly, combinations of terms based on how they interact. Then a trading strategy based on a predetermined investment hypothesis is constructed to evaluate the profitability of the term trading decisions extracted from the DT model. The experiment results based on 122-tweet term trading (TTT) strategies achieve a promising performance and the (TTT) strategies dramatically outperform random investment strategies. Our findings also confirm that StockTwits postings contain valuable information and lead trading activities in capital markets