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The Predictive Power of Stock Micro- Blogging Sentiment in Forecasting Stock Market Behaviour THE PREDICTIVE POWER OF STOCK MICRO- BLOGGING SENTIMENT IN FORECASTING STOCK MARKET BEHAVIOUR This thesis is submitted for the degree of Doctor of Philosophy By Alya Ali AL-Nasseri Brunel Business School Brunel University, London February 2016 ABSTRACT Online 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 markets. i DEDICATION Dedicated to the loving memory of my late grandmother and grandfather (may Allah grant their eternal peace) who always desired and prayed for my success but did not live to see this great accomplishment. Also, to my parents and family for their unconditional love and support which helped me to achieve my aspiration. ii ACKNOWLEDGEMENTS I am immensely indebted to Allah (The Almighty God) for bestowing on me the knowledge of His creation. I pray to Him for forgiveness, guidance, and assistance and to continually support me towards success in my whole life in this world and hereafter. I am most grateful for the wise council of my ‘wonderful’ supervisor, Dr. Sergio de Cesare. I have been fortunate and honoured to know him and to work under his supervision. I would like to express my profound gratitude for all his great help support, encouragement, and continuous guidance on this thesis-writing process. My deepest appreciation also goes to Dr. Allan Tucker (Department of Computer Science) for his invaluable guidance, experience, inspiration, support and encouragement throughout my PhD journey. I am particularly grateful to him for working on a couple of joint papers on Text Mining. I owe him my deepest gratitude not only for his very helpful comments and guidance but also for all his kindness to me in boosting my confidence. I feel very lucky to have worked under his guidance and advice. I would also like to express my deepest appreciation to Dr. Faek Menla Ali (Department of Social Science). I am very grateful to him for his continuous support, guidance and invaluable comments and suggestions on the empirical finance part of this thesis. I am very thankful to him for his great advice and valuable comments in writing up and structuring journal papers. Without the unconditional love and endless support of my parents, I would not have been in a position to complete this PhD research. No words can express my gratitude to them. Thank you, Mum and Dad, for your loving support and endless encouragement. I owe a substantial debt of gratitude and thanks to my lovely sisters, Aisha and Tasnim. Both of you, more than anyone, have always been there for me during the twists and turns and through the ups and downs. I am deeply grateful to you, my gorgeous and amazing sisters, and I can never repay even a little of what you have given me of your exceptional love, constant care and emotional support. My special thanks must also go to my three amazing and wonderful brothers, Hossam, Mohammed and Mansoor, for their invaluable support, care and prayers. All three of you have given me constant support and I truly cannot thank you enough. I love you all. Special thanks and sincere gratitude are due also to my friends and other family members who have been very supportive during this challenging period of my PhD study. Finally, I would like to take the opportunity to express my gratitude to members of the StockTwits website for providing me with the relevant data for this research. Without their valuable input, this study would not have been possible. iii DECLARATION I grant powers of discretion to the Librarian of Brunel University to allow this thesis to be copied in whole or in part without the necessity to contact me for permission. This permission covers only single copies made for study purposes subject to the normal conditions of acknowledgment. iv PUBLICATIONS AND CONFERENCES The following journal and conference papers are outputs based on the research conducted during my PhD study: Al Nasseri, A., Tucker, A. and de Cesare, S., 2015. Quantifying StockTwits semantic terms’ trading behavior in financial markets: An effective application of decision tree algorithms. Expert Systems with Applications, 42(23), pp.9192-9210. Al Nasseri, A., Tucker, A. and de Cesare, S., 2014, January. Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market. In Discovery Science (pp. 13-24). Springer International Publishing. Al Nasseri, A., 2014. The Predictive Value of Stock Micro-blogging Sentiments in Predicting Stock Market Behaviour. Proceeding of the British Academy of Management (BAM) Conference. 09-11 September, Belfast Waterfront, Northern Ireland. Al Nasseri, A., 2014. The Predictive Value of Stock Micro-blogging Sentiments in Predicting Stock Market Behaviour. British Academy of Management (BAM): SIG Workshop, University of East Angelia, Norwich, 12-13 June 2014. Al Nasseri, A., 2016. Dispersion of Stock Returns and Investor Sentiment: StockTwits Evidence. World Finance Conference. 29-31 July, New York, Manhattan. Journal Articles Submitted “Dispersion of Stock Returns and Investor Sentiment: StockTwits Evidence”, submitted to, Journal of Empirical Finance. “Investors’ divergence of opinion and Trading Volume: Evidence from Online Stock Forum”, targeted journal, Journal of Banking and Finance. Best Paper Award “The Predictive Value of Stock Micro-blogging Sentiments in Predicting Stock Market Behaviour”, Paper Presented at Brunel Bussiness School, PhD Doctoral Symposium 2014, Brunel University. (Best Overall Paper Award). v Table of Contents ABSTRACT ................................................................................................................... i DEDICATION ..............................................................................................................ii ACKNOWLEDGEMENTS ...................................................................................... iii DECLARATION ........................................................................................................
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