Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

Does investor sentiment create value for asset pricing? An empirical investigation of the <scp>KOSPI</scp>‐listed firms

This paper proposes the development of an improved investor sentiment index (ISI) to apply on the Korea Composite Stock Price Index (KOSPI) and assess the vitality of sentiment-based factor for explaining critical equity market anomalies in asset pricing in Korea. We follow the methodology of Huang et al. (2015), the align sentiment index, and employ the partial least squares method to overcome the drawbacks of the pioneering BM index of Baker and Wurgler (2006, 2007). Based on the daily trading and price data for individual companies from 2006 to 2021, we construct a novel ISI, which has robust predicting ability for the aggregate stock market return, in comparison to other popular measures of sentiment in the contemporary finance literature. Furthermore, the sentiment-based factor in this paper captures the small firm effect that the asset pricing modelling, containing the more topical Fama–French five factor modelling (5F-FF), has struggled to illuminate completely. Given that our results have shown Korean stock market as fairly well-organised in terms of the availability of the market intelligence, we speculate our results to have important managerial implications for financial regulators in Korea and countries holding similar economic features

The effects of twitter sentiment on renewable energy stock's returns : a Portuguese study about EDP renováveis stocks

Investors’ rationality in the decision-making process has been topic of discussion in the last decades due to conflicts between schools of thought. Several anomalies in the Efficient Market Hypothesis (EMH) led to a new line of thought in the matter of rationality called behavior finance. Sentiment analysis is one branch of this new school of thought who studies investors’ emotions influence on economic variables. There is no consensus between academics if these emotions can make the investment decision biased or not. The aim of this paper is to observe if the prevailing sentiment in tweets can predict the stock returns for a renewable energy company of the Portuguese market. This study looks at the second biggest company by capitalizations of the Portuguese market, EDP Renováveis (EDPR), in the period from the June 1st 2021, to June 1st 2022, and finds no significant evidence of a relationship between Twitter mood and EDP Renováveis stock returns. The reasons for this result might be explained by EDPR belonging to a very small and concentrated market, corroborating the existing theory, as well as the stakeholder composition of the company only having a very small percentage of individual investors, being this kind of investors the most influenced by biases and heuristics present in the tweets. These findings have implications for the development of the sentiment analysis theory, giving more details of the influence of sentiment in smaller and concentrated market, in the renewable energy branch, and in the period of the beginning of the war between Ukraine and Russia and the worldwide economic recovery from the Covid-19 pandemic.A racionalidade dos investidores no processo de decisão de investimento tem sido tópico de discussão nas últimas décadas devido ao conflito entre duas linhas de pensamento diferentes. Várias anomalias que não iam de encontro com a hipótese do mercado eficiente deram origem a uma nova escola de pensamento em relação à racionalidade dos investidores chamada de finanças comportamentais. Análise de sentimentos é um dos ramos desta nova linha de pensamento que estuda a influência das emoções dos investidores em diferentes variáveis económicas. Não existe consenso entre académicos se estas emoções conseguem enviesar as decisões de investimento ou não. O objetivo desta tese é observar se o sentimento presente em tweets consegue fazer prever os retornos das ações de uma empresa de energias renováveis do mercado português. Este estudo analisa a segunda maior empresa portuguesa por capitalizações, a EDP Renováveis (EDPR), no período temporal entre o dia 1 de junho de 2021 e o dia 1 de julho de 2022, e não encontrou evidência com significância de uma relação entre o estado de espírito do Twitter e os retornos das ações da EDP Renováveis. As razões que justificam estes resultados podem ser o facto da EDPR pertencer a um mercado muito pequeno e concentrado como o português, indo de encontro com a evidência empírica, assim como a composição dos proprietários das ações da empresa ter uma percentagem muito reduzida de investidores individuais, que são o tipo de investidor mais facilmente influenciado por heurísticas presentes nos tweets. Este resultado tem implicações para o desenvolvimento da teoria de análise do sentimento, dando mais detalhes da influência deste em mercados mais pequenos e concentrados, no ramo das energias Renováveis, no período de tempo do início da guerra entre a Ucrânia e a Rússia e a recuperação financeira mundial pós-Covid-19

Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Commentary - Much ado about something else. Donald Trump, the US stock market, and the public interest ethics of social media communication

Trump’s use of social media during both his presidential campaign and term questions the principle that institutional responsibility in the digital realm implies treating the infosphere as a commons. We discuss the implications for the functioning of the stock market and the emerging public interest ethical issues related to the breakdown of this principle

The Value of Social Media for Predicting Stock Returns - Preconditions, Instruments and Performance Analysis

The cumulative dissertation of Michael Nofer examines whether Social Media platforms can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which consist largely of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to extract opinions on certain stocks. Taking Social Media platforms as examples, the dissertation examines the forecasting quality of user generated content on the Internet

Investor sentiment as a factor in an APT model: an international perspective using the FEARS index

A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Master of Commerce (M.Com) in Finance, Johannesburg June 2017Traditional finance theory surrounding the risk-return relationship is underpinned by the CAPM which posits that a single risk factor, specifically market risk, is priced into asset returns. Even though it is a popular asset pricing model, the CAPM has been widely criticised due to its unrealistic assumptions and the APT was developed to address the CAPM’s weaknesses. The APT framework allows for a multitude of risk factors to be priced into asset returns; implying that it can be used to model returns using either macroeconomic or microeconomic factors. As such, the APT allows for non-traditional factors, such as investor sentiment, to be included. A macroeconomic APT framework was developed for nine countries using the variables outlined by Chen, Roll, and Ross (1986) and investor sentiment was measured by the FEARS index (Da, Engelberg, & Gao, 2015). Regression testing was used to determine whether FEARS is a statistically significant explanatory variable in the APT model for each country. The results show that investor sentiment is a statistically significant explanatory variable for market returns in five out of the nine countries examined. These results add to the existing APT literature as they show that investor sentiment has a significant explanatory role in explaining asset prices and their associated returns. The international nature of this study allows it to be extended by considering the role that volatility spill-over or the contagion effect would have on each model.XL201

Investor Sentiment and Attention in Capital Markets - A (Social) Media Perspective

This dissertation examines the impact of social and traditional media on capital markets. The empirical tests focus on investor sentiment which, for example, can be captured by postings on social media platforms, innovative news databases and the textual analysis of traditional media press. The research direction of this dissertation implicitly questions the assumptions stated by the traditional finance theory. Our new empirical findings and their explanations are, hence, closely linked with the behavioral finance theory. The Efficient Market Hypothesis constitutes one of the fundamental pillars of the traditional finance theory. In this concept, the availability of information is the basic requirement for the functionality of efficient capital markets. New information is quickly and correctly incorporated into an asset’s price. The new price of an asset, therefore, immediately reflects the updated fundamental value (Fama, 1969; 1970). However, various studies have recently shown that stock market movements are not always associated with rational information about an asset’s value. The observation of over- and underreaction of asset prices to news signals or distinctive return patterns gave reason for the gaining importance of the behavioral finance theory since the 1990’s. The changing availability and the easier access to information for institutional and individual investors play an important role in this recent development. For example, Figure 1 1 (p. 3) depicts the circulation of US newspapers between 1970 and 2017. The number of households covered by traditional media press decreased from more than 60 million to around 30 million households in 2017. The establishment of the internet, on the other hand, parallelly accelerated the digital development in the media landscape. Figure 1 3 (p. 5) describes the global development of social media users since 2010. The number of social media users is expected to increase from 1 billion users in 2010 to around 3 billion users in 2021. This development not only affects the society but also a specific focus group of this dissertation: the financial investors. The way investors gather, process, and disseminate information also experienced a significant change in recent decades (Puppis et al., 2017). In this connection, the development of investor attention and sentiment for individual assets is sustainably impacted by the digitalization of media channels. Consequently, we derive four fundamental research questions, which accompany the empirical analyses of this dissertation: 1. What role does investor sentiment play in financial markets? Do investors solely follow the market, or do beliefs of investors predict future returns or other market variables? 2. How does (social) media relate to financial markets in the general daily context and specifically around news events, such as earnings or M&A announcements? 3. What kind of firms are more sensitive to investor sentiment than others? 4. Does arbitrage stabilize financial markets against noise traders? The following structure of this dissertation aims to answer these questions in the best possible way: The first chapter introduces the reader to the relevance of the topic and the leading research questions of the dissertation. The second chapter lays the theoretical foundation and describes the fundamental concepts of the traditional and also the behavioral finance theory, which aims to comprehensively explain selected market anomalies. Also, we summarize selected psychological concepts that help to explain irrational actions of investors, which potentially cause market volatility and asset prices to deviate from their fundamental value. Literature reviews on investor sentiment in close relationship with traditional and social media complete the second chapter. The third chapter encompasses the first empirical work of this dissertation and primarily explores the impact of social media on capital markets. The empirical analysis falls back to more than 4.5 million posts on the leading Australian financial internet message board HotCopper between January 2008 and May 2016. The findings suggest that social media activity is price relevant for capital markets. Positive investor sentiment, for example, is in this connection contemporaneously and significantly correlated with a stock’s abnormal return. However, the effect diminishes after one month. Arbitrage of presumably informed investors only partially countervail this effect. Postings by individual investors on social media, hence, cause capital markets to overreact to potentially non-relevant information in the short-term. However, negative investor sentiment expressed in internet message boards provides a differentiated picture. Negative investor sentiment is significantly related with the next month’s abnormal returns. Also, an increasing rate of agreement on negative investor sentiment before earnings announcements forecasts negative earnings surprises. Both findings support the information hypothesis that negative internet message board postings contain value-relevant information. The question whether social media activity induces market volatility remains ambiguous. The Granger-tests and the reactions of the impulse-response functions show a bilateral relationship between return volatility and the number of internet message board postings. However, we find in this context that individual investors react more sensitive to market volatility on social media than the other way around. In summary, the results of the first empirical work provide evidence for the economic significance of investor sentiment measured on social media and its asymmetric role in capital markets. We extend the empirical analysis in the fourth chapter of this dissertation and investigate the impact of traditional and social media on target price run-ups before bid announcements. The literature previously documented an increase in the target stock price two months prior to the official bid announcement (e.g., Keown and Pinkerton, 1981). This phenomenon is also referred to as the target run-up. One group of researchers find explanations within the insider hypothesis (leakage of insider information prior to the bid announcement). Another group argues based on the market expectation hypothesis (the market anticipates publicly available information to predict upcoming mergers). Our second empirical work considers 2,765 bid announcements in Australia between January 2008 and August 2015. We use more than 15 thousand news articles, more than 80 thousand posts on the internet message board HotCopper, analyst recommendations, and Google search queries to analyze their relationship with target run-ups before official bid announcements. Thus, we specifically examine the varying impact of investor attention of different investor groups (institutional and individual investors) on target run-ups. The results let us conclude that target run-ups of smaller, unprofitable, and growth firms are significantly related with social media coverage on HotCopper. On the contrary, similar firms that lack media coverage do not experience a significant target run-up prior to a bid announcement. Target run-ups of larger capitalization stocks are, on the other hand, more sensitive to analyst recommendations. The results are consistent with the anecdotal evidence that smaller firms are usually less covered by analysts. Social media closes the information gap for small firms in this perspective. Google search inquiries for target firms are not found to be significantly related to target run-ups. The overall findings of the second empirical work support the market expectation hypothesis. In this regard, social media contributes to the increase of market efficiency and partially closes informational blind spots for smaller firms which might exist due to inefficient allocations of resources or costly information sourcing for smaller firms. The fifth chapter comprises the last empirical work of this dissertation and explores the relationship between media press sentiment and capital markets. We specifically examine the im-pact of aggregated news sentiment indices on the cross-section of returns in the asset pricing context. The literature around asset pricing especially focuses on the determination of risk premia that help to explain stock returns. A central question of our third empirical work is, therefore, whether stock returns are associated with their underlying risk or whether these returns are just a result of irrational market movements in the spirit of the behavioral finance theory. We calculate monthly aggregated news sentiment indices based on more than 120 million unique classified news articles from the Ravenpack News Analytics database between 2000 and 2017. Thus, we construct monthly zero-investment portfolios that go long on (sell) stocks which exhibit on average positive (negative) news sentiment in the previous month. The portfolio yields an annual return of 7.5% even if we control for widely-accepted risk fac-tors, such as market, size, momentum, liquidity, profitability, and investments. The results are mainly driven by positive news sentiment. Hence, we refer this premium to the “premium on optimism”. One possible explanation could be the persistent positive news coverage in the respective time period. The probability of the publication of good news is in particularly high-er if a firm experienced positive news in the prior months. The total results of our third empirical work support the view that news sentiment reflects a risk factor. The overall results of this dissertation have several implications for firms, investors, regulators and researchers in the field of behavioral finance. Firms must learn today to early anticipate crowd movements on (social) media and to deal with putatively fake news. The investor relations department of a firm must engage in this topic more sophistically content-wise and in the communicative interaction with its stakeholders. Selective communication strategies for specific firm events are required to early prevent a potentially negative public perception of the firm. Fake news and volatile markets are also gaining in importance for regulators. The identification of manipulative activities or the stabilization of financial markets in the presence of ambiguous information is of special interest for regulators. This task is even more relevant in the time of increased digitalization of media channels and the networks behind them. The more important is, hence, a better understanding of the stakeholders in financial markets and their actions for the functionality of efficient markets. Finally, the results of this dissertation create new connection points for future research. The asymmetric role of investor sentiment and its underlying mechanism are still controversial and elusive. Current studies especially fail to shed light on the long-term impact of investor sentiment on capital markets. This dissertation, hence, provides a substantiated baseline for future empirical work. Also, this work could not fully answer the question in which situation investors specifically use different media channels for information sourcing and dissemination. An intraday-based analysis on various media channels could provide new answers to this question. In summary, this dissertation shows that investor sentiment is an integral part of today’s financial markets and its important role cannot be anymore neglected by advocates of the traditional finance theory

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