Differential Emotions and the Stock Market - The Case of Company-Specific Trading

Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness, satisfaction, fear, anger, depression, and contempt).To investigate the connection between the differential emotions and stock movements we analyse approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months. Subsequently, we conduct a lagged fixed-effects panel regression on the daily closing value differences. The results generally support the assumption of the necessity of considering a more differentiated sentiment. Moreover, comparing positive and negative valence, we find that only the average negative emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength isassociated with a significant decrease in company-specific stock prices

How do Securities Laws Influence Affect, Happiness, & Trust?

This Article advocates that securities regulators promulgate rules based upon taking into consideration their impacts upon investors\u27 and others\u27 affect, happiness, and trust. Examples of these impacts are consumer optimism, financial stress, anxiety over how thoroughly securities regulators deliberate over proposed rules, investor confidence in securities disclosures, market exuberance, social moods, and subjective well-being. These variables affect and are affected by traditional financial variables, such as consumer debt, expenditures, and wealth; corporate investment; initial public offerings; and securities market demand, liquidity, prices, supply, and volume. This Article proposes that securities regulators can and should evaluate rules based upon measures of affect, happiness, and trust in addition to standard observable financial variables. This Article concludes that the organic statutes of the United States Securities and Exchange Commission are indeterminate despite mandating that federal securities laws consider efficiency among other goals. This Article illustrates analysis of affective impacts of these financial regulatory policies: mandatory securities disclosures; gun-jumping rules for publicly registered offerings; financial education or literacy campaigns; statutory or judicial default rules and menus; and continual reassessment and revision of rules. These regulatory policies impact and are impacted by investors\u27 and other people\u27s affect, happiness, and trust. Thus, securities regulators can and should evaluate such affective impacts to design effective legal policy

Heroes and Victims:Fund Manager Sense-making, Self-legitimation and Storytelling

This paper explores how fund managers continue to do their job when on one level they know they cannot all be exceptional. They do this by telling stories, constructing satisfying narratives to explain to themselves, as well as others, why their investments work out and providing equally plausible reasons for when they underperform. Using the story typology of Gabriel (2000. Storytelling in Organizations: Facts, Fictions, and Fantasies. Oxford: Oxford University Press.) – epic, tragic, comic and romantic, we explore two sets of fund manager narratives. First, we analyse the transcripts of interviews with 50 equity fund managers in some of the world's largest investment houses. Second, we examine a similar number of published fund manager reports to their investors. In both cases, we show how storytelling is used by asset managers to make sense of what they do and justify their value to themselves as well their clients and employers. Similar processes are employed in both sets of narratives, one verbal and informal, the other written and formal. Our study serves to highlight how storytelling is an integral part of the work of the professional investor

Good news or bad news, which do you want first? The importance of the sequence and organization of Information for financial decision-making: a neuro-electrical imaging study

Investment decisions are largely based on the information investors received from the target firm. Thaler introduced the hedonic editing framework, in which suggests that integration/segregation of information influence individual's perceived value. Meanwhile, when evaluating the evidence and information in a sequence, order effect and biases have been found to have an impact in various areas. In this research, the influence of the Organization of Information (Integration vs. Segregation) and the Sequence of Information (Negative-Positive order vs. Positive-Negative order) on individual's investment decision-making both at the behavioral level (decision) and neurometrix level (measured by an individual's emotion and Approach Withdraw tendency) was assessed for the three groups of information: a piece of Big Positive Information and a piece of Small Negative Information, a piece of Big Negative Information and a piece of Small Positive Information, and a piece of Small Negative information. The behavioral results, which are an individual's final investment decision, were consistent for all three scenarios. In general, individuals will invest more/retire less when receiving two pieces of information in a Negative-Positive order. However, the neurometric results (Emotional Index, Approach Withdraw Index and results from LORETA) show differences among information groups. An effect of the Sequence of Information and the Organization of Information was found for the different scenarios. The results suggest that in the scenarios that involve large-scale information, the organization of information (Integration vs. Segregation) influences the emotion and Approach Withdraw tendency. The results of this investigation should provide insight for effective communication of information, especially when large-scale information is involved

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

WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference

Terrorist attacks, analyst sentiment, and earnings forecasts

We examine whether exogenous and extremely negative events, such as terrorist attacks and mass shootings, influence the sentiment and forecasts of sell-side equity analysts. We find that analysts who are local to these attacks issue forecasts that are relatively more pessimistic than the consensus forecast. This effect is stronger when the analyst is closer to the event and located in a low-crime region. Impacted analysts are also relatively more pessimistic around the one- and two-year anniversaries of the attacks. Collectively, these findings indicate that exposure to extreme negative events affects the behavior of information intermediaries and the information dissemination process in financial markets

Investors Behavioural Bias and Trading Behaviour - A Systematic Review

A systematic review of literature has been conducted on investor’s trading behaviour, andscientific literature until the year 2020 has been reviewed. It comprises research articles, reviews, and research reports. Appropriate keywords have been used, and each chosen literature has been assessed for itsquality. Based on the quantitative and qualitative research, evidence has been made, and a multi-dimensional framework has been developed, which is the basis for further research.The review examinesthe factors influencing women investors trading in thestock market and the behavioural biases affecting women in due course of investment and trading.Findings from research evidence have been integrated through thematic synthesis. The findings broadly indicate that there is less participation by females trading inthe stock market, and the behavioural biases explored by past researchers have not explored on females alone. Hence, this study can be further used for analyzing thebehavioural biases inherent in women investors trading inthe stock market

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