Attention, search, and information diffusion : study of stock network dynamics and returns

textThere is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research.Information, Risk, and Operations Management (IROM

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

Proceedings of 2012 Annual Meeting of the Academy of International Business-US North East Chapter: Business Without Borders

Proceedings of the 2012 Academy of International Business-US North East Chapter Fairfield, Connecticut, October 11-13, 2012. Business Without Borders. Host, John F. Welch College of Business, Sacred Heart University. Editor, Jang\u27an Tang. AIB-NE 2012 Conference Co-Chairs, Khawaja Mamun, Ph.D. and Jang\u27an Tang

Sensegiving Word-of-Mouth and Collective Sensemaking About Epistemic Objects

Using theories of sensegiving and sensemaking, I explore how people engage in word-of-mouth about stocks, which are conceptualized as epistemic objects. I draw on netnographic and interview data related to an online investment community, and find that people employ five broad types of word-of-mouth strategies – framing, cuing, connecting, action facilitating, and unsettling – in giving sense about epistemic objects. I also identify the ways in which audiences respond to this form of word-of-mouth, as a part of a collective sensemaking process, and find that their responses pertain to the speaker, the account, as well as their own behavior. Finally, I develop propositions that describe the relationships between sensegiving word-of-mouth strategies and the responses they elicit. This research makes a number of conceptual contributions. It develops the concept of discursive response, an important component of networked word-of-mouth and a manifestation of engagement, and identifies the word-of-mouth strategies associated with higher volumes and types of discursive response. It generates knowledge about word-of-mouth processes. It also provides learning about collective sensemaking and sensegiving. Along with these contributions, this research offers important insights for managers and public policy makers, such as how to elicit online engagement and assist in the collective sensemaking process

Predictability of GARCH-Type Models in Estimating Stock Returns Volatility. Evidence from Kenya

Purpose: The aim of this paper was to  evaluate which of the seven GARCH-type models, namely sGARCH, IGARCH, EGARCH, TGARCH, GJRGARCH, APARCH, and CGARCH, was suitable for predicting the Nairobi Securities Exchange-listed firms' volatility.   Theoritical framework: The Efficient Market Hypothesis is crucial in predicting market value of stocks. Therefore, this study employed the efficient market hypothesis to the the predictability of the stocks returns volatility.   Design/Methodology/Approach: In  this study, we used census approach to collect data from 49 Nairobi Securities Exchange listed firms. The data was collected from 1st January 2011 to 31st December 2020. TO evaluate the volatility, we used the GARCH-type models.   Findings: The study found that the APARCH model as the best suitable for forecasting the volatility of Nairobi Securities Exchange-listed firms.   Research, Practical & Social implications: We propose the the APARCH model as the best suitable model for predicting volatility of stock returns. The findings can be used by investors in making judicious financial decisions. For acedmic purpose, the findings are essential in supporting new knowledge of which model is best fit in predicting the NSE stocks returns volatility.   Original/ Value: The study contributes to the literature on the best suitable model in predicting the volatility of the stocks returns

Thought and Behavior Contagion in Capital Markets

Prevailing models of capital markets capture a limited form of social influence and information transmission, in which the beliefs and behavior of an investor affects others only through market price, information transmission and processing is simple (without thoughts and feelings), and there is no localization in the influence of an investor on others. In reality, individuals often process verbal arguments obtained in conversation or from media presentations, and observe the behavior of others. We review here evidence concerning how these activities cause beliefs and behaviors to spread, affect financial decisions, and affect market prices; and theoretical models of social influence and its effects on capital markets. Social influence is central to how information and investor sentiment are transmitted, so thought and behavior contagion should be incorporated into the theory of capital markets.capital markets; thought contagion; behavioral contagion; herd behavior; information cascades; social learning; investor psychology; accounting regulation; disclosure policy; behavioral finance; market efficiency; popular models; memes