Is Attention Contagious? Estimating the Spillover Effect of Investor Attention in Digital Networks

This study constructs networks based on visitors’ online co-searches of firms to explore the economic value of visible network linkages on digital platforms. To achieve this, we investigate whether exogenous attention shocks of some firms can diffuse through network linkages and spill over to proximate firms. We design a quasi-experiment by leveraging the attention shocks based on multiple external sources and employ an identification strategy based on difference-in-differences models with propensity score-based matching. We find strong evidence on the existence of attention spillover. Both the abnormal return and risk of neighboring firms will significantly increase after “catching” the contagion. Besides, the spillover effect persists only in a short time window and decays over time and the distance from the sources of attention shocks. Finally, we find heterogeneous spillover effects across firms: firms with small size or negative public sentiment are more susceptible to the contagion

FinTech in Information Systems Research: A 2010-2020 Review of the AIS Senior Scholars’ Basket

Financial technology (FinTech) has emerged as a significant innovative and transformative force where the primary drivers are disruptive information systems technologies. As a result of the amplified role of FinTech, this article presents a review of FinTech research published in the top Information Systems (IS) journals over the 2010-2020 time period to assess the FinTech contributions made during the 10-year period by IS researchers. There is a unique opportunity for FinTech researchers to learn from and extend the work that has already been published in the highly correlated IS field. Our analysis reviewed 74 articles on a variety of FinTech topics published in the “Association for Information Systems Senior Scholars’ Basket of the top eight ranked IS academic journals. Across the selected IS publications, our findings compared research methodologies, topic areas investigated, and research trends. Our findings demonstrate that several methodologies are understudied or absent and a variety of FinTech topic areas require further exploration

Using supply chain databases in academic research: A methodological critique

This article outlines the main methodological implications of using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain for academic purposes. These databases provide secondary data on buyer–supplier relationships that have been publicly disclosed. Despite the growing use of these databases in supply chain management (SCM) research, several potential validity and reliability issues have not been systematically and openly addressed. This article thus expounds on challenges of using these databases that are caused by (1) inconsistency between data, SCM constructs, and research questions (data fit); (2) errors caused by the databases' classifications and assumptions (data accuracy); and (3) limitations due to the inclusion of only publicly disclosed buyer–supplier relationships involving specific focal firms (data representativeness). The analysis is based on a review of previous studies using Bloomberg SPLC, FactSet Supply Chain Relationships, and Mergent Supply Chain, publicly available materials, interviews with information service providers, and the direct experience of the authors. Some solutions draw upon established methodological literature on the use of secondary data. The article concludes by providing summary guidelines and urging SCM researchers toward greater methodological transparency when using these databases

Three Essays in Empirical Asset Pricing

This dissertation consists of three essays in empirical asset pricing concerning how investor attention and social interactions impact information diffusion in financial markets. In the first essay, I investigate how different investor attention facilitates information diffusion through the customer-supplier network. I find that retail attention improves information incorporation into asset prices and plays a stabilizing role in financial markets. In contrast, institutional attention exhibits a diminished role if I control for retail attention. In addition, I show that the attention of a potential group of informed retail investors, local retail investors, plays a price-stabilizing role beyond that of uninformed (non-local) retail investors. My results provide a refinement on the view of the role played by retail investors. While the literature argues that retail investors destabilize prices, my findings suggest that at least a group of informed retail investors can stabilize financial markets. In the second essay, I examine how social connectedness affects fund manager stock holdings during the COVID19 pandemic. I exploit the recent outbreak of COVID-19 as an exogenous shock to people's beliefs on the future economic condition to examine how fund managers from different regions make different decisions on stock holdings. By applying a unique dataset from Facebook that measures social interaction among different regions, I am able to identify managers from COVID-19 hotspot counties and those highly socially connected to the hotspot counties. I am also able to identify fund managers that are skilled using standard methodologies exploiting fund alpha and other performance metrics. The results show that managers located in or socially connected to hotspot counties sold more stock holdings during the outbreak of COVID-19 in the first quarter of 2020. However, such reductions appear panic-driven given subsequent behavior and outcomes and in particular given the contrasting behavior and outcomes for skilled versus unskilled fund managers. The evidence suggests that social interaction can intensify salience bias even for institutional investors if they are unskilled, but skilled managers appear relatively impervious to the deleterious effect of social networking. Finally, in the third essay, I explore the role of institutional and retail attention in the context of the media news releases and find nuanced evidence of the costs and benefits to market price adjustments flowing from investor attention to news. I show that retail attention does indeed destabilize financial markets by inducing price overreactions to positive news, but only if it is from uninformed retail investors. I find that when retail attention destabilizes the market, it is when retail investors appear to struggle digesting complex business information and then only if the news is of a positive sentiment; negative sentiment news and retail investor attention are not associated with market instability, possibly a result of the well-known reluctance of retail investors to short sell. I also find that institutional attention plays a stabilizing role in any context I explore, complex or simple news, positive or negative news sentiment, with or without retail investor attention

Three Essays in Empirical Asset Pricing

This dissertation consists of three essays in empirical asset pricing concerning how investor attention and social interactions impact information diffusion in financial markets. In the first essay, I investigate how different investor attention facilitates information diffusion through the customer-supplier network. I find that retail attention improves information incorporation into asset prices and plays a stabilizing role in financial markets. In contrast, institutional attention exhibits a diminished role if I control for retail attention. In addition, I show that the attention of a potential group of informed retail investors, local retail investors, plays a price-stabilizing role beyond that of uninformed (non-local) retail investors. My results provide a refinement on the view of the role played by retail investors. While the literature argues that retail investors destabilize prices, my findings suggest that at least a group of informed retail investors can stabilize financial markets. In the second essay, I examine how social connectedness affects fund manager stock holdings during the COVID19 pandemic. I exploit the recent outbreak of COVID-19 as an exogenous shock to people's beliefs on the future economic condition to examine how fund managers from different regions make different decisions on stock holdings. By applying a unique dataset from Facebook that measures social interaction among different regions, I am able to identify managers from COVID-19 hotspot counties and those highly socially connected to the hotspot counties. I am also able to identify fund managers that are skilled using standard methodologies exploiting fund alpha and other performance metrics. The results show that managers located in or socially connected to hotspot counties sold more stock holdings during the outbreak of COVID-19 in the first quarter of 2020. However, such reductions appear panic-driven given subsequent behavior and outcomes and in particular given the contrasting behavior and outcomes for skilled versus unskilled fund managers. The evidence suggests that social interaction can intensify salience bias even for institutional investors if they are unskilled, but skilled managers appear relatively impervious to the deleterious effect of social networking. Finally, in the third essay, I explore the role of institutional and retail attention in the context of the media news releases and find nuanced evidence of the costs and benefits to market price adjustments flowing from investor attention to news. I show that retail attention does indeed destabilize financial markets by inducing price overreactions to positive news, but only if it is from uninformed retail investors. I find that when retail attention destabilizes the market, it is when retail investors appear to struggle digesting complex business information and then only if the news is of a positive sentiment; negative sentiment news and retail investor attention are not associated with market instability, possibly a result of the well-known reluctance of retail investors to short sell. I also find that institutional attention plays a stabilizing role in any context I explore, complex or simple news, positive or negative news sentiment, with or without retail investor attention

Stock market correlation and investor attention

This thesis deals with three separate problems in �nance related to covariance. First, I assess the forecasting performance of popular multivariate GARCH, hybrid implied and realised covariance models in terms of statistical and economic criteria. I perform a rigorous analysis across major equity indices using di�erent forecasting horizons, market regimes, loss functions and tests. A Vector Heterogeneous Autoregressive speci�cation is the best among competing models. Less complex models that rely on high-frequency data yield superior forecasts and reduce the portfolio risk. Hybrid estimators that combine optionimplied and high-frequency information also have merit when option-implied volatilities are corrected for the volatility risk-premium. During �nancial turmoil the ranking does not change signi�cantly but forecast accuracy deteriorates. Second, I investigate comovement in investor attention as a determinant of excess stock market comovement proposing a novel proxy, \co-attention". Co-attention is estimated as the correlation in demand for market-wide information across stock markets approximated by the Google Search Volume Index (SVI). My results reveal signi�cant co-attention driven to some extent by correlated news and fundamentals. Most importantly, I �nd that coattention is positively related to excess comovement. This e�ect is more pronounced in developed economies and during recessions. I fail to document signi�cant e�ects of correlated news supply on stock markets, lending support to the idea that information demand governs investing decisions. Co-attention is not only induced through international investors, but domestic investors as well. My results provide evidence of attention-induced �nancial contagion in unrelated economies. However, international investors' co-attention appears to facilitate volatility transmission indirectly across markets. Third, I solve the optimal budget allocation problem across keywords for paid search adiv v vertising accounting for the risk induced by maintaining a portfolio of volatile and correlated keywords. In a mean-variance context, I maximise the growth rates in keyword popularities. Advertising costs and conversion rates are shown to be irrelevant. I demonstrate practical implementation using readily available data from Google Trends database estimating averages, variances and co-variances as growth rates in SVIs. Based on keyword sets for major sectors, I form e�cient frontiers consisting of optimal combinations of keywords. Optimal keyword portfolios o�er statistically higher risk-adjusted performance against portfolios constructed using popular heuristics. A proposed heuristic based on risk-adjusted performance reduces the computational cost and provides competing results