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

True or False Prosperity? The Effect of Token Incentives in Decentralized Autonomous Organizations

Decentralized autonomous organizations (DAO) received many discussions and attempts recently with the rapid development of blockchain. Token incentive is one of its most important features and owns multiple attributes of equity, property, and currency. To explore its unknown effect, we utilize a quasi-experiment setting in the NFT marketplaces. We find that the token incentives with DAO implementation in Rarible can significantly motivate users’ participation compared with SuperRare at the platform level. At the seller level, by the comparison of cross-platform users and only-OpenSea users, we find it significantly changes users’ trading behavior which reflects in the increment in transactions number and average prices. However, through the equilibrium analysis based on the supply and demand model, the growth rate of the average prices is far beyond the magnitude it should be at the equilibrium state. Therefore, we argue that buyers’ purchase decision is driven by the high expectations of token value

LU Factorization of Sparse, Unsymmetric Jacobian Matrices on Multicomputers: Experience, Strategies, Performance

Efficient sparse linear algebra cannot be achieved as a straightforward extension of the dense case, even for concurrent implementations. This paper details a new, general-purpose unsymmetric sparse LU factorization code built on the philosophy of Harwell’s MA28, with variations. We apply this code in the framework of Jacobian-matrix factorizations, arising from Newton iterations in the solution of nonlinear systems of equations. Serious attention has been paid to the data-structure requirements, complexity issues and communication features of the algorithm. Key results include reduced communication pivoting for both the “analyze” A-mode and repeated B-mode factorizations, and effective general-purpose data distributions useful incrementally to trade-off process-column load balance in factorization against triangular solve performance. Future planned efforts are cited in conclusion

A Portable Multicomputer Communication Library atop the Reactive Kernel

Sophisticated multicomputer applications require efficient, flexible, convenient underlying communication primitives. In the work described here, Zipcode, a new, portable communication library, has been designed, developed, articulated and evaluated. The primary goals were: high efficiency compared to lowest-level primitives, user-definable message receipt selectivity, as well as abstraction of collections of processes and message selectivity to allow multiple, independently conceived libraries to work together without conflict. Zipcode works atop the Caltech Reactive Kernel, a portable, minimalistic multicomputer node operating system. Presently, the Reactive Kernel is implemented for Intel iPSC/1, iPSC/2, and Symult s2010 multicomputers and emulated on shared-memory computers as well as networks of Sun workstations. Consequently, Zipcode addresses an equally wide audience, and can plausibly be run in other environments

Indirect Financial Loss of Phishing to Global Market

This research studies the indirect financial impact of phishing announcements on firm value. Using about 3,000 phishing announcements, we showed that phishing has a significantly negative impact on firms regardless of their size. We also discovered that place of incorporation, type of ownership, industry, and time are significant factors exacerbating the impact. Our research findings may give some insights to industrial practitioners about attitude of investors towards phishing. Compared to other similar event studies, our research has also made several significant breakthroughs. Firstly, we used the largest data set ever in prior event studies. Secondly, our research is the first to analyze global phenomena concerning phishing. Thirdly, we enhanced the robustness of a regression model by introducing the criterion of selection of best fit market index based on R square. We believe that our research can add value to the literature in the subjects of phishing research and event studies

Online Retailer vs. Click and Mortar Retailer: Who Performs Better?

Retail business is characterized by different business models: pure-play model and dual channel approach, which uses both physical and online channels to reach customers. There is conflicting evidence regarding the relative value of these business models to the consumers. We take a market valuation approach to evaluate the relative merits of both business models. We consider a panel of publicly traded US retailers and evaluate how their sales performance impacts their Tobin’s q. We find that the dual channel retailers receive a market premium for their sales revenue as compared to the pure-play retailers. This higher valuation can be associated with higher customer satisfaction with dual channel firms leading to a higher intangible value as compared to the pure-play firms. Our results have important implications for retailers as we demonstrate the value of different channels. Our work also contributes to the existing literature on online consumer retailing and multichannel research

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

Gamified System Effectiveness on Social Trading Platforms

To motivate user engagement and generate desired engagement outcomes, some social trading platforms have introduced gamified systems with hierarchical badges and financial incentives. However, previous studies have not examined the effectiveness of such an application. Based on data collected from a popular social trading platform, eToro, we empirically examine the effectiveness of the gamified system and its mechanism. Results indicate that the gamified system under social trading is effective in inducing user-to-user and user-to-system interactions, thus leading to some desired engagement outcomes on social media. However, the gamified system does not contribute to the engagement outcomes on financial investment. Our paper contributes to the literature on both gamification and social trading, highlighting gamification’s important practical implications for platform managers, cautioning against the possible ineffectiveness of the dual-outcome gamified system and shedding light on the design of gamified systems

Online Search: Identifying New Investment Habitats

Researchers often refer to investment habitats/categories to explain the patterns of co-movement in asset returns that cannot be fully clarified by fundamentals. Many factors determine these habitats including investor preferences to size, industry, price-levels and risk-levels. This paper investigates a unique method to explore investment habitat based on the search behavior of investors on the Internet without actually using proprietary trading data. Using Yahoo! Finance data on investors’ frequently co-viewing stocks of Russell 3000 stocks between September 15, 2011 and February 24, 2012, we evaluate the return co-movement within investment search habitats/clusters. We find that stocks within a search cluster show strong co-movement with other stocks in the same cluster that is not fully explained by other traditional habitat characteristics. The behavior persists even when a stock moves from one cluster to another. The study provides direct empirical evidence that investors prefer to search among stocks that have similar co-movement

A Study of Search Attention and Stock Returns Cross Predictability

This study investigates a novel application of correlated online searches in predicting stock performance across supply chain partners. If two firms are economically dependent through supply-chain relationship and if information related to both firms diffuses in the market slowly (rapidly), then our ability to predict stock returns increases (vanishes). Using supply-chain data provided by Bloomberg and weekly co-search network of supply-chain partners from Yahoo! Finance, we find that when investors of a focal stock pay less attention to its supply-chain partners, we can use lagged partner returns to predict the future return of the focal stock. When investors’ co-attention to focal and partner stocks is high, the predictability is low. We contribute to the growing literature on aggregate search and economics of networks by demonstrating the inferential power and economic implications of search networks