Developing a Composite Measure to Represent Information Flows in Networks: Evidence from a Stock Market

Wuyue (Phoebe) Shangguan (), Alvin Chung Man Leung (), Ashish Agarwal (), Prabhudev Konana () and Xi Chen ()
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Wuyue (Phoebe) Shangguan: Department of Management Science, School of Management, Xiamen University, Xiamen, Fujian 361005, China
Alvin Chung Man Leung: Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong
Ashish Agarwal: Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712
Prabhudev Konana: Robert H. Smith School of Business, University of Maryland, University of Maryland, College Park, Maryland 20742-1815
Xi Chen: Department of Data Science and Management Engineering, School of Management, Zhejiang University, Hangzhou 310058, China; Center for Research on Zhejiang Digital Development and Governance, Hangzhou 310058, China

Information Systems Research, 2022, vol. 33, issue 2, 413-428

Abstract: There is increasing interest in information systems research to model information flows from different sources (e.g., social media, news) associated with a network of assets (e.g., stocks, products) and to study the economic impact of such information flows. This paper employs a design science approach and proposes a new composite metric, eigen attention centrality (EAC), as a proxy for information flows associated with a node that considers both attention to a node and coattention with other nodes in a network. We apply the EAC metric in the context of financial market where nodes are individual stocks and edges are based on coattention relationships among stocks. Composite information from different channels is used to measure attention and coattention. To evaluate the effectiveness of the EAC metric on predicting outcomes, we conduct an in-depth performance evaluation of the EAC metric by (1) using multiple linear and nonlinear prediction methods and (2) comparing EAC with a benchmark model without EAC and models with a set of alternative network metrics. Our analysis shows that EAC significantly outperforms other measures in predicting the direction and magnitude of abnormal returns of stocks. Besides, our EAC specification has better predictive performance than alternative specifications, and EAC outperforms direct attention in predicting abnormal returns. Using the EAC metric, we derive a stock portfolio and develop a trading strategy that provides significant and positive excess returns. Lastly, we find that composite information has significantly better predictive performance than separate information sources, and such superior performance owes to information from social media instead of traditional media.

Keywords: Eigen attention centrality; information flow; attention; coattention; network structure; network analysis; return prediction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:33:y:2022:i:2:p:413-428

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