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

Forthcoming in Information Systems Research

65 Pages Posted: 18 Aug 2021 Last revised: 5 Oct 2021

See all articles by Wuyue (Phoebe) Shangguan

Wuyue (Phoebe) Shangguan

Xiamen University, School of Management

Alvin Leung

City University of Hong Kong (CityU) - Department of Information Systems

Ashish Agarwal

University of Texas at Austin - Red McCombs School of Business

Prabhudev Konana

University of Texas at Austin - Department of Information, Risk and Operations Management

Xi Chen

Zhejiang University - School of Management

Date Written: August 12, 2021

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 co-attention 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 co-attention relationships among stocks. Composite information from different channels is used to measure attention and co-attention. 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 also 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, co-attention, network structure, network analysis, return prediction

JEL Classification: C52,C53

Suggested Citation

Shangguan, Wuyue (Phoebe) and Leung, Alvin and Agarwal, Ashish and Konana, Prabhudev and Chen, Xi, Developing a Composite Measure to Represent Information Flows in Networks: Evidence from a Stock Market (August 12, 2021). Forthcoming in Information Systems Research, Available at SSRN: https://ssrn.com/abstract=3905684

Wuyue (Phoebe) Shangguan (Contact Author)

Xiamen University, School of Management ( email )

422 Siming South Rd
Xiamen, Fujian 361005
China

Alvin Leung

City University of Hong Kong (CityU) - Department of Information Systems ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Ashish Agarwal

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

Prabhudev Konana

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

Xi Chen

Zhejiang University - School of Management ( email )

Hangzhou, Zhejiang Province 310058
China

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