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

SSRN Electronic Journal · 2021

• Computer Science
• Data Mining
• Machine Learning

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.

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

Information Systems Research · 2021 · 19 citations

• Computer Science
• Data Mining
• Computer Science

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 a 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. We evaluate the effectiveness of the EAC metric on predicting abnormal returns of stocks by (1) using multiple prediction methods and (2) comparing EAC with a set of alternative network metrics. Our analysis shows that EAC significantly outperforms alternative models in predicting the direction and magnitude of abnormal returns of stocks. 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.