A study of search attention and stock returns cross predictability

Alvin Chung Man Leung, Prabhudev Konana, Ashish Agarwal, Alok Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 and weekly co-search network of supply-chain partners from Bloomberg and Yahoo! Finance, respectively, 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.

Original languageEnglish (US)
Title of host publication2015 Americas Conference on Information Systems, AMCIS 2015
PublisherAmericas Conference on Information Systems
ISBN (Print)9780996683104
StatePublished - 2015
Event21st Americas Conference on Information Systems, AMCIS 2015 - Fajardo, Puerto Rico
Duration: Aug 13 2015Aug 15 2015

Other

Other21st Americas Conference on Information Systems, AMCIS 2015
CountryPuerto Rico
CityFajardo
Period8/13/158/15/15

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Keywords

  • Attention
  • Prediction
  • Search
  • Stock
  • Supply-chain

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Cite this

Leung, A. C. M., Konana, P., Agarwal, A., & Kumar, A. (2015). A study of search attention and stock returns cross predictability. In 2015 Americas Conference on Information Systems, AMCIS 2015 Americas Conference on Information Systems.