Description
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.
Recommended Citation
Agarwal, Ashish; Konana, Prabhudev; Kumar, Alok; and Leung, Alvin, "A Study of Search Attention and Stock Returns Cross Predictability" (2015). AMCIS 2015 Proceedings. 14.
https://aisel.aisnet.org/amcis2015/BizAnalytics/GeneralPresentations/14
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.