Analyses of users’ digital footprints and online search history can provide valuable behavioral information. Typically, online correlated searches for stock information can provide insights into revealing certain investor preferences and market phenomenon. Prior research primarily focuses on mature stock markets such as those in the U.S. markets. The emerging Chinese stock markets, which are characterized by unique investment features, are relatively unexplored. In this study, we explore the uniqueness of stock co-search networks in the Chinese context and investigate whether we can identify some interesting investment patterns such as return comovement where returns of stocks within the same search clusters tend to move in the same direction. Making use of network analysis, we construct search clusters from co-search data obtained from Sina Finance. We find some interesting behavioral patterns from online correlated searches. The empirical results provide evidence on the existence of within-cluster return comovement as well as a cluster-level factor that moderates return comovement in the context of the Chinese stock markets. This research is of practical relevance for investors in an investment decision-making scenario. It also contributes to Information Systems (IS) research in applying online correlated searches to revealing behavioral outcomes (i.e. return comovement) in an emerging market.