We introduce a novel method to identify information networks in stock markets, which explicitly accounts for the impact of public information on investor trading decisions. We show that public information has a clear effect on the empirical investor networks' topology. Most importantly, our...

The starting point of this paper is that neighboring investors may talk to each other sharing information about their transactions in stock markets, leading to similar trading behavior. We find that pairwise trade timing similarities between investor pairs are negatively associated to...

According to information theory, central agents are better informed. Thus to gain higher returns, investors are motivated to seek connections. At the same time, previous studies provide evidence on homophily in social networks: individuals tend to establish relationships with the alike to...

We find that investors' future trading decisions are driven by the patterns of their social neighborhood and the trading activity therein. Moreover, we provide evidence that investors weigh their social connections differently in terms of information transfer. Methodologically, we tackle the...

Investors in stock markets face a huge amount of financial information. For that reason, they must decide how to distribute their trading effort across different securities. We propose a new measure of investor trade allocation between securities, called the trading signature . This measure,...

Recent studies using data on social media and stock markets have mainly focused on predicting stock returns. Instead of predicting stock price movements, we examine the relation between Facebook data and investors' decision making in stock markets with a unique data on investors' transactions on...

Investors in stock markets distribute their limited attention across different securities. Drawing on recent findings on social behavior, we ask whether there are persistent, characteristic patterns in how household investors distribute their attention. Our study builds on a large data set that...

We introduce the formation a network of money flows between assets in stock markets, which captures directed relations between assets in terms of how investors have re-allocated money in the stock exchange. Our approach is based on identifying a directed link, or money flow, that occurs when an...

Many of the real-world data sets can be portrayed as bipartite networks. Such data sets are particularly abundant where human behavior is being recorded. Because typically direct observations of the relationships between different agents are lacking, they need to be inferred by converting...

According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint...