Information arrivals may drive investors to require immediacy, generating sudden liquidity demand across multiple price levels in limit order books. We document significant intraday changes in stock limit order book characteristics and liquidity beyond the best levels around scheduled and...

This paper proposes a new method for predicting jump arrivals in stock markets with high-frequency limit order book data. We introduce a new model architecture, based on Convolutional Long Short-Term Memory with attention, to apply time series representation learning with memory and to focus 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,...

We provide evidence that recent losses amplify order book illiquidity shocks caused by non-scheduled news. Moreover, the faster markets' reaction to scheduled and non-scheduled news arrivals is in terms of order book illiquidity, the more illiquid the order book becomes; that is, a fast reaction...

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 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...