Abstract
AbstractUsing comprehensive electronic data collected directly from NASDAQ systems, we assess the impact of changes in electronic message traffic on predicting short‐term changes in prices, spreads and quoted depth levels. We document evidence that message traffic at, and nearby, the inside quotes predicts upcoming price and quoted depth changes as much as 75 seconds in advance. Controlling for the time series properties of silent information, past price, volume, electronic communication network volume, time‐of‐day, and firm‐specific fixed effects, we find that message traffic is strongly related to short‐term returns. Our results demonstrate that modern electronic trading systems can be employed by high‐frequency traders to effectively forecast short‐term market conditions.
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