Do empirical (high and low frequency) measures of price impact capture informed trading?
Seminar Room 2, Newton Institute Gatehouse
AbstractCo-author: Vyacheslav Fos (University of Illinois at Urbana Champaign)
This talk is based on two papers.
The first paper ("Do prices reveal the presence of informed trading?") tests empirical measures or price impact. Using a comprehensive sample of trades by Schedule 13D filers, who possess valuable private information when they accumulate stocks of targeted companies, this paper studies whether several empirical (high and low frequency) measures of adverse selection reveal the presence of informed trading. The evidence suggests that on days when Schedule 13D filers accumulate shares, both high-frequency and low-frequency measures of stock liquidity and adverse selection indicate higher stock liquidity and lower adverse selection, even though prices are positively a ected. We document three channels that help explain this phenomenon: (a) informed traders select times of higher liquidity when they trade, (b) liquidity increases in response to informed traders' trades, (c) informed traders use limit orders.
The second paper ("Insider Trading, Stochastic Liquidity and Equilibrium Prices") proposes a theoretical model to explain the empirical findings. In that paper, we extend Kyle's (1985) model of insider trading to the case where liquidity provided by noise traders follows a general stochastic process. Even though the level of noise trading volatility is observable, in equilibrium, measured price impact is stochastic. If noise trading volatility is mean-reverting, then the equilibrium price follows a multivariate stochastic volatility `bridge' process. More private information is revealed when volatility is higher. This is because insiders choose to optimally wait to trade more aggressively when noise trading volatility is higher. In equilibrium, market makers anticipate this, and adjust prices accordingly. In time series, insiders trade more aggressively, when measured price impact is lower. Therefore, aggregate execution costs to uninformed traders can be higher when price impact is lower. The model provides some guidance about how to improve existing empirical measures of adverse selection.