We develop a multi-period model of strategic trading in an asset market where traders are uncertain about market liquidity. In our model, informed traders strategically trade against competitive market makers to exploit their short-lived private information. Unlike market makers, informed traders do not know whether the liquidity ("noise") trades are generated from a distribution with high or low variance. Instead, informed traders have to learn about liquidity from past prices. We find the following. (1) Prices that deviate markedly from the forecast of terminal asset value based on public news tend to lead to revisions of informed traders' beliefs in favor of the low liquidity state. (2) This revision in beliefs results in less aggressive trading on private information by informed traders. (3) In turn, informational efficiency and trading volume are dependent on the path of prices. (4) Moreover, learning about liquidity also has interesting effects on the unconditional properties of optimal strategic trading policies.
Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know whether the liquidity (¶noise¶) trades are generated from a distribution with high or low variance. Instead, they have to learn about liquidity from past prices and trading volume. Extreme price deviations from forecasts of fundamentals based on public news or low trading volume tend to lead to revisions of beliefs in favor of the low liquidity state. This revision in beliefs implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper has a number of normative implications for practitioners concerned with gauging the potential price impact of their trades.
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