Incomplete Information and the Liquidity Premium Puzzle
研究了投资者在面临不可观测的市场状态转换时，从历史价格中学习并承担交易成本，发现不完全信息导致流动性溢价显著高于完全信息情形，主要源于次优风险暴露，有助于解释流动性溢价理论与实证之间的长期差距。
We examine the problem of an investor who trades in a market with unobservable regime shifts. The investor learns from past prices and is subject to transaction costs. Our model generates significantly larger liquidity premia compared with a benchmark model with observable market shifts. The larger premia are driven primarily by suboptimal risk exposure, as turnover is lower under incomplete information. In contrast, the benchmark model produces (mechanically) high turnover and heavy trading costs. We provide empirical support for the amplification effect of incomplete information on the relation between trading costs and future stock returns. We also show empirically that such amplification is not driven by turnover. Overall, our results can help explain the large disconnect between theory and evidence regarding the magnitude of liquidity premia, which has been a longstanding puzzle in the literature. This paper was accepted by Kay Giesecke, finance.