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Incomplete Information and the Liquidity Premium Puzzle

Author

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  • Yingshan Chen

    (School of Mathematics, South China University of Technology, Guangzhou 510640, China)

  • Min Dai

    (Department of Mathematics, Risk Management Institute, and Chong-Qing and Suzhou Research Institutes, National University of Singapore, Singapore 119076)

  • Luis Goncalves-Pinto

    (School of Banking and Finance, University of New South Wales, Sydney NSW 2052, Australia; Department of Finance, Chinese University of Hong Kong, Hong Kong)

  • Jing Xu

    (School of Finance, Renmin University of China, Beijing 100872, China)

  • Cheng Yan

    (Essex Business School, University of Essex, Colchester CO4 3SQ, United Kingdom)

Abstract

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.

Suggested Citation

  • Yingshan Chen & Min Dai & Luis Goncalves-Pinto & Jing Xu & Cheng Yan, 2021. "Incomplete Information and the Liquidity Premium Puzzle," Management Science, INFORMS, vol. 67(9), pages 5703-5729, September.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:9:p:5703-5729
    DOI: 10.1287/mnsc.2020.3726
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    References listed on IDEAS

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