Incomplete Information and the Liquidity Premium Puzzle

Yingshan Chen (), Min Dai (), Luis Goncalves-Pinto (), Jing Xu () and Cheng Yan ()
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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

Management Science, 2021, vol. 67, issue 9, 5703-5729

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.

Keywords: regime shifts; incomplete information; transaction costs; liquidity premia (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:9:p:5703-5729

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