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Does liquidity drive stock market returns? The role of investor risk aversion

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Abstract

In this paper, we explore the relations between liquidity, stock returns, and investor risk aversion as captured by the variance risk premium (VRP). This is motivated by theoretical and empirical evidence in the literature which suggests that investor risk aversion negatively correlates with asset liquidity, and ample empirical evidence documenting liquidity risk premium. We use monthly US data from January 1999 to December 2018 and show that innovations in the VRP Granger-cause stock returns, which in turn drive liquidity. Our findings are consistent with predictions of prior theories and highlight the predictability of the VRP. They also contribute to the on-going debate on the causal relation between stock returns and liquidity. Finally, we explore the channels through which the VRP impacts liquidity and find that the VRP influences market and momentum factors, and that movements in these factors lead to changes in liquidity.

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Notes

  1. Please see https://sites.google.com/site/haozhouspersonalhomepage/ for Hao Zhou’s website.

  2. For Kenneth French’s website, please see http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/.

  3. Please see Lettau and Ludvigson (2001) for details of computing the deviations from the common trend in consumption, asset wealth, and labor income.

  4. For the full sample period and all sub-periods, the results of unit root tests show that all variables at level are stationary, i.e. I(0), except for the liquidity measures (ILLIQNYSE and ILLIQSP500), which are integrated at order one, i.e. I(1). We used the augmented Dickey and Fuller (1981) ADF and Phillips and Perron (1988) PP models to test the null of a unit root against the alternative of stationarity, whereas the Kwiatkowski et al. (1992) KPSS tests the null of stationarity against the alternative of a unit root. Detailed results from the unit root tests are not reported here to conserve space but are available upon request from the authors.

  5. Asymmetric VAR means that the AVAR system has the same explanatory variables in each equation, but the explanatory variables can have different number of lags. Hence, it is more flexible in modeling dynamic systems.

  6. The maximal eigenvalues of the coefficient matrix of all the VAR models are smaller than 1, which suggests that all the VAR models are stable. Moreover, our VAR models show no serial correlation in the residuals.

  7. The number of lags is selected by a general-to-specific approach to satisfy the assumption of no serial correlation and the stationary condition of VAR models.

  8. We have also conducted the impulse response functions with Cholesky decomposition and structural vector autoregression (SVAR) and obtained consistent results. These results are available upon request from the authors.

  9. Stambaugh (1999) shows that coefficients in predictive regressions such as those in Eq. (8) suffer from finite sample bias and the normal t-test could be misleading when the predictors are highly persistent.

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Authors and Affiliations

  1. Southampton Business School, University of Southampton, Southampton, SO17 1BJ, UK

    Qingjing Zhang & Taufiq Choudhry

  2. Birmingham Business School, University of Birmingham, Birmingham, B15 2TY, UK

    Jing-Ming Kuo

  3. University of Nottingham Business School China, University of Nottingham, Ningbo, 315100, China

    Xiaoquan Liu

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  1. Qingjing Zhang
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  2. Taufiq Choudhry
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Correspondence to Xiaoquan Liu.

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Zhang, Q., Choudhry, T., Kuo, JM. et al. Does liquidity drive stock market returns? The role of investor risk aversion. Rev Quant Finan Acc 57, 929–958 (2021). https://doi.org/10.1007/s11156-021-00966-5

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