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

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.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

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

  2. 2.

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

  3. 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. 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. 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. 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. 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. 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. 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.

References

  1. Alexander C (2001) Market models: a guide to financial data analysis, 1st edn. Wiley, Chichester

    Google Scholar 

  2. Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5:31–56

    Article  Google Scholar 

  3. Amihud Y, Mendelson H (1986) Asset pricing and the bid-ask spread. J Financ Econ 17:223–249

    Article  Google Scholar 

  4. Ang A, Bekaert G (2007) Stock return predictability: is it there? Rev Financ Stud 20:651–707

    Article  Google Scholar 

  5. Ascioglu A, Hegde SP, Krishnan GV, McDermott JB (2012) Earnings management and market liquidity. Rev Quant Financ Account 38:257–274

    Article  Google Scholar 

  6. Baker M, Stein JC (2004) Market liquidity as a sentiment indicator. J Financ Mark 7:271–299

    Article  Google Scholar 

  7. Bakshi G, Madan D (2006) A theory of volatility spreads. Manag Sci 52:1945–1956

    Article  Google Scholar 

  8. Baradarannia MR, Peat M (2013) Liquidity and expected returns-evidence from 1926–2008. Int Rev Financ Anal 29:10–23

    Article  Google Scholar 

  9. Bekaert G, Harvey CR, Lundblad C (2007) Liquidity and expected returns: lessons from emerging markets. Rev Financ Stud 20:1783–1831

    Article  Google Scholar 

  10. Bernardo AE, Welch I (2004) Liquidity and financial market runs. Q J Econ 119:135–158

    Article  Google Scholar 

  11. Bollerslev T, Gibson M, Zhou H (2011) Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities. J Econom 160:235–245

    Article  Google Scholar 

  12. Bollerslev T, Marrone J, Xu L, Zhou H (2014) Stock return predictability and variance risk premia: statistical inference and international evidence. J Financ Quant Anal 49:633–661

    Article  Google Scholar 

  13. Bollerslev T, Tauchen G, Zhou H (2009) Expected stock returns and variance risk premia. Rev Financ Stud 22:4463–4492

    Article  Google Scholar 

  14. Brennan MJ, Subrahmanyam A (1996) Market microstructure and asset pricing: on the compensation for illiquidity in stock returns. J Financ Econ 41:441–464

    Article  Google Scholar 

  15. Brunnermeier MK, Pedersen LH (2009) Market liquidity and funding liquidity. Rev Financ Stud 22:2201–2238

    Article  Google Scholar 

  16. Campbell JY, Thompson SB (2008) Predicting excess stock returns out of sample: can anything beat the historical average? Rev Financ Stud 21:1509–1531

    Article  Google Scholar 

  17. Carhart MM (1997) On persistence of mutual fund performance. J Finance 52:57–82

    Article  Google Scholar 

  18. Carr P, Wu L (2009) Variance risk premiums. Rev Financ Stud 22:1311–1341

    Article  Google Scholar 

  19. Chen AS, Cheng LY, Cheng KF (2009) Intrinsic bubbles and granger causality in the S&P 500: evidence from long-term data. J Bank Financ 33:2275–2281

    Article  Google Scholar 

  20. Chen Y, Eaton GW, Paye BS (2018) Micro(structure) before macro? The predictive power of aggregate illiquidity for stock returns and economic activity. J Financ Econ 130:48–73

    Article  Google Scholar 

  21. Chiu YC (2020) Macroeconomic uncertainty, information competition, and liquidity. Financ Res Lett 34:101262

    Article  Google Scholar 

  22. Chong TTL, Tsui SC, Chan WH (2017) Factor pricing in commodity futures and the role of liquidity. Quant Financ 17:1745–1757

    Article  Google Scholar 

  23. Chordia T, Huh S-W, Subrahmanyam A (2007) The cross-section of expected trading activity. Rev Financ Stud 20:709–740

    Article  Google Scholar 

  24. Chordia T, Roll R, Subrahmanyam A (2001) Market liquidity and trading activity. J Finance 56:501–530

    Article  Google Scholar 

  25. Chordia T, Roll R, Subrahmanyam A (2002) Order imbalance, liquidity, and market returns. J Financ Econ 65:111–130

    Article  Google Scholar 

  26. Chung KH, Chuwonganant C (2014) Uncertainty, market structure, and liquidity. J Financ Econ 113:476–499

    Article  Google Scholar 

  27. Clark TE, West KD (2007) Approximately normal tests for equal predictive accuracy in nested models. J Econom 138:291–311

    Article  Google Scholar 

  28. Daniel K, Hirshleifer D (2015) Overconfident investors, predictable returns, and excessive trading. J Econ Perspect 29:61–88

    Article  Google Scholar 

  29. De Prado ML (2018) The 10 reasons most machine learning funds fail. J Portf Manag 44:120–133

    Article  Google Scholar 

  30. Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072

    Article  Google Scholar 

  31. Drechsler I, Yaron A (2011) What’s vol got to do with it. Rev Financ Stud 24:1–45

    Article  Google Scholar 

  32. Durand RB, Lim D, Zumwalt JK (2011) Fear and the fama-french factors. Financ Manag 40:409–426

    Article  Google Scholar 

  33. Easley D, O’Hara M (1987) Price, trade size, and information in securities markets. J Financ Econ 19:69–90

    Article  Google Scholar 

  34. Fama EF, French KR (1992) The cross-section of expected stock returns. J Finance 47:427–465

    Article  Google Scholar 

  35. Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33:3–56

    Article  Google Scholar 

  36. Fama EF, French KR (1995) Size and book-to-market factors in earnings and returns. J Finance 50:131–155

    Article  Google Scholar 

  37. Feunou B, Jahan-Parvar MR, Okou C (2018) Downside variance risk premium. J Financ Econ 16:341–383

    Google Scholar 

  38. Gallant AR, Rossi PE, Tauchen G (1992) Stock prices and volume. Rev Financ Stud 5:199–242

    Article  Google Scholar 

  39. Gervais S, Odean T (2001) Learning to be overconfident. Rev Financ Stud 14:1–27

    Article  Google Scholar 

  40. Glosten LR, Milgrom PR (1985) Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14:71–100

    Article  Google Scholar 

  41. Goyenko RY, Holden CW, Trzcinka CA (2009) Do liquidity measures measure liquidity? J Financ Econ 92:153–181

    Article  Google Scholar 

  42. Griffin JM, Nardari F, Stulz RM (2007) Do investors trade more when stocks have performed well? Evidence from 46 countries. Rev Financ Stud 20:905–951

    Article  Google Scholar 

  43. Grinblatt M, Keloharju M (2009) Sensation seeking, overconfidence, and trading activity. J Finance 64:549–578

    Article  Google Scholar 

  44. Grossman SJ, Miller MH (1988) Liquidity and market structure. J Finance 43:617–633

    Article  Google Scholar 

  45. Guo H, Mortal S, Savickas R, Wood R (2017) Market illiquidity and conditional equity premium. Financ Manag 46:743–766

    Article  Google Scholar 

  46. Hameed A, Kang W, Viswanathan S (2010) Stock market declines and liquidity. J Finance 65:257–293

    Article  Google Scholar 

  47. Hiemstra C, Jones J (1994) Testing for linear and nonlinear granger causality in the stock price-volume relation. J Finance 49:1639–1664

    Google Scholar 

  48. Hollstein F, Simen CW (2020) Variance risk: a bird’s eye view. J Econom 215:517–535

    Article  Google Scholar 

  49. Huang HY, Ho KC (2020) Liquidity, earnings management, and stock expected returns. North Am J Econ Financ 54:101261

    Article  Google Scholar 

  50. Jones CM (2002) A century of stock market liquidity and trading costs. SSRN Electron J https://ssrn.com/abstract=313681

  51. Kalli M, Studies ME (2019) Stock market liquidity and return predictability: A bayesian nonparametric approach. Rev Financ Stud In press

  52. Karady G (1982) The effect of temporal risk aversion on liquidity preference. J Financ Econ 10:467–483

    Article  Google Scholar 

  53. Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econom 74:119–147

    Article  Google Scholar 

  54. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root? J Econom 54:159–178

    Article  Google Scholar 

  55. Lakonishok J, Smidt S (1986) Volume for winners and losers: taxation and other motives for stock trading. J Finance 41:951–974

    Article  Google Scholar 

  56. Lesmond DA, Ogden JP, Trzcinka CA (1999) A new estimate of transaction costs. Rev Financ Stud 12:1113–1141

    Article  Google Scholar 

  57. Lettau M, Ludvigson S (2001) Consumption, aggregate wealth, and expected stock returns. J Finance 56:815–849

    Article  Google Scholar 

  58. Li G, Lu L, Wu B, Zhang Z (2014) Asymmetric information, illiquidity and asset returns: evidence from China. Quant Financ 14:947–957

    Article  Google Scholar 

  59. Liu G, Gregoriou A, Bo Y (2020) How do markets value stock liquidity? Comparative evidence from the UK, the US. Gers China Econ Lett 186:108500

    Article  Google Scholar 

  60. Liu W (2006) A liquidity-augmented capital asset pricing model. J Financ Econ 82:631–671

    Article  Google Scholar 

  61. Marshall BR, Nguyen NH, Visaltanachoti N (2012) Commodity liquidity measurement and transaction costs. Rev Financ Stud 25:599–638

    Article  Google Scholar 

  62. Mazouz K, Daya W, Yin S (2014) Index revisions, systematic liquidity risk and the cost of equity capital. J Int Financ Mark Inst Money 33:283–298

    Article  Google Scholar 

  63. Merton RC (1973) An intertemporal capital asset pricing model. Econometrica 41:867

    Article  Google Scholar 

  64. Neely CJ, Rapach DE, Tu J, Zhou G (2014) Forecasting the equity risk premium: the role of technical indicators. Manage Sci 60:1772–1791

    Article  Google Scholar 

  65. Odean T (1998) Volume, volatility, price, and profit when all traders are above average. J Finance 53:1887–1934

    Article  Google Scholar 

  66. Oded J (2009) Optimal execution of open-market stock repurchase programs. J Financ Mark 12:832–869

    Article  Google Scholar 

  67. Orosel GO (1998) Participation costs, trend chasing, and volatility of stock prices. Rev Financ Stud 11:521–557

    Article  Google Scholar 

  68. Pástor Ľ, Stambaugh RF (2003) Liquidity risk and expected stock returns. J Polit Econ 111:642–685

    Article  Google Scholar 

  69. Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346

    Article  Google Scholar 

  70. Rosenberg JV, Engle RF (2002) Empirical pricing kernels. J Financ Econ 64:341–372

    Article  Google Scholar 

  71. Ruan X, Zhang JE (2018) Equilibrium variance risk premium in a cost-free production economy. J Econ Dyn Control 96:42–60

    Article  Google Scholar 

  72. Saad M, Samet A (2017) Liquidity and the implied cost of equity capital. J Int Financ Mark Inst Money 51:15–38

    Article  Google Scholar 

  73. Segal G, Shaliastovich I, Yaron A (2015) Good and bad uncertainty: macroeconomic and financial market implications. J Financ Econ 117:369–397

    Article  Google Scholar 

  74. Shefrin H, Statman M (1985) The disposition to sell winners too early and ride losers too lLong: theory and evidence. J Finance 40:777–790

    Article  Google Scholar 

  75. Smirlock M, Starks L (1988) An empirical analysis of the stock price-volume relationship. J Bank Financ 12:31–41

    Article  Google Scholar 

  76. Stambaugh RF (1999) Predictive regressions. J Financ Econ 54:375–421

    Article  Google Scholar 

  77. Statman M, Thorley S, Vorkink K (2006) Investor overconfidence and trading volume. Rev Financ Stud 19:1531–1565

    Article  Google Scholar 

  78. Stoll HR (1978) The supply of dealer services in securities markets. J Finance 33:1133–1151

    Article  Google Scholar 

  79. Toda HY, Yamamoto T (1995) Statistical inference in vector autoregressions with possibly integrated processes. J Econom 66:225–250

    Article  Google Scholar 

  80. Vayanos D, Wang J (2012) Liquidity and asset returns under asymmetric information and imperfect competition. Rev Financ Stud 25:1339–1365

    Article  Google Scholar 

  81. Wang Q, Zhang J (2015) Individual investor trading and stock liquidity. Rev Quant Financ Account 45:485–508

    Article  Google Scholar 

  82. Welch I, Goyal A (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud 21:1455–1508

    Article  Google Scholar 

  83. Yildiz S, Van Ness B, Van Ness R (2020) VPIN, liquidity, and return volatility in the U.S. equity markets. Glob Financ J 45:100479

    Article  Google Scholar 

  84. Zapata HO, Rambaldi AN (1997) Monte carlo evidence on cointegration and causation. Oxf Bull Econ Stat 59:285–298

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiaoquan Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Systematic factors
  • Toda-Yamamoto Granger non-causality test
  • Investor risk aversion
  • Liquidity

JEL Classification

  • C32
  • C53
  • G12
  • G13
  • G14