Predictive distributions and the market return: The role of market illiquidity

Michael Ellington and Maria Kalli

European Journal of Operational Research, 2025, vol. 323, issue 1, 309-322

Abstract: This paper evaluates the role of volatility-free stock market illiquidity proxies in forecasting monthly stock market returns. We adopt a probabilistic approach to multivariate time-series modelling using Bayesian nonparametric vector autoregressions. These models flexibly capture complex joint dynamics among financial variables through data-driven regime switching. Out-of-sample forecasts maintain accuracy as the horizon increases. Adding illiquidity generates statistical improvements in out-of-sample predictive accuracy. We highlight the operational importance of market illiquidity after selecting the most appropriate forecasting model that delivers profitable strategies that outperform a range of multivariate models; as well as the historical mean.

Keywords: Stock market illiquidity; Return predictability; Bayesian methods; Bayesian non-parametrics; Vector autoregression (search for similar items in EconPapers)
JEL-codes: C14 C53 G10 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:323:y:2025:i:1:p:309-322

DOI: 10.1016/j.ejor.2025.01.006

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