Machine Learning and Liquidity Dynamics in European Stock Markets

Veni Arakelia, Guglielmo Maria Caporale, Mirto M. Gasparinatou and Menelaos Karanasos

No 12829, CESifo Working Paper Series from CESifo

Abstract: This paper examines the forecasting of liquidity dynamics in European stock markets by means of traditional econometric models and machine learning techniques. It uses daily data for the DAX, CAC 40, FTSE 100, FTSE MIB, and IBEX 35 over 2010–2026, liquidity being measured by the logarithmic Amihud illiquidity indicator. The empirical framework compares ARIMA models and a dynamic panel specification with Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) within a common rolling one-step-ahead forecasting framework. The results show that liquidity is highly persistent and that the dynamic panel model achieves the lowest forecast errors, although Diebold–Mariano tests indicate no significant predictive advantage over the leading machine learning models. SHAP analysis reveals that trading activity, lagged liquidity, and market uncertainty are the main determinants of liquidity forecasts. The findings highlight the complementary role of explainable machine learning in empirical finance.

Keywords: liquidity dynamics; european stock markets; forecasting; econometric models; machine learning (ML); artificial intelligence (AI) (search for similar items in EconPapers)
JEL-codes: C22 C33 C53 G17 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_12829

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