Nonparametric determinants of market liquidity

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TL;DR

This study employs fully nonparametric, explainable machine learning methods to identify nonlinear factors affecting equity market liquidity, finding that liquidity increases with market activity but remains stable within certain volume ranges; key predictors include price volatility, broker efficiency, and trade impact, with bid-ask spreads linked to uncertainty and economic activity.

Abstract
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We examine the factors influencing equity market liquidity through explainable machine learning techniques. Unlike previous studies, our approach is entirely nonparametric. By studying daily placement orders for equity securities managed by a European asset management institution, we uncover multiple nonlinear relationships between market liquidity and placement characteristics. As expected, the results show that liquidity tends to increase in highly active markets. However, we also note that liquidity remains relatively stable within certain trading volume ranges. Price volatility, broker efficiency, and the market impact of the trade are important predictors of liquidity. Price volatility shows a linear relationship with bid-ask spreads, whereas broker efficiency and market impact have nonsymmetric convex effects. Large bid-ask spreads are linked to increased uncertainty and weak economic activity.

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