Nonparametric determinants of market liquidity
用可解释的机器学习方法，完全非参数地研究欧洲资产管理机构股权证券的每日订单数据，发现市场流动性与交易特征之间存在多种非线性关系。
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.