Machine Learning for liquidity classification and its applications to portfolio selection

Abstract

Liquidity refers to the ease of asset conversion into cash, playing a crucial role in investment decisions for achieving optimal returns. This study proposes a novel stock liquidity classification method using machine learning algorithms, trained, and tested on ten years of Brazilian stock market (B3) data. Achieving an accuracy of 99.2%, the classifier, when integrated with the mean-variance portfolio optimization model, reduces portfolio uncertainty by preventing an average of 11.5% of illiquid asset sales.