High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification

TL;DR

This paper develops a machine learning framework to predict high-frequency market movements using extensive liquidity metrics, demonstrating improved accuracy with a comprehensive feature set and highlighting the importance of liquidity in market stability.

Contribution

It introduces a novel application of multiple machine learning classifiers to liquidity analysis, emphasizing the value of diverse liquidity measures for prediction accuracy.

Findings

Random Forest outperforms other models in accuracy

Broader liquidity metrics improve prediction performance

Liquidity measures are key predictors of price movements

Abstract

This research presents a comprehensive framework for analyzing liquidity in financial markets, particularly in the context of high-frequency trading. By leveraging advanced machine learning classification techniques, including Logistic Regression, Support Vector Machine, and Random Forest, the study aims to predict minute-level price movements using an extensive set of liquidity metrics derived from the Trade and Quote (TAQ) data. The findings reveal that employing a broad spectrum of liquidity measures yields higher predictive accuracy compared to models utilizing a reduced subset of features. Key liquidity metrics, such as Liquidity Ratio, Flow Ratio, and Turnover, consistently emerged as significant predictors across all models, with the Random Forest algorithm demonstrating superior accuracy. This study not only underscores the critical role of liquidity in market stability and transaction costs but also highlights the complexities involved in short-interval market predictions. The research suggests that a comprehensive set of liquidity measures is essential for accurate prediction, and proposes future work to validate these findings across different stock datasets to assess their generalizability.