Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading

Akash Deep (), Abootaleb Shirvani, Chris Monico, Svetlozar Rachev and Frank Fabozzi
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Akash Deep: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Abootaleb Shirvani: Department of Mathematical Sciences, Kean University, Union, NJ 07083, USA
Chris Monico: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Svetlozar Rachev: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Frank Fabozzi: Carey Business School, Johns Hopkins University, Baltimore, MD 21218, USA

JRFM, 2025, vol. 18, issue 3, 1-24

Abstract: Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with Random Forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with R 2 values deteriorating from 0.749–0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14–15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from −2.4% to −3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model.

Keywords: high-frequency data; technical indicators; machine learning; stock price prediction; risk-adjusted performance; Random Forest regression (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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