Risk management in investment decisions: a machine learning approach

Abstract

Risk management is a critical component of investment decision-making, yet traditional methods often fall short in capturing the complexities of modern financial markets. This study explores the application of machine learning (ML) techniques to enhance risk management practices, leveraging advanced algorithms to predict and mitigate financial risks. Using a comprehensive dataset that includes historical stock prices, macroeconomic indicators, and alternative data sources such as social media sentiment, we evaluate the performance of various ML models, including Long Short-Term Memory (LSTM) networks, XGBoost, Random Forest, and Support Vector Machines (SVMs). The results demonstrate that LSTM outperforms other models, achieving an accuracy of 93% and the lowest root mean squared error (RMSE) of 0.18. Statistical analysis identifies stock price volatility and interest rates as the most influential variables, while SHapley Additive exPlanations (SHAP) provide interpretability, highlighting the key drivers of risk predictions. Comparative analysis reveals that ML models significantly outperform traditional methods like Value-at-Risk (VaR) and Monte Carlo simulations, underscoring their potential to revolutionize risk management. The integration of alternative data sources further enhances predictive accuracy, offering actionable insights for investors. Despite challenges such as data quality and model interpretability, this study demonstrates the transformative potential of ML in financial risk management. By combining advanced algorithms with robust statistical techniques, this research provides a framework for more accurate, transparent, and actionable risk assessment, paving the way for improved investment strategies in an increasingly data-driven market environment.