Machine Learning Approaches for Enhanced Portfolio Optimization: A Comparative Study of Regularization and Cross-Validation Techniques

One of the ways to optimize an investment portfolio is by way of diversification across multiple asset classes including stocks, bonds, mutual funds, etc. Machine Learning is becoming an important tool for portfolio optimization given the dynamics and the non-linearities inherent in financial markets besides providing computational speed and accuracy. In the present study, we evaluate various machine learning models utilizing cross-validation and regularization for portfolio optimization. We begin with the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models and machine learning models like long short-term memory networks (LSTMs) and recurrent neural networks (RNNs). We also utilize the mean-variance and mean-C VaR optimization within the Least Absolute Shrinkage, Selection Operator (LASSO) and ridge regression. Our study has important implications for investors and professional wealth managers including enhanced prediction accuracy, dynamic asset allocation, and portfolio diversification.