Adaptive Portfolio Asset Allocation Optimization with Deep Learning

Portfolio management is a well-known multi-factor optimization problem facing investment advisors. The system described in this work can assist in automating portfolio management, and improving risk-adjusted returns. The asset allocation action recommendations were personalized to the portfolio under consideration, and were examined empirically in this work in comparison to standard portfolio management techniques. This work presents a Long Short-Term Memory approach to adaptive asset allocation, building upon prior work on training neural networks to model causality. The neural network model discussed in this work ingests historical price data and ingests macroeconomic data and market indicators using Principal Components Analysis. The model then estimates the expected return, volatility, and correlation for the selected assets. These neural network outputs were then turned into action recommendations using a MeanVariance Optimization framework augmented to use a forwardlooking rolling window technique. Testing was performed on a dataset with a 7.66 year duration. The observed mean annualized return for classical passive portfolio management approaches were 4.67%, 3.49%, and 4.57%, with mean Sharpe ratios of 0.46, 0.20, and 0.54. 10 simulations using the new Long ShortTerm Memory model from this work provided a mean annualized return of 10.07%, with a Sharpe ratio of 0.98. This work provides the conclusion that a Long Short-Term Memory model can generate better risk-adjusted returns than conventional strategic passive portfolio management. Keywords–Recommender systems; Deep learning; Portfolio management.