Machine Learning Adaptive Portfolio Asset Allocation
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Strategy in a nutshell
The strategy invests in diversified ETFs across equities, bonds, real estate, and natural resources. It uses an LSTM neural network to predict returns, with inputs including macroeconomic, market, and historical asset data, reduced via PCA. Two LSTM models were tested: LSTM1 (MAE loss, 70 PCA dimensions, one hidden layer) and LSTM2 (MSE loss, 150 PCA dimensions, three hidden layers, early stopping). LSTM2 outperforms and feeds predictions into a Mean-Variance Optimizer to determine asset weights (0.05–0.35, 0 for TIPS), respecting constraints (no short selling, weights sum to 1). The portfolio is rebalanced monthly, extending the training period by one month per prediction.
Economic rationale
Mean-Variance Optimization (MVO) seeks maximum expected return for a given risk. LSTMs, a type of recurrent neural network, use hidden and cell states with input/output gates to retain and filter past information, enabling effective return predictions. LSTM-based strategies have shown strong performance in crises (e.g., European debt crisis, China 2015 crash), though they may underperform in certain events (e.g., Taper Tantrum).