Machine Learning-Enhanced Dynamic Asset Allocation in Target-Date Investment Strategies for Pension Funds

Abstract

Target-date funds constitute the dominant default investment vehicle in defined contribution pension systems, managing approximately $3.4 trillion globally. Traditional glide path designs employ static allocation rules failing to adapt to evolving market regimes. This research develops a machine learning framework integrating temporal feature engineering with ensemble prediction models to construct adaptive asset allocation strategies. Our probabilistic optimization transforms static age-based allocation into a dynamic system responsive to macroeconomic indicators, volatility patterns, and correlation structures. Empirical analysis across 15-year backtesting demonstrates ML-enhanced strategies achieve 1.8% annual excess returns while reducing maximum drawdown by 34% compared to conventional glide paths. The framework incorporates gradient boosting machines for regime classification and LSTM networks for return forecasting, establishing differentiable optimization objectives balancing growth with capital preservation. Implementation protocols address overfitting through walk-forward validation and transaction cost constraints.