Introduction

Target-date funds employ fixed glide paths reducing equity exposure as retirement approaches. Reinforcement Learning enables dynamic glide paths optimizing equity exposure based on market conditions and individual circumstances.

Dynamic Glide Path Optimization

RL systems optimize equity allocation considering valuation metrics, interest rates, individual factors, and market regimes.

Results and Implementation

Dynamic glide paths improve retirement outcomes 8-12% on average versus static approaches while reducing sequence-of-returns risk near retirement.

Conclusion

Dynamic glide paths leveraging RL enable lifecycle funds to optimize equity exposure over time, improving retirement outcomes.