6 Financial planning using reinforcement learning

 

This chapter covers

  • Solving a goals-based investing problem using dynamic programming
  • Solving the same goals-based problem using AI
  • Using utility functions in financial planning
  • Applying reinforcement learning to optimize spending using utility functions
  • Extending the model to include longevity risk

When we looked at asset allocation earlier, we were essentially performing a single-period optimization. However, most financial planning decisions involve making decisions over multiple periods. And the decisions made today—not only how to allocate assets but also how much to spend, whether to claim Social Security, when to retire, what accounts to withdraw from, etc.—affect decisions in the future. These multiperiod, dynamic models, which economists sometimes call lifecycle models, are much more complicated to optimize.

6.1 A goals-based investing example

6.2 An introduction to reinforcement learning

6.2.1 Solution using dynamic programming

6.2.2 Solution using Q-learning

6.3 Utility function approach

6.3.1 Understanding utility functions

6.3.2 Optimal spending using utility functions

6.4 Longevity risk

6.5 Other extensions

Summary