Part 2 Financial planning tools

This part of the book shows how to automate some of the financial planning services offered by advisors. Chapter 5 introduces Monte Carlo simulations and how to model various sources of risk, including market risk, inflation risk, and longevity risk (using mortality tables), to estimate the probability of running out of money in retirement. The chapter also covers historical simulation and bootstrapping.

Chapter 6 provides several fully worked examples, from start to finish, of how you can apply reinforcement learning, a powerful branch of AI, to solving financial planning problems, including an example of when to claim Social Security that takes risk into account. The chapter also explains how to incorporate utility functions into reinforcement learning, which is a useful tool for handling financial planning decisions involving risk, but one that has not been widely adopted by financial planners.

Chapter 7 takes a detour to discuss returns: first, how to measure returns when there are inflows and outflows; and second, how to evaluate risk-adjusted returns of actively managed funds. As an example, the chapter analyzes the risk-adjusted returns of a popular environmental, social, and governance (ESG) fund.