This paper introduces AIPlanner, a financial planner based upon deep reinforcement learning.
AIPlanner provides an investment and consumption strategy intended to optimize lifetime …

In contrast to target date funds that vary asset allocation by age alone, it is important to take
into account both the client's age and the client's portfolio size relative to spending goals …

Despite adaptive mutations being the basis of evolution, many population genetics models
of mutation-selection balance have focused on deleterious and neutral mutations. Here, a …

The creation-selection-mutation model makes predictions regarding the fitness of asexual
and sexual populations in an environment that incorporates both positive and negative …

Abstract Machine learning provides a new approach to solving problems in many fields.
This article explores the use of machine learning to solve the retirement portfolio problem: …

… It was created using the AACalc program developed by Gordon Irlam, which does stochastic
dynamic programming (SDP). The “stochastic” term refers to the program being able to …

The first half of this paper provides a primer on machine learning and relates it to problems
in financial planning. Machine learning proves capable of handling real world complications …

Financial planning via deep reinforcement learning holds much promise. One implementation,
AIPlanner, delivered near optimal financial results, but had a major shortcoming. It …

I investigate optimal stock/bond asset allocation and consumption decisions using a utility
function that is intended to be representative of a retired individual's need for an income floor …

Merton’s portfolio problem involves finding the optimal asset allocation between a risky and
a risk free asset, and the optimal consumption over time, so as to maximize aggregate utility …