Optimal consumption-investment choices under wealth-driven risk aversion

TL;DR

This paper introduces a neural network approach to solve optimal consumption-investment problems under wealth-driven risk aversion using a jump-diffusion model for data simulation.

Contribution

It presents a novel neural network method, specifically LSTM, to numerically solve investment problems with wealth-driven risk aversion, which is rarely addressed in literature.

Findings

Neural network LSTM effectively models the investment problem.

The approach successfully optimizes investment and consumption parameters.

Promising results demonstrate potential for practical application.

Abstract

CRRA utility where the risk aversion coefficient is a constant is commonly seen in various economics models. But wealth-driven risk aversion rarely shows up in investor's investment problems. This paper mainly focus on numerical solutions to the optimal consumption-investment choices under wealth-driven aversion done by neural network. A jump-diffusion model is used to simulate the artificial data that is needed for the neural network training. The WDRA Model is set up for describing the investment problem and there are two parameters that require to be optimized, which are the investment rate of the wealth on the risky assets and the consumption during the investment time horizon. Under this model, neural network LSTM with one objective function is implemented and shows promising results.