Optimal consumption-investment choices under wealth-driven risk aversion
This work addresses a specific gap in economics models for investors, but it is incremental as it applies existing neural network methods to a new data setup.
The paper tackles the problem of optimal consumption-investment choices under wealth-driven risk aversion, a rarely modeled scenario, by using an LSTM neural network to optimize investment and consumption parameters, showing promising results.
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.