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.

翻译：CRRA 在风险反向系数不变的情况下,CRRA效用在各种经济学模型中常见。但财富驱动的风险反向很少出现在投资者的投资问题中。本文主要侧重于在神经网络的财富驱动反向情况下,对最佳消费-投资选择的数字解决方案。一个跳跃扩散模型用于模拟神经网络培训所需的人工数据。WDRA模型是用来描述投资问题的,需要优化两个参数,即对风险资产和投资时间范围内的消费的财富投资率。在这个模型下,一个目标功能的神经网络LSTM得到实施,并显示有希望的结果。