论文标题
优于随机基准目标的最佳资产分配
Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target
论文作者
论文摘要
我们提出了一个数据驱动的神经网络（NN）优化框架，以确定优于一般随机目标的最佳多周期动态资产分配策略。我们将问题提出为最佳随机控制，具有不对称的分布形状，目标函数。在定义的贡献养老金计划的积累阶段中，提出的框架用资产分配问题进行了说明，其目的是获得比随机基准更高的终端财富。我们证明，数据驱动的方法能够直接从历史市场收益中学习自适应资产分配策略，而无需假设金融市场动态的任何参数模型。遵循最佳的自适应策略，投资者可以仅根据投资组合的当前状态做出分配决策。最佳的自适应策略的表现优于基准恒定比例策略，获得了更高的终端财富，概率为90％，中位终端财富高46％，右手终端财富分布明显更大。我们通过测试Bootstrap上的策略的性能重新采样市场数据，进一步证明了最佳自适应策略的鲁棒性，与培训数据相比，该数据具有不同的分布。
We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution. We further demonstrate the robustness of the optimal adaptive strategy by testing the performance of the strategy on bootstrap resampled market data, which has different distributions compared to the training data.
