论文标题
资产分配：从Markowitz到深入的强化学习
Asset Allocation: From Markowitz to Deep Reinforcement Learning
论文作者
论文摘要
资产分配是一种投资策略，旨在通过根据某些目标，风险承受能力和投资视野不断重新分配投资组合的资产来平衡风险和回报。不幸的是，没有一个简单的公式可以为每个人找到正确的分配。结果，投资者可能会使用不同的资产分配策略来实现其财务目标。在这项工作中，我们进行了一项广泛的基准研究，以确定许多优化技术的功效和可靠性。特别是，我们专注于基于现代投资组合理论的传统方法，以及基于深度强化学习的机器学习方法。我们在不同的市场趋势下（即看涨和看跌市场）评估该模型的绩效。为了获得可重复性，我们在此存储库中提供代码实现代码。
Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository.
