Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse requirements such as time frame and risk aversion level, thereby total earning and individual earning cannot be optimized at the same time. Orders executed earlier would affect the market price level, so late execution usually means additional implementation cost. In this paper, we propose a novel scheme that utilizes multi-agent reinforcement learning systems to derive stock trading strategies for all clients which keep a balance between revenue and fairness. First, we demonstrate that Reinforcement learning (RL) is able to learn from experience and adapt the trading strategies to the complex market environment. Secondly, we show that the Multi-agent RL system allows developing trading strategies for all clients individually, thus optimizing individual revenue. Thirdly, we use the Generalized Gini Index (GGI) aggregation function to control the fairness level of the revenue across all clients. Lastly, we empirically demonstrate the superiority of the novel scheme in improving fairness meanwhile maintaining optimization of revenue.

中文翻译：

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股票交易多智能体强化学习的公平性

不公平的股票交易策略已被证明是客户对交易的最负面看法之一，并可能导致公司长期亏损。投资银行通常会为多个目标资产相同但订单规模不同、时间范围、风险规避水平等要求不同的客户下单，从而无法同时优化总收益和个人收益。提前执行的订单会影响市场价格水平，因此延迟执行通常意味着额外的执行成本。在本文中，我们提出了一种新颖的方案，利用多智能体强化学习系统为所有客户推导出股票交易策略，在收入和公平之间保持平衡。第一的，我们证明了强化学习 (RL) 能够从经验中学习并使交易策略适应复杂的市场环境。其次，我们展示了多代理 RL 系统允许为所有客户单独开发交易策略，从而优化个人收入。第三，我们使用广义基尼指数（GGI）聚合函数来控制所有客户的收入公平水平。最后，我们通过经验证明了新方案在提高公平性同时保持收入优化方面的优越性。我们使用广义基尼指数 (GGI) 聚合函数来控制所有客户的收入公平水平。最后，我们通过经验证明了新方案在提高公平性同时保持收入优化方面的优越性。我们使用广义基尼指数 (GGI) 聚合函数来控制所有客户的收入公平水平。最后，我们通过经验证明了新方案在提高公平性同时保持收入优化方面的优越性。