  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.
