Fairness-Aware Multi-Agent Reinforcement Learning for Stock Execution

Summary

The paper addresses how a trading desk should execute orders for multiple clients seeking the same assets when their order sizes, horizons, and risk preferences differ. Earlier fills can move prices, so optimizing aggregate trading revenue may disadvantage clients whose orders execute later. The proposed framework uses multi-agent reinforcement learning to develop strategies for clients individually while balancing their outcomes across the group.

A Generalized Gini Index aggregates client revenues and provides a way to control the fairness objective alongside revenue optimization. The authors report empirical evidence that the approach improves fairness while maintaining revenue optimization, and argue that reinforcement learning can adapt strategies to changing market conditions. The provided description gives no dataset, experimental setup, numerical results, or comparison details, so the strength and generality of the empirical claim cannot be assessed from this text alone.

Key ideas

• Client order execution can create conflicts between maximizing total revenue and treating clients fairly.
• Earlier orders may affect prices and raise implementation costs for later executions.
• The framework uses multi-agent reinforcement learning to learn client-specific execution strategies.
• A Generalized Gini Index incorporates distributional fairness into revenue aggregation.
• The document claims improved fairness while maintaining revenue optimization, but supplies few details for evaluating the evidence.

Tags

# Fairness in Multi-agent Reinforcement Learning for Stock Trading


# Fairness in Multi-agent Reinforcement Learning for Stock Trading









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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