Muchen Zhao
ABSTRACT
Market makers provide liquidity by placing limit orders on both sides of the market (bids and offers) while aiming to earn the bid-offer (bid-ask) spread. Their long-term performance is significantly determined by their ability to mitigate the risk of adverse selection when their limit orders are picked off by informed traders possessing relevant information that moves the market to a new level resulting in losses to market makers. This paper proposes a high-frequency feature Book Exhaustion Rate (BER) and shows theoretically and empirically that the BER can serve as a direct measurement of the adverse selection risk from an equilibrium point of view. We train a market making algorithm via Reinforcement Learning using three years of limit order book data on Chicago Mercantile Exchange (CME) S&P 500 and 10-year Treasury note futures and demonstrate that with utilizing the BER allows the algorithm to avoid large losses due to adverse selection and achieve stable performance.
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- High frequency automated market making algorithms with adverse selection risk control via reinforcement learning
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