🤖 AI Summary
This study investigates how market makers in electronic over-the-counter trading balance immediate spread-based profits against the long-term value of client flow. To this end, the authors develop a stochastic control model that explicitly incorporates reputation dynamics through a performance-based order flow feedback mechanism—where future order arrival intensity depends on both request-for-quote (RFQ) win rates and streaming execution rates. By integrating stochastic dynamic programming with optimal control theory, the model naturally yields an optimal strategy characterized by alternating phases of reputation accumulation and profit realization. The analysis further reveals that, even under a single market maker, multiple stable client-flow equilibria can emerge, thereby elucidating the dynamic trade-off between building reputation and monetizing franchise value.
📝 Abstract
Electronic over-the-counter (OTC) liquidity provision is increasingly shaped not only by the price of the next quote, but also by a dealer's accumulated standing with clients and platforms. We develop a stochastic-control model in which request-for-quote (RFQ) win ratios and streaming fill ratios feed back into future flow through performance-based flow gates, creating an explicit trade-off between immediate spread capture and long-term franchise value. The resulting policy naturally alternates between reputation-building campaigns and franchise monetization phases, and can generate multiple stable client-flow regimes even in a parsimonious single dealer control problem.