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Quality-Adjusted Hit-Ratio Targeting in Corporate Bond Market Making

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  • Bouna Niang

Abstract

Hit ratio is a common service metric for electronic corporate bond market making, but raw hit-ratio targets can be economically misleading when client flow has heterogeneous adverse-selection content. This paper extends a stochastic-control framework for OTC bond RFQ market making with hit-ratio constraints by replacing raw hit ratio with a residual-quality-adjusted hit ratio. The key modelling distinction is that adverse post-trade markouts are first decomposed into observable credit factors, carry/rolldown, issuer-relative-value effects, index or ETF demand effects, and residual adverse selection. Only the residual component is treated as client-flow toxicity. The resulting control problem remains tractable: after dualizing the quality-hit-ratio penalty, the HJB retains separable Hamiltonians, and the dual variable is the solution of an exact one-dimensional nonlinear fixed point for each targeted tier. Under a quadratic value-function approximation, optimal quotes decompose into a riskless spread, inventory skew, credit-alpha skew, residual-toxicity charge, and quality-hit-ratio subsidy. Synthetic multi-bond simulations with nonlinear dual solves illustrate that raw hit-ratio targeting can subsidize residual-toxic flow, while residual-quality targeting reallocates service toward low-residual-toxicity flow and improves the attained service/economics frontier. A final reduced-form extension studies inventory-recycling value through risk-aware style-aligned client-flow warehousing. Sweep or portfolio-trade opportunities fill randomly, and participation is sized using the same quadratic value approximation as the RFQ quoting problem. A passive/index-demand experiment is reported in the appendix as a special case of forecastable client flow. The numerical evidence is synthetic and mechanism-oriented; no proprietary RFQ data are used.

Suggested Citation

  • Bouna Niang, 2026. "Quality-Adjusted Hit-Ratio Targeting in Corporate Bond Market Making," Papers 2605.30643, arXiv.org.
  • Handle: RePEc:arx:papers:2605.30643
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    References listed on IDEAS

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    1. Olivier Guéant & Iuliia Manziuk, 2019. "Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 26(5), pages 387-452, September.
    2. Olivier Gu'eant & Charles-Albert Lehalle & Joaquin Fernandez Tapia, 2011. "Dealing with the Inventory Risk. A solution to the market making problem," Papers 1105.3115, arXiv.org, revised Aug 2012.
    3. Olivier Gu'eant & Iuliia Manziuk, 2019. "Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality," Papers 1910.13205, arXiv.org.
    4. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    5. Alexander Barzykin & Axel Ciceri, 2026. "Bond Market Making with a Hit-Ratio Target," Papers 2604.20406, arXiv.org, revised Jul 2026.
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