发表机构
Texas A&M University; University of California, Berkeley（德克萨斯农工大学; 加利福尼亚大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
REFLEX是一个用于场外公司债券市场报价模型的框架，通过交易商行为的可测量特征构建稳定性裕度，可预测再训练的收敛性，在模拟和市场数据中验证了其有效性，能将抽象收敛定理转化为市场安全裕度。
AI 中文摘要
在场外公司债券市场中，交易商通过报出买入价和卖出价来争夺客户交易。更紧的报价能吸引更多业务，但也会吸引更可能在不利价格变动前交易的知情客户，使交易商承担风险。随着交易商越来越多地使用机器学习来设定报价，他们会根据自己的报价吸引的交易重新训练这些模型，形成一个反馈循环，其中每个模型都会重塑产生其下一批训练数据的市场。因此，问题不仅在于报价模型表现是否良好，还在于该模型从中学习的市场是否保持稳定。现有的表演性预测理论给出了明确的稳定性条件，但该条件通过交易台在部署前无法测量的学习目标的抽象属性来表达。我们引入REFLEX，这是一个用交易商行为的三个可测量特征替代那些不可观测量的框架：交易量对更紧报价的响应强度、交易商目标在其最优值附近的弯曲程度，以及价差缩小时知情资金流的增长速度。REFLEX将这些特征组合成一个单一的再训练模数，这是一个从交易台自身的报价和执行历史中估计的部署前稳定性裕度，用于预测重复再训练是会收敛还是会自我放大。在模拟中，预测的稳定性与测量的稳定性相差在8%以内，且正如预测的那样，当有两个竞争交易商时，不稳定性增加1.74倍，有三个时增加3.16倍。当普通再训练在模数1.21时变得不稳定，而结构锚定校正会在盲目再训练崩溃时收敛。在36年的公开市场数据上进行校准后，从平静期到危机期，投资级债券的稳定性余量下降约4.4倍，高收益债券下降约4.3倍。最终，REFLEX将一个抽象的收敛定理转化为市场层面的安全裕度。
英文摘要
In over-the-counter corporate bond markets, dealers compete for client trades by quoting bid and ask prices. Tighter quotes attract more business, but also informed customers more likely to trade ahead of adverse price moves, leaving the dealer holding the risk. As dealers increasingly use machine learning to set quotes, they retrain these models on the trades their own quotes attract, creating a feedback loop in which each model reshapes the market that generates its next training data. The question is therefore not only whether a quoting model performs well, but whether the market it creates stays stable as the model learns from it. Existing performative prediction theory gives a sharp stability condition, yet expresses it through abstract properties of the learning objective a trading desk cannot measure before deployment. We introduce REFLEX, a framework that replaces those unobservable quantities with three measurable features of dealer behavior: how strongly trading volume responds to tighter quotes, how sharply the dealer's objective bends around its optimum, and how quickly informed flow increases as spreads narrow. REFLEX combines these into a single retraining modulus, a pre-deployment stability margin estimated from a desk's own quote and execution history that predicts whether repeated retraining will converge or amplify itself. In simulation, predicted and measured stability agree within 8%, and competing dealers increase instability by 1.74x with two and 3.16x with three, as predicted. Where ordinary retraining becomes unstable at modulus 1.21, a structurally anchored correction converges as blind retraining collapses. Calibrated over 36 years of public market data, stability headroom falls roughly 4.4x for investment grade and 4.3x for high yield from calm to crisis regimes. Ultimately, REFLEX turns an abstract convergence theorem into a market-level safety margin.
Comments8 pages, 6 figures, 5 tables, 24 references
