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鲁棒性还是拥挤:交易策略容量的实验设计

Robustness or Crowding: Experimental Design for Trading Strategy Capacity

Alejandro Rodriguez Dominguez, Miquel Noguer i Alonso

arXiv 2608.08405首次发表:更新:

AI 中文总结

本文探究交易策略容量的实验设计,明确固定持有期的效应低估程度及校正方法,为专用面板校准设计规则并为遵循规则的研究定价。

AI 中文摘要

交易策略在其优势消失前可吸收多少资本,是一个关于部署资本规模的因果问题,但目前该问题的答案依赖于基于不兼容假设的观测代理变量。本文探究何种实验可回答该问题,并表明该问题的两个特征相互作用,制约着任何可能的答案:部署资本会逐渐侵蚀策略优势,因此固定时长的试验所测结果小于最终效应;同一策略的并行实现交易相同证券,故并非独立单元。比较同一日期的实现可消除市场整体冲击,这是该比较可信的原因;但策略自身累积头寸造成的拥挤也存在于这些实现中,而任意日期效应恰好吸收了该拥挤,因此使实验具有鲁棒性的比较,同时阻止其测量与拥挤容量相关的内容。同一日期设计可恢复某一实现在现有总头寸水平下的私人响应,若要获得总效应,需要么让实现对该头寸具有刻意不同的敞口,要么随时间改变头寸。本文明确了每条路径所识别的内容及其成本,确定了固定持有期低估最终效应的程度及校正方法,并展示了有限部署水平集所能揭示和不能揭示的内容。对专用面板的校准说明了所得设计规则,并为遵循这些规则的研究定价。

英文摘要

How much capital a trading strategy can absorb before its edge disappears is a causal question about how much is deployed, but it is answered with observational proxies that rest on incompatible assumptions. We ask what experiment would answer it instead, and show that two features of the problem interact to constrain any answer. Deployed capital erodes the edge gradually, so a trial of fixed length measures less than the eventual effect; and parallel implementations of one strategy trade the same securities, so they are not independent units. Comparing implementations on the same date removes market-wide shocks, which is what makes the comparison credible. But the crowding created by the strategy's own accumulated position is common to those implementations too, and an arbitrary date effect absorbs it exactly: the comparison that makes the experiment robust is the one that prevents it from measuring the crowding capacity is about. A same-date design recovers one implementation's private response at the prevailing level of aggregate positioning, and reaching the aggregate effect requires either implementations with deliberately different exposure to that position or variation in it over time. We characterise what each route identifies and what it costs, establish how far a fixed holding period understates the eventual effect and how to correct for it, and show what a finite set of deployment levels can and cannot reveal. A calibration on a purpose-built panel illustrates the resulting design rules and prices a study that would follow them.

Comments38 Pages, 9 Tables, 5 Figures

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