arXiv：cs.LG· Dennis Thumm, Ying Chen·· 11 小时前
因果基础模型如何估计市场冲击：Causal Market Simulator 在 ABIDES 与 BTC/ETH/SOL 上的零样本验证
Market Impact Estimation via Causal Foundation Models
AI 导读
研究者提出 Causal Market Simulator，一个在合成市场先验上训练的连续时间因果 PFN，用于估计"若不交易价格会是多少"的市场冲击。该先验中隐藏信息流按构造混淆 flow-price 关系，朴素估计器偏差最高达 68% 高估，而 PFN 在高混淆下贴近无偏真实参数 oracle。
正文
Abstract:When a trader executes a large order, the relevant question is not \emph{what will the price be?} but \emph{what would the price have been had I not traded?} Market impact is thus a causal quantity, yet classical models (Almgren--Chriss, propagators) regress price on flow, which is biased whenever informed flow moves prices through information as well as trading. We present a proof-of-concept \emph{Causal Market Simulator}: a continuous-time causal prior-data fitted network (PFN) trained on a synthetic market prior in which a hidden informational flow confounds the flow--price relation by construction, with confounding strength an exact per-episode knob. On this prior, the naive estimator's bias follows a closed form we confirm empirically (up to 68\% overestimation), while the PFN tracks the unbiased true-parameter oracle at high confounding. Its \emph{do-ablated twin}, the same model with the intervention inputs removed, collapses onto the pre-window mean. Zero-shot, the model responds at the ground-truth scale to metaorders in an ABIDES limit-order book ($+0.38$ pre-onset standard deviations against seed-replay counterfactuals), and its intervention response replicates on about 2,300 detected flow bursts in BTC/ETH/SOL perpetuals and 3,800 on Nasdaq equities, evidence consistent with, though not proof of, causal transfer. Off-prior the response degrades gracefully, and split-conformal recalibration repairs interval calibration. Transfer hinges on encoding the metaorder as added flow rather than a clamped price level, not on more training. A recurrent state-space variant closes the remaining shape gap: its impact accrues over execution duration where the transformer's stays flat, raising the correlation with measured metaorder impact from $+0.68$ to between $+0.92$ and $+0.96$, at the cost of weaker on-prior deconfounding that traces to the training recipe rather than to recurrence.
| Comments: | In 7th ACM International Conference on AI in Finance (ICAIF '26), November 14-17, 2026, Milan, Italy |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Trading and Market Microstructure (q-fin.TR) |
| Cite as: | arXiv:2610.12537 [cs.LG] |
| (or arXiv:2610.12537v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12537 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dennis Thumm [view email]
[v1]
Thu, 8 Oct 2026 13:29:39 UTC (182 KB)
来源：arXiv：cs.LG · arxiv.org