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pith:EAKE5DYU

pith:2026:EAKE5DYUVTTMLJPZ5S2IZMNDSJ

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The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence

Ao Hu, Danilo Mandic, Danny Dongning Sun, Juncheng Bu, Jun Han, Liangjian Wen, Xu Yinghui, Yiyi Chen, Yuxuan Ye, Zenglin Xu

Reported alpha from LLM trading agents should not be treated as deployment evidence.

arxiv:2605.16895 v1 · 2026-05-16 · cs.CE · cs.AI · cs.CL

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Before such returns can support claims of deployable trading capability, they must survive structural validity tests for temporal integrity, real-world frictions, counterfactual robustness, predictive calibration, numerical execution, and multi-agent disaggregation.

C2weakest assumption

Current public evidence cannot yet distinguish robust predictive ability from temporal contamination, unmodeled frictions, short-window Sharpe uncertainty, narrative fitting, and parametric priors (abstract, paragraph on the gap between architecture research and deployment claim).

C3one line summary

Reported alpha from end-to-end LLM trading agents does not constitute deployment evidence until it passes structural tests for temporal integrity, frictions, robustness, calibration, execution, and disaggregation.

References

41 extracted · 41 resolved · 4 Pith anchors

[1] Optimal execution of portfolio transactions.Journal of Risk, 3(2):5–39, 2000 2000

[2] Stockbench: Can llm agents trade stocks profitably in real-world markets? 2025

[3] Empirical asset pricing via machine learning.The Review of Financial Studies, 33(5):2223–2273, 2020 2020

[4] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. InInternational Conference on Machine Learning (ICML), pages 1321–1330. PMLR, 2017 2017

[5] Harvey, Yan Liu, and Heqing Zhu 2016

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:28.871089Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

20144e8f14ace6c5a5f9ecb48cb1a39257386d03c5be62b016e10c95bcc72747

Aliases

arxiv: 2605.16895 · arxiv_version: 2605.16895v1 · doi: 10.48550/arxiv.2605.16895 · pith_short_12: EAKE5DYUVTTM · pith_short_16: EAKE5DYUVTTMLJPZ · pith_short_8: EAKE5DYU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EAKE5DYUVTTMLJPZ5S2IZMNDSJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 20144e8f14ace6c5a5f9ecb48cb1a39257386d03c5be62b016e10c95bcc72747

Canonical record JSON

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    "abstract_canon_sha256": "deffee6cd4d05005f2cf0ab71753b439f5e4e02f33ac1afa8df8b072a5f599c6",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CE",
    "submitted_at": "2026-05-16T09:14:35Z",
    "title_canon_sha256": "4d2c551a2f69e874d91d215833902ee4eeb50de431a0f10d752beb9b80cba793"
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  "source": {
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    "kind": "arxiv",
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}