---
title: Short Windows, Unmodelled Costs, Unknown Cutoffs: A Pre-Registered Evidence Audit of 91 LLM Trading Papers
url: https://www.ml-quant.com/papers/ssrn/7687481/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-11-14
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7687481
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687481
featured: 2026-11-13
citations: unknown
topic: ML & AI Methods
---


# Short Windows, Unmodelled Costs, Unknown Cutoffs: A Pre-Registered Evidence Audit of 91 LLM Trading Papers

Pre-registers and audits 91 LLM trading papers, finding 51% ignore trading costs, median test window is 12 months, and 98% fail to control for look-ahead bias in model training.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687481
- Identifier: SSRN 7687481
- Released: 2026-11-13
- First featured: Quant Letter No. 139 (2026-11-13): https://www.ml-quant.com/issues/2026-11-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Heng Zhang

## Related

- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://www.ml-quant.com/papers/arxiv/2405.07960/): AI Evaluation in Clinical Environments: The paper introduces AgentClinic, a benchmark for assessing large language models in simulated clinical environments, highlighting the significant impact of biases on diagnostic accuracy and patient interactions.
- [Learning Performance-Improving Code Edits](https://www.ml-quant.com/papers/arxiv/2302.07867/): The research presents a framework for optimizing programs using large language models, achieving a mean speedup of 6.86, outperforming average individual programmers.
- [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://www.ml-quant.com/papers/arxiv/2312.03664/): Concordia is a library designed to help build and operate Generative Agent-Based Models (GABMs), using Large Language Models (LLMs) to simulate physical or digital environments.
- [Jamba-1.5: Hybrid Transformer-Mamba Models at Scale](https://www.ml-quant.com/papers/arxiv/2408.12570/): Transformer-Mamba Models: Jamba-1.5 is a new large language model with enhanced conversational and instruction-following capabilities, featuring a unique quantization technique for cost-effective inference.
- [MindSearch: Mimicking Human Minds Elicits Deep AI Searcher](https://www.ml-quant.com/papers/arxiv/2407.20183/): Mimicking Human Minds for Search: MindSearch is a Large Language Model-based framework that simulates human cognitive processes for web information seeking, greatly enhancing response quality.
- [What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books](https://www.ml-quant.com/papers/arxiv/2610.20104/): Autoregressive transformers trained on limit-order-book message streams learn amortized Bayesian inference over market regimes, matching optimal prediction and enabling accurate parameter calibration.