arXiv:2609.30316v1 Announce Type: cross Abstract: Language models used in financial backtests suffer from look-ahead bias, as a model trained on text published after the study period has already obse...
The paper addresses look‑ahead bias in large language models (LLMs) used for financial prediction, which arises because LLMs are trained on long time‑series data. It proposes a low‑cost solution that adjusts the logits of a base model at inference time using two smaller, specialized models—one fine‑tuned to forget certain information and another to retain it. Experiments show that this method removes both verbatim and semantic knowledge, corrects biases, and outperforms previous approaches.
arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
arXiv:2604. 16197v2 Announce Type: replace Abstract: Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods suffer from scalability challenges on LLMs.
arXiv:2512. 23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs).
The paper introduces point‑in‑time language models that are trained only on text available up to each calendar date, thereby eliminating lookahead bias that plagues standard large language models. By scaling decoder‑only transformers to 4 billion parameters and training on 1 trillion chronologically filtered tokens from FineWeb, the authors produce monthly checkpoints from 2013 to 2024 that achieve performance close to unconstrained models of similar size on common‑sense reasoning and language understanding tasks. Instruction fine‑tuning with LoRA further enhances downstream usability, and the authors release the full pipeline—including dataset construction, training infrastructure, and evaluation code—for reproducible research.
The study investigates whether adding inference-time reasoning to large language models (LLMs) improves trading performance. Using a controlled experiment across DeepSeek, GPT, and Gemini models, the authors varied reasoning effort while keeping other variables constant and evaluated over a full year of U.S. equities under three input conditions. Results show that additional reasoning does not reliably increase net portfolio returns and can even lead to nonmonotonic performance and unstable outcomes.
arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.
MemGuard-Alpha evaluates whether membership inference attacks (MIA) can detect memorization in large language models (LLMs) used for financial alpha signals. The study combines five MIA methods with a temporal proximity feature and a cross-model disagreement metric, then audits them across seven LLMs, 50 S&P 100 stocks, and 299,600 prompt-model pairs. Findings show that temporal proximity alone perfectly predicts in-sample status, MIA discriminative power largely stems from model scale differences, and filtering based on contamination scores does not improve risk-adjusted performance once transaction costs are considered.
arXiv:2607. 12248v1 Announce Type: cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets.
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
arXiv:2610.30113v1 Announce Type: new Abstract: Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical da...