arXiv AI By Weixian Waylon Li, Mengyu Wang, Tiejun Ma

Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computation and Language
Sep 28

PALM: Point-in-Time Adaptation for Financial Language Models

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...

By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
arXiv Machine Learning
Sep 25

A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs

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.

By Humzah Merchant, Bradford Levy
arXiv Computation and Language
Sep 30

Can Language Models Learn to Forecast Stock Prices

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,...

By Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang
arXiv AI
Oct 15

Scaling Point-in-Time Language Models: Enabling Economic Evaluation of Embeddings

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.

By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu