# Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis

arXiv: https://arxiv.org/abs/2309.17322 · HTML: https://mlsanity.com/paper/2309.17322/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://mlsanity.com/paper/2205.10770/) (2022)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [ChatGPT Informed Graph Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2306.03763/) (2023)

## Built on by (most cited first, 30 of 31)

- [Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking](https://mlsanity.com/paper/2505.11065/) (2025)
- [Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance](https://mlsanity.com/paper/2601.13770/) (2026)
- [A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs](https://mlsanity.com/paper/2512.06607/) (2025)
- [DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining](https://mlsanity.com/paper/2603.11838/) (2026)
- [A Scoping Review of ChatGPT Research in Accounting and Finance](https://mlsanity.com/paper/2412.05731/) (2024)
- [Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis](https://mlsanity.com/paper/2506.04574/) (2025)
- [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025)
- [A Financial Brain Scan of the LLM](https://mlsanity.com/paper/2508.21285/) (2025)
- [Instruction Tuning Chronologically Consistent Language Models](https://mlsanity.com/paper/2510.11677/) (2025)
- [Scaling Point-in-Time Language Models: Enabling Economic Evaluation of Embeddings](https://mlsanity.com/paper/2607.11889/) (2026)
- [Dutch Books for Language Models](https://mlsanity.com/paper/2609.02797/) (2026)
- [Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scores](https://mlsanity.com/paper/2608.02985/) (2026)
- [Do Large Language Models (LLMs) Understand Chronology?](https://mlsanity.com/paper/2511.14214/) (2025)
- [Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading](https://mlsanity.com/paper/2510.10526/) (2025)
- [How AI Forecasts AI Jobs: Benchmarking LLM Predictions of Labor Market Changes](https://mlsanity.com/paper/2510.23358/) (2025)
- [Seeing the Goal, Missing the Truth: Human Accountability for AI Bias](https://mlsanity.com/paper/2602.09504/) (2026)
- [MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting](https://mlsanity.com/paper/2603.26797/) (2026)
- [Forecasting With LLMs: Improved Generalization Through Feature Steering](https://mlsanity.com/paper/2606.27199/) (2026)
- [HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks](https://mlsanity.com/paper/2607.18867/) (2026)
- [Frontier Financial Judgement: Can agents tell what might move a stock?](https://mlsanity.com/paper/2607.20645/) (2026)
- [Talking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Media](https://mlsanity.com/paper/2608.01181/) (2026)
- [Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment, Macroeconomic Indicators, and Technical Signals](https://mlsanity.com/paper/2608.12283/) (2026)
- [FORESIGHT-9: Prospective and Process-Aware Evaluation of Adaptive Trading Agents](https://mlsanity.com/paper/2608.29372/) (2026)
- [Agentic Empirical Asset Pricing: Methodological Foundations](https://mlsanity.com/paper/2609.00731/) (2026)
- [Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems](https://mlsanity.com/paper/2609.19789/) (2026)
- [Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target](https://mlsanity.com/paper/2609.26303/) (2026)
- [LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs](https://mlsanity.com/paper/2609.33470/) (2026)
- [Frontier Autolab: Organizational Memory, Adversarial Dissent and Temporal Leakage in Multi-Agent LLM Firms Across Fifty Years of Technological Change](https://mlsanity.com/paper/2609.36739/) (2026)
- [Can Language Models Learn to Forecast Stock Prices](https://mlsanity.com/paper/2609.36914/) (2026)
- [Say, Echo, Do: Strategic Narratives and Revealed Positioning in Financial Markets](https://mlsanity.com/paper/2609.38545/) (2026)

## Abstract (arXiv)

Large language models (LLMs), including ChatGPT, can extract profitable trading signals from the sentiment in news text. However, backtesting such strategies poses a challenge because LLMs are trained on many years of data, and backtesting produces biased results if the training and backtesting periods overlap. This bias can take two forms: a look-ahead bias, in which the LLM may have specific knowledge of the stock returns that followed a news article, and a distraction effect, in which general knowledge of the companies named interferes with the measurement of a text's sentiment. We investigate these sources of bias through trading strategies driven by the sentiment of financial news headlines. We compare trading performance based on the original headlines with de-biased strategies in which we remove the relevant company's identifiers from the text. In-sample (within the LLM training window), we find, surprisingly, that the anonymized headlines outperform, indicating that the distraction effect has a greater impact than look-ahead bias. This tendency is particularly strong for larger companies--companies about which we expect an LLM to have greater general knowledge. Out-of-sample, look-ahead bias is not a concern but distraction remains possible. Our proposed anonymization procedure is therefore potentially useful in out-of-sample implementation, as well as for de-biased backtesting.