Look-Ahead Bias in Large Language Models (LLMs): Implications and Applications in Finance

5 Pages Posted: 10 Jan 2025

See all articles by Miquel Noguer I Alonso

Miquel Noguer I Alonso

Artificial Intelligence in Finance Institute

Date Written: November 15, 2024

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, but ensuring their robustness and reliability requires addressing key methodological challenges such as look-ahead bias. This paper discusses the concept of look-ahead bias in LLMs, with particular emphasis on its implications for financial applications. We explore common sources of bias, its manifestations in training and evaluation, and propose strategies to mitigate its effects. Special attention is given to backtesting methodologies, data cutoffs, and the challenges posed by entity embeddings. These mitigation strategies, while computationally and financially expensive, are crucial for accurately evaluating LLM prediction tasks. By addressing these challenges, we aim to enhance the applicability and trustworthiness of LLMs in sequential and time-sensitive tasks.

Keywords: artificial intelligence, large language models, look ahead bias, backtesting

JEL Classification: C45, C53, C58, G17, Q47

Suggested Citation

Noguer I Alonso, Miquel, Look-Ahead Bias in Large Language Models (LLMs): Implications and Applications in Finance (November 15, 2024). Available at SSRN: https://ssrn.com/abstract=5022165 or http://dx.doi.org/10.2139/ssrn.5022165

Miquel Noguer I Alonso (Contact Author)

Artificial Intelligence in Finance Institute ( email )

New York
United States

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