Enhancing Few-Shot Stock Trend Prediction with Large Language Models

TL;DR

This paper introduces a novel two-step 'denoising-then-voting' approach using Large Language Models for few-shot stock trend prediction, effectively handling noisy and lengthy financial news to outperform standard methods.

Contribution

The paper proposes a new method that classifies irrelevant news and aggregates individual predictions, improving few-shot stock trend prediction with LLMs.

Findings

Achieves 66.59% accuracy on S&P 500

Outperforms standard few-shot methods by 4-7%

Performs comparably to state-of-the-art supervised models

Abstract

The goal of stock trend prediction is to forecast future market movements for informed investment decisions. Existing methods mostly focus on predicting stock trends with supervised models trained on extensive annotated data. However, human annotation can be resource-intensive and the annotated data are not readily available. Inspired by the impressive few-shot capability of Large Language Models (LLMs), we propose using LLMs in a few-shot setting to overcome the scarcity of labeled data and make prediction more feasible to investors. Previous works typically merge multiple financial news for predicting stock trends, causing two significant problems when using LLMs: (1) Merged news contains noise, and (2) it may exceed LLMs' input limits, leading to performance degradation. To overcome these issues, we propose a two-step method 'denoising-then-voting'. Specifically, we introduce an `Irrelevant' category, and predict stock trends for individual news instead of merged news. Then we aggregate these predictions using majority voting. The proposed method offers two advantages: (1) Classifying noisy news as irrelevant removes its impact on the final prediction. (2) Predicting for individual news mitigates LLMs' input length limits. Our method achieves 66.59% accuracy in S&P 500, 62.17% in CSI-100, and 61.17% in HK stock prediction, outperforming the standard few-shot counterparts by around 7%, 4%, and 4%. Furthermore, our proposed method performs on par with state-of-the-art supervised methods.