Linking microblogging sentiments to stock price movement: An application of GPT-4

TL;DR

This study compares GPT-4 and BERT in sentiment analysis of microblogging messages to predict stock price movements of Apple and Tesla, demonstrating GPT-4's superior accuracy and emphasizing prompt engineering's importance.

Contribution

It introduces a novel prompt engineering method for GPT-4 to improve sentiment analysis accuracy for stock prediction tasks.

Findings

GPT-4 outperformed BERT in 5 of 6 months in accuracy

GPT-4 achieved up to 71.47% accuracy in May

Prompt engineering significantly enhances GPT-4's contextual sentiment analysis

Abstract

This paper investigates the potential improvement of the GPT-4 Language Learning Model (LLM) in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. We recorded daily adjusted closing prices and translated them into up-down movements. Sentiment for each day was extracted from messages on the Stocktwits platform using both LLMs. We develop a novel method to engineer a comprehensive prompt for contextual sentiment analysis which unlocks the true capabilities of modern LLM. This enables us to carefully retrieve sentiments, perceived advantages or disadvantages, and the relevance towards the analyzed company. Logistic regression is used to evaluate whether the extracted message contents reflect stock price movements. As a result, GPT-4 exhibited substantial accuracy, outperforming BERT in five out of six months and substantially exceeding a naive buy-and-hold strategy, reaching a peak accuracy of 71.47 % in May. The study also highlights the importance of prompt engineering in obtaining desired outputs from GPT-4's contextual abilities. However, the costs of deploying GPT-4 and the need for fine-tuning prompts highlight some practical considerations for its use.