# Predicting stock prices with ChatGPT-annotated Reddit sentiment

arXiv: https://arxiv.org/abs/2507.22922 · HTML: https://mlsanity.com/paper/2507.22922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 21 Jul 2025.

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

- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis](https://mlsanity.com/paper/2312.04720/) (2023)
- [Self-induced consensus of Reddit users to characterise the GameStop short squeeze](https://mlsanity.com/paper/2112.07059/) (2021)
- [Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach](https://mlsanity.com/paper/2105.02728/) (2021)

## Built on by (most cited first, 1 of 1)

- [Divide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification](https://mlsanity.com/paper/2511.03830/) (2025)

## Abstract (arXiv)

The surge of retail investor activity on social media, exemplified by the 2021 GameStop short squeeze, raised questions about the influence of online sentiment on stock prices. This paper explores whether sentiment derived from social media discussions can meaningfully predict stock market movements. We focus on Reddit's r/wallstreetbets and analyze sentiment related to two companies: GameStop (GME) and AMC Entertainment (AMC). To assess sentiment's role, we employ two existing text-based sentiment analysis methods and introduce a third, a ChatGPT-annotated and fine-tuned RoBERTa-based model designed to better interpret the informal language and emojis prevalent in social media discussions. We use correlation and causality metrics to determine these models' predictive power. Surprisingly, our findings suggest that social media sentiment has only a weak correlation with stock prices. At the same time, simpler metrics, such as the volume of comments and Google search trends, exhibit stronger predictive signals. These results highlight the complexity of retail investor behavior and suggest that traditional sentiment analysis may not fully capture the nuances of market-moving online discussions.