Predicting S&P 500 Motion using Reddit Posts

Introduction

Predicting stock market trends is a challenging task due to the high volatility of stock markets. In this project, we explore the potential of using Reddit data to forecast the S&P 500 index's behavior. We focus on two relevant subreddit pages, r/WallStreetBets and r/Stocks, and analyze their posts from 2008-01-01 to 2022-12-31.

Dataset

We collected Reddit posts from r/WallStreetBets and r/Stocks using data dumps as Reddit's API is no longer available. The data was preprocessed to remove irrelevant posts and transformed into a pandas DataFrame. The posts were labeled as "Increased," "Decreased," or "Neutral" based on the change in the S&P 500 index on the following day.

Methods

We experimented with different models to predict market trends:

1. Baseline: A pre-trained RoBERTa-based sentiment analysis model.
2. Comment-weighted naive model: A model that predicted the daily sentiment based on the weighted average of the comments on each post.
3. Comment-weighted fine-tuned model: A model fine-tuned on our dataset using Huggingface trainer and AdamW optimizer.
4. Unified-signal model: A novel architecture that utilized gradients from all available posts per day and predicted a unified daily sentiment.

Results

The unified-signal model achieved the best performance in predicting stock market trends, but the results did not surpass random-level choice. More hyper-parameter tuning and computational resources could potentially improve the model's performance.

References

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