Yejin Hwang

It was early 2025. I was browsing a Korean stock community late at night when I noticed something: a user with a special "stock guru" badge posted about quantum computing stocks going up. People followed. The stock jumped over 200%. Real money moved because of an internet post.

Then I thought about GameStop. The Dumb Money movie. Reddit's r/WallStreetBets — still active, still moving markets. The question formed itself: what if you could quantify that signal?

GameStop's 1700% surge (Jan–Mar 2021) · Beyond Meat 1300% spike — both driven by social media momentum

Can a pre-trained Transformer model (Temporal Fusion Transformer) trained on historical stock prices perform better when you add social media sentiment scraped from Reddit — analyzed through FinBERT, a finance-tuned BERT model?

Full research pipeline: Yahoo Finance API + Reddit API → Feature Engineering → Modeling (TFT) → Evaluation

Reddit posts from r/WallStreetBets were scraped via the Reddit API and analyzed using FinBERT (finance-domain BERT) to extract daily sentiment scores. These were engineered into lag features, rolling statistics, and spike indicators — then combined with OHLCV price data as multimodal input to the Temporal Fusion Transformer (TFT). Three model tiers were compared: univariate baselines (LSTM, PatchTST, TimesFM), TFT without sentiment, and TFT with sentiment.

Beyond the technical results, this project taught me that the most interesting data problems live at the intersection of human behavior and quantitative systems. People talking on the internet is data. Communities form opinions that move markets. The challenge — and the opportunity — is figuring out how to listen to that signal responsibly.