Research case study
Retail Sentiment Trading Signals
NLP-driven sentiment analysis for equity trading signals.
1–3 days
Forward-return horizon
Focus
Python · NLP
Methods
FinBERT · Time Series
01 / Context
The research question
Can retail investor sentiment on social media predict short-term equity price movements?
02 / Method
Research design
Built an NLP pipeline with VADER and FinBERT to score Reddit/Twitter posts, then trained classifiers on sentiment-price lag features.
03 / Evaluation
How it was tested
- 1–3 day forward returns; Reddit/Twitter posts (2020–2023)
- Split: 70% train / 15% validation / 15% test, walk-forward
- Metrics: directional hit rate, Sharpe ratio, return attribution
04 / Findings
What the work showed
Observed a short-horizon relationship between sentiment features and 1–3 day returns in the selected-ticker backtest; broader-universe and transaction-cost validation remain open.
05 / Next iteration
Where I would take it next
Incorporate real-time streaming data and test on a broader universe of mid-cap stocks.