Sentiment Prediction for Market Volatility

This project presents an automated framework for generating sentiment metrics from SEC 10-K filings, aiming to predict stock market returns and volatility at the sector, portfolio, and firm levels. The system comprises two core models: an SEC Filing Extraction Model, which preprocesses filings, and a Supervised Lexicon Learning Model, which analyzes sentiment using a four-step process. This includes identifying sentimentrelated words, assigning predictive weights, aggregating sentiment scores, and applying the Kalman Filter for trend analysis. Empirical results demonstrate the effectiveness of sentiment metrics from 10-K filings, particularly the Item 1A risk factor section, in forecasting market movements.