Lazy Prices: Vector Representations of Financial Disclosures and Market Outperformance

The ”Efficient Market Hypothesis” (EMH) states that market outperformance is impossible through expert selection because each stock price efficiently incorporates and reflects all relevant evaluative information. We study the validity of EMH by analyzing the latent information of financial disclosures year over year. Specifically, we explore the concept of ”Lazy Prices”, the idea that changes in financial disclosures are correlated with a decrease in market capitalization, using natural language processing methods to factor in these changes the market may not capture. We created a novel database of financial disclosures represented as GloVe vectors from 60,000 raw 10-K documents filed with the Securities and Exchange Commission (SEC) from 1994-2016, and trained several models to predict future market performace. Because our best model did not acheive cross-validated prediction accuracy greater than 56%, our model provides evidence in favor of Efficient Markets. We present our dataset, methodology for latent information mining, and results as well as a discussion of future improvements.