"Agree to Disagree": Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis

This paper studies the predictability of implied volatility indices of stocks using financial reports tone disagreement from U.S. firms. For this purpose, we build a novel measure of tone disagreement based on financial report tone synchronization of U.S. corporations scattered across five Fama-French industries. The research uses tree network methods to calculate the minimum spanning tree length utilizing data from text mining sentiments features extracted from all U.S. firms that considers 837,342 financial reports. The results show that periods of increased disagreement predict higher implied volatility indices. We contribute to the literature that proposes that a high level of expectations dispersion leads to higher stock volatility and fills a gap in understanding how firms' disagreement level of financial report tone forecast the aggregate stock market behavior. The findings also have implications for financial stability and delegated portfolio management, as accurate volatility prediction is critical for practitioners.