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

Nicolas S. Magner (), Nicolas Hardy, Tiago Ferreira and Jaime F. Lavin
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Nicolas S. Magner: Facultad de Administración y Economía, Universidad Diego Portales, Santiago 8370191, Chile
Tiago Ferreira: Facultad de Economía y Negocios, Universidad Alberto Hurtado, Santiago 6500620, Chile
Jaime F. Lavin: Escuela de Negocios, Universidad Adolfo Ibáñez, Santiago 7941169, Chile

Mathematics, 2023, vol. 11, issue 7, 1-16

Abstract: 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.

Keywords: disagreement; textual analysis; predictability; stock returns; implied volatility; network methods; forecast models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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