“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-Fren...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/7/1591 |
| _version_ | 1827746732062015488 |
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| author | Nicolas S. Magner Nicolás Hardy Tiago Ferreira Jaime F. Lavin |
| author_facet | Nicolas S. Magner Nicolás Hardy Tiago Ferreira Jaime F. Lavin |
| author_sort | Nicolas S. Magner |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:30:28Z |
| format | Article |
| id | doaj.art-31c39ac302d240cf9620e4a41487d7cc |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T05:30:28Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-31c39ac302d240cf9620e4a41487d7cc2023-11-17T17:07:49ZengMDPI AGMathematics2227-73902023-03-01117159110.3390/math11071591“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement AnalysisNicolas S. Magner0Nicolás Hardy1Tiago Ferreira2Jaime F. Lavin3Facultad de Administración y Economía, Universidad Diego Portales, Santiago 8370191, ChileFacultad de Administración y Economía, Universidad Diego Portales, Santiago 8370191, ChileFacultad de Economía y Negocios, Universidad Alberto Hurtado, Santiago 6500620, ChileEscuela de Negocios, Universidad Adolfo Ibáñez, Santiago 7941169, ChileThis 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.https://www.mdpi.com/2227-7390/11/7/1591disagreementtextual analysispredictabilitystock returnsimplied volatilitynetwork methods |
| spellingShingle | Nicolas S. Magner Nicolás Hardy Tiago Ferreira Jaime F. Lavin “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis Mathematics disagreement textual analysis predictability stock returns implied volatility network methods |
| title | “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis |
| title_full | “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis |
| title_fullStr | “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis |
| title_full_unstemmed | “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis |
| title_short | “Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis |
| title_sort | agree to disagree forecasting stock market implied volatility using financial report tone disagreement analysis |
| topic | disagreement textual analysis predictability stock returns implied volatility network methods |
| url | https://www.mdpi.com/2227-7390/11/7/1591 |
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