JOURNAL ARTICLE

Dividend policy effect on common stock price volatility: An empirical evidence from Indian companies.

  • Published In: International Journal of Financial Engineering, 2023, v. 10, n. 4. P. 1 1 of 3

  • Database: Mathematics Source 2 of 3

  • Authored By: Goud, N. Narsa 3 of 3

Abstract

Despite empirical research, the relationship between dividend policy and stock price volatility is continuously debatable. This study investigated the relationship between dividend policy and stock price volatility of Indian listed companies. The study examined 260 listed companies based on the reliable dividend-paying manners of nonfinancial companies listed on the Bombay Stock Exchange (BSE) for the financial period from 2014–2015 to 2020–2015. To analyze the data, this study used the panel data models: fixed effects, random effects, and the Hausman test. Finally, this study applied the fixed effect model after careful examination of multicollinearity, endogeneity, and causality issues related to the dataset. The analyses revealed a significant negative relationship between dividend payout and stock price volatility meanwhile, dividend yield and stock price volatility have a positive association. The study outcomes provide information for investors and managers about dividend decision. This study provides an extensive understanding of the emerging stock market fluctuation on the relationship with the dividend policy. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Financial Engineering. 2023/12, Vol. 10, Issue 4, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:2424-7863
  • DOI:10.1142/S2424786323500287
  • Accession Number:174344997
  • Copyright Statement:Copyright of International Journal of Financial Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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