Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/45090
Título : Dividend distribution prediction for small- and medium-sized enterprises in an emerging economy
Otros títulos : Previsão de distribuição de dividendos para pequenas e médias empresas numa economia emergente
Autor : Correa Mejía, Diego Andrés
Correa García, Jaime Andrés
Castaño Ríos, Carlos Eduardo
metadata.dc.subject.*: Dividendos
Dividends
Pequeñas empresas
Small enterprises
Predicciones económicas
Economic forecasts
Economias emergentes
Política de dividendos
Fecha de publicación : 2024
Editorial : Fundação Instituto Capixaba de Pesquisas em Contabilidade, Economia e Finanças (FUCAPE)
Citación : Correa-Mejía, Diego & Correa García, Jaime & Castaño, Carlos. (2024). Dividend distribution prediction for small- and medium-sized enterprises in an emerging economy. Brazilian Business Review. 22. 1-23. 10.15728/bbr.2023.1578.en.
Resumen : ABSTRACT: The possibility of predicting dividends is a determining factor for shareholders, investors, and management, since dividends are an important variable for firm value creation. This study aims to predict small and medium-sized enterprises’ (SMEs) dividend decisions in an emerging Latin American economy. Supported by a sample of 20,418 observations for the years 2016-2018, a boosting model is applied to establish the incidence of a set of financial indicators for dividend prediction. The findings reveal that none of the financial indicators alone can explain future dividend expectations, demonstrating the robustness of the boosting model applied, and confirming that companies that have distributed dividends in the past are consistent with their dividend policy. This work has methodological, academic, and practical contributions to the SME segment, to the context in which it is studied, and to other emerging economies. The empirical evidence provided opens the door to future research that will enhance and deepen the study of this financial decision.
ISSN : 1808-2386
metadata.dc.identifier.doi: https://doi.org/10.15728/bbr.2023.1578.en
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