Forecasting the Dividend Policy Using Machine Learning Approach: Decision Tree Regression Models

Hanaan Yaseen () and Victor Dragotă
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Hanaan Yaseen: Bucharest University of Economic Studies, Doctoral School of Finance, CEFIMO

A chapter in Eurasian Business and Economics Perspectives, 2021, pp 19-39 from Springer

Abstract: Abstract Dividend policy is still one of the most discussed issues in corporate finance. Many papers are determined to find which are the most relevant factors influencing dividend payments. The list of possible determining factors of dividend policy is very large, being difficult to integrate all of them in the decision-making process. In our paper, we propose an approach based on machine learning methods, using decision tree regression models. Using a database of 11,248 companies from 70 countries, for the period 2008–2014, we found the most relevant input factors which determine the level of the dividend payout ratio. On this shortlist of factors, both companies’ financial indicators (size, return on equity, beta, leverage, market-to-book ratio, foreign holdings) and sociocultural factors at the country level (legal origin, GDP/capita, pluralism index, social progress index, democracy index, Hofstede’s harmony hierarchy and egalitarianism indexes) are present. The best prediction models are similar for both developed and developing countries.

Keywords: Dividend policy; Dividend policy forecasting; Decision tree; Dividend payout ratio (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:eurchp:978-3-030-71869-5_2

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DOI: 10.1007/978-3-030-71869-5_2

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