Determinants of dividend payout and dividend propensity in an emerging market, Iran: an application of the LASSO. Issue 42 (8th September 2019)

• Record Type: Journal Article
• Authors: Elyasiani, Elyas; Jia, Jingyi; Movaghari, Hadi
• Is Part Of: Applied economics. Volume 51:Issue 42(2019)
• Page Start: 4576
• Page End: 4596
• Publication Date: 2019-09-08
• Subjects: Dividend payout prediction -- emerging markets -- Iran -- least absolute shrinkage & selection operator (LASSO) -- variable selection; G35 -- C51 -- C52 -- C53; Economics -- Periodicals; 330
• DOI: 10.1080/00036846.2019.1593315
• Languages: English
• ISSNs: 0003-6846

Abstract

ABSTRACT: Accurate prediction of dividends is important for market participants such as investors, firm managers, and monitoring authorities, as they can, respectively, invest, manage dividend decisions, and monitor dividend policies more effectively. We identify the most relevant variables for predicting the dividend payout of the firms in an emerging market, Iran, using the least absolute shrinkage and selection operator (LASSO). The advantages of the LASSO include: enhancing the prediction accuracy of the dividend model, improving interpretation of the results, and applicability to high-dimensional data. We obtain several results. First, some fundamental determinants of dividends in the industrialized economies such as market-to-book ratio and current ratio, do not play a role in deciding dividends in Iran. Second, LASSO-selected variables outperform the variables commonly used in the literature in terms of model fit and prediction accuracy. Third, business risk, leverage, return on assets and effective tax rate are the most important predictors of dividend propensity of the Iranian firms. Fourth, if the support vector machine algorithm, an often-used classification method, is combined with LASSO-selected variables, it can better discriminate between dividend-paying and dividend non-paying firms than other methods such as logistic regression and linear discriminant analysis. Abbreviations: LASSO: Least Absolute Shrinkage and Selection Operator; TSE: Tehran Stock Exchange; RMSE: Root Mean Squared Errors; MAE: Mean Absolute Errors; ROC: Receiver Operating Characteristics; GMM: Generalized Method of Moments; MENA: Middle East and North Africa region; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; LARS: Least Angel Regression; OLS: Ordinary Least Squares; AUC: Area Under Curve; BS: Brier Score ; OA: Overall Accuracy; LDA: Linear Discriminant Analysis; SVM: Support Vector Machine algorithm; LR: Logistic Regression.