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Determinants of Dividend Payouts: The Relative Contribution of Dividend Predictors in KOSPI and KOSDAQ FirmsDeterminants of Dividend Payouts: The Relative Contribution of Dividend Predictors in KOSPI and KOSDAQ Firms

Other Titles
Determinants of Dividend Payouts: The Relative Contribution of Dividend Predictors in KOSPI and KOSDAQ Firms
Authors
김인중
Issue Date
2021
Publisher
한국자료분석학회
Keywords
dividend; firm characteristic; relative weight; KOSPI; KOSDAQ
Citation
Journal of The Korean Data Analysis Society, v.23, no.3, pp.1007 - 1020
Journal Title
Journal of The Korean Data Analysis Society
Volume
23
Number
3
Start Page
1007
End Page
1020
URI
https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/15833
DOI
10.37727/jkdas.2021.23.3.1007
ISSN
1229-2354
Abstract
We decompose the traditional dividend model of Fama and French to investigate the relative contribution of major dividend predictors and the evolution of the model’s explanatory power over the economic crisis periods using KOSPI and KOSDAQ sampled firms. The relative weight analysis suggests that the predictive power of the traditional dividend model mainly comes from the earnings related firm characteristics, such as profitability and firm lifecycle, while the simple firm size, investment opportunity or risks can only play the supplementary role. The rank and the magnitude of this relative contribution remain very stable over time and the model as a whole explain both KOSPI and KOSDAQ firms’ dividend behaviors fairly well during the noncrisis period. However, the model’s dividend predictors all lose explanatory power during the crisis period. Particularly, KOSDAQ firms are more severely damaged by macroeconomic shocks and the changes in their firm characteristics can no longer explain majority of the variations in their dividend policy. Rather, the credit rating information that could only marginally contribute to the model accuracy, now emerges as a top tier influencer during the crisis period. Therefore, caution should be taken because the naive universal application of the model may bear noisy results.
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