Firm-Specific Determinants on Dividend Changes: Insights from Data Mining

Luebke, Karsten; Rojahn, Joachim

doi:10.1007/978-3-319-25226-1_29

Cited by 5 publications

(1 citation statement)

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“…However, variable importance and prediction performance in terms of misclassification error rates may vary with the technique applied (Dietterich, 1998; Bolón-Canedo et al. , 2013; Luebke and Rojahn, 2016). Therefore, we employ different classification techniques to analyze the importance of features in explaining financial resilience.…”

Section: Introductionmentioning

confidence: 99%

The influence of financial literacy on financial resilience – New evidence from Europe during the COVID-19 crisis

Erdem

Rojahn

2022

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PurposeThis study examines the importance of financial literacy in explaining financial resilience in four continental European countries during the coronavirus disease 2019 (COVID-19) crisis while controlling for a wide set of additional determinants.Design/methodology/approachVariable importance may vary with the technique applied. Therefore, different classification techniques, such as logistic regression, partial proportional odds regression, and conditional random forest, have been employed. The analysis relies on the Survey of Health, Ageing and Retirement in Europe in the context of COVID-19, collecting 4,781 observations from France, Germany, Italy, and Spain.FindingsIn line with previous studies, financial resilience is found to increase with financial literacy that consistently ranks in the midfield in terms of variable importance among all explanatory variables.Practical implicationsThe findings reveal the most important features that improve financial resilience. Financial literacy is one of the few determinants of financial resilience that can be actively shaped. To increase preparedness for future crises, a policy mix of financial education, regulation, and nudging may help increase financial literacy and, subsequently, financial resilience.Originality/valueThe better the financial literacy, the more protected individuals are from macroeconomic shocks. However, most previous studies do not rely on data samples that cover such crises. Moreover, most of the previous studies rely on single classification techniques, while this study applied traditional and data-mining techniques to assess feature importance.

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Section: Introductionmentioning

confidence: 99%