Abstract
In recent years, machine learning techniques have assumed an increasingly central role in many areas of research, from computer science to medicine, including finance. In the current study, we applied it to financial literacy to test its accuracy, compared to a standard parametric model, in the estimation of the main determinants of financial knowledge. Using recent data on financial literacy and inclusion among Italian adults, we empirically tested how tree-based machine learning methods, such as decision trees, random, forest and gradient boosting techniques, can be a valuable complement to standard models (generalized linear models) for the identification of the groups in the population in most need of improving their financial knowledge.
Highlights
In the wake of the global financial crisis of 2007–2008 and of the recent events concerning the COVID-19 global pandemic crisis, the debate on the importance of financial literacy (FL) has gained further momentum, because more vulnerable and less informed investors are the most exposed to crises, and financial ones
This paper aims to contribute to the analysis of FL, extending the common methodology to machine learning (ML) techniques
Demonstrating that analytical steps of the econometric processes, like the logistic regression model that we apply to real data, has a homologous step in ML analyses, we clearly find a correspondence between parametric and ML techniques, with the goal to facilitate and reconcile the adoption of ML techniques in the context of financial literacy
Summary
In the wake of the global financial crisis of 2007–2008 and of the recent events concerning the COVID-19 global pandemic crisis, the debate on the importance of financial literacy (FL) has gained further momentum, because more vulnerable and less informed investors are the most exposed to crises, and financial ones. Lo Prete (2013, 2018) empirically tested how the ability to take advantage of different financial opportunities, measured by financial knowledge, may help to reduce inequality across countries and over time She found that the level of economic literacy is associated with income inequality across countries, using a sample of advanced and developing countries observed over the 1980–2007 period. Our analysis provides preliminary evidence that ML techniques can produce reliable information for financial literacy that is consistent with the literature, it can identify different patterns of correlations than traditional parametric models (i.e., high variable importance of financial behavior and attitude as determinants of financial knowledge). The proposed ML methodology can be used above and beyond our empirical analysis, because ML offers the opportunity to gain insight from: (a) new datasets that cannot be modelled with econometric methods; and (b) old datasets that incorporate complex relationships that are still unexplored
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