Non-parametric methods have been proposed to capture complex relationships between variables in high-dimensional datasets with the aim of classifying or predicting variables of interest. Among non-parametric methods, decision trees and random forests became popular because they do not require any assumptions regarding the distribution of the dependent variable, the explanatory variables, and the functional form of the relationships between them. These methods partition the space of the explanatory variables and associate a value of the target variable to each element of the partition. Their output is easy to interpret and allows for making predictions conditionally on the values of the explanatory variables. This chapter reviews some important aspects of Breiman's CART algorithm and discusses some features of classification and regression trees and random forests. Some original applications to nowcasting of financial time series and to default prediction in small and medium-size enterprises are given.

The paper provides an overview of the existing literature on decision trees and random forests and presents two new applications in economics and finance. More specifically, it shows how deep learners such as random forests can be used to evaluate the impact of macroeconomic variables on bonds and stocks inexes and how they can produce default predictions based on accountancy data.

Decision trees and random forests

Casarin R.;Sorice D.;Tonellato S.
2021-01-01

Abstract

The paper provides an overview of the existing literature on decision trees and random forests and presents two new applications in economics and finance. More specifically, it shows how deep learners such as random forests can be used to evaluate the impact of macroeconomic variables on bonds and stocks inexes and how they can produce default predictions based on accountancy data.
2021
The Essentials of Machine Learning in Finance and Accounting
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3733844
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