6533b7d9fe1ef96bd126c025

RESEARCH PRODUCT

Statistical Learning Algorithms to Forecast the Equity Risk Premium in the European Union

David Cortés-sánchezPilar Soriano-felipe

subject

Boosting (machine learning)business.industryRisk premiumBig dataEnsemble learningRegressionRandom forestParametric modelEconomicsmedia_common.cataloged_instanceEuropean unionbusinessAlgorithmmedia_common

description

With the explosion of “Big Data”, the application of statistical learning models has become popular in multiple scientific areas as well as in marketing, finance or other business disciplines. Nonetheless, there is not yet an abundant literature that covers the application of these learning algorithms to forecast the equity risk premium. In this paper we investigate whether Classification and Regression Trees (CART) algorithms and several ensemble methods, such as bagging, random forests and boosting, improve traditional parametric models to forecast the equity risk premium. In particular, we work with European Monetary Union data for a period that spans from the EMU foundation at the beginning of 2000 to half of 2017.

yearjournalcountryeditionlanguage
2018-01-01
https://doi.org/10.1007/978-3-319-89824-7_47