On tree-structured linear and quantile regression-based asset pricing

Abstract

Purpose

This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.

Design/Methodology/Approach

The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.

Findings

The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.

Originality/Value

To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

Citation

Galakis, J., Vrontos, I. and Xidonas, P. (2022), "On tree-structured linear and quantile regression-based asset pricing", Review of Accounting and Finance, Vol. 21 No. 3, pp. 204-245. https://doi.org/10.1108/RAF-10-2021-0283