Stock Return Prediction and Anomaly Detection by Regression Trees

79 Pages Posted: 14 Sep 2011

Date Written: September 1, 2011

Abstract

Noisy markets need extensive descriptions that are noisy themselves, such as deep regression trees that capture many data-local nonlinear anomalies and that do not require arbitrary weighting schemes like traditional linear multifactor models often do. Simple tools allow extraction of general and stock specific insightful information from such regression trees and facilitate factor selection. Large tree-models show excellent out-of-sample predictive power, that we measure and discuss extensively. The method is described by a seven-factor example model for the worldwide industrial sector that delivers 20% quintile-separation excess return out-of-sample and that reveals for instance how following price falls analysts react belatedly and investors overreact and thus create reversal outperformance opportunities.

Keywords: Stock Prediction, Nonlinear multifactor model, CART, Regression tree, Local anomaly, Model interpretation paradigm

JEL Classification: C18, C38, G11, G14, G17

Suggested Citation

Verbiest, Eddy Hector, Stock Return Prediction and Anomaly Detection by Regression Trees (September 1, 2011). Available at SSRN: https://ssrn.com/abstract=1927327 or http://dx.doi.org/10.2139/ssrn.1927327

Eddy Hector Verbiest (Contact Author)

affiliation not provided to SSRN ( email )

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