Quantitative Tactical Asset Allocation Using Ensemble Machine Learning Methods

8 Pages Posted: 27 May 2014 Last revised: 7 Dec 2015

Date Written: May 18, 2014


Beating the SP500 Index benchmark is a do-or-die among active portfolio managers. We propose a new method to add a 2-layer augmentation to relative strength and momentum based active portfolio management methods; first layer is to add a filtering mechanism to add a momentum filter in the recommendation engine and second is to include a multi level- multi layer machine learning method to integrate an ensemble model to decision making process. The ensemble model consists of gradient boosted decision trees and neural network models. Our initial results show that it is possible to beat the SP500 benchmark index by 600 basis points (in the calculations industry standard trading costs are included) as it is demonstrated by comparing the overall performance of the proposed method.

Keywords: Tactical Asset Allocation, Stocks, Bonds, Real Estate, Quantitative, Momentum, ETFs, GTAA, Machine Learning, Ensemble, alpha, neural network, CAGR

JEL Classification: C10, C50, E00, G10, G11

Suggested Citation

Oflus, Kemal, Quantitative Tactical Asset Allocation Using Ensemble Machine Learning Methods (May 18, 2014). Available at SSRN: https://ssrn.com/abstract=2438522 or http://dx.doi.org/10.2139/ssrn.2438522

Kemal Oflus (Contact Author)

Independent ( email )

Los Angeles, CA

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