Enhancing the momentum strategy through deep regression

Abstract

Momentum is a pervasive and persistent phenomenon in financial economics that has been found to generate abnormal returns not explainable by the traditional asset pricing models. This paper investigates some variations of the existing momentum strategies to increase profit and gain other desirable properties such as low kurtosis, small negative skewness and small maximum drawdown. We investigate these by using regression that is based on the latest techniques from deep learning such as stacked autoencoders and denoising autoencoders. Empirical results indicate that our regression-based variations can generate increased returns, and improved higher-order moments and maximum drawdown characteristics. Furthermore, our results reveal such improved performance can only be attained through the use of the latest deep learning technologies.

Keywords:

• Momentum strategy
• Deep learning
• Deep neural networks
• Autoencoders
• Denoising autoencoders

JEL Classification:

• C45
• G11

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Correction

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032722).