Using neural networks to improve cross-sectional momentum strategy performance
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School of Business |
Master's thesis
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Authors
Date
2019
Department
Major/Subject
Mcode
Degree programme
Finance
Language
en
Pages
39+5
Series
Abstract
Machine learning and in particular, neural networks, are powerful tools for predictive analysis and used in a wide range of applications. I combine the neural networks and momentum investing with the aim to construct a neural network enhanced momentum strategy. I train the neural network to predict future relative performance of stocks in US between 1964 and 2012 using past returns and risk indicators as a feature set. I find that neural network momentum strategy generates monthly average returns of 3.18% (t-stat 11.81), outperforming the traditional momentum strategy by 1.39%-points. I also find that long-term momentum is the main driver of the relative performance predictions and that short and mid-term historical volatilities are the most important risk factors explaining variation in the predictions.Description
Thesis advisor
Nyberg, PeterKeywords
neural, networks, neural networks, momentum, momentum crashes