Global Stock Selection with Hidden Markov Model

Nguyet Nguyen and Dung Nguyen
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Nguyet Nguyen: Department of Mathematics and Statistics, Youngstown State University, 31 Lincoln Ave., Youngstown, OH 44503, USA
Dung Nguyen: Ned Davis Research Group, 600 Bird Bay Drive West, Venice, FL 34285, USA

Risks, 2020, vol. 9, issue 1, 1-18

Abstract: Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and scored based on its historical performances. Second, HMM is used to predict the regimes of six global economic indicators and find the time periods in the past during which these indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All country world index (ACWI) in the identified time periods to assign a weighted score for each stock factor and to calculate the composite score of the five factors. Finally, we make a monthly selection of 10% of the global stocks that have the highest composite scores. This strategy is shown to outperform those relying on either ACWI, any single stock factor, or the simple average of the five stock factors.

Keywords: global stocks; trading; machine learning; hidden Markov model; economics; regimes; stock ranking; stocks’ factors; economics indicators (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
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