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Open AccessArticle

Global Asset Allocation Strategy Using a Hidden Markov Model

1
Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
2
School of Economics and Trade, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2019, 12(4), 168; https://doi.org/10.3390/jrfm12040168
Received: 29 August 2019 / Revised: 20 October 2019 / Accepted: 2 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue AI and Financial Markets)
This study uses the hidden Markov model (HMM) to identify the phases of individual assets and proposes an investment strategy using price trends effectively. We conducted empirical analysis for 15 years from January 2004 to December 2018 on universes of global assets divided into 10 classes and the more detailed 22 classes. Both universes have been shown to have superior performance in strategy using HMM in common. By examining the change in the weight of the portfolio, the weight change between the asset classes occurs dynamically. This shows that HMM increases the weight of stocks when stock price rises and increases the weight of bonds when stock price falls. As a result of analyzing the performance, it was shown that the HMM effectively reflects the asset selection effect in Jensen’s alpha, Fama’s Net Selectivity and Treynor-Mazuy model. In addition, the strategy of the HMM has positive gamma value even in the Treynor-Mazuy model. Ultimately, HMM is expected to enable stable management compared to existing momentum strategies by having asset selection effect and market forecasting ability. View Full-Text
Keywords: price momentum; hidden markov model; asset allocation price momentum; hidden markov model; asset allocation
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Kim, E.-C.; Jeong, H.-W.; Lee, N.-Y. Global Asset Allocation Strategy Using a Hidden Markov Model. J. Risk Financial Manag. 2019, 12, 168.

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