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Analysis and Comparison of Bitcoin and S and P 500 Market Features Using HMMs and HSMMs

by David Suda *,‡ and Luke Spiteri
Faculty of Science, University of Malta, Msida 2080, Malta
*
Author to whom correspondence should be addressed.
This is the extended version of our previous conference paper presented in the 2nd International Workshop on Blockchain and Smart Contract Technologies (BSCT 2019) which is in conjunction with the 22nd International Conference on Business Information Systems, 26–28 June 2019.
These authors contributed equally to this work.
Information 2019, 10(10), 322; https://doi.org/10.3390/info10100322
Received: 28 September 2019 / Revised: 11 October 2019 / Accepted: 16 October 2019 / Published: 18 October 2019
(This article belongs to the Special Issue Blockchain and Smart Contract Technologies)
We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist of several studies in finance. Popular labels given to market phases are “bull”, “bear”, “correction”, and “rally”. In the first part, we fit HMMs and HSMMs and look at the evolution of hidden state parameters and state persistence parameters over time to ensure that states are correctly classified in terms of market phase labels. We conclude that our modelling approaches yield positive results in both BTC/USD and the S and P 500, and both are best modelled via four-state HSMMs. However, the two assets show different regime volatility and persistence patterns—BTC/USD has volatile bull and bear states and generally weak state persistence, while the S and P 500 shows lower volatility on the bull states and stronger state persistence. In the second part, we put our models to the test of detecting different market phases by devising investment strategies that aim to be more profitable on unseen data in comparison to a buy-and-hold approach. In both cases, for select investment strategies, four-state HSMMs are also the most profitable and significantly outperform the buy-and-hold strategy. View Full-Text
Keywords: hidden Markov models; hidden semi-Markov models; cryptocurrencies; filtering; nowcasting hidden Markov models; hidden semi-Markov models; cryptocurrencies; filtering; nowcasting
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Suda, D.; Spiteri, L. Analysis and Comparison of Bitcoin and S and P 500 Market Features Using HMMs and HSMMs . Information 2019, 10, 322.

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