Abstract
Regime detection is vital for the effective operation of trading and investment strategies. However, the most popular means of doing this, the two-state Markov-switching regression model (MSR), are not an optimal solution, as two volatility states do not fully capture the complexity of the market. Past attempts to extend this model to a multi-state MSR have proved unstable, potentially expensive in terms of trading costs, and can only divide the market into states with varying levels of volatility, which is not the only aspect of market dynamics relevant to trading. We demonstrate it is possible and valuable to instead segment the market into more than two states not on the basis of volatility alone, but on a combined basis of volatility and trend, by combining the two-state MSR with an adaptive moving average. A realistic trading framework is used to demonstrate that using two selected states from the four thus generated leads to better trading performance than traditional benchmarks, including the two-state MSR. In addition, the proposed model could serve as a label generator for machine learning tasks used in predicting financial regimes ex ante.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goldfeld, S. M., & Quandt, R. E. (1973). A Markov model for switching regressions. Journal of Econometrics, 1(1), 3–15.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 357–384.
Krolzig, H.-M. (1997). Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis. Springer.
Hauptmann, J., Hoppenkamps, A., Min, A., Ramsauer, F., & Zagst, R. (2014). Forecasting market turbulence using regime-switching models. Financial Markets and Portfolio Management, 139–164.
Kaufman, P. J. (1995). Smarter trading: Improving performance in changing markets. McGraw-Hill.
Timmermann, A. (2000). Moments of Markov switching models. Journal of Econometrics, 96(1), 75–111.
Kim, C.-J., & Nelson, C. R. (1998). State space models with regime switching: Classical and Gibbs-sampling approaches with applications. MIT Press.
Kim, C.-J. (1993). Unobserved-component time series models with Markov-switching heteroscedasticity: Changes in regime and the link between inflation rates and inflation uncertainty. Journal of Business & Economic Statistics, 11(3), 341–349.
Jeanne, O., & Masson, P. (2000). Currency crises, sunspots and Markov-switching regimes. Journal of International Economics, 50(2), 327–350.
Bulla, J. (2011). Hidden Markov models with t components. Increased persistence and other aspects. Quantitative Finance, 11(3), 459–475.
Srivastava, S., & Bhattacharyya, R. (2018). Evaluating the building blocks of a dynamically adaptive systematic trading strategy. Available at SSRN 3144169.
Wyckoff, R. D. (1937). The Richard D. Wyckoff method of trading in stocks. Wyckoff Associates, Inc.
Ruiz-Franco, L., Jiménez-Gómez, M., & Lambis-Alandete, E. (2018). Trading strategy on the future mini S & P 500. International Journal of Applied Engineering Research, 13(13), 11018–11024.
Arthur, D., & Vassilvitskii, S. (2006). k-means++: The advantages of careful seeding.
Boldin, M. D. (1996). A check on the robustness of Hamilton's Markov switching model approach to the economic analysis of the business cycle. Studies in Nonlinear Dynamics & Econometrics, 1(1).
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423–427.
Edelen, R., Evans, R., & Kadlec, G. (2013). Shedding light on “invisible” costs: Trading costs and mutual fund performance. Financial Analysts Journal, 69(1), 33–44.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pomorski, P., Gorse, D. (2023). Improving on the Markov-Switching Regression Model by the Use of an Adaptive Moving Average. In: Gartner, W.C. (eds) New Perspectives and Paradigms in Applied Economics and Business. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-23844-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-23844-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23843-7
Online ISBN: 978-3-031-23844-4
eBook Packages: Economics and FinanceEconomics and Finance (R0)