Abstract
This paper employs the hidden semi-Markov model and a novel model selection procedure to determine different regimes in the US stock market. The empirical results suggest that the US stock market is switching between five states that can be classified into three bull states and two bear states. The three bull states are categorized as a low-volatility bull market, a high-volatility bull market, and a stock market bubble. One of the bear states represents a regular bear market, while the other corresponds to either a stock market crash or a market correction. The paper demonstrates that the five-state model is consistent with a number of stylized facts and provides many valuable insights into the regime-switching dynamics of the US stock market and the risk-reward pattern of each regime. Besides, the paper demonstrates that the five-state model enables investors to make better asset allocation decisions. Specifically, in out-of-sample tests, the asset allocation strategy based on the five-state model achieves higher performance with lower risk than the strategy based on the two-state model and the buy-and-hold benchmark.
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Notes
The duration time is also known as the dwell time, occupancy time, holding time, or sojourn time.
We remind the reader that the best model is the model with the smallest value of AIC.
A somewhat similar critique of using the standard model selection criteria to determine the number of states in the HMM is presented by Pohle et al. (2017). These authors emphasize, among other things, that in selecting the number of states, one should consider the realism of the fitted candidate models assessed using expert knowledge.
In other words, N(J) is the number of transitions between the states.
The estimated mean return in state j is computed as the mean return over all months in the data sample decoded as being state j.
Note that \(r_t\) denotes the time t total return on the stocks that consists of the capital gain return \(x_t\) and the dividend yield. In this section, variables \(\mu\) and \(\sigma\) denote the mean total return and the standard deviation of the total returns.
References
Albeverio, S., V. Steblovskaya, and K. Wallbaum. 2013. Investment Instruments with Volatility Target Mechanism. Quantitative Finance 13 (10): 1519–1528.
Bodie, Z., A. Kane, and A.J. Marcus. 2013. Investments (10 edition). New York: McGraw Hill.
Bry, G., and Boschan, C. 1971. Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. NBER.
Bulla, J., I. Bulla, and O. Nenadic. 2010. hsmm - An R Package for Analyzing Hidden Semi-Markov Models. Computational Statistics & Data Analysis 54 (3): 611–619.
Butler, A., and M. Philbrick. 2012. Volatility Management for Better Absolute and Risk-Adjusted Performance. White paper: Macquarie Private Wealth Inc.
Carlson, M.A. 2007. A Brief History of the 1987 Stock Market Crash with a Discussion of the Federal Reserve Response. Board of Governors Federal Reserve System (FRS) Finance and Economics Discussion Paper.
Chauvet, M., and S. Potter. 2000. Coincident and Leading Indicators of yhe Stock Market. Journal of Empirical Finance 7 (1): 87–111.
Cochran, S.J., and R.H. Defina. 1995. Duration Dependence in yhe US Stock Market Cycle: A Parametric Approach. Applied Financial Economics 5 (5): 309–318.
Collie, R., M. Sylvanus, and M. Thomas. 2011. Volatility-Responsive Asset Allocation. Russell Investments: White paper.
Daniel, K., D. Hirshleifer, and A. Subrahmanyam. 1998. Investor Psychology and Security Market Under- and Overreactions. Journal of Finance 53 (6): 1839–1885.
De Angelis, L., and L.J. Paas. 2013. A Dynamic Analysis of Stock Markets Using a Hidden Markov Model. Journal of Applied Statistics 40 (8): 1682–1700.
De Bondt, W.F.M., and R. Thaler. 1985. Does the Stock Market Overreact? Journal of Finance 40 (3): 793–805.
Dias, J.G., J.K. Vermunt, and S. Ramos. 2015. Clustering Financial Time Series: New Insights From an Extended Hidden Markov Model. European Journal of Operational Research 243 (3): 852–864.
French, K.R., G. Schwert, and R.F. Stambaugh. 1987. Expected Stock Returns and Volatility. Journal of Financial Economics 19 (1): 3–29.
Gonzalez, L., J.G. Powell, J. Shi, and A. Wilson. 2005. Two Centuries of Bull and Bear Market Cycles. International Review of Economics and Finance 14 (4): 469–486.
Hardy, M.R. 2001. A Regime-Switching Model of Long-Term Stock Returns. North American Actuarial Journal 5 (2): 41–53.
Harman, Y.S., and T.W. Zuehlke. 2007. Nonlinear Duration Dependence in Stock Market Cycles. Review of Financial Economics 16 (4): 350–362.
Jiang, Y., and X. Fang. 2015. Bull, Bear or Any Other States in US Stock Market? Economic Modelling 44: 54–58.
Jobson, J.D., and B.M. Korkie. 1981. Performance Hypothesis Testing with the Sharpe and Treynor Measures. Journal of Finance 36 (4): 889–908.
Liu, Z., and S. Wang. 2017. Decoding Chinese Stock Market Returns: Three-State Hidden Semi-Markov Model. Pacific-Basin Finance Journal 44: 127–149.
Lunde, A., and A. Timmermann. 2004. Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets. Journal of Business and Economic Statistics 22 (3): 253–273.
Maheu, J.M., and T.H. McCurdy. 2000. Identifying Bull and Bear Markets in Stock Returns. Journal of Business and Economic Statistics 18 (1): 100–112.
Maheu, J.M., T.H. McCurdy, and Y. Song. 2012. Components of Bull and Bear Markets: Bull Corrections and Bear Rallies. Journal of Business and Economic Statistics 30 (3): 391–403.
Memmel, C. 2003. Performance Hypothesis Testing with the Sharpe Ratio. Finance Letters 1: 21–23.
Moreira, A., and T. Muir. 2017. Volatility-Managed Portfolios. Journal of Finance 72 (4): 1611–1644.
Nystrup, P., H. Madsen, and E. Lindstrom. 2015. Stylised Facts of Financial Time Series and Hidden Markov Models in Continuous Time. Quantitative Finance 15 (9): 1531–1541.
Ohn, J., L.W. Taylor, and A. Pagan. 2004. Testing for Duration Dependence in Economic Cycles. Econometrics Journal 7 (2): 528–549.
Pagan, A.R., and K.A. Sossounov. 2003. A Simple Framework for Analysing Bull and Bear Markets. Journal of Applied Econometrics 18 (1): 23–46.
Perchet, R., R.L. de Carvalho, T. Heckel, and P. Moulin. 2015. Predicting the Success of Volatility Targeting Strategies: Application to Equities and Other Asset Classes. Journal of Alternative Investments 18 (3): 21–38.
Phillips, P.C.B., S. Shi, and J. Yu. 2015. Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500. International Economic Review 56 (4): 1043–1078.
Pohle, J., R. Langrock, F.M. van Beest, and N.M. Schmidt. 2017. Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement. Journal of Agricultural, Biological and Environmental Statistics 22: 270–293.
Shiller, R.J. 1988. Portfolio Insurance and Other Investor Fashions as Factors in the 1987 Stock Market Crash. NBER Macroeconomics Annual 3: 287–297.
Sornette, D., A. Johansen, and J.-P. Bouchaud. 1996. Stock Market Crashes, Precursors And Replicas. Journal of Physics I France 6 (1): 167–175.
Timmermann, A. 2000. Moments of Markov Switching Models. Journal of Econometrics 96 (1): 75–111.
Zakamulin, V. 2014. Dynamic Asset Allocation Strategies Based on Unexpected Volatility. Journal of Alternative Investments 16 (4): 37–50.
Zakamulin, V. 2016. Optimal Dynamic Portfolio Risk Management. Journal of Portfolio Management 43 (1): 85–99.
Zakamulin, V. 2022. Revisiting the Duration Dependence in the US Stock Market Cycles. Forthcoming in Applied Economics.
Zhou, H., and S.E. Rigdon. 2011. Duration Dependence in Bull and Bear Stock Markets. Modern Economy 2 (3): 279–286.
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Zakamulin, V. Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model. Risk Manag 25, 5 (2023). https://doi.org/10.1057/s41283-022-00112-y
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DOI: https://doi.org/10.1057/s41283-022-00112-y