Realized Probability Index is a Better Market Timing Indicator
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Haibin Xie
,
Boyao Wu
Abstract
In practice, market timing is a well-known trading strategy among investors, and different indicators have been proposed for market timing methods. This paper compares three indicators used in market timing strategy: the return, the 0–1 binary index, and the realized probability index. It is shown that realized probability index is more informative and more efficient than the 0–1 binary index in terms of market timing, and tends to be more predictable than the return itself. This finding is interesting and important as it proves for the first time that the realized probability index is a more efficient market timing indicator and thus should be given more attention by both academic researchers and practitioners. An empirical study is performed on different stock indices, and the results confirm our finding.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 72271055
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 72073126
Award Identifier / Grant number: 71988101
Award Identifier / Grant number: 72322016
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Competing interests: The authors declare no competing interests regarding this article.
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Research funding: This research is supported by National Natural Science Foundation of China under Grant Nos. 72271055, 72073126, 71988101 and 72322016.
References
Anatolyev, S., and N. Gospodinov. 2010. “Modeling Financial Return Dynamics by Decomposition.” Journal of Business & Economic Statistics 28 (2): 232–45. https://doi.org/10.1198/jbes.2010.07017.Search in Google Scholar
Andersen, T. G., T. Bollerslev, F. X. Diebold, and H. Ebens. 2001a. “The Distribution of Realized Stock Return Volatility.” Journal of Financial Economics 61: 43–76. https://doi.org/10.1016/s0304-405x(01)00055-1.Search in Google Scholar
Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys. 2001b. “The Distribution of Exchange Rate Volatility.” Journal of the American Statistical Association 96: 42–55. https://doi.org/10.1198/016214501750332965.Search in Google Scholar
Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys. 2003. “Modeling and Forecasting Realized Volatility.” Econometrica 71: 579–625. https://doi.org/10.1111/1468-0262.00418.Search in Google Scholar
Barndorff-Nielsen, O. E., and N. Shephard. 2002. “Econometric Analysis of Realised Volatility and its Use in Estimating Stochastic Volatility Models.” Journal of the Royal Statistical Society: Series B 64: 253–80. https://doi.org/10.1111/1467-9868.00336.Search in Google Scholar
Bollerslev, T., N. Meddahi, and S. Nyawa. 2019. “High-dimensional Multivariate Realized Volatility Estimation.” Journal of Econometrics 212: 116–36. https://doi.org/10.1016/j.jeconom.2019.04.023.Search in Google Scholar
Bollerslev, T., A. J. Patton, and R. Quaedvlieg. 2018. “Modeling and Forecasting (Un)reliable Realized Covariances for More Reliable Financial Decisions.” Journal of Econometrics 207: 71–91. https://doi.org/10.1016/j.jeconom.2018.05.004.Search in Google Scholar
Bollerslev, T., A. J. Patton, and R. Quaedvlieg. 2020. “Realized Semicovariances.” Econometrica 88 (4): 1515–51. https://doi.org/10.3982/ecta17056.Search in Google Scholar
Chou, R. Y. 2005. “Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model.” Journal of Money, Credit, and Banking 37 (3): 561–82. https://doi.org/10.1353/mcb.2005.0027.Search in Google Scholar
Christoffersen, P., and F. Diebold. 2006. “Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics.” Management Science 52 (8): 1273–87. https://doi.org/10.1287/mnsc.1060.0520.Search in Google Scholar
Chung, J., and Y. Hong. 2007. “Model-free Evaluation of Directional Predictability in Foreign Exchange Markets.” Journal of Applied Econometrics 22 (5): 855–89. https://doi.org/10.1002/jae.965.Search in Google Scholar
Engle, R. F., D. M. Lilien, and R. P. Robins. 1987. “Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model.” Econometrica 55: 391–407. https://doi.org/10.2307/1913242.Search in Google Scholar
Hong, Y., and J. Chung. 2006. Are the Directions of Stock Price Changes Predictable? A Generalized Cross-Spectral Approach. Department of Economics, Cornell University. Working Paper.Search in Google Scholar
Kauppi, H., and P. Saikkonen. 2008. “Predicting U.S. Recessions with Dynamic Binary Response Models.” The Review of Economics and Statistics 90: 777–91. https://doi.org/10.1162/rest.90.4.777.Search in Google Scholar
Leitch, G., and J. Tanner. 1991. “Economic Forecast Evaluation: Profits versus the Conventional Error Measures.” The American Economic Review 81: 580–90.Search in Google Scholar
Leitch, G., and J. Tanner. 1995. “Professional Economic Forecasts: Are They Worth Their Costs?” Journal of Forecasting 14: 143–57. https://doi.org/10.1002/for.3980140206.Search in Google Scholar
Leung, M. T., H. Daouk, and A. S. Chen. 2000. “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models.” International Journal of Forecasting 16: 173–90. https://doi.org/10.1016/s0169-2070(99)00048-5.Search in Google Scholar
Merton, R. 1981. “On Market Timing and Investment Performance: An Equilibrium Theory of Value for Market Forecasts.” Journal of Business 54: 363–406. https://doi.org/10.1086/296137.Search in Google Scholar
Neuberger, A. 2012. “Realized Skewness.” Review of Financial Studies 25 (11): 3423–55. https://doi.org/10.1093/rfs/hhs101.Search in Google Scholar
Newey, W. K., and K. D. West. 1987. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55 (3): 703–8. https://doi.org/10.2307/1913610.Search in Google Scholar
Nyberg, H. 2011. “Forecasting the Direction of the US Stock Market with Dynamic Binary Probit Models.” International Journal of Forecasting 27 (2): 561–78. https://doi.org/10.1016/j.ijforecast.2010.02.008.Search in Google Scholar
Nyberg, H., and H. Ponka. 2016. “International Sign Predictability of Stock Returns: The Role of the United States.” Economic Modelling 58: 323–38. https://doi.org/10.1016/j.econmod.2016.06.013.Search in Google Scholar
Pring, M. J. 1991. Technical Analysis Explained: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points. New York: McGraw-Hill.Search in Google Scholar
Rydberg, T., and N. Shephard. 2003. “Dynamics of Trade-By-Trade Price Movements: Decomposition and Models.” Journal of Financial Econometrics 1: 2–25. https://doi.org/10.1093/jjfinec/nbg002.Search in Google Scholar
Welch, I., and A. Goyal. 2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” It Review of Financial Studies 21 (4): 1455–508. https://doi.org/10.1093/rfs/hhm014.Search in Google Scholar
Xie, H. B., J. J. Zhang, Y. Chen, and Z. D. Lu. 2024. “Realized Probability.” Journal of Systems Science and Complexity. https://sysmath.cjoe.ac.cn/jssc/EN/abstract/abstract52982.shtml.Search in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/snde-2024-0060).
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