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Realized Probability Index is a Better Market Timing Indicator

  • Haibin Xie EMAIL logo , Boyao Wu , Yuying Sun und Shouyang Wang
Veröffentlicht/Copyright: 20. Januar 2025
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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.

JEL Classification: G17; C22
PACS: 05.45.Tp

Corresponding author: Haibin Xie, China School of Banking and Finance, University of International Business and Economics, Beijing, China, E-mail: 

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

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Competing interests: The authors declare no competing interests regarding this article.

  3. 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche in Google Scholar

Neuberger, A. 2012. “Realized Skewness.” Review of Financial Studies 25 (11): 3423–55. https://doi.org/10.1093/rfs/hhs101.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche 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.Suche in Google Scholar


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/snde-2024-0060).


Received: 2024-06-13
Accepted: 2024-12-16
Published Online: 2025-01-20

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 21.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/snde-2024-0060/pdf
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