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Forecasting the direction of the US stock market with dynamic binary probit models

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  • Nyberg, Henri

Abstract

Several empirical studies have documented that the signs of excess stock returns are, to some extent, predictable. In this paper, we consider the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns. The recession forecast obtained from the model for a binary recession indicator appears to be the most useful predictive variable, and once it is employed, the sign of the excess return is predictable in-sample. The new dynamic "error correction" probit model proposed in the paper yields better out-of-sample sign forecasts, with the resulting average trading returns being higher than those of either the buy-and-hold strategy or trading rules based on ARMAX models.

Suggested Citation

  • Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.
  • Handle: RePEc:eee:intfor:v:27:y::i:2:p:561-578
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    References listed on IDEAS

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    Cited by:

    1. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Con?dence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics.
    2. Candelon Bertrand & Ahmed Jameel & Straetmans Stefan, 2012. "Predicting and Capitalizing on Stock Market Bears in the U.S," Research Memorandum 019, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    3. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, pages 72-86.
    4. Thomas Bury, 2013. "Predicting trend reversals using market instantaneous state," Papers 1310.8169, arXiv.org, revised Mar 2014.
    5. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, pages 72-86.
    6. Pönkä, Harri, 2016. "Real oil prices and the international sign predictability of stock returns," Finance Research Letters, Elsevier, pages 79-87.
    7. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    8. Ginker, Tim & Lieberman, Offer, 2017. "Robustness of binary choice models to conditional heteroscedasticity," Economics Letters, Elsevier, vol. 150(C), pages 130-134.
    9. Pönkä, Harri, 2017. "Sentiment and sign predictability of stock returns," MPRA Paper 81861, University Library of Munich, Germany.
    10. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, pages 1451-1480.
    11. de Resende, Charlene C. & Pereira, Adriano C.M. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2017. "Investigating market efficiency through a forecasting model based on differential equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 199-212.
    12. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 193-220, February.
    13. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    14. repec:eee:riibaf:v:42:y:2017:i:c:p:39-60 is not listed on IDEAS
    15. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2017. "Further evidence on bear market predictability: The role of the external finance premium," International Review of Economics & Finance, Elsevier, pages 106-121.
    16. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, pages 181-186.
    17. Stanislav Anatolyev & Jozef Barunik, 2017. "A simple model for forecasting conditional return distributions," Papers 1711.05681, arXiv.org.
    18. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    19. Rafik Nazarian & Ashkan Amiri, 2014. "Asymmetry of the Oil Price Pass–Through to Inflation in Iran," International Journal of Energy Economics and Policy, Econjournals, pages 457-464.
    20. repec:spr:mathme:v:86:y:2017:i:2:d:10.1007_s00186-017-0602-4 is not listed on IDEAS
    21. Nyberg, Henri, 2010. "QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles," MPRA Paper 23724, University Library of Munich, Germany.
    22. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    23. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    24. Luis H. R. Alvarez E. & Paavo Salminen, 2016. "Timing in the Presence of Directional Predictability: Optimal Stopping of Skew Brownian Motion," Papers 1608.04537, arXiv.org.
    25. Konstantinos Angelopoulos & George Economides & Apostolis Philippopoulos, "undated". "What is the best environmental policy? Taxes, permits and rules under economic and environmental uncertainty," DEOS Working Papers 1014, Athens University of Economics and Business.

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