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International Stock Return Predictability: Is the Role of U.S. Time-Varying?

Author

Listed:
  • Goodness C. Aye

    () (Department of Economics, University of Pretoria)

  • Mehmet Balcilar

    () (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

This study investigates the predictability of 11 industrialized stock returns with emphasis on the role of U.S. returns. Using monthly data spanning 1980:2 to 2014:12, we show that there exist multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the U.S. returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (2013). Our results show that while the U.S. returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, it played no role based on the pooled version of the OLS model and its role based on the adaptive elastic net model is weak relative to Switzerland. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that U.S. stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the U.S. returns and those of other industrialized countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.

Suggested Citation

  • Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2015. "International Stock Return Predictability: Is the Role of U.S. Time-Varying?," Working Papers 15-07, Eastern Mediterranean University, Department of Economics.
  • Handle: RePEc:emu:wpaper:15-07.pdf
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    References listed on IDEAS

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    1. repec:eee:riibaf:v:42:y:2017:i:c:p:1173-1195 is not listed on IDEAS
    2. Rangan Gupta & Christian Pierdzioch & Andrew J. Vivian & Mark E. Wohar, 2018. "The Predictive Value of Inequality Measures for Stock Returns: An Analysis of Long-Span UK Data Using Quantile Random Forests," Working Papers 201809, University of Pretoria, Department of Economics.
    3. Christina Christou & Rangan Gupta & Fredj Jawadi, 2017. "Does Inequality Help in Forecasting Equity Premium in a Panel of G7 Countries?," Working Papers 201720, University of Pretoria, Department of Economics.
    4. Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2018. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," Working Papers 201830, University of Pretoria, Department of Economics.
    5. Nicholas Apergis & Matteo Bonato & Rangan Gupta & Clement Kyei, 2016. "Does Geopolitical Risks Predict Stock Returns and Volatility of Leading Defense Companies? Evidence from a Nonparametric Approach," Working Papers 201671, University of Pretoria, Department of Economics.
    6. Mehmet Balcilar & Deven Bathia & Riza Demirer & Rangan Gupta, 2017. "Credit Ratings and Predictability of Stock Returns and Volatility of the BRICS and the PIIGS: Evidence from a Nonparametric Causality-in-Quantiles Approach," Working Papers 201719, University of Pretoria, Department of Economics.
    7. Suleman, Tahir & Gupta, Rangan & Balcilar, Mehmet, 2017. "Does country risks predict stock returns and volatility? Evidence from a nonparametric approach," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1173-1195.

    More about this item

    Keywords

    Stock returns; predictability; structural breaks; nonlinearity; time varying causality;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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