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Nonparametric prediction of stock returns with generated bond yields

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  • Michael Scholz

    ()

  • Stefan Sperlich

    ()
    (Université de Genéve
    Karl-Franzens University of Graz)

  • Jens Perch Nielsen

    ()
    ( Cass Business School)

Abstract

The question of whether empirical models are able to forecast the equity premium more accurately than the simple historical mean is intensively debated in the nancial literature. The low prediction power is disappointing, even when using nonparametric models that make use of typical predictor variables. Motivated by the co-movement of bond and stock returns, the inclusion of the current bond yield in a prediction model is proposed. This results in a notable improvement in the prediction of stock returns, as measured by the validated R2. Since the current bond yield is unknown, it is predicted in a prior step. The essential point is that the inclusion of the generated bond can be seen as a kind of dimension and complexity reduction that imposes more structure in an appropriate way that circumvents the curse of dimensionality and complexity.

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File URL: http://www-classic.uni-graz.at/vwlwww/forschung/RePEc/wpaper/2012-10.pdf
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Bibliographic Info

Paper provided by University of Graz, Department of Economics in its series Graz Economics Papers with number 2012-10.

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Date of creation: Dec 2012
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Handle: RePEc:grz:wpaper:2012-10

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Keywords: Prediction; Stock returns; Bond yield; Cross validation; Generated regressors;

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  1. Campbell, John, 1987. "Stock Returns and the Term Structure," Scholarly Articles 3207699, Harvard University Department of Economics.
  2. Martin Lettau & Stijn Van Nieuwerburgh, 2006. "Reconciling the Return Predictability Evidence," NBER Working Papers 12109, National Bureau of Economic Research, Inc.
  3. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
  4. Engsted, Tom & Tanggaard, Carsten, 2001. "The Danish stock and bond markets: comovement, return predictability and variance decomposition," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 243-271, July.
  5. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
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  7. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
  8. Stefan Sperlich, 2009. "A note on non-parametric estimation with predicted variables," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 382-395, 07.
  9. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
  10. Mammen, Enno & Martínez Miranda, María Dolores & Nielsen, Jens Perch & Sperlich, Stefan, 2011. "Do-Validation for Kernel Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 651-660.
  11. Vieu, Philippe, 1994. "Choice of regressors in nonparametric estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(5), pages 575-594, June.
  12. Kim, Tae Yoon & Park, Byeong U. & Moon, Myung Sang & Kim, Chiho, 2009. "Using bimodal kernel for inference in nonparametric regression with correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1487-1497, August.
  13. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
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