Nonparametric prediction of stock returns with generated bond yields
AbstractThe 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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Bibliographic InfoPaper provided by University of Graz, Department of Economics in its series Graz Economics Papers with number 2012-10.
Date of creation: Dec 2012
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Find related papers by JEL classification:
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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