Out-of-Sample Return Predictability: A Quantile Combination Approach

2016 ◽  
Vol 32 (4) ◽  
pp. 877-895 ◽  
Author(s):  
Luiz Renato Lima ◽  
Fanning Meng
Keyword(s):  
Return Predictability ◽  
Sample Return ◽  
Out Of Sample ◽  
Combination Approach

scholarly journals Out-of-sample stock return predictability in Australia

2012 ◽  
Vol 37 (3) ◽  
pp. 461-479 ◽  
Author(s):  
Yiwen (Paul) Dou ◽  
David R. Gallagher ◽  
David Schneider ◽  
Terry S. Walter
Keyword(s):  
Stock Return ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Out Of Sample

Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis

2018 ◽  
Vol 44 (3) ◽  
pp. 10-24
Author(s):  
Giulia Dal Pra ◽  
Massimo Guidolin ◽  
Manuela Pedio ◽  
Fabiola Vasile
Keyword(s):  
Stock Return ◽  
Return Predictability ◽  
Portfolio Analysis ◽  
Regime Shifts ◽  
Stock Return Predictability ◽  
Out Of Sample

scholarly journals Break Risk

2020 ◽  
Author(s):  
Simon C Smith ◽  
Allan Timmermann
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Prediction Models ◽  
Return Predictability ◽  
Present Value ◽  
New Approach ◽  
Present Value Model ◽  
Out Of Sample ◽  
Rate Processes ◽  
Value Model

Abstract We develop a new approach to modeling and predicting stock returns in the presence of breaks that simultaneously affect a large cross-section of stocks. Exploiting information in the cross-section enables us to detect breaks in return prediction models with little delay and to generate out-of-sample return forecasts that are significantly more accurate than those from existing approaches. To identify the economic sources of breaks, we explore the asset pricing restrictions implied by a present value model which links breaks in return predictability to breaks in the cash flow growth and discount rate processes.

scholarly journals Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

2019 ◽  
Vol 11 (12) ◽  
pp. 50
Author(s):  
Anwen Yin
Keyword(s):  
Structural Breaks ◽  
Equity Premium ◽  
Model Averaging ◽  
Forecast Combination ◽  
Two Stage ◽  
Explicit Theory ◽  
Out Of Sample ◽  
Combination Approach ◽  
Averaged Models ◽  
Averaged Model

This paper introduces a two-stage out-of-sample predictive model averaging approach to forecasting the U.S. market equity premium. In the first stage, we combine the break and stable specifications for each candidate model utilizing schemes such as Mallows weights to account for the presence of structural breaks. Next, we combine all previously averaged models by equal weights to address the issue of model uncertainty. Our empirical results show that the double-averaged model can deliver superior statistical and economic gains relative to not only the historical average but also the simple forecast combination when forecasting the equity premium. Moreover, our approach provides an explicit theory-based linkage between forecast combination and structural breaks which distinguishes this study from other closely related works.

Out-of-sample stock return predictability in emerging markets

2016 ◽  
Vol 58 (3) ◽  
pp. 727-750
Author(s):  
Afsaneh Bahrami ◽  
Abul Shamsuddin ◽  
Katherine Uylangco
Keyword(s):  
Emerging Markets ◽  
Stock Return ◽  
Return Predictability ◽  
Stock Return Predictability ◽  
Out Of Sample
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