scholarly journals Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?

Author(s):  
John Y. Campbell ◽  
Samuel Brodsky Thompson
Keyword(s):  



2019 ◽  
Vol 11 (12) ◽  
pp. 50
Author(s):  
Anwen Yin

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.



2018 ◽  
Vol 37 (5) ◽  
pp. 604-626
Author(s):  
Xiaoxiao Tang ◽  
Feifang Hu ◽  
Peiming Wang




Author(s):  
Loukia Meligkotsidou ◽  
Ekaterini Panopoulou ◽  
Ioannis D. Vrontos ◽  
Spyridon D. Vrontos


2015 ◽  
Vol 52 (8) ◽  
pp. 1935-1955 ◽  
Author(s):  
Rangan Gupta ◽  
Mampho P. Modise ◽  
Josine Uwilingiye


2020 ◽  
Vol 24 (6) ◽  
pp. 1313-1355 ◽  
Author(s):  
Philipp Adämmer ◽  
Rainer A Schüssler

Abstract We introduce a novel strategy to predict monthly equity premia that is based on extracted news from more than 700,000 newspaper articles, which were published in The New York Times and Washington Post between 1980 and 2018. We propose a flexible data-adaptive switching approach to map a large set of different news-topics into forecasts of aggregate stock returns. The information that is embedded in our extracted news is not captured by established economic predictors. Compared with the prevailing historical mean between 1999 and 2018, we find large out-of-sample (OOS) gains with an ROOS2 of 6.52% and sizeable utility gains for a mean–variance investor. The empirical results indicate that geopolitical news are at times more valuable than economic news to predict the equity premium and we also find that forecasting gains arise in down markets.



2019 ◽  
Vol 33 (8) ◽  
pp. 3583-3623 ◽  
Author(s):  
Alexis Akira Toda ◽  
Kieran James Walsh

Abstract We show that in a general equilibrium model with heterogeneity in risk aversion or belief, shifting wealth from an agent who holds comparatively fewer stocks to one who holds more reduces the equity premium. From an empirical view, the rich hold more stocks, so inequality should predict excess stock market returns. Consistent with our theory, we find that when the U.S. top ($\textrm{e.g.}$, 1%) income share rises, subsequent 1-year excess market returns significantly decline. This negative relation is robust to controlling for classic return predictors, predicting out-of-sample, and instrumenting inequality with estate tax rate changes. It also holds in international markets. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.



2019 ◽  
Vol 27 (1-2) ◽  
pp. 110-135 ◽  
Author(s):  
Loukia Meligkotsidou ◽  
Ekaterini Panopoulou ◽  
Ioannis D. Vrontos ◽  
Spyridon D. Vrontos


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