A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

By Bruce D. Phelps. 2009

1,881 citations recorded by the source corpus.

Subfields: Accounting, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Predicting Asset Returns (2018, methodology): “Cross-sectional time series predictability is studied using predictive regressions, see Kandel and Stam-baugh (1996), Barberis (2000) and Welch and Goyal (2008).”
Predictable Returns and Asset Allocation: Should a Skeptical Investor Time the Market? (2009, positive): “The various priors specified in the above studies all contain the same flaw: they require that the investor has knowledge of 15 Goyal and Welch (2008) present an ''encompassing forecast'', which, while not Bayesian, has similar implications in that it downweights the predictability coefficient…”
Technological Innovations and Aggregate Risk Premiums (2009, neutral): “This observation is not uncommon in the literature (e.g., Goyal and Welch, 2008) and may be attributed to the following factors: (1) U.S. economy has experienced some technology revolution waves since the 1970s, such as personal computers in the 1980s and the internet in the 1990s, which may cause…”
To Combine Forecasts or to Combine Information? (2010, methodology): “Our data construction replicates what Goyal and Welch (2008) did.”
Complete subset regressions (2013, methodology): “Variable definitions and data sources are described in more detail in Goyal and Welch (2008).”
Predictability of stock returns and asset allocation under structural breaks (2011, positive): “(4)Consistent with our results, Johannes et al. (2009) also find that parameter estimation uncertainty has a smaller effect on the asset allocation than uncertainty about changes to model parameters.”
Short Interest and Aggregate Stock Returns (2016, positive): “To relate our findings to the voluminous literature on market return predictability, we compare the predictive ability of aggregate short interest to that of 14 monthly predictor variables from Goyal and Welch (2008),6 which constitute a set of popular predictors from the literature.”
Volatility‐Managed Portfolios (2017, neutral): “Moreover, parameter instability for an agent that estimates volatility in real time is negligible, in contrast to strategies that try to time expected returns (Goyal and Welch (2008)).”
Reality Checks and Comparisons of Nested Predictive Models (2012, positive): “DGP 2 is based on the empirical properties of the quarterly stock return (corresponding to our predictand y t + ) and predictor ( x i;t ) data of Goyal and Welch (2008).”
Choice of Sample Split in Out-of-Sample Forecast Evaluation (2012, methodology): “This is consistent with the nding by Pesaran & Timmermann (1995) and Welch & Goyal (2008) that return predictability was particularly high after the rst oil shock in the seventies.”