Does Academic Research Destroy Stock Return Predictability

By R. D. McLean, Jeffrey Pontiff, Pierluigi Balduzzi, Turan G. Bali, Brad Barber, David Chapman, Mark Bradshaw, Shane Corwin, Alex Edmans, Lian Fen and 25 more. 2016

878 citations recorded by the source corpus.

Subfields: Accounting, Finance

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Citations in the corpus

Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, background): “In particular, many authors attempt to explain the cross-section of stock returns by using various factors, such as earnings–price ratio, company size and stock price momentum, and the efficacy of using such factors [1-3] .”
Forecasting: theory and practice (2020, background): “bination forecasts of cross-sectional stock returns based on a large number of firm characteristics from the cross-sectional literature (e.g., Harvey et al., 2016; McLean and Pontiff, 2016; Hou et al., 2020), extending the conventional OLS approach of Haugen and Baker (1996), Lewellen (2015), and Gr”
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, neutral): “McLean and Pontiff (2016) suggest a 32% lower return from publication-informed trading.”
Dissecting Characteristics Nonparametrically (2020, background): “…the parameters using only the selected variables and no penalty function addresses this problem. example, some characteristics might lose their predictive power for expected returns.18 McLean and Pontiff (2016) show that for 97 return predictors, predictability decreases by 58% post publication.”
The Cross Section of Expected Stock Returns (2014, methodology): “An investor using FM-based estimates of expected return would forecast, on the low end, excess returns that are zero or negative for many large stocks and, on the high end, excess returns greater than 15% annualized (the average monthly Tbill rate during the sample is 0.45%).”
Discussion of “disclosure processing costs, investors' information choice, and equity market outcomes: A review” (2020, background): “This phenomenon is also discussed in the review by Richardson et al. (2010), and in the taxonomy of anomalies by Novy-Marx and Velikov (2016) and Green et al. (2017), who refer to it as CAR PEAD and EAR, respectively.”
ESG Rating Disagreement and Stock Returns (2019, background): “First, the control variables are known return predictors, and there is evidence of lower post-publication return predictability for them (McLean and Pontiff 2016), especially in the United States (Jacobs and Müller 2020).”