The importance of large shocks to return predictability

• Published in: Pacific-Basin finance journal 2021-09, Vol.68, p.101518, Article 101518
• Main Authors: Diaz, Juan, Duarte, Diogo, Galindo, Hamilton, Montecinos, Alexis, Truffa, Santiago
• Format: Article
• Language: English
• Subjects: Bias correction; China trade shock; Directional trading; In- and out-of-sample forecast; Return predictability
• ISSN: 0927-538X; 1879-0585
• DOI: 10.1016/j.pacfin.2021.101518

Based on the rare disasters literature of Barro and Ursúa (2008), Barro and Ursúa (2009), and Barro and Jin (2011), we show that the predictability of the S&P500 returns increases substantially when we control the regressions for major historical events, such as the Great Depression, World War I, World War II, the oil crisis of 1973-1974, and the subprime mortgage crisis. Controlling for these large shocks, the model with the dividend-earnings ratio as the regressor reaches an in-sample performance with an R2 of 27.6%, while all the other models increase their R2 after correcting for these large shocks. In addition, we show that controlling for major historical events improves the prediction performance, reducing the RSME in all of the 21 models we investigate. We check the robustness of our method by investigating the effects of controlling for the China trade shock of 2001 on the R2 and RMSE of the bias-corrected regressions. Our findings suggest that correcting for these shocks is critical to improve prediction performance. •We study how a correction for large shocks can improve the prediction performance of the S&P500 return regressions.•We use Robert Barro's famous literature about disasters to correct the typical return regressions for these big shocks.•We find that when we correct for these large shocks, the in sample R2 of all regressions improves considerable.•We find the RSME out of sample also improves when we use the corrected parameters for the new data.