David Rapach is a research economist and adviser on the financial markets team in the research department at the Federal Reserve Bank of Atlanta. His research interests include asset pricing, forecasting, machine learning, international finance, and macroeconomics.
Before joining the Atlanta Fed in 2022, Dr. Rapach was a professor (and John Simon Endowed Chair in Economics), associate professor, and assistant professor in the Department of Economics at Saint Louis University from 2003 to 2022. Prior to that, he was an assistant professor at Seattle University and Trinity University and a visiting assistant professor at American University. He also served as a visiting professor of finance at Washington University in St. Louis from 2019 to 2021.Dr. Rapach has published widely in leading peer-reviewed journals, including the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Management Science, Journal of Empirical Finance, International Journal of Forecasting, Journal of Applied Econometrics, Journal of International Economics, Journal of International Money and Finance, and Journal of Urban Economics. He has presented his research at numerous seminars and conferences around the world.
Dr. Rapach is an associate editor for the International Journal of Forecasting and has served as a referee for many other journals, including the Journal of Finance, Review of Financial Studies, Management Science, Journal of Empirical Finance, Journal of Financial and Quantitative Analysis, American Economic Review, Journal of Political Economy, Journal of Business and Economic Statistics, Journal of Applied Econometrics, Journal of Econometrics, Journal of International Economics, Journal of International Money and Finance, Journal of Monetary Economics, and Journal of Urban Economics.
Dr. Rapach earned his bachelor's degree in economics, phi beta kappa and magna cum laude, from Randolph-Macon College in Ashland, Virginia, and his doctoral degree in economics from American University in Washington, DC.
The Anatomy of Out-of-Sample Forecasting Accuracy
Daniel Borup, Philippe Goulet Coulombe, David E. Rapach, Erik Christian Montes Schütte, and Sander Schwenk-Nebbe
Abstract | Full text
"Mixed-Frequency Machine Learning: Nowcasting and Backcasting Weekly Initial Claims with Daily Internet Search Volume Data" (with D. Borup and E.C.M. Schütte), forthcoming in the International Journal of Forecasting
"Asset Pricing: Time-Series Predictability" (with G. Zhou), Oxford Research Encyclopedia of Economics and Finance, June 20, 2022
"Forecasting: Theory and Practice" (with 79 co-authors), International Journal of Forecasting, 2022, 38(3), 705–871
"Anomalies and the Expected Market Return" (with X. Dong, Y. Li, and G. Zhou), Journal of Finance, 2022, 77(1), 639–681
"Industry Return Predictability: A Machine Learning Approach" (with J.K. Strauss, J. Tu, and G. Zhou), Journal of Financial Data Science, 2019, 1(3), 9–28
"Metro Business Cycles" (with M.A. Arias and C.S. Gascon), Journal of Urban Economics, 2016, 94, 90–108
"Short Interest and Aggregate Stock Returns" (with M.C. Ringgenberg and G. Zhou), Journal of Financial Economics, 2016, 121(1), 46–65
"Forecasting the Equity Risk Premium: The Role of Technical Indicators" (with C.J. Neely, J. Tu, and G. Zhou), Management Science, 2014, 60(7), 1772–1791
"International Stock Return Predictability: What is the Role of the United States?" (with J.K. Strauss and G. Zhou), Journal of Finance, 2013, 68(4), 1633–1662
"Forecasting US State-Level Employment Growth: An Amalgamation Approach" (with J.K. Strauss), International Journal of Forecasting, 2012, 28(2), 315–327
"International Comovements in Inflation Rates and Country Characteristics" (with C.J. Neely), Journal of International Money and Finance, 2011, 30(7), 1471–1490
"Bagging or Combining (or Both)? An Analysis Based on Forecasting US Employment Growth" (with J.K. Strauss), Econometric Reviews, 2010, 29(5), 511–533
"Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy" (with J.K. Strauss and G. Zhou), Review of Financial Studies, 2010, 23(2), 821–862
"Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods" (with G. Zhou), Machine Learning for Asset Management: New Developments and Financial Applications, 2020, Emmanuel Jurczenko (Ed.), Wiley, pp. 1–34
"Forecasting Stock Returns" (with G. Zhou), Handbook of Economic Forecasting, 2013, Vol. 2A, G. Elliott and A. Timmermann (Eds.), Elsevier, pp. 328–383