Meet with PWL Capital: https://pages.pwlcapital.com/en-ca/contact-us?utm_source=content&utm_medium=youtube&utm_campaign=rationalreminder_yt Are you curious about the hidden factors driving your investment decisions? Today’s guest is Andrew Chen, a Principal Economist at the Federal Reserve Board who focuses on monetary policy and financial stability. Published in leading journals, his research informs key policy decisions and helps shape the Federal Reserve’s strategy for managing economic challenges effectively. In this episode, Andrew delves into the intricacies of meta-research and asset pricing, focusing on cross-sectional asset pricing predictors, replication, and out-of-sample performance in factor investing. We discuss the significance of open-source data and transparency, highlighting Andrew's creation of the Open Source Asset Pricing project, an indispensable and comprehensive dataset for asset pricing predictors. We also address the challenges of replicating financial studies, publication bias, data mining, and false discovery rates, with Andrew offering practical insights on how these factors impact financial research and investment decisions. For actionable insights that could refine your investment strategies and enhance your understanding of financial research, don’t miss this fascinating conversation! Timestamps: 0:00:00 Intro 0:04:44 Andrew defines asset pricing factors and how it is different from a predictor 0:06:22 Andrew explains how many predictors there are 0:10:55 How many asset pricing factors Andrew was successfully able to reproduce 0:15:58 The implications of this research for the supposed “replication crisis” in cross sectional asset pricing 0:22:01 How the false discovery rate relates to publication bias and out of sample returns 0:27:10 Whether these are the worst-case transaction costs, or if Andrew uses cost mitigation techniques 0:34:09 Which factors, or factor combinations, had the strongest investable expected returns in Andrew's data 0:38:33 How peer-reviewed factors with strong theoretical underpinnings perform relative to naively data mined factors 0:43:54 What this tells us about the academic peer review process 0:47:10 What this tells us about the usefulness of machine learning for asset pricing research 0:51:06 The implications for people using peer-reviewed research for asset allocation decisions 0:54:37 Andrew describes the current state of cross sectional asset pricing 0:58:54 Andrew defines success in his life Links From Today’s Episode: Rational Reminder on Apple Podcasts — https://podcasts.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582 Rational Reminder Website — https://rationalreminder.ca/ Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on X — https://x.com/RationalRemind Rational Reminder on YouTube — https://www.youtube.com/@rationalreminder/ Rational Reminder Email — info@rationalreminder.ca Benjamin Felix — https://www.pwlcapital.com/author/benjamin-felix/ Benjamin on X — https://x.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/ Cameron Passmore — https://www.pwlcapital.com/profile/cameron-passmore/ Cameron on X — https://x.com/CameronPassmore Cameron on LinkedIn — https://www.linkedin.com/in/cameronpassmore/ Mark McGrath on LinkedIn — https://www.linkedin.com/in/markmcgrathcfp/ Mark McGrath on X — https://x.com/MarkMcGrathCFP Andrew Chen — https://sites.google.com/site/chenandrewy/ Federal Reserve Board — https://www.federalreserve.gov/ Andrew Chen on LinkedIn — https://www.linkedin.com/in/andrew-chen-63394169/ Andrew Chen on X — https://x.com/achenfinance Books From Today’s Episode: The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics — https://www.amazon.com/dp/0199681147 Papers From Today’s Episode: Andrew Chen, Tom Zimmermann, ’Open Source Cross-Sectional Asset Pricing’— https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3604626 Kewei Hou, Chen Xue, Lu Zhang, ’Replicating Anomalies’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275496 R. David McLean, Jeffrey Pontiff, ’Does Academic Research Destroy Stock Return Predictability?’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623 Ilia D. Dichev, ’Is the Risk of Bankruptcy a Systematic Risk?’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=99868 Campbell R. Harvey, Yan Liu, Caroline Zhu, ‘...and the Cross-Section of Expected Returns’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2249314 Andrew Chen, Mihail Velikov, ‘Zeroing in on the Expected Returns of Anomalies’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3073681 Victor DeMiguel paper relating to which factors have the strongest investable expected returns — Andrew Chen, Alejandro Lopez-Lira, Tom Zimmermann, ‘Does Peer-Reviewed Research Help Predict Stock Returns?’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4308069