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Who is Andrew Chen
Meet the investor who predicted every major startup trend of the last decade. While others chase the latest buzz, Andrew Chen has consistently identified the patterns that define successful startups. As General Partner at Andreessen Horowitz, he’s not just investing in tech’s future – he’s writing its playbook.
His newsletter isn’t another collection of startup tips. It’s a masterclass in growth strategy, backed by 650+ essays that have become required reading in Silicon Valley. Want to know why some startups explode while others implode? His bestseller “The Cold Start Problem” reveals the hidden mechanics behind companies like Uber, Airbnb, and Slack.
From coining “Growth Hacker is the new VP Marketing” to exposing “The Law of Shitty Clickthroughs,” Andrew’s insights have guided countless founders through the treacherous waters of user acquisition and network effects. His work spans the practical to the profound, breaking down everything from CAC calculations to the psychology of viral growth.
Whether you’re building the next tech unicorn or simply fascinated by how great companies scale, Andrew’s expertise in tech, entertainment, and AI offers a rare glimpse into tomorrow’s innovation landscape. Join the thousands of founders and operators who consider his analysis essential reading for navigating the startup ecosystem.
The Rational Reminder Podcast
Episode 316 – Andrew Chen: “Is everything I was taught about cross-sectional asset pricing wrong?!”
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!
Key Points From This Episode:
(0:03:43) What an asset pricing factor is and how it differs from a predictor.
(0:04:25) Three plausible explanations for why cross-sectional predictors exist.
(0:05:45) Insight into Andrew’s Open Source Asset Pricing project and why it’s so important.
(0:09:49) Where the results of his research diverge from other papers on the subject.
(0:11:42) How the returns on anomalies in his data sample change post-publication.
(0:12:33) Implications of this research for the “replication crisis” in cross-sectional asset pricing.
(0:14:14) Challenges of false discovery rates, publication bias, and out-of-sample returns.
(0:18:37) The effect of transaction costs on expected returns from factor investing.
(0:22:02) Problems with estimating factor expected returns using historical data.
(0:26:08) A big-picture view of the factors with the strongest investable expected returns.
(0:29:12) The relative value of peer-reviewed factors with strong theoretical underpinnings.
(0:35:13) Whether or not machine learning can be useful for asset pricing research.
(0:37:39) Practical advice for using financial research to inform your investment decisions.
(0:40:08) Andrew’s take on the current state of cross-sectional asset pricing.
(0:42:58) The simple way that Andrew defines success for himself.
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 — [email protected]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
Open Source Asset Pricing Project — https://www.openassetpricing.com/
Center for Research in Security Prices — https://www.crsp.org/
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
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
