Ph.D. in Finance: empirical asset pricing, mutual funds

Joined September 2019
a cool paper!
We have a new working paper out today. Demand, supply AND their interaction matter for risky financial assets. How does this interaction affect demand estimation for risky bonds? What can one do about it? What are some macro implications? This and more in this paper and 🧵!
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A must-read paper.
Just accepted at RFS: Firms that hire AI workers have a labor share of income that is 5% lower. But such AI workers earn $22k+ more per year. The firms that own the data profit more and grow bigger. w/Simona Abis. doi.org/10.1093/rfs/hhad059
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New 150+ (!) page survey on Financial #MachineLearning by Bryan Kelly and Dacheng Xiu! 👉 papers.ssrn.com/sol3/papers.… Unfortunately, only limited discussion of international results…
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#RFS #Forthcoming: The Partisanship of Financial Regulators Joseph Engelberg @RadySchool Matthew Henriksson @HaslamUT Asaf Manela @AsafManela @WUSTL Jared Williams @USFMuma doi.org/10.1093/rfs/hhad029
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Thanks NBER for creating a video of the plenary talk on Valuing Data as an Asset that I gave Friday night at the Chicago NBER meetings. And thanks to all my co-authors who made this work possible. youtu.be/sTDdibkkEMI #dataeconomy #EconTwitter @nberpubs
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An excellent paper that everyone interested in empirical asset pricing should read. Great to see this.
Happy to see this coming finally out. Open Access link: doi.org/10.1111/jofi.13197
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In case the preview link and picture do not work: papers.ssrn.com/sol3/papers.…
New paper out: Can machine learning boost stock selection in emerging markets? 🚀 🚀 🚀 @SSRN: papers.clicksocialimage.top/… We also investigate: 📈 Relationship to ML forecasts from DM 📈 Do limits to arbitrage explain the results? 📈 Net returns under real-life investment frictions
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A great podcast! As a follower of the podcasts and a former quant trader, really like the guys, and best wishes for the podcast. Also, as a researcher focusing on mutual funds, I really enjoy reading the works of BvB, but I still learn a lot from the podcast.
Episode 220: Jonathan Berk and Jules van Binsbergen: The Arithmetic of Active Management, Revisited rationalreminder.ca/podcast/…
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"Machine Learning and Fund Characteristics Help to Select Mutual Funds:" @SSRN papers.ssrn.com/sol3/papers.…, with @JGilBazo, @fjnogales, @AndrePortelaSa1 . We unveil interactions between fund activeness and past performance--active funds help, but only those with good past performance
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The 2022 MFS Virtual Summer School is one month away. With Jesus Fernandez-Villaverde (Penn), @WenxinDu (Chicago), @HannoLustig (Stanford), @DimitrisPapan20 (Northwestern), and @laura_veldkamp (Columbia). Sign up today at: macrofinancesociety.org/mfs-…
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yangyi liu retweeted
Instead, we need to imagine what it's like for the person reading our paper, or scanning titles, or the editor reading our revision. And if you take their perspective, you'll realize that we assume way too much common knowledge.
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Unpalatable @JohnHCochrane? Perhaps. But necessary. And about time. Perhaps we don’t need to abandon all of modern inter temporal macro. But we DO need to abandon the NRH hypothesis. This paper static1.squarespace.com/stat… and the literature it cites, shows how to do that. 1
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New paper "Internationalizing like China"! We empirically characterizes the Renminbi’s bond market internationalization. We find that foreigners increasingly buy these bonds as substitute for developed government debt. We provide a model to understand China's strategy.
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Forthcoming soon: Berk @StanfordGSB, van Binsbergen @Wharton model a market for a skill in short supply where charlatans are present in equilibrium. Reducing the number of charlatans via regulation lowers consumer surplus by reducing producer competition. doi.org/10.1111/jofi.13112
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yangyi liu retweeted
How do long-term investors choose portfolios in a world in which risk and returns change over time? The lead article in the Review of Finance is "Portfolios for Long-Term Investors" by @JohnHCochrane Open Access at academic.oup.com/rof/article…
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yangyi liu retweeted
Behavioural economics has been very successful at showing deviations from the old economic model. The next step is to explain them! from my forthcoming “Optimally Irrational”
Also worth reading: "Behavioural economics has some similarities to the state of astronomy in 1500 – it is still at the collection of deviation stage. There aren’t 165 human biases. There are 165 deviations from the wrong model." evonomics.com/please-not-ano…
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yangyi liu retweeted
"The Purpose of a Finance Professor", my keynote speech at the 2021 FMA Annual Meeting. @finmgmtassoc youtube.com/watch?v=2OnZ52pE…
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yangyi liu retweeted
R has a reputation of being terrible at running regressions. This is no longer true, thanks to -fixest- package. I made a Stata -> R Cheatsheet to try and prove it @lrberge single-handedly remade all the good features of stata. It's truly a feat kylebutts.com/pages/fixest_c…
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OK, done.
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