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20 results

The Value of ETF Liquidity

Review of Financial Studies 2024 37(10), 3092-3148 open access
We analyze how ETFs compete. Drawing on a new model and empirical analysis, we show that ETF secondary market liquidity plays a key role in determining fees. More liquid ETFs for a given index charge higher fees and attract short-horizon investors who are more sensitive to liquidity than to fees. Higher turnover from these investors sustains the ETF’s high liquidity, allowing the ETF to extract a rent through its fee, and creating a first-mover advantage. Liquidity segmentation through clientele effects generates welfare losses. Our findings resolve the apparent paradox that higher-fee ETFs not only survive but also flourish in equilibrium.

The investment effects of dark trading

Journal of Financial Markets 2026 open access
Almost half of US share trading volume occurs in dark markets, prompting regulatory concerns. We examine the effects of dark trading on issuers and show that, at moderate levels, dark trading improves the quality of corporate investment decisions by increasing the amount of information in prices that is new to managers. Consistent with this mechanism, higher dark trading is associated with greater investment–price sensitivity, improved managerial forecast accuracy, stronger M&A-price sensitivity, and superior future operating performance. These benefits diminish, and can reverse, at high levels of dark trading. We establish causality using exogenous changes in dark trading.

Biased risk perceptions: Evidence from the laboratory and financial markets

Journal of Banking & Finance 2023 154, 106685
Applying a well-established neuroscience framework to the issue of investor perception of volatility, we propose that after prolonged exposure to high volatility, investors tend to underestimate volatility due to adaptation to the high volatility, and vice versa. Using a combination of field and laboratory tests, we find strong support for this hypothesis. The evidence suggests that this neurobiologically-grounded perceptual bias can cause distortions of asset prices in sophisticated and liquid financial markets.

Shorting at close range: A tale of two types

Journal of Financial Economics 2016 121(3), 546-568 open access
We examine returns, order flow, and market conditions in the minutes before, during, and after NYSE and Nasdaq short sales. We find two distinct types of short sales: those that provide liquidity, and those that demand it. Liquidity-supplying shorts are strongly contrarian at intraday horizons. They trade when spreads are unusually wide, facing greater adverse selection. Liquidity-demanding shorts trade when spreads are narrow and tend to follow short-term price declines. These results support a competitive rational expectations model where both market-makers and informed traders short, indicating that these two shorting types are integral to both price discovery and liquidity provision.

Benchmarking benchmarks

Journal of Financial Economics 2025 168, 104018 open access
Financial benchmarks such as LIBOR underpin the pricing of trillions of dollars of contracts around the world. We evaluate the quality of benchmark prices using a state-space model to separate information from noise. Applying the method to LIBOR benchmarks and their replacements, we find that alternative reference rates (ARRs) are less noisy in four of the five currencies. However, the USD ARR is considerably more noisy, resulting in billions of dollars of noise-related wealth transfers between contract counterparties. We show that benchmark reforms such as expanding the reference market and using a trimmed mean can reduce noise in ARRs.

Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?

Review of Financial Studies 2019 32(5), 1798-1853 open access
Cryptocurrencies are among the largest unregulated markets in the world. We find that approximately one-quarter of bitcoin users are involved in illegal activity. We estimate that around $76 billion of illegal activity per year involve bitcoin (46% of bitcoin transactions), which is close to the scale of the U.S. and European markets for illegal drugs. The illegal share of bitcoin activity declines with mainstream interest in bitcoin and with the emergence of more opaque cryptocurrencies. The techniques developed in this paper have applications in cryptocurrency surveillance. Our findings suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.” Received June 1, 2017; editorial decision December 8, 2018 by Editor Andrew Karolyi.

The Active World of Passive Investing

Review of Finance 2021 25(5), 1433-1471
We investigate the new reality of exchange-traded funds (ETFs). We show that most ETFs are active investments in form (designed to generate alpha) or function (serve as building blocks of active portfolios). We define a new activeness index to capture these dimensions, finding that the cross-section of ETFs is now increasingly characterized by highly active investment vehicles. Active-in-form ETFs have positive flow-performance sensitivity, charge the highest fees among ETFs, and have high within-portfolio turnover. Active-in-function ETFs have more concentrated holdings, less within-portfolio turnover, but higher turnover in the secondary market. We show how more active ETFs are gaining market share over less active ETFs, leading to competitive fee pressure both within the ETF space and across the investment management industry. We suggest that the growing activeness of ETFs may assuage concerns about ETFs harming price discovery.

What Moves Stock Prices? The Roles of News, Noise, and Information

Review of Financial Studies 2022 35(9), 4341-4386
We develop a return variance decomposition model to distinguish the roles of different types of information and noise in stock price movements. We disentangle four components: noise, private firm-specific information revealed through trading, firm-specific information revealed through public sources and market-wide information. Overall, we find that 31% of the return variance is from noise, 24% from private firm-specific information, 37% from public firm-specific information and 8% from market-wide information. Since the mid-1990s, there has been a dramatic decline in noise and an increase in firm-specific information, consistent with increasing market efficiency. The Internet Appendix that accompanies this paper can be obtained here: https://bit.ly/3FcV9UR

Nonstandard Errors

Albert J. Menkveld; Anna Dreber; Felix Holzmeister; Jürgen Huber; Magnus Johannesson; Michael Kirchler; SEBASTIAN NEUSÜß; Michael Razen; Utz Weitzel; DAVID ABAD-DÍAZ; Menachem Abudy; Tobias Adrian; Yacine Aït-Sahalia; Olivier Akmansoy; Jamie Alcock; Vitali Alexeev; Arash Aloosh; LIVIA AMATO; Diego Amaya; James J. Angel; ALEJANDRO T. AVETIKIAN; AMADEUS BACH; EDWIN BAIDOO; GAETAN BAKALLI; LI BAO; Andrea Barbon; OKSANA BASHCHENKO; Parampreet Christopher Bindra; Geir Høidal Bjønnes; Jeffrey R. Black; Bernard S. Black; DIMITAR BOGOEV; SANTIAGO BOHORQUEZ CORREA; Oleg Bondarenko; CHARLES S. BOS; Ciril Bosch-Rosa; ELIE BOURI; Christian T. Brownlees; ANNA CALAMIA; Viet Nga Cao; Gunther Capelle-Blancard; LAURA M. CAPERA ROMERO; Massimiliano Caporin; Allen Carrion; TOLGA CASKURLU; Bidisha Chakrabarty; Jian Chen; Mikhail Chernov; WILLIAM CHEUNG; LUDWIG B. CHINCARINI; Tarun Chordia; SHEUNG-CHI CHOW; BENJAMIN CLAPHAM; Jean-Edouard Colliard; Carole Comerton-Forde; EDWARD CURRAN; THONG DAO; WALE DARE; Ryan J. Davies; RICCARDO DE BLASIS; GIANLUCA F. DE NARD; Fany Declerck; OLEG DEEV; Hans Degryse; SOLOMON Y. DEKU; CHRISTOPHE DESAGRE; Mathijs A. van Dijk; Chukwuma Dim; Thomas Dimpfl; YUN JIANG DONG; PHILIP A. DRUMMOND; Tom L. Dudda; TEODOR DUEVSKI; Ariadna Dumitrescu; Teodor Dyakov; Anne Haubo Dyhrberg; Michał Dzieliński; ASLI EKSI; Izidin El Kalak; Saskia ter Ellen; Nicolas Eugster; Martin D. D. Evans; Michael Farrell; ESTER FELEZ-VINAS; Gerardo Ferrara; EL MEHDI FERROUHI; Andrea Flori; JONATHAN T. FLUHARTY-JAIDEE; Sean Foley; Kingsley Y. L. Fong; Thierry Foucault; TATIANA FRANUS; Francesco A. Franzoni; Bart Frijns; MICHAEL FRÖMMEL; SERVANNA M. FU; Sascha Füllbrunn; BAOQING GAN; GE GAO; Thomas Gehrig; ROLAND GEMAYEL; DIRK GERRITSEN; Javier Gil-Bazo; Dudley Gilder; Lawrence R. Glosten; THOMAS GOMEZ; Arseny Gorbenko; Joachim Grammig; Vincent Grégoire; Ufuk Güçbilmez; Björn Hagströmer; JULIEN HAMBUCKERS; ERIK HAPNES; Jeffrey H. Harris; Lawrence Harris; SIMON HARTMANN; JEAN-BAPTISTE HASSE; Nikolaus Hautsch; XUE-ZHONG (TONY) HE; Davidson Heath; SIMON HEDIGER; Terrence Hendershott; Ann Marie Hibbert; Erik Hjalmarsson; Seth A. Hoelscher; Peter Hoffmann; Craig W. Holden; Alex R. Horenstein; Wenqian Huang; DA HUANG; Christophe Hurlin; KONRAD ILCZUK; ALEXEY IVASHCHENKO; Subramanian R. Iyer; Hossein Jahanshahloo; NAJI JALKH; Charles M. Jones; SIMON JURKATIS; Petri Jylhä; ANDREAS T. KAECK; GABRIEL KAISER; ARZÉ KARAM; Egle Karmaziene; BERNHARD KASSNER; Markku Kaustia; EKATERINA KAZAK; Fearghal Kearney; Vincent van Kervel; SAAD A. KHAN; MARTA K. KHOMYN; Tony Klein; OLGA KLEIN; Alexander Klos; Michael Koetter; Aleksey Kolokolov; Robert A. Korajczyk; Roman Kozhan; Jan P. Krahnen; PAUL KUHLE; Amy Kwan; QUENTIN LAJAUNIE; F. Y. Eric C. Lam; Marie Lambert; Hugues Langlois; JENS LAUSEN; Tobias Lauter; Markus Leippold; VLADIMIR LEVIN; YIJIE LI; HUI LI; CHEE YOONG LIEW; THOMAS LINDNER; Oliver Linton; JIACHENG LIU; Anqi Liu; Guillermo Llorente; Matthijs Lof; ARIEL LOHR; FRANCIS LONGSTAFF; Alejandro Lopez-Lira; Shawn Mankad; NICOLA MANO; ALEXIS MARCHAL; Charles Martineau; Francesco Mazzola; Debrah Meloso; MICHAEL G. MI; Roxana Mihet; Vijay Mohan; Sophie Moinas; DAVID MOORE; Liangyi Mu; Dmitriy Muravyev; Dermot Murphy; GABOR NESZVEDA; CHRISTIAN NEUMEIER; Ulf Nielsson; Mahendrarajah Nimalendran; Sven Nolte; LARS L. NORDEN; Peter O’Neill; Khaled Obaid; BERNT A. ØDEGAARD; Per Östberg; EMILIANO PAGNOTTA; Marcus Painter; Stefan Palan; IMON J. PALIT; Andreas Park; Roberto Pascual; Paolo Pasquariello; Ľuboš Pástor; VINAY PA℡; Andrew J. Patton; Neil D. Pearson; Loriana Pelizzon; MICHELE PELLI; Matthias Pelster; Christophe Pérignon; CAMERON PFIFFER; Richard Philip; TOMÁŠ PLÍHAL; PUNEET PRAKASH; OLIVER-ALEXANDER PRESS; TINA PRODROMOU; Marcel Prokopczuk; Talis Putnins; YA QIAN; GAURAV RAIZADA; David Rakowski; Angelo Ranaldo; Luca Regis; Stefan Reitz; Thomas Renault; REX W. RENJIE; Roberto Renò; Steven J. Riddiough; Kalle Rinne; PAUL RINTAMÄKI; Ryan Riordan; THOMAS RITTMANNSBERGER; IÑAKI RODRÍGUEZ LONGARELA; Dominik Roesch; LAVINIA ROGNONE; Brian Roseman; Ioanid Roşu; Saurabh Roy; NICOLAS RUDOLF; STEPHEN R. RUSH; Khaladdin Rzayev; ALEKSANDRA A. RZEŹNIK; Anthony Sanford; Harikumar Sankaran; Asani Sarkar; Lucio Sarno; Olivier Scaillet; STEFAN SCHARNOWSKI; KLAUS R. SCHENK-HOPPÉ; ANDREA SCHERTLER; MICHAEL SCHNEIDER; FLORIAN SCHROEDER; Norman Schürhoff; Philipp Schuster; MARCO A. SCHWARZ; Mark S. Seasholes; Norman J. Seeger; Or Shachar; Andriy Shkilko; JESSICA SHUI; MARIO SIKIC; Giorgia Simion; Lee A. Smales; Paul Söderlind; Elvira Sojli; Konstantin Sokolov; JANTJE SÖNKSEN; Laima Spokeviciute; Denitsa Stefanova; Marti G. Subrahmanyam; BARNABAS SZASZI; Oleksandr Talavera; Yuehua Tang; Nick Taylor; Wing Wah Tham; Erik Theissen; Julian Thimme; Ian Tonks; Hai Tran; Luca Trapin; Anders B. Trolle; M. ANDREEA VADUVA; Giorgio Valente; Robert A. Van Ness; Aurelio Vasquez; Thanos Verousis; Patrick Verwijmeren; ANDERS VILHELMSSON; Grigory Vilkov; Vladimir Vladimirov; SEBASTIAN VOGEL; Stefan Voigt; Wolf Wagner; THOMAS WALTHER; Patrick Weiss; Michel van der Wel; Ingrid M. Werner; P. Joakim Westerholm; Christian Westheide; HANS C. WIKA; Evert Wipplinger; Michael Wolf; Christian C. P. Wolff; LEONARD WOLK; WING-KEUNG WONG; Jan Wrampelmeyer; Zhen-Xing Wu; Shuo Xia; Dacheng Xiu; KE XU; CAIHONG XU; Pradeep K. Yadav; JOSÉ YAGÜE; Cheng Yan; Antti Yang; Woongsun Yoo; WENJIA YU; YIHE YU; Shihao Yu; Bart Z. Yueshen; Darya Yuferova; MARCIN ZAMOJSKI; Abalfazl Zareei; STEFAN M. ZEISBERGER; LU ZHANG; S. Sarah Zhang; Xiaoyu Zhang; LU ZHAO; Zhuo Zhong; Z. IVY ZHOU; CHEN ZHOU; XINGYU S. ZHU; Marius Zoican; REMCO ZWINKELS
Journal of Finance 2024 79(3), 2339-2390 open access
In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.