To make high-quality research more accessible and easier to explore.

Fields:
4 results ✕ Clear filters

Evaluating VPIN as a trigger for single-stock circuit breakers

Journal of Banking & Finance 2018 86, 21-36 open access
We study if VPIN (Easley et al., 2012a) is an efficient advance indicator of toxicity-induced liquidity crises and related sharp price movements. We find that high VPIN readings rarely signal abnormal illiquidity, and very occasionally anticipate large intraday price changes leading to actual trading halts. We find significant differences in illiquidity and price impact between VPIN-identified toxic and non-toxic halts, but they tend to vanish when we control for ex ante realized volatility. We conclude that the capacity of VPIN to anticipate truly toxic events is limited.

Adverse selection costs, trading activity and price discovery in the NYSE: An empirical analysis

Journal of Banking & Finance 2004 28(1), 107-128 open access
This paper studies the role that trading activity plays in the price discovery process of a NYSE-listed stock. We measure the expected information content of each trade by estimating its permanent price impact. It depends on observable trade features and market conditions. We also estimate the time required for quotes to incorporate all the information content of a particular trade. Our results show that price discovery is faster after risky trades and also at the extreme intervals of the session. The quote adjustment to trade-related shocks is progressive and this causes risk persistency and unusual short-term market conditions.

Order Exposure in High-Frequency Markets

Journal of Financial and Quantitative Analysis 2026 61(1), 61-98 open access
We examine hidden orders usage by algorithmic traders (ATs) and nonATs. ATs extensively use hidden orders but of smaller size than nonATs, who are the primary contributors to hidden volume. ATs’ relative share of hidden volume decreases with volatility, adverse selection costs, and the relative tick-size. Proprietary ATs (HFTs), who differ from agency ATs (AATs) in their information sets and potential gains from trade, hide orders to reduce competition for liquidity provision, whereas AATs use hidden orders to conceal information in their more informed orders and manage picking-off risk. Finally, superior technology provides limited benefit for hidden order execution.

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.