The Journal of Finance

Infrequent Rebalancing, Return Autocorrelation, and Seasonality

Published: 11/10/2016,  Volume: 71,  Issue: 6  |  DOI: 10.1111/jofi.12436  |  Cited by: 138

VINCENT BOGOUSSLAVSKY

A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross‐section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross‐sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross‐section of stock returns, which can help explain available empirical evidence.

Liquidity, Volume, and Order Imbalance Volatility

Published: 6/5/2023,  Volume: 78,  Issue: 4  |  DOI: 10.1111/jofi.13248  |  Cited by: 40

VINCENT BOGOUSSLAVSKY, PIERRE COLLIN‐DUFRESNE

We examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post‐decimalization period. Motivated by a continuous‐time inventory model, we compute a high‐frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high‐frequency order imbalance volatility is an important driver of liquidity and explains the often positive time‐series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross‐section of stock returns.

Informed Trading Intensity

Published: 2/27/2024,  Volume: 79,  Issue: 2  |  DOI: 10.1111/jofi.13320  |  Cited by: 79

VINCENT BOGOUSSLAVSKY, VYACHESLAV FOS, DMITRIY MURAVYEV

We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.

Liquidity Fluctuations in Over‐the‐Counter Markets

Published: 2/2022,  Volume: 77,  Issue: 2  |  DOI: 10.1111/jofi.13106  |  Cited by: 8

VINCENT MAURIN

This paper proposes a theory of excess price fluctuations in over‐the‐counter secondary markets. When heterogeneous assets trade under asymmetric information, a quality effect emerges: high liquidity lowers the quality of the pool of sellers and decreases future liquidity. Cyclical equilibria can arise even without fundamental shocks. In a cycle, investors speculate by bidding up the price of low‐quality assets, anticipating a high resale price at the peak. When this resale effect is strong, cycles disappear and multiple steady states coexist with different levels of liquidity. The model rationalizes empirical patterns for corporate bonds and housing in particular.

PUBLIC FINANCING FOR SMALL CORPORATIONS*

Published: 3/1960,  Volume: 15,  Issue: 1  |  DOI: 10.1111/j.1540-6261.1960.tb04844.x  |  Cited by: 0

Vincent M. Jolivet

AN ECONOMETRIC STUDY OF EURODOLLAR BORROWING BY NEW YORK BANKS AND THE RATE OF INTEREST ON EURODOLLARS: COMMENT

Published: 9/1972,  Volume: 27,  Issue: 4  |  DOI: 10.1111/j.1540-6261.1972.tb01325.x  |  Cited by: 0

Vincent G. Massaeo

Compensating Financial Experts

Published: 11/10/2016,  Volume: 71,  Issue: 6  |  DOI: 10.1111/jofi.12372  |  Cited by: 38

VINCENT GLODE, RICHARD LOWERY

We propose a labor market model in which financial firms compete for a scarce supply of workers who can be employed as either bankers or traders. While hiring bankers helps create a surplus that can be split between a firm and its trading counterparties, hiring traders helps the firm appropriate a greater share of that surplus away from its counterparties. Firms bid defensively for workers bound to become traders, who then earn more than bankers. As counterparties employ more traders, the benefit of employing bankers decreases. The model sheds light on the historical evolution of compensation in finance.

The Delisting Bias in CRSP's Nasdaq Data and Its Implications for the Size Effect

Published: 12/1999,  Volume: 54,  Issue: 6  |  DOI: 10.1111/0022-1082.00192  |  Cited by: 480

Tyler Shumway, Vincent A. Warther

We investigate the bias in CRSP's Nasdaq data due to missing returns for delisted stocks. We find that the missing returns are large and negative on average, and that delisted stocks experience a substantial decrease in liquidity. We estimate that using a corrected return of −55 percent for missing performance‐related delisting returns corrects the bias. We revisit previous work which finds a size effect among Nasdaq stocks. After correcting for the delisting bias, there is no evidence that there ever was a size effect on Nasdaq. Our results are inconsistent with most risk‐based explanations of the size effect.

Dividends, Asymmetric Information, and Agency Conflicts: Evidence from a Comparison of the Dividend Policies of Japanese and U.S. Firms

Published: 6/1998,  Volume: 53,  Issue: 3  |  DOI: 10.1111/0022-1082.00038  |  Cited by: 233

Kathryn L. Dewenter, Vincent A. Warther

We compare dividend policies of U.S. and Japanese firms, partitioning the Japanese data into keiretsu, independent, and hybrid firms. We examine the correlation between dividend changes and stock returns, and the reluctance to change dividends. Results are consistent with the joint hypotheses that Japanese firms, particularly keiretsu‐member firms, face less information asymmetry and fewer agency conflicts than U.S. firms, and that information asymmetries and/or agency conflicts affect dividend policy. Japanese firms experience smaller stock price reactions to dividend omissions and initiations, they are less reluctant to omit and cut dividends, and their dividends are more responsive to earnings changes.

High‐Frequency Trading around Large Institutional Orders

Published: 3/21/2019,  Volume: 74,  Issue: 3  |  DOI: 10.1111/jofi.12759  |  Cited by: 207

VINCENT VAN KERVEL, ALBERT J. MENKVELD

Liquidity suppliers lean against the wind. We analyze whether high‐frequency traders (HFTs) lean against large institutional orders that execute through a series of child orders. The alternative is HFTs trading with the wind, that is, in the same direction. We find that HFTs initially lean against these orders but eventually change direction and take positions in the same direction for the most informed institutional orders. Our empirical findings are consistent with investors trading strategically on their information. When deciding trade intensity, they seem to trade off higher speculative profits against higher risk of being detected and preyed on by HFTs.

Financial Expertise as an Arms Race

Published: 9/12/2012,  Volume: 67,  Issue: 5  |  DOI: 10.1111/j.1540-6261.2012.01771.x  |  Cited by: 108

VINCENT GLODE, RICHARD C. GREEN, RICHARD LOWERY

We show that firms intermediating trade have incentives to overinvest in financial expertise. In our model, expertise improves firms’ ability to estimate value when trading a security. Expertise creates asymmetric information, which, under normal circumstances, works to the advantage of the expert as it deters opportunistic bargaining by counterparties. This advantage is neutralized in equilibrium, however, by offsetting investments by competitors. Moreover, when volatility rises the adverse selection created by expertise triggers breakdowns in liquidity, destroying gains to trade and thus the benefits that firms hope to gain through high levels of expertise.

Second Chance: Life with Less Student Debt

Published: 12/14/2025,  Volume: 81,  Issue: 1  |  DOI: 10.1111/jofi.70002  |  Cited by: 2

MARCO DI MAGGIO, ANKIT KALDA, VINCENT YAO

We exploit an episode of plausibly random debt discharge due to the loss of paperwork for thousands of defaulted borrowers to examine the effects of private student debt relief on borrower outcomes. We find that borrowers who receive debt relief (treated) experience declines in debt balances and delinquency rates on other accounts, and increases in mobility and income relative to those who bear the costs of default like wage garnishment and collections (control). Borrowers in both groups contribute to our findings through different mechanisms. While our estimates may not directly apply to blanket student loan forgiveness, they speak to the benefits of forgiveness in reducing the consequences of debt burden for distressed borrowers.

Nonstandard Errors

Published: 4/17/2024,  Volume: 79,  Issue: 3  |  DOI: 10.1111/jofi.13337  |  Cited by: 111

ALBERT J. MENKVELD, ANNA DREBER, FELIX HOLZMEISTER, JUERGEN HUBER, MAGNUS JOHANNESSON, MICHAEL KIRCHLER, SEBASTIAN NEUSÜß, MICHAEL RAZEN, UTZ WEITZEL, DAVID ABAD‐DÍAZ, MENACHEM (MENI) ABUDY, TOBIAS ADRIAN, YACINE AIT‐SAHALIA, OLIVIER AKMANSOY, JAMIE T. 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 C. BINDRA, GEIR H. BJØNNES, JEFFREY R. BLACK, BERNARD S. BLACK, DIMITAR BOGOEV, SANTIAGO BOHORQUEZ CORREA, OLEG BONDARENKO, CHARLES S. BOS, CIRIL BOSCH‐ROSA, ELIE BOURI, CHRISTIAN 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 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 D. V. FOLEY, KINGSLEY Y. L. FONG, THIERRY FOUCAULT, TATIANA FRANUS, FRANCESCO FRANZONI, BART FRIJNS, MICHAEL FRÖMMEL, SERVANNA M. FU, SASCHA C. FÜLLBRUNN, BAOQING GAN, GE GAO, THOMAS P. 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, LUBOS PASTOR, VINAY PATEL, 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 RENO, 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

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.