The Journal of Finance

Noise as Information for Illiquidity

Published: 11/12/2013,  Volume: 68,  Issue: 6  |  DOI: 10.1111/jofi.12083  |  Cited by: 408

GRACE XING HU, JUN PAN, JIANG WANG

We propose a market‐wide liquidity measure by exploiting the connection between the amount of arbitrage capital in the market and observed “noise” in U.S. Treasury bonds—the shortage of arbitrage capital allows yields to deviate more freely from the curve, resulting in more noise in prices. Our noise measure captures episodes of liquidity crises of different origins across the financial market, providing information beyond existing liquidity proxies. Moreover, as a priced risk factor, it helps to explain cross‐sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market.

ON OPTIMAL FINANCING OF CYCLICAL CASH NEEDS: COMMENT

Published: 9/1975,  Volume: 30,  Issue: 4  |  DOI: 10.1111/j.1540-6261.1975.tb01029.x  |  Cited by: 0

H. Stephen Grace

Default Risk in Equity Returns

Published: 3/25/2004,  Volume: 59,  Issue: 2  |  DOI: 10.1111/j.1540-6261.2004.00650.x  |  Cited by: 1419

Maria Vassalou, Yuhang Xing

This is the first study that uses Merton's (1974) option pricing model to compute default measures for individual firms and assess the effect of default risk on equity returns. The size effect is a default effect, and this is also largely true for the book‐to‐market (BM) effect. Both exist only in segments of the market with high default risk. Default risk is systematic risk. The Fama–French (FF) factors SMB and HML contain some default‐related information, but this is not the main reason that the FF model can explain the cross section of equity returns.

Taxes on Tax‐Exempt Bonds

Published: 3/19/2010,  Volume: 65,  Issue: 2  |  DOI: 10.1111/j.1540-6261.2009.01545.x  |  Cited by: 109

ANDREW ANG, VINEER BHANSALI, YUHANG XING

Implicit tax rates priced in the cross section of municipal bonds are approximately two to three times as high as statutory income tax rates, with implicit tax rates close to 100% using retail trades and above 70% for interdealer trades. These implied tax rates can be identified because a portion of secondary market municipal bond trades involves income taxes. After valuing the tax payments, market discount bonds, which carry income tax liabilities, trade at yields around 25 basis points higher than comparable municipal bonds not subject to any taxes. The high sensitivities of municipal bond prices to tax rates can be traced to individual retail traders dominating dealers and other institutions.

The Dark Side of Circuit Breakers

Published: 2/23/2024,  Volume: 79,  Issue: 2  |  DOI: 10.1111/jofi.13310  |  Cited by: 23

HUI CHEN, ANTON PETUKHOV, JIANG WANG, HAO XING

Market‐wide circuit breakers are trading halts aimed at stabilizing the market during dramatic price declines. Using an intertemporal equilibrium model, we show that a circuit breaker significantly alters market dynamics and affects investor welfare. As the market approaches the circuit breaker, price volatility rises drastically, accelerating the chance of triggering the circuit breaker—the so‐called “magnet effect,” returns exhibit increasing negative skewness, and trading activity spikes up. Our empirical analysis supports the model's predictions. Circuit breakers can affect overall welfare negatively or positively, depending on the relative significance of investors' trading motives for risk sharing versus irrational speculation.

Model Ambiguity versus Model Misspecification in Dynamic Portfolio Choice

Published: 1/21/2026,  Volume: 81,  Issue: 3  |  DOI: 10.1111/jofi.70027  |  Cited by: 0

PASCAL J. MAENHOUT, HAO XING, ANNE G. BALTER

We study aversion to model ambiguity and misspecification in dynamic portfolio choice. Risk‐averse investors (relative risk aversion ) fear return persistence, while risk‐tolerant investors () fear mean reversion, when confronting model misspecification concerns of identically and independently distributed (IID) returns. The intuition is that risk‐averse investors, who want to hedge intertemporally, endogenously fear return persistence, which precludes hedging. A log investor is myopic and unaffected by model misspecification, therefore only worrying about model ambiguity. Our model can generate belief scarring, nonparticipation in equity markets, and extrapolative return expectations. Extending beyond IID returns, we study model misspecification for a mean‐reverting Sharpe ratio.

Persuading Investors: A Video‐Based Study

Published: 8/14/2025,  Volume: 80,  Issue: 5  |  DOI: 10.1111/jofi.13471  |  Cited by: 30

ALLEN HU, SONG MA

Persuasive communication functions through not only content but also delivery—facial expression, tone of voice, and diction. This paper examines the persuasiveness of delivery in startup pitches. Using machine learning algorithms to process full pitch videos, we quantify persuasion in visual, vocal, and verbal dimensions. We find that positive (i.e., passionate, warm) pitches increase funding probability. However, conditional on funding, startups with higher levels of pitch positivity underperform. Women are more heavily judged on delivery when evaluated in single‐gender teams, but they are neglected when copitching in mixed‐gender teams. Using an experiment, we show that persuasion delivery works mainly through leading investors to form inaccurate beliefs.

A Theory of Zombie Lending

Published: 4/26/2021,  Volume: 76,  Issue: 4  |  DOI: 10.1111/jofi.13022  |  Cited by: 89

YUNZHI HU, FELIPE VARAS

An entrepreneur borrows from a relationship bank or the market. The bank has a higher cost of capital but produces private information over time. While the entrepreneur accumulates reputation as the lending relationship continues, asymmetric information is also developed between the bank/entrepreneur and the market. In this setting, zombie lending is inevitable: Once the entrepreneur becomes sufficiently reputable, the bank will roll over loans even after learning bad news, for the prospect of future market financing. Zombie lending is mitigated when the entrepreneur faces financial constraints. Finally, the bank stops producing information too early if information production is costly.

The Cross‐Section of Volatility and Expected Returns

Published: 1/20/2006,  Volume: 61,  Issue: 1  |  DOI: 10.1111/j.1540-6261.2006.00836.x  |  Cited by: 3952

ANDREW ANG, ROBERT J. HODRICK, YUHANG XING, XIAOYAN ZHANG

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

Attention‐Induced Trading and Returns: Evidence from Robinhood Users

Published: 10/20/2022,  Volume: 77,  Issue: 6  |  DOI: 10.1111/jofi.13183  |  Cited by: 489

BRAD M. BARBER, XING HUANG, TERRANCE ODEAN, CHRISTOPHER SCHWARZ

We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are −4.7% for the top stocks purchased each day.

A Horizon‐Based Decomposition of Mutual Fund Value Added Using Transactions

Published: 4/4/2024,  Volume: 79,  Issue: 3  |  DOI: 10.1111/jofi.13331  |  Cited by: 15

JULES VAN BINSBERGEN, JUNGSUK HAN, HONGXUN RUAN, RAN XING

We decompose mutual fund value added by the length of funds' holdings using transaction‐level data. We motivate our decomposition with a model featuring horizon‐specific investment ideas, where short‐term ideas are less scalable because the associated trades cannot be spread over time. Fund turnover correlates negatively with the horizon over which value is added and positively with price impact costs. As predicted, holdings of high‐turnover funds add a substantial amount of value in the first two weeks, of which more than 80% is earned on Federal Open Market Committee (FOMC) and earnings announcement days. Holdings of low‐turnover funds add value only over longer horizons.

Advance Refundings of Municipal Bonds

Published: 5/15/2017,  Volume: 72,  Issue: 4  |  DOI: 10.1111/jofi.12506  |  Cited by: 47

ANDREW ANG, RICHARD C. GREEN, FRANCIS A. LONGSTAFF, YUHANG XING

The advance refunding of debt is a widespread practice in municipal finance. In an advance refunding, municipalities retire callable bonds early and refund them with bonds with lower coupon rates. We find that 85% of all advance refundings occur at a net present value loss, and that the aggregate losses over the past 20 years exceed $15 billion. We explore why municipalities advance refund their debt at loss. Financially constrained municipalities may face pressure to advance refund since it allows them to reduce short‐term cash outflows. We find strong evidence that financial constraints are a major driver of advance refunding activity.

The “Actual Retail Price” of Equity Trades

Published: 7/25/2025,  Volume: 80,  Issue: 5  |  DOI: 10.1111/jofi.13467  |  Cited by: 5

CHRISTOPHER SCHWARZ, BRAD BARBER, XING HUANG, PHILIPPE JORION, TERRANCE ODEAN

We compare execution quality of six brokerage accounts across five brokers by generating a sample of 85,000 simultaneous market orders. Commission levels and payment for order flow (PFOF) differ across our accounts. We find that execution prices vary significantly across brokers: the mean account‐level round‐trip cost ranges from 0.07% to 0.46%, excluding any commissions. The dispersion is due to off‐exchange wholesalers systematically giving different execution prices for the same trades to different brokers. Across brokers, variation in PFOF does not explain the large variation in price execution. We provide several suggestions for more informative disclosures on execution quality.

The Allocation of Capital Between Residential and Nonresidential Uses: Taxes, Inflation and Capital Market Constraints

Published: 6/1983,  Volume: 38,  Issue: 3  |  DOI: 10.1111/j.1540-6261.1983.tb02502.x  |  Cited by: 13

PATRIC H. HENDERSHOTT, SHENG CHENG HU

We have constructed a simple two‐sector model of the demand for housing and corporate capital. Economic growth and an increase in the inflation rate were then simulated with a number of model variants. The model and simulation experiments illustrate both the tax bias in favor of housing and the manner in which the increase in inflation between 1965 and 1978 magnified it. The existence of capital‐market constraints offsets the bias against corporate capital, but it introduces a sharp, inefficient reallocation of housing from less wealthy, constrained households to wealthy households who do not have gains on mortgages and are not financially constrained.

A (Sub)penny for Your Thoughts: Tracking Retail Investor Activity in TAQ

Published: 5/3/2024,  Volume: 79,  Issue: 4  |  DOI: 10.1111/jofi.13334  |  Cited by: 104

BRAD M. BARBER, XING HUANG, PHILIPPE JORION, TERRANCE ODEAN, CHRISTOPHER SCHWARZ

We placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.

The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks

Published: 3/2/2005,  Volume: 60,  Issue: 2  |  DOI: 10.1111/j.1540-6261.2005.00742.x  |  Cited by: 576

JOHN H. BOYD, JIAN HU, RAVI JAGANNATHAN

We find that on average, an announcement of rising unemployment is good news for stocks during economic expansions and bad news during economic contractions. Unemployment news bundles three types of primitive information relevant for valuing stocks: information about future interest rates, the equity risk premium, and corporate earnings and dividends. The nature of the information bundle, and hence the relative importance of the three effects, changes over time depending on the state of the economy. For stocks as a group, information about interest rates dominates during expansions and information about future corporate dividends dominates during contractions.

Pledgeability, Industry Liquidity, and Financing Cycles

Published: 7/26/2019,  Volume: 75,  Issue: 1  |  DOI: 10.1111/jofi.12831  |  Cited by: 33

DOUGLAS W. DIAMOND, YUNZHI HU, RAGHURAM G. RAJAN

Why do firms choose high debt when they anticipate high valuations, and underperform subsequently? We propose a theory of financing cycles where the importance of creditors’ control rights over cash flows (“pledgeability”) varies with industry liquidity. The market allows firms take on more debt when they anticipate higher future liquidity. However, both high anticipated liquidity and the resulting high debt limit their incentives to enhance pledgeability. This has prolonged adverse effects in a downturn. Because these effects are hard to contract upon, higher anticipated liquidity can also reduce a firm's current access to finance.

Nonstandard Errors

Published: 4/17/2024,  Volume: 79,  Issue: 3  |  DOI: 10.1111/jofi.13337  |  Cited by: 106

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.