The Journal of Finance

Momentum, Business Cycle, and Time‐varying Expected Returns

Published: 4/2002,  Volume: 57,  Issue: 2  |  DOI: 10.1111/1540-6261.00449  |  Cited by: 661

Tarun Chordia, Lakshmanan Shivakumar

A growing number of researchers argue that time‐series patterns in returns are due to investor irrationality and thus can be translated into abnormal profits. Continuation of short‐term returns or momentum is one such pattern that has defied any rational explanation and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time‐varying expected returns as an explanation for momentum payoffs.

Trading Volume and Cross‐Autocorrelations in Stock Returns

Published: 4/2000,  Volume: 55,  Issue: 2  |  DOI: 10.1111/0022-1082.00231  |  Cited by: 500

Tarun Chordia, Bhaskaran Swaminathan

This paper finds that trading volume is a significant determinant of the lead‐lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross‐autocorrelation patterns in short‐horizon stock returns.

Brokerage Commission Schedules

Published: 9/1993,  Volume: 48,  Issue: 4  |  DOI: 10.1111/j.1540-6261.1993.tb04758.x  |  Cited by: 46

MICHAEL J. BRENNAN, TARUN CHORDIA

It is generally optimal for risk‐sharing reasons to base a charge for information on the signal realization. When this is not possible, a charge based on the amount of trading, a brokerage commission, may be a good alternative. The optimal brokerage commission schedule is derived for a risk‐neutral information seller faced with risk‐averse purchasers who may differ in their risk aversion. Revenues from the brokerage commission are compared with those from a fixed charge for information and the optimal mutual fund management fee.

True Spreads and Equilibrium Prices

Published: 10/2001,  Volume: 56,  Issue: 5  |  DOI: 10.1111/0022-1082.00390  |  Cited by: 39

Clifford A. Ball, Tarun Chordia

Stocks and other financial assets are traded at prices that lie on a fixed grid determined by the minimum tick size. Observed prices and quoted spreads do not correspond to the equilibrium prices and true spreads that would exist in a market with no minimum tick size. Using Monte Carlo Markov Chain methods, this paper estimates the equilibrium prices and true spreads. For large stocks, most of the quoted spread is attributable to the rounding of prices and the adverse selection component is small. The true spread and the adverse selection component are greater for mid‐sized stocks.

Liquidity and Autocorrelations in Individual Stock Returns

Published: 9/19/2006,  Volume: 61,  Issue: 5  |  DOI: 10.1111/j.1540-6261.2006.01060.x  |  Cited by: 401

DORON AVRAMOV, TARUN CHORDIA, AMIT GOYAL

This paper documents a strong relationship between short‐run reversals and stock illiquidity, even after controlling for trading volume. The largest reversals and the potential contrarian trading strategy profits occur in high turnover, low liquidity stocks, as the price pressures caused by non‐informational demands for immediacy are accommodated. However, the contrarian trading strategy profits are smaller than the likely transactions costs. This lack of profitability and the fact that the overall findings are consistent with rational equilibrium paradigms suggest that the violation of the efficient market hypothesis due to short‐term reversals is not so egregious after all.

Market Liquidity and Trading Activity

Published: 4/2001,  Volume: 56,  Issue: 2  |  DOI: 10.1111/0022-1082.00335  |  Cited by: 1077

Tarun Chordia, Richard Roll, Avanidhar Subrahmanyam

Previous studies of liquidity span short time periods and focus on the individual security. In contrast, we study aggregate market spreads, depths, and trading activity for U.S. equities over an extended time sample. Daily changes in market averages of liquidity and trading activity are highly volatile and negatively serially dependent. Liquidity plummets significantly in down markets. Recent market volatility induces a decrease in trading activity and spreads. There are strong day‐of‐the‐week effects; Fridays accompany a significant decrease in trading activity and liquidity, while Tuesdays display the opposite pattern. Long‐ and short‐term interest rates influence liquidity. Depth and trading activity increase just prior to major macroeconomic announcements.

Momentum and Credit Rating

Published: 9/4/2007,  Volume: 62,  Issue: 5  |  DOI: 10.1111/j.1540-6261.2007.01282.x  |  Cited by: 320

DORON AVRAMOV, TARUN CHORDIA, GERGANA JOSTOVA, ALEXANDER PHILIPOV

This paper establishes a robust link between momentum and credit rating. Momentum profitability is large and significant among low‐grade firms, but it is nonexistent among high‐grade firms. The momentum payoffs documented in the literature are generated by low‐grade firms that account for less than 4% of the overall market capitalization of rated firms. The momentum payoff differential across credit rating groups is unexplained by firm size, firm age, analyst forecast dispersion, leverage, return volatility, and cash flow volatility.

The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium

Published: 3/27/2012,  Volume: 67,  Issue: 2  |  DOI: 10.1111/j.1540-6261.2012.01723.x  |  Cited by: 49

TARUN RAMADORAI

Rational theories of the closed‐end fund premium puzzle highlight fund share and asset illiquidity, managerial ability, and fees as important determinants of the premium. Several of these attributes are difficult to measure for mutual funds, and easier to measure for hedge funds. This paper employs new data from a secondary market for hedge funds, discovers a closed‐hedge fund premium that is highly correlated with the closed‐end mutual fund premium, and shows that the closed‐hedge fund premium is well explained by variables suggested by rational theories. Sentiment‐based explanations do not find support in the data.

Is Group Affiliation Profitable in Emerging Markets? An Analysis of Diversified Indian Business Groups

Published: 4/2000,  Volume: 55,  Issue: 2  |  DOI: 10.1111/0022-1082.00229  |  Cited by: 1914

Tarun Khanna, Krishna Palepu

Emerging markets like India have poorly functioning institutions, leading to severe agency and information problems. Business groups in these markets have the potential both to offer benefits to member firms, and to destroy value. We analyze the performance of affiliates of diversified Indian business groups relative to unaffiliated firms. We find that accounting and stock market measures of firm performance initially decline with group diversification and subsequently increase once group diversification exceeds a certain level. Unlike U.S. conglomerates' lines of business, and similar to the affiliates of U.S. LBO associations, affiliates of the most diversified business groups outperform unaffiliated firms.

On the High‐Frequency Dynamics of Hedge Fund Risk Exposures

Published: 3/7/2013,  Volume: 68,  Issue: 2  |  DOI: 10.1111/jofi.12008  |  Cited by: 168

ANDREW J. PATTON, TARUN RAMADORAI

We propose a new method to model hedge fund risk exposures using relatively high‐frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within‐month variation is more important for hedge funds than for mutual funds. We consider different within‐month functional forms, and uncover patterns such as day‐of‐the‐month variation in risk exposures. We also find that changes in portfolio allocations, rather than in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation.

Currency Returns, Intrinsic Value, and Institutional‐Investor Flows

Published: 5/3/2005,  Volume: 60,  Issue: 3  |  DOI: 10.1111/j.1540-6261.2005.00769.x  |  Cited by: 231

KENNETH A. FROOT, TARUN RAMADORAI

We decompose currency returns into (permanent) intrinsic‐value shocks and (transitory) expected‐return shocks. We explore interactions between these shocks, currency returns, and institutional‐investor currency flows. Intrinsic‐value shocks are: dwarfed by expected‐return shocks (yet currency returns overreact to them); unrelated to flows (although expected‐return shocks correlate with flows); and related positively to forecasted cumulated‐interest differentials. These results suggest flows are related to short‐term currency returns, while fundamentals better explain long‐term returns and values. They also rationalize the long‐observed poor performance of exchange‐rate models: by ignoring the distinction between permanent and transitory exchange‐rate changes, prior tests obscure the connection between currencies and fundamentals.

Asset Fire Sales and Purchases and the International Transmission of Funding Shocks

Published: 11/19/2012,  Volume: 67,  Issue: 6  |  DOI: 10.1111/j.1540-6261.2012.01780.x  |  Cited by: 340

CHOTIBHAK JOTIKASTHIRA, CHRISTIAN LUNDBLAD, TARUN RAMADORAI

We identify a new channel for the transmission of shocks across international markets. Investor flows to funds domiciled in developed markets force significant changes in these funds' emerging market portfolio allocations. These forced trades or “fire sales” affect emerging market equity prices, correlations, and betas, and are related to but distinct from effects arising purely from fund holdings or from overlapping ownership of emerging markets in fund portfolios. A simple model and calibration exercise highlight the importance to these findings of “push” effects from funds' domicile countries and “co‐ownership spillover” between markets with overlapping fund ownership.

Change You Can Believe In? Hedge Fund Data Revisions

Published: 5/11/2015,  Volume: 70,  Issue: 3  |  DOI: 10.1111/jofi.12240  |  Cited by: 70

ANDREW J. PATTON, TARUN RAMADORAI, MICHAEL STREATFIELD

We analyze the reliability of voluntary disclosures of financial information, focusing on widely‐employed publicly‐available hedge fund databases. Tracking changes to statements of historical performance recorded between 2007 and 2011, we find that historical returns are routinely revised. These revisions are not merely random or corrections of earlier mistakes; they are partly forecastable by fund characteristics. Funds that revise their performance histories significantly and predictably underperform those that have never revised, suggesting that unreliable disclosures constitute a valuable source of information for investors. These results speak to current debates about mandatory disclosures by financial institutions to market regulators.

Change You Can Believe In? Hedge Fund Data Revisions: Erratum

Published: 7/23/2015,  Volume: 70,  Issue: 4  |  DOI: 10.1111/jofi.12306  |  Cited by: 1

ANDREW J. PATTON, TARUN RAMADORAI, MICHAEL STREATFIELD

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Published: 12/16/2021,  Volume: 77,  Issue: 1  |  DOI: 10.1111/jofi.13090  |  Cited by: 441

ANDREAS FUSTER, PAUL GOLDSMITH‐PINKHAM, TARUN RAMADORAI, ANSGAR WALTHER

Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.

Who Owns What? A Factor Model for Direct Stockholding

Published: 3/27/2023,  Volume: 78,  Issue: 3  |  DOI: 10.1111/jofi.13220  |  Cited by: 55

VIMAL BALASUBRAMANIAM, JOHN Y. CAMPBELL, TARUN RAMADORAI, BENJAMIN RANISH

We build a cross‐sectional factor model for investors' direct stockholdings and estimate it using data from almost 10 million retail accounts in the Indian stock market. Our model identifies strong investor clienteles for stock characteristics, most notably firm age and share price, and for particular clusters of stock characteristics. These clienteles are intuitively associated with investor attributes such as account age, size, and diversification. Coheld stocks tend to have higher return covariance, inconsistent with simple models of diversification but suggestive that clientele demands influence stock returns.

Hedge Funds: Performance, Risk, and Capital Formation

Published: 7/19/2008,  Volume: 63,  Issue: 4  |  DOI: 10.1111/j.1540-6261.2008.01374.x  |  Cited by: 485

WILLIAM FUNG, DAVID A. HSIEH, NARAYAN Y. NAIK, TARUN RAMADORAI

We use a comprehensive data set of funds‐of‐funds to investigate performance, risk, and capital formation in the hedge fund industry from 1995 to 2004. While the average fund‐of‐funds delivers alpha only in the period between October 1998 and March 2000, a subset of funds‐of‐funds consistently delivers alpha. The alpha‐producing funds are not as likely to liquidate as those that do not deliver alpha, and experience far greater and steadier capital inflows than their less fortunate counterparts. These capital inflows attenuate the ability of the alpha producers to continue to deliver alpha in the future.

Nonstandard Errors

Published: 4/17/2024,  Volume: 79,  Issue: 3  |  DOI: 10.1111/jofi.13337  |  Cited by: 103

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.