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Credit composition and the severity of post-crisis recessions

Journal of Financial Stability 2019 42, 52-66 open access
Unsustainable credit growth leads to financial stability risks, with real repercussions. But what is unsustainable? In this paper we suggest that the balance between the growth in mortgage credit and business credit is a key factor in post-crisis macroeconomic vulnerability. The sample-average rise in credit composition (the share of household mortgage credit in total credit) in 51 economies in the five years before the 2007 global financial crisis is associated with a 2.1% average growth loss in the five years after the crisis. This finding is robust for total-credit growth and for post-crisis fiscal and monetary policy responses. Delving into the channels, we find that larger changes in credit composition before the crisis are linked to less efficient capital reallocation and larger investment cutbacks afterwards.

An Augmented q -Factor Model with Expected Growth

Review of Finance 2021 25(1), 1-41 open access
In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin’s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967–2018 sample. The q5 model, which augments the Hou–Xue–Zhang (2015, Rev. Finan. Stud., 28, 650–705) q-factor model with the expected growth factor, shows strong explanatory power in the cross-section and outperforms the Fama–French (2018, J. Finan. Econom., 128, 234–252) six-factor model.

Which Factors?

Review of Finance 2019 23(1), 1-35 open access
Many recently proposed, seemingly different factor models are closely related. In spanning tests, the q-factor model largely subsumes the Fama–French five- and six-factor models, and the q5 model subsumes the Stambaugh–Yuan four-factor model. Their “mispricing” factors are sensitive to the construction procedure, and once replicated via the traditional approach, are close to the q-factors, with correlations of 0.8 and 0.84. Finally, consistent with the investment CAPM, valuation theory predicts a positive relation between the expected investment and the expected return.

Digesting Anomalies: An Investment Approach

Review of Financial Studies 2015 28(3), 650-705 open access
This paper is a new incarnation of the defunct work previously circulated under the titles "Neoclassical Factors," "An equilibrium three-factor model," "Production-based factors," "A better three-factor model that explains more anomalies," and "An alternative three-factor model." We are extremely grateful to Robert Novy-Marx

A Supply Approach to Valuation

Review of Financial Studies 2013 26(12), 3029-3067 open access
A new methodology for equity valuation arises from the perspective of managers' supply of capital assets. Under q-theory, managers optimally adjust the supply of assets to changes in their market value. The first-order condition of investment then provides a valuation equation that infers asset prices from managers' costs of supplying the assets. This equation fits well the Tobin's q levels across many testing assets, including portfolios formed on q. With current investment-to-capital as the only input, the supply approach does not require cash flow forecasts or discount rate estimates, both of which are notoriously difficult to obtain in practice.

Idiosyncrasy as a Leading Indicator

Journal of Financial and Quantitative Analysis 2023 58(8), 3547-3576 open access
Disequilibrating macro shocks affect different firms’ prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return variation forecasts next-quarter real GDP growth, industrial production growth, and consumption growth both in-sample and out-of-sample. These effects persist after controlling for other leading economic indicators.

Financially Constrained Stock Returns

Journal of Finance 2009 64(4), 1827-1862 open access
We study the effect of financial constraints on risk and expected returns by extending the investment‐based asset pricing framework to incorporate retained earnings, debt, costly equity, and collateral constraints on debt capacity. Quantitative results show that more financially constrained firms are riskier and earn higher expected stock returns than less financially constrained firms. Intuitively, by preventing firms from financing all desired investments, collateral constraints restrict the flexibility of firms in smoothing dividend streams in the face of aggregate shocks. The inflexibility mechanism also gives rise to a convex relation between market leverage and expected stock returns.

Does mortgage lending impact business credit? Evidence from a new disaggregated bank credit data set

Journal of Banking & Finance 2020 113, 105760 open access
We present a new data set for 74 economies over 1990–2016 on domestic bank credit distinguishing between four categories: household mortgages, household consumption credit, loans to non-bank financial institutions, and loans to non-financial business. We offer a description of sources and data collection methods and a comparison with similar data sets. We document the key data trends including a shift in bank credit allocation away from business lending. The literature suggests that this ‘debt shift’ has substantial consequences for growth, income distribution and macroeconomic resilience, which motivates the construction of a new data set. We apply the data to analyze the effect of an increase in mortgage lending flows on business credit flows. We identify several theoretical transmission channels which involve collateral and bank balance sheet effects and wealth effects on consumption. The impact of mortgage credit expansion on business credit growth is found to be positive in advanced economies and negative in emerging and developing economies.

The CAPM strikes back? An equilibrium model with disasters

Journal of Financial Economics 2019 131(2), 269-298 open access
Embedding disasters into a general equilibrium model with heterogeneous firms induces strong nonlinearity in the pricing kernel, helping explain the empirical failure of the (consumption) CAPM. Our single-factor model reproduces the failure of the CAPM in explaining the value premium in finite samples without disasters and its relative success in samples with disasters. Due to beta measurement errors, the estimated beta-return relation is flat, consistent with the beta “anomaly,” even though the true beta-return relation is strongly positive. Finally, the consumption CAPM fails in simulations, even though a nonlinear model with the true pricing kernel holds exactly by construction.

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.