The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
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The Time‐Varying Price of Financial Intermediation in the Mortgage Market
Published: 06/04/2024 | DOI: 10.1111/jofi.13358
ANDREAS FUSTER, STEPHANIE H. LO, PAUL S. WILLEN
We introduce a new measure of the price charged by financial intermediaries for connecting mortgage borrowers with capital market investors. Based on administrative lender pricing data, we document that the price of intermediation reacts strongly to variation in demand, reflecting capacity constraints of mortgage originators. This positive comovement of price with quantity reduced the pass‐through of quantitative easing. We also find a notable upward trend in this price between 2008 and 2014, likely due to increased legal and regulatory burden in the mortgage market. The trend led to an implicit cost to borrowers of nearly $100 billion over this period.
Predictably Unequal? The Effects of Machine Learning on Credit Markets
Published: 10/28/2021 | DOI: 10.1111/jofi.13090
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.