本文精选了金融学国际顶刊《Journal of Finance》近期发表的论文，提供金融学研究领域最新的学术动态。

Pricing Currency Risks

原刊和作者：

Journal of Finance Volume78, Issue2

MIKHAIL CHERNOV （UCLA Anderson School）

MAGNUS DAHLQUIST （Stockholm School of Economics）

LARS LOCHSTOER （UCLA Anderson School）

Abstract

The currency market features a small cross-section, and conditional expected returns can be characterized by few signals: interest differential, trend, and mean reversion. We exploit these properties to construct the ex ante mean-variance efficient portfolio of individual currencies. The portfolio is updated in real time and prices all prominent currency trading strategies, conditionally and unconditionally. The fraction of risk in these assets that does not affect their risk premiums is at least 85%. Extant explanations of carry strategies based on intermediary capital or global volatility are related to these unpriced components, while consumption growth is related to the priced component of returns.

Link: https://doi.org/10.1111/jofi.13190

A Model of Systemic Bank Runs

原刊和作者：

Journal of Finance Volume78, Issue2

XUEWEN LIU （University of Hong Kong）

Abstract

We develop a tractable model of systemic bank runs. The market-based banking system features a two-layer structure: banks with heterogeneous fundamentals face potential runs by their creditors while they trade short-term funding in the asset (interbank) market in response to creditor withdrawals. The possibility of a run on a particular bank depends on its assets' interim liquidation value, and this value depends endogenously in turn on the status of other banks in the asset market. The within-bank coordination problem among creditors and the cross-bank price externality feed into each other. A small shock can be amplified into a systemic crisis.

Link: https://doi.org/10.1111/jofi.13213

Biased Auctioneers

原刊和作者：

Journal of Finance Volume78, Issue2

MATHIEU AUBRY （Ecole des Ponts）

ROMAN KRÄUSSL （Stanford University）

GUSTAVO MANSO （University of California）

CHRISTOPHE SPAENJERS （University of Colorado Boulder）

Abstract

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.

Link: https://doi.org/10.1111/jofi.13203

Discount-Rate Risk in Private Equity: Evidence from Secondary Market Transactions

原刊和作者：

Journal of Finance Volume78, Issue2

BRIAN H. BOYER （Brigham Young University）

TAYLOR D. NADAULD （Brigham Young University）

KEITH P. VORKINK （Brigham Young University）

MICHAEL S. WEISBACH （Ohio State University）

Abstract

Measures of private equity (PE) performance based on cash flows do not account for a discount-rate risk premium that is a component of the capital asset pricing model (CAPM) alpha. We create secondary market PE indices and find that PE discount rates vary considerably. Net asset values are too smooth because they fail to reflect variation in discount rates. Although the CAPM alpha for our index is zero, the generalized public market equivalent based on cash flows is large and positive. We obtain similar results for a set of synthetic funds that invest in small cap stocks. Ignoring variation in PE discount rates can lead to a misallocation of capital.

Link: https://doi.org/10.1111/jofi.13202

Do Municipal Bond Dealers Give Their Customers “Fair and Reasonable” Pricing?

Journal of Finance Volume78, Issue2

JOHN M. GRIFFIN （University of Texas at Austin）

NICHOLAS HIRSCHEY （Universidade NOVA de Lisboa）

SAMUEL KRUGER （University of Texas at Austin）

Abstract

Municipal bonds exhibit considerable retail pricing variation, even for same-size trades of the same bond on the same day, and even from the same dealer. Markups vary widely across dealers. Trading strongly clusters on eighth price increments, and clustered trades exhibit higher markups. Yields are often lowered to just above salient numbers. Machine learning estimates exploiting the richness of the data show that dealers that use strategic pricing have systematically higher markups. Recent Municipal Securities Rulemaking Board rules have had only a limited impact on markups. While a subset of dealers focus on best execution, many dealers appear focused on opportunistic pricing.

Link: https://doi.org/10.1111/jofi.13214