Abstract We construct optimal portfolios of mutual funds whose objectives include socially responsible investment (SRI). Comparing portfolios of these funds to those constructed from the broader fund universe reveals the cost of imposing the SRI constraint on investors seeking the highest Sharpe ratio. This SRI cost crucially depends on the investor’s views about asset pricing models and stock-picking skill by fund managers. To an investor who strongly believes in the CAPM and rules out managerial skill, that is, a market index investor, the cost of the SRI constraint is typically just a few basis points per month, measured in certainty-equivalent loss. To an investor who still disallows skill but instead believes to some degree in pricing models that associate higher returns with exposures to size, value, and momentum factors, the SRI constraint is much costlier, typically by at least 30 basis points per month. The SRI constraint imposes large costs on investors whose beliefs allow a substantial amount of fund-manager skill, that is, investors who heavily rely on individual funds’ track records to predict future performance. ( JEL G11, G12, C11) In 2005, when we released what ultimately proved to be the final version of this study, socially responsible investment (SRI) had already become a major presence on the investment landscape. In the years since, this approach, now often called “sustainable” investment, has grown even more rapidly and often encompasses a broad set of “ESG” (environmental, social, and governance) criteria. As evidence of the rapid growth, Morningstar (2020) notes, “one need look no further than the nearly fourfold increase in assets that flowed into sustainable funds in the United States in 2019.” Sustainable investing has also received increased attention in the academic literature, in subsequent studies too numerous to list. Some of the studies are especially related to ours in that they also examine mutual funds. In our study, mutual funds constitute an asset universe faced by an investor imposing an SRI/ESG constraint. A number of the subsequent studies use mutual funds to address other dimensions of sustainable investing. For example, Bollen (2007), Benson and Humphrey (2008), Renneboog, Ter Horst, and Zhang (2011), Bialkowski and Starks (2016) and Hartzmark and Sussman (2019) investigate determinants of mutual fund flows into sustainable funds versus other funds. Riedl and Smeets (2017) use survey and experimental data to explore investors’ preferences for sustainable funds. Madhavan et al. (2020) examine sustainable active equity mutual funds, relating factor loadings and residual returns to ESG characteristics. While we focus on mutual funds, our study also intends that the basic aspects of the SRI setting extend to other institutional investors. That intent is supported, for example, by the recent evidence of Bolton and Kacperczyk (forthcoming, 2020) providing broader perspectives on the SRI portfolio tilts of various types of institutional investors.One conclusion of our study is that an SRI/ESG constraint is especially binding for investors wishing to tilt toward value or small-cap funds. It seems reasonable to infer that such is still the case, though we have not updated our formal analysis. For example, Morningstar (2020) identifies, as of 2019, 99 sustainable U.S. equity funds categorized within its 3 × 3 style box that sorts along the dimensions of value/blend/growth and small/mid-cap/large. Of those 99 funds, only 8 are classified as value, versus 24 as growth and 67 as blend. Only 7 of the 99 are small-cap funds, versus 79 large-cap and 13 mid-cap. More generally, our 2005 study is early in noting meaningful differences in factor loadings between sustainable versus other funds, in both three- and four-factor models.An SRI/ESG constraint is also especially binding for investors who see much information in individual funds’ historical alphas. The basic reason we discuss in our study is seemingly still at work. That is, despite the rapid growth noted earlier, the number of sustainable funds is still well less than those in the total fund universe, so many of the highest track records appear among funds outside that subset. Not mentioned in our original study is that the case of an investor who sees much information in historical alpha confronts the argument of Berk and Green (2004): if fund flows rationally respond to historical alpha, an investor will not view historical alpha as being informative about future alpha. That argument relies on investors correctly assessing the degree of fund-level decreasing returns to scale. One might view an investor who sees historical alpha as informative about future alpha as also having beliefs that favor a lower degree of decreasing returns to scale, as compared to other investors. Moreover, the equilibrating effects of fund flows might interact with the nonpecuniary utility that SRI-conscious investors derive from their fund choices, as suggested by the evidence of Bollen (2007) that flows respond to returns differently for SRI funds versus conventional funds. In any event, when prior beliefs admit substantial information from historical alphas, Busse and Irvine (2006) find that Bayesian predictive alphas computed as in Pástor and Stambaugh (2002a, 2002b), as are the alphas in our study, do predict future performance.While not one we address, a question often asked is whether sustainable investments perform better or worse than other investments. A number of studies do pursue this question, obtaining a range of findings that include both higher and lower performance for sustainable investments. Pástor, Stambaugh, and Taylor (forthcoming) discuss the challenge in interpreting such findings’ implications about expected future performance. A wedge between ex ante and ex post performance of sustainable investments arises during any period that witnesses unanticipated shifts in either customers’ demands for sustainable products or investors’ demands for sustainable holdings.1 As those authors note, sorting out such effects is an important challenge for future research. Our study conducts its analysis under a variety of asset pricing models and prior beliefs. In each case, an investor conditions on funds’ past returns and thus takes account of any historical performance differences between the sustainable funds and other funds in our sample. We do not, however, include models in which expected asset returns depend on sustainability. In this respect, our study does not attempt to provide direct evidence about a potential relation between sustainability and expected investment performance.We are grateful to the Review of Asset Pricing Studies for the opportunity to publish our original study, which follows below with only the references updated to reflect subsequent publications. The study’s abstract is also unchanged from its original version.

Abstract We examine the role private equity (PE) sponsors play in the resolution of financial distress of portfolio companies. PE-backed firms have higher leverage and default at higher rates than other companies borrowing in leveraged loan markets. But, PE-backed firms restructure more quickly, avoid bankruptcy court more often, and liquidate less often compared to other highly leveraged firms experiencing financial distress. PE owners are also more likely to retain control post-restructuring, often by infusing capital as firms approach distress. While default frequencies are higher among PE-backed firms, PE investors appear to manage financial distress at lower cost compared to other owners. (JEL G23, G32, G33)

In a 2014 paper, Garthwaite, Gross, and Notowidigdo examined the employment impact of the 2005 TennCare contraction. We extend their approach in several directions. First, we use consistent Conley-Taber estimation. Second, we transform their estimates to make them comparable to previous work; the transformed effects have large confidence intervals. We estimate their models using several larger data sets in an attempt to get more precise estimates but find that the results can be quite different. We consider two modifications to account for a major disruption to coverage in 2002, and one of these reduces the differences in the results.

Abstract Non-fundamental demand shocks have significant effects on asset prices, but observing these shocks is challenging. We use the exchange-traded fund (ETF) primary market to study non-fundamental demand. Unique to the ETF market, specialized arbitrageurs called authorized participants correct violations of the law of one price between an ETF and its underlying assets by creating or redeeming ETF shares. We show theoretically and empirically that creation and redemption activities (ETF flows) provide signals of non-fundamental demand shocks. A portfolio that is short high-flow ETFs and long low-flow ETFs earns excess returns of 1.1–2.0% per month, consistent with non-fundamental demand distorting asset prices away from fundamental values. Moreover, we show non-fundamental demand imposes non-trivial costs on investors, leading to underperformance.

How does personal exposure to economic conditions affect individual human capital investment choices? Focusing on bachelor’s degree recipients, we find that cohorts exposed to higher unemployment rates during typical schooling years select majors that earn higher wages, have better employment prospects, and lead to work in a related field. Conditional on expected earnings, recessions also encourage women to enter male-dominated fields, and students of both genders pursue more difficult majors. We conclude that economic environments change how students select majors, and we find evidence that students who respond to the business cycle enjoy earnings typical of their new majors.

Abstract We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.

Abstract We consider a macroprudential approach to analyze the optimal lending policy for the central bank, focusing on spillover effects that policy exerts on money markets. Lending against high-quality collateral protects central banks against losses, but can adversely affect liquidity creation in markets since high-quality collateral gets locked up with the central bank rather than circulating in markets. Lending against low-quality collateral creates counterparty risk but can improve liquidity in markets. We illustrate the optimal policy incorporating these trade-offs. Contrary to what is generally accepted, lending against high-quality collateral can have negative effects, whereas it may be optimal to lend against low-quality collateral.

Until the 1970s, both banks and nonfinancial corporations relied on performance targets linked to their earnings per share (EPS). Over the next few decades, banks rapidly changed to emphasize return on equity (ROE) as a performance target. Investors seem aware of this change because ROE growth (EPS growth) better explains banks’ (nonfinancials’) stock market values. Also, manager compensation linked to ROE is more common for banks than for nonfinancials. This paper presents a model of a bank subject to fixed-rate deposit insurance and facing increasing competition that erodes its charter value. When the bank chooses its capital to maximize its shareholder value, its performance based on ROE appears better than its performance based on EPS. Thus, the increase in competition that started in the 1970s, along with fixed-rate deposit insurance, may explain banks’ growing preference for ROE over EPS as a performance target.

When valuing a firm, investors must assess not only its expected future cash flows but also the systematic risk inherent in these cash flows. In this paper, we model the process by which investors may learn about firms' betas from earnings and how this learning process affects the relationship between earnings, announcement returns, and expected future returns. The model's main predictions are: (i) earnings response coefficients vary with macroeconomic conditions and are lower in upswings than downturns; (ii) earnings positively and negatively predict future returns in economic upswings and downturns, respectively, leading to return autocorrelation; and (iii) real earnings management rises in economic downturns and contributes to systematic risk in the economy. These predictions are directly attributable to investors' uncertainty regarding firms' exposures to systematic risk.

Abstract I document wage convergence in conglomerates using detailed plant-level data: Workers in low-wage industries collect higher-than-industry wages when the diversified firm also operates in high-wage industries. I confirm this effect by exploiting the implementation of the North American Free Trade Agreement (NAFTA) and changes in minimum wages at the state level as sources of exogenous increases in wages in some plants. I then track the evolution of wages of the remaining workers of the firm, relative to workers of unaffiliated plants. Plants where workers collect higher-than-industry wages operate with higher capital intensity, suggesting that internal labor markets may affect investment decisions in internal capital markets.