Knowledge that Transforms

This paper focuses on emerging market government bonds issued in local currency with different maturities. Foreign investors face interest rate, currency, and credit risks. We consider the entire term structure of carry trade returns and find that, while the default premium does not contribute to carry trade strategies, the contribution of interest rate risk, captured by the term premium, is large and increases with maturity. We introduce default risk in an otherwise standard affine model; we show that the volatility of the permanent component of the SDFs must be different across emerging markets in order to match these stylized facts. (JEL F31, F34, G15) Received September 9, 2019; editorial decision March 25, 2021 by Editor: Nikolai Roussanov.

Amihud’s stock (il)liquidity measure averages daily ratios of the absolute close-to-close return to dollar volume, including overnight returns. Our modified measure uses open-to-close returns matching return and trading volume measurement windows. It is more strongly correlated with trading-cost measures (by 8%–37%) and better explains cross-sections of returns, doubling estimated liquidity premiums. Using nonsynchronous trading near close, we show overnight returns are primarily information driven: including them in Amihud’s proxy for price impacts of trading magnifies measurement error, understating liquidity premiums. Our modification helps wherever Amihud’s measure is required. Our measures are publicly available for 1964–2019 and can be updated. (JEL G12, G14) Received June 2, 2020; editorial decision September 11, 2020 by Editor Jeffrey Pontiff.

Mutual fund returns are significantly related to stock characteristics in the cross-section after controlling for risk via factor models. We develop a new double-adjusted approach that controls for both factor model betas and stock characteristics in one performance measure. The new measure substantially affects performance rankings, with a quarter of funds experiencing a change in their percentile ranking greater than 10. Double-adjusted performance produces strong evidence of persistence in relative performance. Inference based on the new measure often differs, sometimes dramatically, from that based on traditional performance estimates. Received November 22, 2019; editorial decision June 28, 2020; Editor: Jeffrey Pontiff.

I estimate a dynamic term structure model on an unbalanced panel of Treasury coupon bonds, without relying on an interpolated zero-coupon yield curve. A linearity-generating model, which separates the parameters that govern the cross-sectional and time-series moments of the model, takes about 8 min to estimate on a sample of over 1 million bond prices. The traditional exponential affine model takes about 2 hr, because of a convexity term in coupon-bond prices that cannot be concentrated out of the cross-sectional likelihood. I quantify the on-the-run premium and a “notes versus bonds” premium from 1990 to 2017 in a single, easy-to-estimate no-arbitrage model. (JEL G12, G14, C33) Received: April 30, 2018; editorial decision November 3, 2020 by Editor Nikolai Roussanov

Portfolio performance measures using holdings data are panel regressions. The returns of a fund’s stocks are regressed on its lagged portfolio weights. Stock fixed effects isolate average performance from time-series predictive ability. Control variables condition for fund performance on the characteristics of the stocks held. The long-term performance of average holdings drives some of the classical measures, while predictive ability drives others. A “buy-and-hold drift,” where portfolio weights increase over time in the higher alpha stocks, affects performance measures. Investor flows respond to average performance net of the buy-and-hold drift.

This paper proposes that computational complexity generates noise. The same asset is often held for completely different reasons by many funds following a wide variety of threshold-based trading rules. Under these conditions, we show it can be computationally infeasible to predict how these various trading rules will interact with one another, turning the net demand from these funds into unpredictable noise. This noise-generating mechanism can operate in a wide range of markets and also predicts how noise volatility will vary across assets. We confirm this prediction empirically using data on exchange-traded funds. (JEL G00, G02, G14). Received May 28 2019; editorial decision December 16 2020 by Editor Thierry Foucault.

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.

Value premiums, which we define as value portfolio returns in excess of market portfolio returns, are on average much lower in the second half of the July 1963–June 2019 period. But the high volatility of monthly premiums prevents us from rejecting the hypothesis that expected premiums are the same in both halves of the sample. Regressions that forecast value premiums with book-to-market ratios in excess of market (BM–BMM) produce more reliable evidence of second-half declines in expected value premiums, but only if we assume the regression coefficients are constant during the sample period. Received: January 21, 2020; editorial decision: July 21, 2020; Editor: Jeffrey Pontiff.

The causes and consequences of the 2008 mortgage meltdown and 2020 COVID-19 crisis are quite different: the 2008 mortgage meltdown reflected infection of the financial system due to excess leverage and poor-quality mortgage loans, and the recent crisis reflects a substantial global economic shock to contain the viral outbreak of the coronavirus. Yet the financial and medical systems share many elements, such as opacity and interconnectedness as well as adequate buffers and reserves. We examine these themes as well as asset pricing, moral hazard (though it was at the root of the crisis only in the Great Recession), the consequences for government as a systemic actor, economic concentration, and capital market regulation in the two crises. In both crises, interventions in financial markets and disruptions in the housing market played important, but differing, roles. The recent crisis elucidates open questions about the foundation of financial economics and risk sharing.

Utilizing transaction-level financial data, we explore how household consumption responded to the onset of the COVID-19 pandemic. As case numbers grew and cities and states enacted shelter-in-place orders, Americans began to radically alter their typical spending across a number of major categories. In the first half of March 2020, individuals increased total spending by over 40% across a wide range of categories. This was followed by a decrease in overall spending of 25%–30% during the second half of March coinciding with the disease spreading, with only food delivery and grocery spending as major exceptions to the decline. Spending responded most strongly in states with active shelter-in-place orders, though individuals in all states had sizable responses. We find few differences across individuals with differing political beliefs, but households with children or low levels of liquidity saw the largest declines in spending during the latter part of March.