We derive new estimates of total wealth, the returns on total wealth, and the wealth effect on consumption. We estimate the prices of aggregate risk from bond yields and stock returns using a no-arbitrage model. Using these risk prices, we compute total wealth as the price of a claim to aggregate consumption. We find that U.S. households have a surprising amount of total wealth, most of it human wealth. This wealth is much less risky than stock market wealth. Events in long-term bond markets, not stock markets, drive most total wealth fluctuations. The wealth effect on consumption is small and varies over time with real interest rates.

If an investor wants to form a portfolio of risky assets and can exert effort to collect information on the future value of these assets before he invests, which assets should he learn about? The best assets to acquire information about are ones the investor expects to hold. But the assets the investor holds depend on the information he observes. We build a framework to solve jointly for investment and information choices, with general preferences and information cost functions. Although the optimal research strategies depend on preferences and costs, the main result is that the investor who can first collect information systematically deviates from holding a diversified portfolio. Information acquisition can rationalize investing in a diversified fund and a concentrated set of assets, an allocation often observed, but usually deemed anomalous.

Many cities have attracted a flurry of out‐of‐town (OOT) home buyers. Such capital inflows affect house prices, rents, construction, labor income, wealth, and ultimately welfare. We develop an equilibrium model to quantify the welfare effects of OOT home buyers for the typical U.S. metropolitan area. When OOT investors buy 10% of the housing in the city center and 5% in the suburbs, welfare among residents falls by 0.61% in consumption‐equivalent units. House prices and rents rise substantially, resulting in welfare gains for owners and losses for renters. Policies that tax OOT buyers or mandate renting out vacant property mitigate welfare losses.

The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.

We estimate the benefit of life-extending medical treatments to life insurance companies. Our main insight is that life insurance companies have a direct benefit from such treatments because they lower the insurer’s liabilities by pushing the death benefit further into the future and raising future premium income. We apply this insight to immunotherapy, treatments associated with durable gains in survival rates for a growing number of cancer patients. We estimate that the life insurance sector’s aggregate benefit from FDA-approved immunotherapies is $9.8 billion a year. Such life-extending treatments are often prohibitively expensive for patients and governments alike. Exploiting this value creation, we explore various ways life insurers could improve stress-free access to treatment. We discuss potential barriers to integration and the long-run implications for the industrial organization of life and health insurance markets, as well as the broader implications for medical innovation and long-term care insurance markets.

How much capital should financial intermediaries hold? We propose a general equilibrium model with a financial sector that makes risky long‐term loans to firms, funded by deposits from savers. Government guarantees create a role for bank capital regulation. The model captures the sharp and persistent drop in macro‐economic aggregates and credit provision as well as the sharp change in credit spreads observed during financial crises. Policies requiring intermediaries to hold more capital reduce financial fragility, reduce the size of the financial and non‐financial sectors, and lower intermediary profits. They redistribute wealth from savers to the owners of banks and non‐financial firms. Pre‐crisis capital requirements are close to optimal. Counter‐cyclical capital requirements increase welfare.

We show remote work led to large drops in lease revenues, occupancy, and market rents in the commercial office sector. We revalue New York City office buildings, taking into account both the cash flow and discount rate implications of these shocks, and find a 46 percent decline in long-run value. For all US office markets combined, we find a $556.8 billion value destruction. Higher-quality buildings were buffered against these trends due to a flight to quality, while lower-quality offices are at risk of becoming a stranded asset. These valuation changes have repercussions for financial stability and local public finances.

Johannes Stroebel is the David S. Loeb Professor of Finance at New York University's Stern School of Business. Johannes joined Stern as an Assistant Professor of Finance in 2013 and received tenure in 2016. He started his career in 2012 as the Neubauer Family Assistant Professor of Economics at the University of Chicago Booth School of Business after earning a Ph.D. in Economics at Stanford University. Stroebel's prolific body of work, which looks more like that of somebody 20 rather than only 10 years out of the Ph.D., spans a broad range of topics and uses a variety of methods. He has made important contributions to at least four areas: household finance, asset pricing, climate risk, and social networks. Several papers forge connections between these areas. I highlight key contributions to each of these research agendas. The first strand of Johannes' work focuses on household finance. In two papers published in the Quarterly Journal of Economics (Agarwal et al. (2015, 2018)), Stroebel combines a large microlevel data set of credit card accounts with careful identification. The first paper finds that regulation that protected consumers by limiting fees was not undone by banks charging higher costs elsewhere, but rather resulted in higher consumer surplus. The second paper uses the cross-section of credit card accounts to show that the monetary pass-through via the bank lending channel is limited because the borrowers with a high marginal propensity to borrow and consume are those for which the banks have a low marginal willingness to lend. Another important paper in household finance is the American Economic Review paper (Giglio et al. (2021b)) that establishes a modest response of financial portfolio decisions to households' beliefs about expected returns. Johannes returns to the topic of consumer credit in a forthcoming Journal of Finance paper (Howell et al. (2023)) on lender automation and racial disparities in credit access. Much of Johannes' work, some of which I describe below, touches on housing, the largest asset in households' portfolios. Stroebel's second main research pillar is asset pricing. In three connected papers, Giglio, Maggiori, and Stroebel infer the discount rates that investors apply to cash flows that accrue in the very far future. Such very long discount rates are important, for example, for analyses of greenhouse gas abatement investments that trade off the uncertain future benefits against current costs (Giglio et al. (2021a)). More on the climate implications below. In traditional financial markets, we have very few very long-lived assets from which to infer long-run discount rates. The authors turn to real estate markets. In Singapore and the United Kingdom, investors can buy houses either as freeholds, which grant perpetual ownership rights to land and structure, or as leaseholds, which grant tradeable temporary ownership rights ranging between 75 and 999 years. From the price difference between freeholds and leaseholds of different maturities, the authors back out a term structure of discount rates under reasonable assumptions on growth rates of rents. The observed price discounts imply low long-run discount rates of 2.6% per year for very far-out payoffs. Combined with the observation that the overall (maturity-weighted) return on housing is around 6% per year, they conclude that the term structure of discount rates is downward sloping (Giglio, Maggiori, and Stroebel (2015)). These discount rate estimates suggest that there was no bubble in these housing markets in the 2000s (Giglio, Maggiori, and Stroebel (2016)). Put differently, the risk premium applied to cash flows in the very far future is high enough to make its present discounted value equal to zero; the transversality condition for long-lived assets is likely to be satisfied. The third and most recent area Johannes has focused on is climate finance, as summarized in a recent review article (Giglio, Kelly, and Stroebel (2021)). A central issue in this literature is how to think of the uncertainty associated with future benefits of climate abatement investments. More economic activity creates larger climate damages as a by-product. But climate risk also directly affects the economy, think of a natural disaster. Under the first view, states of the world with lots of climate change are states of the world with high GDP (Nordhaus (2013)), whereas in the second view, they are states with low GDP (Barro (2015), Weitzman (2012)). Whether states of the world with rapid climate change are good or bad states has major quantitative implications for the discount rate to be used when calculating the benefits of climate mitigation and the social cost of carbon. Giglio et al. (2021a) argue that real estate markets are informative for which discount rate to use when evaluating climate abatement investments. It establishes that real estate returns are risky, performing poorly in low-growth and consumption disaster states. It also documents that real estate values are indeed exposed to climate risk by studying how coastal house prices change when the perception of climate risk increases. A disaster risk model, where economic activity increases the likelihood of a climate disaster and rebounds after a disaster, generates a downward-sloping discount rate curve, consistent with the aforementioned evidence from the real estate market. Since climate abatement investments hedge climate change risk, the appropriate discount rates are below the risk-free rate at all maturities and rising in maturity. Johannes has several more interesting papers in climate finance (Engle et al. (2020), Stroebel and Wurgler (2021), van Benthem et al. (2022), Alekseev et al. (2022)) that zoom in on risk measurement and management. The fourth, and maybe most well-known area of Johannes' research portfolio is his work on how social networks affect economic decision making, summarized in Bailey et al. (2018a) and Kuchler and Stroebel (2021). Together with Theresa Kuchler and other coauthors, Johannes uses Facebook (now Meta) data to construct a social graph of friendship links and shows that this network is important for the transmission of beliefs about all kinds of real outcomes. Their first and best-known paper in this agenda is in the Journal of Political Economy (Bailey et al. (2018b)). It shows that friends' experiences with house price growth shape the homeownership choices and the price paid for houses of their geographically distant Facebook friends. This influence occurs by changing their beliefs. In Bailey et al. (2019), the authors explore how variation in house price beliefs that is induced by the same type of social network variation affects mortgage leverage choice. Kuchler et al. (2022) shows that mutual fund investment decisions are in part determined by social networks. In more recent worth with Raj Chetty, Matt Jackson, Theresa Kuchler, and other coauthors (Chetty et al. (2022a, 2022b)), Johannes investigates that the effect friendship networks have on upward income mobility. They also study the factors that influence interactions across people of different socioeconomic backgrounds and suggest policy interventions that could increase such interactions. The Fischer Black Prize honors individual financial research. It is awarded for a body of work that best exemplifies the Fischer Black hallmark of developing original research that is relevant to finance practice. The winner should either be under age 40, or under age 45 for a winner who had not been awarded a Ph.D. (or equivalent) by age 35. The American Finance Association appreciates the generosity of the original donors who made this prize possible and the recent 2018 donors who helped to substantially increase the endowment. The names of the donors can be found at https://afajof.org/fischer-black-prize/.

Evidence of stock-return predictability by financial ratios is still controversial, as documented by inconsistent results for in-sample and out-of-sample regressions and by substantial parameter instability. This article shows that these seemingly incompatible results can be reconciled if the assumption of a fixed steady state mean of the economy is relaxed. We find strong empirical evidence in support of shifts in the steady state and propose simple methods to adjust financial ratios for such shifts. The in-sample forecasting relationship of adjusted price ratios and future returns is statistically significant and stable over time. In real time, however, changes in the steady state make the in-sample return forecastability hard to exploit out-of-sample. The uncertainty of estimating the size of steady-state shifts rather than the estimation of their dates is responsible for the difficulty of forecasting stock returns in real time. Our conclusions hold for a variety of financial ratios and are robust to changes in the econometric technique used to estimate shifts in the steady state.

We set up and solve a spatial, dynamic equilibrium model of the housing market based on two main assumptions: households with heterogenous abilities flow in and out metropolitan areas in response to local wage shocks, and the housing supply cannot adjust instantly because of regulatory constraints. In our equilibrium, house prices compensate for cross-sectional productivity differences. We increase productivity dispersion in the calibrated model in order to match the 30-year increase in cross-sectional wage dispersion that we document based on metropolitan-level data. We show that the model quantitatively matches the observed 30-year increase in dispersion of house prices across US metropolitan areas. It is consistent with several other features of the cross-sectional distribution of house prices and wages.