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.

We use a standard single-agent model to conduct a simple consumption growth accounting exercise.

[We use a standard single-agent model to conduct a simple consumption growth accounting exercise. Consumption growth is driven by news about current and expected future returns on the market portfolio. We impute the residual of consumption growth innovations that cannot be attributed to either news about financial asset returns or future labor income growth to news about expected future returns on human wealth, and we back out the implied human wealth and market return process. Innovations in current and future human wealth returns are negatively correlated with innovations in current and future financial asset returns, regardless of the elasticity of intertemporal substitution.]

[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.]

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.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

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.

Housing affordability is the main policy challenge for most large cities in the world. Zoning changes, rent control, housing vouchers, and tax credits are the main levers employed by policymakers. How effective are they at combatting the affordability crisis? We build a dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on the well-being of its citizens. The model endogenizes house prices, rents, construction, labour supply, output, income, and wealth inequality, the location decisions of households within the city as well as inter-city migration. Its main novel features are risk, risk aversion, and incomplete risk-sharing. We calibrate the model to the New York metropolitan statistical area. Housing affordability policies carry substantial insurance value but affect aggregate housing and labour supply and cause misallocation in labour and housing markets. Housing affordability policies that enhance access to this insurance especially for the neediest households create substantial net welfare gains.