I use asymptotic arguments to show that the t-statistics in long-horizon regressions do not converge to well-defined distributions. In some cases, moreover, the ordinary least squares estimator is not consistent and the R2 is an inadequate measure of the goodness of fit. These findings can partially explain the tendency of long-horizon regressions to find “significant” results where previous short-term approaches find none. I propose a rescaled t-statistic, whose asymptotic distribution is easy to simulate, and revisit some of the long-horizon evidence on return predictability and of the Fisher effect.

All‐cash homebuyers account for one‐third of U.S. home purchases between 1980 and 2017. We use multiple data sets and research designs to robustly estimate that mortgaged buyers pay an 11% premium over all‐cash buyers to compensate home sellers for mortgage transaction frictions. A dynamic, representative‐seller model implies only a 3% premium, which would suggest an 8% puzzle. Accounting for heterogeneity in selling conditions explains half of this difference, but a puzzle holds in conditions with high transaction risk. An experimental survey of U.S. homeowners replicates these patterns and suggests that belief distortions can explain the puzzle in these high‐risk states.

[Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate—that is, the rent-price ratio in commercial real estate—captures fluctuations in expected returns for apartments and retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on offices are also time-varying. As implied by the present value relation, cap rates marginally forecast office rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data-generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land-use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market and Treasury bills, as well as commercial real estate.]

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.

[We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP- Compustat data set, exploiting the size, value, and momentum anomalies.]

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP–Compustat data set, exploiting the size, value, and momentum anomalies.

We investigate whether the returns of industry portfolios predict stock market movements. In the US, a significant number of industry returns, including retail, services, commercial real estate, metal, and petroleum, forecast the stock market by up to two months. Moreover, the propensity of an industry to predict the market is correlated with its propensity to forecast various indicators of economic activity. The eight largest non-US stock markets show remarkably similar patterns. These findings suggest that stock markets react with a delay to information contained in industry returns about their fundamentals and that information diffuses only gradually across markets.

Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate—that is, the rent-price ratio in commercial real estate—captures fluctuations in expected returns for apartments and retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on offices are also time-varying. As implied by the present value relation, cap rates marginally forecast office rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data-generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land-use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market and Treasury bills, as well as commercial real estate.

The excess return in the stock market is higher under Democratic than Republican presidencies: 9 percent for the value‐weighted and 16 percent for the equal‐weighted portfolio. The difference comes from higher real stock returns and lower real interest rates, is statistically significant, and is robust in subsamples. The difference in returns is not explained by business‐cycle variables related to expected returns, and is not concentrated around election dates. There is no difference in the riskiness of the stock market across presidencies that could justify a risk premium. The difference in returns through the political cycle is therefore a puzzle.

The response of corporate bond credit spreads to three exogenous macro shocks—oil supply, investment‐specific technology, and government spending—is large, significant, and a mirror image of macroeconomic activity. This countercyclicality is driven largely by credit risk premia and translates into significant return predictability. Equity risk premia exhibit similar responses, providing external validity. Information rigidities and leverage play a key role in the transmission of the shocks. Since causal evidence linking macro shocks to credit markets is scarce and recent work highlights the real effects of credit fluctuations, our findings contribute to understanding the joint dynamics of credit markets and the macroeconomy.