Stig Vinther Møller

Forecasting US Recessions: The Role of Sentiment

We study the role of sentiment variables as predictors for US recessions. We combine sentiment variables with either classical recession predictors or common factors based on a large panel of macroeconomic and financial variables. Sentiment variables hold vast predictive power for US recessions in excess of both the classical recession predictors and the common factors. The strong importance of the sentiment variables is documented both in-sample and out-of-sample.

Housing Price Forecastability: A Factor Analysis

We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets.

Global Economic Growth and Expected Returns Around the World: The End-of-the-Year Effect

Global economic growth at the end of the year strongly predicts returns from a wide spectrum of international assets, such as global, regional, and individual-country stocks, FX, and commodities. Global economic growth at other times of the year does not predict international returns. Low growth in the global economy at the end of the year predicts higher returns over the following year. It also predicts the global business cycle. When global economic growth at the end of the year is low, investors expect a worsening of the global business cycle and increase their required returns. This paper was accepted by Lauren Cohen, finance.

Cross-Sectional Consumption-Based Asset Pricing: A Reappraisal

By using a beginning-of-period timing convention for consumption, and by including the Great Depression years in the analysis, we show that on annual data from 1926 to 2009 a standard contemporaneous consumption risk model goes a long way in explaining the size and value premiums in cross-sectional data that include both the Fama-French portfolios and industry portfolios. A long run consumption risk variant of the model also produces a high cross-sectional fit. In addition, the equity premium puzzle is significantly reduced in the models. We argue that in evaluating consumption based models, it is important to include both boom and crises periods, i.e. periods with severe consumption declines as well as periods with strong growth, and that the standard post-war data sample may not be well suited in this respect.The consumption-based asset pricing model with constant relative risk aversion explains the size and value premiums in US data over the period 1929 to 2014. The timing convention used for consumption is crucial for this result. The model matches the cross-sectional variation in mean returns on size and value portfolios with beginning-of-period consumption, but the fit of the model completely breaks down with end-of-period consumption.

Metro Area Common House Price Declines and US Recessions

We investigate the relation between large negative house price co-movements in the cross-section of US cities and the national business cycle. The occurrences of large negative house price co-movements across cities cluster over time and these clusters are closely linked to NBER recession dates. A simple co-movement measure that aggregates large negative city-level house price returns reliably predicts future recession periods. Weighting cities according to population or GDP when constructing the negative co-movement variable yields the largest forecasting power, indicating that larger cities that contribute more to the national GDP are more influential in terms of correctly signaling future recessions. Moreover, large negative house price co-movements contribute above and beyond traditional recession predictors, suggesting an important role for city-level housing information as an early warning indicator.

Online Appendix: Bond Market Asymmetries Across Recessions and Expansions: New Evidence on Risk Premia

This paper provides new evidence on bond risk premia by conditioning the classic Campbell-Shiller regressions on the business cycle. In expansions, we find mostly positive intercepts and negative slopes, but the results are completely reversed in recessions with negative intercepts and positive slopes. The pattern in these coefficients is explained by a term structure model with business cycle dependent loadings in the market price of risk. We argue that such a re-pricing of risk may be induced by a switch in monetary policy, as a standard Taylor-rule displays significantly less interest rate smoothing in recessions compared to expansions. Robustness checks and all model derivations are provided in this Online Appendix. The paper is available at: http://ssrn.com/abstract=2834898.

Bond Market Asymmetries Across Recessions and Expansions: New Evidence on Risk Premia

This paper provides new evidence on bond risk premia by conditioning the classic Campbell-Shiller regressions on the business cycle. In expansions, we find mostly positive intercepts and negative slopes, but the results are completely reversed in recessions with negative intercepts and positive slopes. The pattern in these coefficients is explained by a term structure model with business cycle dependent loadings in the market price of risk. We argue that such a re-pricing of risk may be induced by a switch in monetary policy, as a standard Taylor-rule displays significantly less interest rate smoothing in recessions compared to expansions. An Online Appendix is available at http://ssrn.com/abstract=2857802.

Common Real Estate Crashes

We examine joint declines in real estate prices across US metro areas using the non-parametric exceedance methodology from the stock market literature. Large negative real estate returns are identified as returns below the 10% quantile (negative exceedances). Common real estate crashes are months in which many metro areas have negative exceedances (co-exceedances). Using count models, we find that common real estate crashes are strongly related to macroeconomic variables such as the price-income ratio and macroeconomic uncertainty. The larger the price-income ratio and the macroeconomic uncertainty are, the more likely it is that the US real estate market incurs a common crash.

A New Index of Housing Sentiment

We propose a new measure for housing sentiment and show that it accurately tracks expectations about future house price growth rates. We construct the housing sentiment index using partial least squares on household survey responses to questions about buying conditions for houses. We ?find that housing sentiment explains a large share of the time-variation in house prices during both boom and bust cycles and it strongly outperforms several macroeconomic variables typically used to forecast house prices.