Fama-French Factors and Business Cycles
Arnav Sheth and Tee Lim (Saint Mary’s College of California)
December 4, 2017
We examine the behavior of Fama-French factors across business cycles measured in various ways. We first split up the business cycles into four stages and examine the cumulative returns of factors in each of those stages. We then look at the behavior of the factors after a yield curve inversion starts and ends, as the relationship between yield curve inversions and recessions has been well-explored. We finally run a logistic regression to test the predictive power of the term spread on the NBER recession indicator. Our results show that there is an effect on the factors of each of our four stages, and there is limited predictive power from the recession probabilities. We believe this is of practical importance to portfolio managers who are factor-oriented in their approach.

Fearing the Fed: How Wall Street Reads Main Street
Tzuo Hann Law (University of Pennsylvania), et al.
December 1, 2017
We provide strong evidence of persistent cyclical variation in the sensitivity of stock prices to macroeconomic news announcement (MNA) surprises. The stock market sensitivity is muted during the early recession and the late expansion phases of the economy, however, it increases significantly, reaching peak values in the early expansion phase. We show that market expectations and uncertainty about future interest rates are the primary drivers of the cyclical market responses to MNAs — these responses depend on whether the Fed is expected to be reactive. Evidence from survey forecasts and a monetary regime-switching model corroborates the connection between the cyclical stock responses and monetary policy expectations. A decomposition of the stock market responses shows that they primarily reflect news about cash flows and interest rate rather than risk premia news.

Can Stock Volatility Be Benign? New Measurements and Macroeconomic Implications
Yu-Fan Huang and Sui Luo (Capital University of Economics and Business)
December 10, 2017
This paper uses the maximum and minimum stock price within a month to construct two new measures of financial market uncertainty, denoted by good and bad volatility, respectively. We find non-synchronized movements of the good and the bad volatility, and investigate their implications on the business cycle fluctuations. Good (bad) volatility clearly signals acceleration (deceleration) in economic activity based on predictive regression estimates. Output, employment, and stock price plummet rapidly in response to a bad volatility shock, while the responses of them to a good volatility shock are “J”-curved: they fall mildly upon impact but rise persistently afterward. Although both good and bad volatility shock explain a significant portion of variation in output and stock price, the bad volatility shock plays a dominating role.

Asset Co-Movements: Features and Challenges
Nikolay Gospodinov (Federal Reserve Bank of Atlanta)
November 1, 2017
This paper documents and characterizes the time-varying structure of U.S. and international asset co-movements. Although some of the time variation could be genuine, the sampling uncertainty and time series properties of the series can distort significantly the underlying signal dynamics. We discuss examples that illustrate the pitfalls from drawing conclusions from local trends of asset prices. On a more constructive side, we find that the U.S. main asset classes and major international stock indices share a factor that is closely related to the business cycle. At even lower frequency, the common asset co-movement appears to be driven by demographic trends.

Macroeconomic Nowcasting and Forecasting with Big Data
Brandyn Bok (Federal Reserve Bank of New York), et al.
November 22, 2017
Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions—the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before “big data” became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.

Financial Variables and Macroeconomic Forecast Errors
Michelle L. Barnes and Giovanni Olivei (Federal Reserve Bank of Boston)
October 31, 2017
A large set of financial variables has only limited power to predict a latent factor common to the year-ahead forecast errors for real Gross Domestic Product (GDP) growth, the unemployment rate, and Consumer Price Index (CPI) inflation for three sets of professional forecasters: the Federal Reserve’s Greenbook, the Survey of Professional Forecasters (SPF), and the Blue Chip Consensus Forecasts. Even when a financial variable appears to be fairly robust across sample periods in explaining the latent factor, from an economic standpoint its contribution appears modest. Still, several financial variables retain economic significance over certain subsamples; when non-linear effects are accounted for, these variables have an improved ability to consistently predict the latent factor over the business cycle.