Sell Side Recommendations during Booms and Busts
Dieter Hess (University of Cologne), et al. | January 2013
Our study documents that the information content and the information processing of stock recommendations differ fundamentally between expansions and recessions. The initial market reaction to all recommendations is more intense in recessions, but “Buy” recommendations do not have long-term investment value. We find that in recessions sell side analysts are too optimistic about stocks they recommend to buy, while investors initially overreact to these recommended stocks. In expansions, no such contradicting pattern exists. We also document that analysts favor “glamour” over “value” stocks irrespective of the state of the economy.
The Dow is Killing Me: Risky Health Behaviors and the Stock Market
Chad Cotti (University of Wisconsin), et al. | January 2013
We investigate how risky health behaviors and self-reported health vary with the Dow Jones Industrial Average (DJIA) and during stock market crashes. Because stock market indices are leading indicators of economic performance, this research contributes to our understanding of the macro-economic determinants of health. Existing studies in this literature typically rely on the unemployment rate to proxy for economic performance, but this measure captures only one of many channels through which the economic environment may influence individual health decisions. After accounting for associations with the unemployment rate, we find that large, negative monthly DJIA returns, decreases in the level of the DJIA, and the 1987 and 2008-2009 stock market crashes are associated with worsening self-reported mental health and riskier health behaviors including more cigarette smoking, binge drinking, and fatal car accidents involving alcohol. These results are consistent with models of consumption behavior such as rational addiction models, and they have important implications for research studying the association between consumption and stock prices.
The Financial Cycle and Macroeconomics: What Have We Learnt?
Claudio Borio (Bank for International Settlements) | December 2012
It is high time we rediscovered the role of the financial cycle in macroeconomics. In the environment that has prevailed for at least three decades now, it is not possible to understand business fluctuations and the corresponding analytical and policy challenges without understanding the financial cycle. This calls for a rethink of modelling strategies and for significant adjustments to macroeconomic policies. This essay highlights the stylised empirical features of the financial cycle, conjectures as to what it may take to model it satisfactorily, and considers its policy implications. In the discussion of policy, the essay pays special attention to the bust phase, which is less well explored and raises much more controversial issues.
The Failure to Predict the Great Recession - The Failure of Academic Economics? A View Focusing on the Role of Credit
Maria Dolores Gadea Rivas (University of Zaragoza) and Gabriel Pérez-Quirós (Bank of Spain) | December 2012
Much has been written about why economists failed to predict the latest financial and real crisis. Reading the recent literature, it seems that the crisis was so obvious that economists must have been blind when looking at data not to see it coming. In this paper, we analyze whether such claims are justified by looking at one of the most cited and relevant variables in this analysis, the now infamous credit to GDP chart. We compare the conclusions reached in the literature after the crisis with the results that could have been drawn from an ex ante analysis. We show that, even though credit affects the business cycle in both the expansion and the recession phases, this effect is almost negligible and impossible to exploit from a policymaker’s point of view.
Forecast Combination for U.S. Recessions with Real-Time Data
Laurent Pauwels and Andrey Vasnev (University of Sydney) | December 2012
This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model’s own forecasting performance.
The Sign Switch Effect of Macroeconomic News in Foreign Exchange Markets
Walid Ben Omrane (Brock University) and Tanseli Savaser (Williams College) | January 2013
We analyze the effect of macro news on exchange rate returns during the 2005-2009 period and document that the US dollar appreciated (depreciated) against the three major currencies, euro, pound and yen, in response to unfavorable (favorable) US growth news during the financial crisis. We show that about a third of the most significant macro news effects switched sign, primarily announcements relating to consumption, credit, employment and housing markets. We explore possible factors contributing to the sign switch such as investors’ flight to quality and the Federal Reserve Bank’s zero interest rate policy and find that these explanations are not enough to account for the change in the sign of the news response coefficients. Our results suggest that the exchange-rate response to news depends not only on the stages of the business cycle, but also on the sources of the shocks that induce these fluctuations.
Stock Market Liquidity, Aggregate Analyst Forecast Errors, and the Economy
Ji-Chai Lin (Louisiana State University), et al. | January 2013
Næs, Skjeltorp, and Ødegaard (2011) suggest that stock market liquidity is a good leading indicator of the economy because of the “flight-to-quality” behavior of informed investors, whose sells (buys) tend to lead stock market liquidity to decrease (increase) and tend to occur before economic downturns (expansions). To further understand the mechanism in the economic forecastability of stock market liquidity, we hypothesize that analyst earnings forecast errors have a systematic component, which is predictable and related to changes in the economy, and that smart investors exploit analyst forecast errors, which leads to the economic forecastability of stock market liquidity. Consistent with our hypothesis, we find that there is a strong correlation between detrended aggregate analyst forecast errors and concurrent GDP growth and that a large part of the forecast errors can be predicted using lagged macro variables. Once we control for the predictable forecast errors, the economic forecastability of stock market liquidity disappears. Thus, our study reveals that aggregate analyst forecast errors are very informative about business cycle and contain all the relevant information for stock market liquidity as a leading economic indicator.
Financial Stress and Economic Dynamics: The Transmission of Crises
Kirstin Hubrich (European Central Bank) and Robert Tetlow (Federal Reserve) | January 2013
The recent financial crisis and the associated decline in economic activity have raised some important questions about economic activity and its links to the financial sector. This paper introduces an index of financial stress -- an index that was used in real time by the staff of the Federal Reserve Board to monitor the crisis -- and shows how stress interacts with real activity, inflation and monetary policy. We define what we call a stress event -- a period affected by stress in both shock variances and model coefficients -- and describe how financial stress affects macroeconomic dynamics. We also examine what constitutes a useful and credible measure of stress and the role of monetary policy. We address these questions using a richly parameterized Markov-switching VAR model, estimated using Bayesian methods. Our results show that allowing for time variation is important: the constant-parameter, constant-shock-variance model is a poor characterization of the data. We find that periods of high stress coefficients in general, and stress events in particular, line up well with financial events in recent U.S. history. We find that a shift to a stress event is highly detrimental to the outlook for the real economy, and that conventional monetary policy is relatively weak during such periods. Finally, we argue that our findings have implications for DSGE modeling of financial events insofar as researchers wish to capture phenomena more consequential than garden-variety business cycle fluctuations, pointing away from linearized DSGE models toward either MS-DSGE models or fully nonlinear models solved with global methods.