Business Cycle Peaks Research Articles

Help wanted: the drivers and implications of labour shortages

ABSTRACT Labour shortages have become prevalent across advanced economies. Yet, little is known about which firms are more likely to face them or about the impact they have on the labour market. We create a firm-level dataset spanning 28 EU countries, 283 regions and 18 sectors, contributing to closing this gap. We find that structural factors play the dominant role. Firms in regions with limited labour supply as well as innovative and fast-growing firms are particularly prone to face labour shortages. Moreover, shortages tend to aggravate at business cycle peaks. In a second stage, we empirically determine the impact of labour shortages on wages and hiring. Firms with higher shortages pay a wage growth premium to keep and attract workers, increasingly so when they face excess demand. At the same time, these are the firms that hire less than the average.

Accelerating peak dating in a dynamic factor Markov-switching model

The dynamic factor Markov-switching (DFMS) model introduced by Diebold and Rudebusch (1996) has proven to be a powerful framework for measuring the business cycle. We extend the DFMS model by allowing for time-varying transition probabilities, intending to accelerate the real-time dating of business cycle peaks. Time-variation of the transition probabilities is brought about endogenously using the score-driven approach and exogenously using the term spread. In a real-time application using the four components of The Conference Board’s Coincident Economic Index for 1959–2020, we find that signaling power for recessions is significantly improved. We are able to date the 2001 and 2008 recession peaks four and two months after the peak date, which is four and ten months before the NBER.

Forecasting low‐frequency macroeconomic events with high‐frequency data

SummaryHigh‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term spread improves over the monthly‐sampled to predict NBER recessions, (ii) the predictive content of financial variables is supplementary to economic activity for forecasts of vulnerability events, and (iii) a weekly activity index can date the 2020 business cycle peak in real‐time using a mixed‐frequency filtering.

The shape of recovery: Implications of past experience for the duration of the COVID-19 recession

In this paper we seek to make headway on the question of what recovery from Covid-19 recession may look like, focusing on the duration of the recovery – that is, how long it will take to re-attain the levels of output and employment reached at the prior business cycle peak. We start by categorizing all post-1960 recessions in advanced countries and emerging markets into supply-shock, demand-shock and both-shock induced recessions. We measure recovery duration as the number of years required to re-attain pre-recession levels of output or employment. We then rely on the earlier literature on business cycle dynamics to identify candidate variables that can help to account for variations in recovery duration following different kinds of shocks. By asking which of these variables are operative in the Covid-19 recession, we can then draw inferences about the duration of the recovery under different scenarios. A number of our statistical results point in the direction of lengthy recoveries.

Momentum, Reversals, and Business Cycle Turning Points

We study time variation in the profitabilities of medium‐term momentum and long‐term reversals trading strategies over the business cycle. We find reliable evidence that turning points in the business cycle are critically important in determining momentum and reversals profits. Specifically, momentum profits at business cycle peaks are higher than at business cycle troughs. The opposite pattern is found for reversals profits. Business cycle peaks show lower reversals profits than at troughs. The results indicate that momentum profits are not simply procyclical, but instead exhibit an intriguing and complex non‐linear relation with macroeconomic conditions. We also find that the loser stocks are the primary determinant of time variation in momentum and reversals profitabilities across business cycles. Finally, our results remain robust after controlling for market state, cross‐sectional return dispersion, and investor sentiment. Taken together, our findings pose challenges to existing theories of the momentum and reversals effects.

Asset Pricing with Persistence Risk

Persistence risk is an endogenous source of risk that arises when a rational agent learns about the length of business cycles. Persistence risk is positive during recessions and negative during expansions. This asymmetry, which solely results from learning about persistence, causes expected returns, return volatility, and the price of risk to rise during recessions. Persistence risk predicts future excess returns, particularly at 3- to 7-year horizons. Its predictability is strongest around business-cycle peaks and troughs. We confirm the model’s predictions in the data and provide evidence that persistence risk is priced in financial markets.Received October 13, 2017; editorial decision September 19, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Can Firms See into the Future? Survey Evidence from Germany

This paper presents new evidence on the expectation formation process of firms from a survey of the German manufacturing sector. It focuses on the expectation about their future business conditions, which enters the widely followed economic sentiment index and which is an important determinant of their employment and investment decisions. We find that firms extrapolate their experience too much and make predictable forecasting errors. Moreover, firms do not seem to anticipate the upcoming reversals of business cycle peaks and troughs which causes suboptimal adjustment of investment and employment and affects their inventories and profits. However, the impact on expectation errors decreases with the size and the age of the firm as firms learn to reduce their extrapolation bias over time.

Can firms see into the future? Survey evidence from Germany

This paper presents new evidence on the expectation formation process of firms from a survey of the German manufacturing sector. First, firms become too optimistic after their business has improved, consistent with the over-extrapolation of their experience. Second, firms extrapolate improvements in their business more than deteriorations. Third, firms are more optimistic (pessimistic) than usual during business cycle peaks (troughs), suggesting that they do not anticipate reversals. Fourth, the German reunification provided a positive exogenous shock to the business of West German products. The industries most positively affected by the shock initially over-extrapolated the past downward trend just as much as the less affected industries, and thus failed to anticipate the reversal. Fifth, more overoptimistic firms subsequently report larger inventories and lower profits, suggesting that expectation errors entail (opportunity) costs. Sixth, older and larger firms show a smaller extrapolation bias, indicating that time and resources can alleviate the extrapolation bias. Seventh, firms that expect their business to improve tend to also expect their prices to increase, suggesting that firms mostly try to forecast demand shocks as opposed to supply shocks. Overall, these observations suggest that firms have a limited ability to anticipate changes in their activity and extrapolate their experience too much.

Asset Pricing with Persistence Risk

Persistence risk is an endogenous source of risk that arises when a rational agent learns about the length of business cycles. Persistence risk is positive during recessions and negative during expansions. This asymmetry, which solely results from learning about persistence, causes expected returns, return volatility, and the price of risk to rise during recessions. Persistence risk predicts future excess returns, particularly at 3- to 7-year horizons. Its predictability is strongest around business-cycle peaks and troughs. We confirm the model's predictions in the data and provide evidence that persistence risk is priced in financial markets.

Does it Pay to Forecast the Business Cycle? A U.S. Update and an International Perspective

Over the period 1970-2015, investment returns were enhanced by merely knowing concurrently whether the economy was in a state of expansion or contraction, and making the most basic asset allocation decision of whether to be in stocks or bonds. In the United States, an annual excess return of 2.01% was earned by investing in stocks during expansions and in bonds during contractions. In eight foreign markets, the average annual excess return from the same strategy was 1.74%. Forecasting business cycle troughs is more important than business cycle peaks. The authors conclude simple passive timing improves fund performance using business cycle peaks/troughs, and even slight forecasting prowess is rewarded with positive performance. Importantly, even investors who invested one month after the cycle turns could still earn excess returns.

Loan Pricing over the Business Cycle

Based on a screening model, we hypothesize that borrower risk will be over- (under-)priced in recessions (booms), and the loan spreads’ sensitivity to default risk as a function of economic growth will be inverse U-shaped. We test this prediction using a sample of 5,300 U.S. commercial loans contracted between 1987 and 2012, and find that the data support the prediction. The result is particularly strong at business cycle peaks: in the main results, moving from zero real GDP growth to the 99th percentile of economic growth, the responsiveness of the spread to borrower risk (the “price of risk”) is more than halved. The results are robust to a variety of alternative specifications and competing explanations.

The long-run relationship between precious metal prices and the business cycle

Abstract This study examines the long-run relationships between major precious metal prices over the last forty years. Using a vector error correction model, we find that weekly futures log prices of gold and silver, and gold and platinum appear to be cointegrated. The results show that the cointegrating relationships between precious metal prices are not stable over time with significant shifts in the price relations around business cycle peaks and during recessions. Our results indicate that the long-run relationships between precious metal prices are strongly influenced by economic conditions. These findings should contribute to the growing literature on linkages between macroeconomic fundamentals and the exact nature of the price relationships across different precious metals.

Determinants of Consumer Sentiment Over Business Cycles: Evidence from the U.S. Surveys of Consumers

We study the information content of the University of Michigan’s Index of Consumer Sentiment as well as its five components. Using household data from the Surveys of Consumers, we identify the main determinants of these indicators and document their varying role over the business cycle. Our results suggest that while at the aggregate level, macroeconomic conditions explain sentiment well, important and additional information is contained at the level of households. We compare the role of objective and subjective information in determining household level sentiment, and show that significant heterogeneity in the absorption of news from local network sources is a major feature of consumer sentiment. The differential interpretation of current macroeconomic conditions is found to be more pervasive in periods of falling sentiment that typically predates business cycle peaks, and thus helps sentiment to foreshadow recessions.

Predicting the oil prices: Do technical indicators help?

This paper aims to investigate the predictability of technical indicators to directly forecast oil prices and compare their performances with macroeconomic variables. We find that technical indicators do exhibit statistically and economically significant in-sample and out-of-sample forecasting power under OLS regressions and forecast combinations, clearly exceeding that of well-known macroeconomic variables and state-of-the-art oil-macro forecasting variables. Moreover, the strength of the predictive evidence is substantial during recessions and expansions and can detect the typical decline in the oil returns near business-cycle peaks effectively. Furthermore, technical indicators reveal substantial economic value for investors, in terms of superior oil risk premium forecasts and sizable utility gains. The technical indicators' ability to predict the oil price stems in part from its ability to predict changes in sentiment, suggesting the financialization of oil markets.

Discussion

Discussion

Relative Valuations of Fixed Capital and Financial Assets in the United States

This paper examines the relative valuations for fixed capital and financial assets in the United States, illustrating the observed relationships from the valuations of assets on the real productive side of the economy to the financial valuations of equities and other financial assets, and also to private net worth. The analysis examines the various layers of the relationships of asset valuations relative to potential GDP for the private nonfinancial sectors of the U.S. economy, building up from the relative valuations for fixed capital assets (the capital stock), to the relative valuations for total nonfinancial assets (including land), and then to total assets (i.e., including financial assets). The low rate of net investment in recent years is also addressed, including examining the role of higher effective rates of depreciation. Comparisons are made for the observed relative valuations of corporate equities to Shiller’s CAPE and to a measure of Tobin’s q ratio. Relationships for the relative valuation of assets during business cycles are examined, including the observed lead times and threshold changes relative to business cycle peaks.

Comment

Comment

Skyscraper height and the business cycle: separating myth from reality

This article is the first to rigorously test how skyscraper height and output co-move. Because builders can use their buildings for nonrational or nonpecuniary gains, it is widely believed that height competition occurs near the business cycle peaks. This would suggest that extreme building height is a leading indicator of GDP, since the tallest buildings are likely to be completed at or near the peak of a cycle. To test these claims, first we look at both the announcement and the completion dates for record-breaking buildings and find there is very little correlation with the business cycle. Second, cointegration and Granger causality tests show that while height and output are cointegrated, height does not Granger cause output. These results are robust for the United States, Canada, China and Hong Kong.

Skyscraper Height and the Business Cycle: International Time Series Evidence

This paper is the first to rigorously test how height and output co-move. Because builders can use their buildings for non-rational or non-pecuniary gains, it is widely believed that (a) the most severe forms of height competition occur near the business cycle peaks and (b) that extreme height are examples of developers “gone wild.” We find virtually no support for either of these popularly held claims. First we look at both the announcement and completion dates for record breaking buildings and find there is very little correlation with the business cycle. Second, cointegration and Granger causality tests show that height and output are cointegrated and that height does not Granger cause output. These results are robust for the United States, Canada, China and Hong Kong.

Has the structural break slowed down growth rates of stock markets?

In this paper, we use the common structural break test suggested by Bai et al. (1998) to test for a common structural break in the stock prices of the US, the UK, and Japan. On the basis of the structural break, we divide each country's stock price series into sub-samples and investigate whether or not the structural break had slowed down the growth of stock markets. Our main findings are that when stock markets are modelled in a trivariate sense the common structural break turns out to be 1990:02, with the confidence interval including several episodes, such as the asset price bubble when housing prices and stock prices in Japan reached a peak in 1988/1989, the early 1990s recession in the UK, the business cycle peak of July 1990, the August 1990 Iraqi invasion of Kuwait and the March 1991 business cycle trough. Annual average growth rates suggest that the structural break has slowed down the growth rate of the US, the UK and Japanese stock markets.