Business Cycle Forecasts Research Articles

Business News and Business Cycles

ABSTRACTWe propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text‐augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

German forecasters\u2019 narratives: How informative are German business cycle forecast reports?

Based on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

Corporate Loan Spreads and Economic Activity

We use secondary corporate loan market prices to construct a novel loan market-based credit spread. This measure has additional predictive power across macroeconomic outcomes beyond existing bond credit spreads as well as other commonly used predictors in both the U.S. and Europe. Consistent with theoretical predictions, our evidence highlights the joint role of financial intermediary and borrower balance sheet frictions. In particular, loan market borrowers are compositionally different from bond market borrowers, which helps explain the differential predictive power of loan over bond spreads. Exploiting industry specific loan spreads and alternative weighting schemes further improves our business cycle forecasts.

Theories, Techniques and the Formation of German Business Cycle Forecasts

AbstractWe report results of a survey among active forecasters of the German business cycle. Using data for 82 respondents from 37 different institutions, we investigate what models and theories forecasters subscribe to and find that they are pronounced conservative in the sense that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting. DSGE models are mostly used in public institutions. In line with findings in the literature there are tendencies of “leaning towards consensus” (especially for public institutions) and “sticky adjustment of forecasts” with regard to new information. A stable relationship between preferred theories and methods and forecast accuracy cannot be established.

The economic value of business cycle forecasts for potential investors – Evidence from Germany

We examine the economic value of business cycle forecasts for potential investors of financial markets, as opposed to statistical measures of forecast accuracy. Taking Germany as an example, and based on annual data ranging from 1990 to 2016, covering 16 institutions and 18 different forecasts, we calculate performance measures of portfolios that actively react to business cycle forecasts and compare these values with the values of portfolios that are passively managed. We find that actively managed portfolios do not systematically outperform passively managed ones, while statistical measures suggest that forecasts are better than naive ones. Statistical and economic measures of forecast quality often do not point in the same direction, across forecasters. One main reason for the difference between the two views on forecast quality is that the economic value of a correct forecast changes with time. We check the robustness of our results by applying several trading rules referring to business cycle forecasts.

The Economic Value of Business Cycle Forecasts for Potential Investors Evidence from Germany

We evaluate the economic value of business cycle forecasts for potential investors on financial markets as opposed to statistical measures of forecasts accuracy. Taking Germany as an example, and based on annual data ranging from 1990 to 2016 covering 16 institutions and 18 different forecasts, we calculate the value of portfolios that actively react to business cycle forecasts and compare these values with the value of portfolios that are passively managed. We find that actively managed portfolios do not systemically outperform passively managed ones, while statistical measures suggest that forecasts are better than naive ones. Furthermore, statistical and economic measures of forecast quality often correlate negatively across forecasters. One main reason for the difference between the two views on forecast quality is that the economic value of a correct forecast changes during time. We check the robustness of our results by applying several trading rules referring to business cycle forecasts.

Theories, techniques and the formation of German business cycle forecasts

The paper reports results of a survey among active forecasters of the German business cycle. Relying on 82 respondents from 37 different institutions, we investigate what models and theories forecasters subscribe to and find that they are pronounced conservative in the sense, that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting. DSGE models are mostly used in public institutions. In line with findings in the literature there are tendencies of “leaning towards consensus” (especially for public institutions) and “sticky adjustment of forecasts” with regard to new information. We find little evidence that the behaviour of forecasters has changed fundamentally since the Great Recession but there are signs that forecast errors are evaluated more carefully. Also, a stable relationship between preferred theories and methods and forecast accuracy cannot be established. (150 words)

Improving Business Cycle Forecasts' Accuracy - What Can We Learn from Past Errors?

This paper addresses the question whether forecasters could have been able to produce better forecasts by using the available information more efficiently (informational efficiency of forecast). It is tested whether forecast errors covariate with indicators such as survey results, monetary data, business cycle indicators, or financial data. Because of the short sampling period and data problems, a non parametric ranked sign test is applied. The analysis is carried out for GDP and its main components. The study differentiates between two types of errors: Type I error occurs when forecasters neglect the information provided by an indicator. As type II error a situation is labelled in which forecasters have given too much weight to an indicator. In a number of cases forecast errors and the indicators are correlated, though mostly at a rather low level of significance. In most cases type I errors have been found. Additional tests reveal that there is little evidence of institution specific as well as forecast horizon specific effects. In many cases, co-variations found for GDP are not refected in one of the expenditure side components et vice versa.

Heuristic model selection for leading indicators in Russia and Germany

Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling holes in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms

German business cycle forecasts, asymmetric loss and financial variables

We examine the efficiency of German forecasts for output growth and inflation allowing for an asymmetric loss function of the forecasters. We find the loss of output growth forecasts to be approximately symmetric while there is an asymmetry in the loss of the inflation forecasts. The information of financial variables seems to be adequately incorporated into the output forecasts but to a lesser extent into the inflation forecasts.

Business Cycle Forecasts and their Implications for High Frequency Stock Market Returns

ABSTRACTThis article contributes to the literature on business cycle forecasts and their impact on asset prices by investigating how the 15‐second Xetra DAX returns reflect the monthly announcements of the two best‐known business cycle forecasts for Germany, i.e., the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment. The analysis disentangles ‘good’ macroeconomics news from ‘bad’ news and, simultaneously, considers time intervals with and without confounding announcements from other sources. Releases from both institutes lead to an immediate response of returns occurring 15 seconds after the announcements, i.e. within the first possible time interval. Announcements of both institutes are also clearly and immediately reflected in the volatility, which remains at a significantly higher level for approximately 2 minutes. Findings can be used to improve high‐frequency forecasts in stock markets. Copyright © 2011 John Wiley & Sons, Ltd.

Evaluating German Business Cycle Forecasts Under an Asymmetric Loss Function

Based on annual data for growth and inflation forecasts for Germany covering the 1970-2007 period and up to 17 different forecasts per year, we test for a possible asymmetry of the forecasters’ loss function and estimate the degree of asymmetry for each forecasting institution using the approach of Elliot et al. (2005). Furthermore, we test for the rationality of the forecasts under the assumption of a possibly asymmetric loss function and for the features of an optimal forecast under the assumption of a generalised loss function. We find evidence of the existence of an asymmetric loss function of German forecasters only in the case of pooled data and a quad-quad loss function. We can reject the hypothesis of rationality of the growth forecasts based on a pooled dataset, but not on data for single institutions. The rationality of inflation forecasts is frequently rejected in the case of single institutions, and also for pooled data.

Evaluating German business cycle forecasts under an asymmetric loss function

Based on annual data for growth and inflation forecasts for Germany covering the 1970-2007 period and up to 17 different forecasts per year, we test for a possible asymmetry of the forecasters’ loss function and estimate the degree of asymmetry for each forecasting institution using the approach of Elliot et al. (2005). Furthermore, we test for the rationality of the forecasts under the assumption of a possibly asymmetric loss function and for the features of an optimal forecast under the assumption of a generalised loss function. We find evidence of the existence of an asymmetric loss function of German forecasters only in the case of pooled data and a quad-quad loss function. We can reject the hypothesis of rationality of the growth forecasts based on a pooled dataset, but not on data for single institutions. The rationality of inflation forecasts is frequently rejected in the case of single institutions, and also for pooled data.

Evaluating German Business Cycle Forecasts Under an Asymmetric Loss Function

Based on annual data for growth and inflation forecasts for Germany covering the time span from 1970 to 2007 and up to 17 different forecasts per year, we test for a possible asymmetry of the forecasters' loss function and estimate the degree of asymmetry for each forecasting institution using the approach of Elliot et al. (2005). Furthermore, we test for the rationality of the forecasts under the assumption of a possibly asymmetric loss function and for the features of an optimal forecast under the assumption of a generalized loss function. We find evidence for the existence of an asymmetric loss function of German forecasters only in case of pooled data and a quad-quad loss function. We cannot reject the hypothesis of rationality of the growth forecasts based on data for single institutions, but based on a pooled data set. The rationality of inflation forecasts frequently is rejected in case of single institutions and also for pooled data.

Zehn Gebote zum Umgang mit Konjunkturprognosen

The paper discusses the reasons for the low reputation of business cycle forecasts in Germany. We argue that the reasons do not rely exclusively on the supply side of the market, i.e. the accused low quality of the forecasts, but also on the demand side of the market, i.e. the misinformed and naive use of the forecasts by politicians, journalists and other customers. Hence, we formulate „10 commandments“, which an informed user should keep in mind when reading business cycles forecasts.

Accuracy and Properties of German Business Cycle Forecasts

In this paper the accuracy of a wide range of German business cycle forecasters is assessed for the period from 1995 to 2005. For this purpose, a data set is used comprising forecasts published on a monthly basis by Consensus Economics. The application of several descriptive as well as statistical measures reveals that the accuracy of the 2-years forecasts is low relative to a simple naïve forecast. This observation can mainly be explained by a systematic overestimation of the growth rates by the forecasters. Moreover, the lack of accuracy can also be explained partly by insufficient information efficiency as well as imitation behaviour. Finally, it is shown that notwithstanding the common errors which affected the accuracy of all forecasters mainly because of their systematic overestimation, they differ significantly in their forecast accuracy.

Makroökonomische Nachrichten und die Reaktion des 15-Sekunden-DAX: Eine Ereignisstudie zur Wirkung der ZEW-Konjunkturprognose / Announcement of Business Cycle Forecasts and the Reaction of the German Stock Market

Summary We study the response of the German stock market index DAX to the announcement of macroeconomic business cycle forecasts. Returns are computed using high-frequency data observed for 15-second intervals. Publications of macroeconomic US indicators at 2:30 p.m. (CET) have temporary and opening of the New York Stock Exchange at 3:30 p.m. (CET) have permanent effects on the volatility of the German DAX. Moreover, the intraday volatility of the DAX index has a U-shaped form, which has also been identified for other international stock markets. Major reactions of both returns and volatility occur within the first 15 to 60 seconds after the announcement, revealing a high efficiency of the German capital market. Unanticipated shocks cause asymmetric stock market returns: “good” news lead to more pronounced reactions than “bad” news. Moreover, there is evidence of mean reversion and calm-before-thestorm effects. Finally, serial correlation of returns is found to be a potentially spurious result of non-anticipated announcements of macroeconomic news.

Accuracy and Properties of German Business Cycle Forecasts

In this paper the accuracy of a wide range of German business cycle forecasters is assessed for the past 10 years. For this purpose, a data set is used comprising forecasts published on a monthly basis by Consensus Economics. The application of several descriptive as well as statistical measures reveals that the accuracy of the 2-years forecasts is low relative to a simple naive forecast. This observation can mainly be explained by a systematic overestimation of the growth rates by the forecasters. Moreover, the lack of accuracy can also be explained partly by insufficient information efficiency as well as imitation behaviour. Finally, it is shown that notwithstanding the common errors which affected the accuracy of all forecasters mainly because of their systematic overestimation, they differ significantly in their forecast accuracy.

Announcement of Business Cycle Forecasts and the Reaction of the German Stock Market

Summary: We study the response of the German stock market index DAX to the announcement of macroeconomic business cycle forecasts. Returns are computed using highfrequency data observed for 15-second intervals. Results reveal immediate stock market reactions. Major reactions involving both returns and volatility occur within the initial 15 to 60 seconds after the announcement. Unanticipated shocks cause asymmetric stock market returns: ‘good’ news leads to more pronounced reactions than ‘bad’ news. Moreover, there is evidence of mean reversion and calm-before-the-storm effects. Finally, serial correlation of returns is found to be a potentially spurious result of non-anticipated announcements of macroeconomic news.

What causes mean reversion in corporate bond index spreads? The impact of survival

Previous studies document that the spread between the yield on commonly used corporate bond indexes (e.g., Moody’s Baa index) and a comparable maturity treasury bond exhibits mean reversion. An analytical model shows that a part of the observed negative relationship between changes in the spread and the level of spreads is a natural consequence of ratings based classification of bonds included in the index and the related effects of survival. Using data on individual corporate bonds over the period January 1985 to December 1996, I corroborate the analysis and illustrate the effects of survival. The result has several implications for parametric specifications of spread dynamics in the pricing of contingent claims, for the application of spreads in tests of asset pricing models (such as the conditional version of the CAPM) and for the use of spreads in business cycle forecasts.