Business cycle forecasting through economic indicators
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After years of more or less continuous growth and relatively low macroeconomic volatility during the years named “The Great Moderation”1, the US economy entered in December 20072 what seems to have been the deepest recession since The Great Depression3 The recession has been of relatively long duration and contained both a credit-crunch and a significant downturn in the housing market.
This has in turn resulted in rising unemployment and a monthly bankruptcy rate which has increased by almost 67%4 between Q3 2007, which was the quarter before the business cycle peak, and Q4 2008.
Business cycles are returning phenomenons where periods of economic growth are always followed by a downturn associated with negative growth, before the growth turns positive again5, hence the name business cycle. But despite a long history of recurring cycles, the downturns often seem to come as a surprise to many investors and corporations. In each downturn you can hear managers in trouble deny having prepared the wrong strategy in bad periods by explaining their losses through unexpected external changes in the macro economy (Lai 1994).
Since the definition of a downturn in the business cycle indicates falling economic activity and hence profits, external changes can be a viable explanation in some cases. But much research also suggests that managers tend to choose poor strategies ahead of and during changes in the business cycle as a result of misinterpreting the situation (Lai, 1994) (Van Der Stede, 2009). This paper will show how macroeconomic forecasting can help managers in gaining qualified expectations about the future of the business cycle, which creates a broader foundation for managers to prepare their strategies.
In accordance with the fact that recurrent phenomenons are easier to predict than random happenings, together with our extensive experience with business cycles, a large amount of research has concluded that economic indicators can be used to forecast the future developments in the business cycle6. But as the economic environment seems to be ever evolving, there is a constant need for updated research on these fields. This paper will extend on this field of research through an ex post forecast of the US business cycle peak from December 2007, and show how macroeconomic forecasting can play an important role, also in the future, as part of macroeconomic risk management.
Even though we know that business cycles are recurring, and forecasting through economic indicators have proven helpful in gaining qualified expectations about the future developments of economic activity, it still seems as business cycle risks are not given the deserved attention in enterprise risk management. The increased stability during the great moderation, the imperfections of forecasting, and biases in decision making, seemed to make economic forecasting and the management of business cycle risks surplus of requirements in regards of risk management. But as the economy again enters a deep recession the importance of monitoring and managing business cycle risks is back on the agenda.
There is already a wide selection of literature on the subjects of economic forecasting, but as the economic environment seems to be ever evolving, it is important to continuously perform new research on these subjects7. A relevant question is; how do we know whether the forecasting techniques of the past will continue to produce successful predictions in the modern economy? This question makes the research in this paper highly relevant.
The following research will explore the value of economic forecasting through economic indicators. It will provide a detailed introduction to forecasting and the value and characteristics of economic indicators. To take into account the evolving economic environment, I will suggest a flexible and dynamic approach to forecasting which will be more based on judgmental analysis rather than econometric modeling. While this approach has both strengths and weaknesses compared to more structured econometric methods, its flexibility will help ensure its relevance also in the future8.
To contribute to the need of frequent updates on the research of the predictive powers of economic indicators, this paper will also provide an ex post forecast of the business cycle peak from December 2007. Ex post forecasts of the latest business cycle turning points play an important role in such research as they help confirming the forecasting abilities of economic indicators on the evolving economy, and give updated information on the performance of the different approaches to economic forecasting.
To cover these topics I will research economic forecasting through the following two problem statements:
P1: “ Show how US business cycles can be forecasted through a flexible and dynamic analysis of economic indicators. The approach should be flexible enough to easily adjust to future economic evolvement, and hence have the qualities to be a relevant forecasting procedure also in the future ”
P2: “ Was it possible to forecast the U.S. recession following the business cycle peak in December 2007 through an analysis of economic indicators?”
There is a broad range of external factors influencing the health and stability of the economy. Issues such as politics, wars, and extreme weather have indeed influenced the economy in the past and are likely to carry influence in the future. But these issues will not be considered in this paper, as I will only focus on economic indicators9.
There also exist more specialized economic indicators which demand more of the analyst in terms of specialized knowledge. This can for example be specific and detailed information about the important banking sector. Such specialized information will not be included as the approach in this paper will be of a more general structure. As a result of this the relatively specialized issues behind the subprime crisis will not be given much attention.
This does not mean that such indicators do not hold important information. Instead it means that such indicators are of less relevance if the forecaster does not hold a detailed knowledge of developments and innovations within the markets it explains. If the forecaster does hold detailed knowledge about a relevant market or sector, he should include this in his analysis.
As will be explained in detail, understanding history plays a vital role in forecasting the developments of business cycles. Nevertheless, this paper will not base the analysis on empiric statistical relationships. While measures such as correlations can be of great relevance, the empirical analysis in this paper will instead be based on past trends and negative signs ahead of earlier recessions, and not on statistical measures. More details on the reasoning behind this can be found in the review of the strengths and weaknesses of this forecasting approach in section 8.
To answer the problem statements I will start by a detailed description and explanation of the problem before I move on to give an understanding on how these problems can be handled through economic forecasting. Finally I will use the methods and theories generated in the answer of the first problem statement to solve the second problem statement.
A vast amount of research on different approaches to economic forecasting have proven that economic indicators indeed are helpful in predicting future developments in the business cycle. Much of this research is made towards econometric approaches which are often constructed to forecast the probability of recessions. While Andrew J. Filardo states that the different models tested in his article “The 2001 recession; what did recession prediction models tell us?” (Dua 2004, pp 134-160) are indeed good models, this paper will use a different approach. Instead of a static econometric approach I will introduce a more dynamic and judgmental analysis with more flexibility.
The chosen indicators will first be given an empirical and theoretical analysis of their behavior ahead of earlier recessions, to create knowledge on what developments we can expect from the respective indicators. After this, predictive information will be extracted from the indicators through analysis of economic theory and a fundamental examination. The indicators will be analyzed both separate and in conjunction with the help of “The Three D’s”, which is a rule of thumb suggested by The Conference Board (2001).
As a basis of forecasting there need to be a detailed understanding of the different stages of the business cycle. The different stages will be examined through the business cycle model introduced by Victor Zarnowitz from his paper “The anatomy of recent US growth and business cycles” (Dua 2004, pp 43-82). While this model is based on the US economy, it is still relatively broad and many of the components in the discussion from section: ( Managing business cycle risks) should be relevant also for forecasts in other economies.
The forecast produced to solve the second problem statement will be made with an as chronologic timeline as possible. Nevertheless, there are numerous of reasons why an ex post forecast cannot be directly compared to real-time analysis.
First of all, the interpretation of the indicators by the analyst are of vital importance to the conclusions drawn, and because of my hindsight understanding of the crisis, it should be acknowledged that I might be somewhat biased during the analysis.
Second, my data was collected ex post, and some of the time series are likely to have been revised.
Third, I have all the data available at the same time. During real time analysis much of the data comes with a considerable lag which makes forecasting more difficult. But with this said, I still believe that both the analysis of the 2007 recession, and the approach to forecasting business cycles introduced in this paper, is of great relevance to economic forecasting and macroeconomic risk management.
Data and literature
As a basis of the research in this paper I will use a broad mixture of modern and classic literature. Section: (U.S. business cycles), which provides an introduction to the history and structure of US business cycles, will mainly be based on the work of the National Bureau of Economic Research, mostly represented through the papers of Victor Zarnowitz.
There also exists a broad range of literature surrounding the analysis of economic indicators. While I have gathered information from a wide range of relevant research, the forecasting approach is mostly influenced by Bernard Baumohls book; “The secrets of economic indicators” published in 2007 and The Conference Boards; “Business Cycle Indicators Handbook” published in 2001.
I will also include some theories on how the economy and forecasts are biased by the animal spirits of human behavior. That is, some more or less controversial subjects from behavioral finance. This is mainly influenced by the prize winning book Animal Spirits, which was made available in 2009 by Robert J. Shiller and George A. Akerlof.
But also the article; “Enterprise governance: Risk and performance management through the business cycle” by Wim A. Van Der Stede from 2009, and a good and summarizing article by Linda M. H. Lai titled; “The Norwegian banking crisis: Managerial escalation of decline and crisis” published in 1994, have been used as the basis of arguments.
If not stated differently, the quantitative data are all collected through Datastream® on the 1303-2009. As all data were collected at this date, with no updates during the analyzing process, there might have been some revisions and changes which are not updated in this paper. But this will not have any effect on the quality of neither the forecasting approach nor the forecast of the 2007 recession. But as already mentioned; as the data used in the forecast is collected ex post, they are very likely to have been revised both after the download, and during the period between the business cycle peak and the ex post forecast performed in section 7.
The paper will start with a more detailed discussion on why forecasting the business cycle is important, and why it should be implemented in enterprise risk management. After establishing the relevancy of forecasting, I will give an introduction to the history of US business cycles. As it is vital to all types of forecasting that you have detailed knowledge of the environment you are forecasting, the goal of this section is to provide a foundation for the following forecasting approach.
In the next section I will introduce the role of economic indicators to business cycle forecasting. There will be detailed information on the criteria’s that should be met in terms of choosing the relevant indicators for forecasting, and also some information on how these should be analyzed. A broad range of different economic indicators will then be introduced and explained mainly through economic theory, but also from the light of empirical evidence.
After establishing an understanding of the business cycle, and of a list of relevant economic indicators, section 7 will forecast the recession starting in December 2007. Even though this forecast cannot be directly compared with a real time forecast, it will be done in a realistic and relatively chronological manner to give a better practical understanding on how the forecast can be performed.
As for all forecasting there exist much uncertainty, and for economic forecasting there are many different researched approaches towards predicting the future. After performing the ex post forecast of the 2007 recession, I will explain some of the strengths and weaknesses of this particular forecasting approach and some of the biases from human behavior which indeed can disturb the forecast.
1 The years from the early 1990s and up until 2007 were a period of high growth, low nominal short term interest rates together with low and relatively stable inflation. This period has been named the “The Great Moderation” in the US and has by some been marked as an important reason for the magnitude of the 2007 recession (Mizen 2008).
2 The dating of the US business cycle peaks used in this paper is produced by The National Bureau of Economic Research (NBER).
3 This particular recession will from now on be referred to as “the 2007 recession”.
4 The number of bankruptcies in Q3 2007 was 25925. This number increased to 43546 in Q4 2008. (43546- 25925)/25925 = 67,9%. All numbers are collected from Datastream®
6 Among many studies, James H. Stock and Mark W. Watson researched the forecasting abilities of economic indicators ahead of the 2001 recession in their article; “How did leading indicator forecast do during the 2001 recession?” from 2003.
7 New technologies, politics, techniques and financial products are continuously being released, changing the environment of the business cycle.
8 Section 8.5 will also point to the fact that the flexible approach in this paper and econometric forecasting is not mutually exclusive. On the contrary these different approaches can indeed gain from each other’s strengths.
9 Arguably changes in other external factors will in turn influence the economic indicators. In this way the forecaster will get the potential warning signs resulting from changes in factors outside the analysis in this paper.