Today, businesses are facing increasingly fierce global competition and rapidly changing business conditions. Add to the picture financial instability and depressed economies, and it becomes difficult to accurately predict the future. The need for methods to improve forecasting of the future is rising. Business cycle forecasting was employed to address this issue. The objective of the thesis was to study the application of business cycle forecasting on both the macroeconomic and industry level. Forecasting on the industry level has not received much attention, whether academically or practically. By conducting a comprehensive literature review, numerous sources to drivers of business and industry cycles were identified. It is recognized that there is no complete theory of the business cycle, and that in order to gain a complete view on cyclicality, knowledge must be pooled from all theories. The findings from business cycle theories were applicable for both levels of study. Assessment of quantitative approaches revealed that the economic indicator approach had the best fit with the aim of the thesis. The review was guided by expert interviews. The selection of the indicator approach was based on a set of criteria advocating that the approach must be easy to understand and use by nonstatistical expert, practical and effective in predicting business cycle turning points. In order to use the economic indicator approach, the findings of the literature review is applied in the process of identifying potential leading indicators. Once selected, these indicators are tested in their ability to actually lead the business cycle. Indices of indicators have proven to be superior over individual indicators. 10 economic indicators were found for the macro level, and used to create a leading macroeconomic composite index. These 10 macroeconomic indicators were combined with 5 industry specific indicators for each of the three case industries. Several important insights emerged from the empirical analysis. The leading macroeconomic composite index did a good job explaining cyclical movement in the three industry cases. Macroeconomic factors exert a large influence on most industries. However, the leading industry composite indices outperformed the leading macroeconomic composite index for all the industries. Thus, the indicator approach showed great potential for improving forecasting on the industry level.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||125|