Profitability Prediction Using Macroeconomic Forecasts: The Informativeness of GDP Growth Expectations and Geographic Segment Disclosures

Abstract: Many firms today have an international footprint which means that they are exposed to different macroeconomic environments across the world. This Master Thesis investigates the usefulness of macroeconomic forecasts for the prediction of firm profitability. Recent research has shown that firm-level country exposures, determined based on geographic segment disclosures, can be combined with country-level predictions of real GDP growth to create a "MACRO" variable which has a significant relationship with future return on net operating assets, "RNOA". Focusing on the time period 2000-2019, this thesis confirms the relationship on a sample of Swedish-listed manufacturing firms, for which global macroeconomic conditions play an important role. The study extends previous research by conducting a more comprehensive out-of-sample validation. Three different prediction models of one-year-ahead RNOA are estimated and compared. The results suggest that out-of-sample prediction accuracy is improved by including the MACRO variable, although not all tests yield significant results. In addition, a model containing only past RNOA and MACRO is shown to produce significantly lower out-of-sample forecast errors than a model which also contains additional accounting and financial market variables. The results shed light on the strong forecasting power that past RNOA has on its own, which has been documented several times in previous research and which can be attributed to its strong mean reversion properties.