Profit Forecast Accuracy of Time Series Model - A Case Study of Associated British Foods

Abstract

Accurate earnings per share (EPS) forecasting is crucial for financial decision-making. This study explores the potential of improving EPS forecasting accuracy by integrating economic lead indicators into time-series models. By incorporating macroeconomic factors like GDP growth and interest rates, the models capture the influence of the broader economic environment on a company’s financial performance. Results demonstrate that including economic lead indicators significantly enhances EPS predictability beyond traditional time-series models. This integration offers a forward-looking perspective, comprehensive analysis, and context to the forecasting process, enabling stakeholders to make more informed investment decisions and develop better strategies. Further research can investigate additional lead indicators, assess their impact in different industries, and develop hybrid forecasting models for refined EPS predictions.