Multivariate Time Series Forecasting of Earnings Before Interests and Taxes

Abstract

Financial forecasting is an important tool for companies when planning their operations and structuring their organization. At the Packaging Solutions division at Stora Enso they work with three different forecasts for their corrugated business, where one of them is a 15-months rolling forecast. This forecast is updated each month, which is a time consuming process. It is desired to decrease that time to make more time available for analysing the result from the forecast and plan for actions. One of the most important outcomes is the forecast for Earnings Before Interest and Taxes (EBIT). Therefore, this project aims to create an automatic model that can make the forecasting process for EBIT more efficient and more accurate. The input data consists of 16 different features, such as raw material costs, end product price and maintenance costs, that are recorded monthly between 2014-2021, for seven different countries and three regions. Since the amount of data is relatively small and multivariate, the vector autoregressive moving average (VARMA) model was selected. During the model training, five different combinations of features were tested for all countries and regions. The result showed that the accuracy increased compared to the company model for four countries and one region. It could be seen that the countries that had more stable EBIT data worked well with the VARMA model while the ones that included sudden increases or decreases were more difficult to model, as expected. To conclude, the VARMA model is a good option to make the forecasting process more efficient but the model would benefit from some fine adjustments before it can be implemented in the daily work at Stora Enso.