Ensemble Forecasting Methods in DCF Modelling of the Fair Value of Enterprises

Abstract

The purpose of this study is to improve the methodology for assessing the fair value of enterprises based on the DCF model with the use of ensemble methods for predicting the selling price of their products. The study compares the fair values of three Ukrainian agricultural enterprises. The value of each company was calculated in several ways: using forecast data from statistical agencies, calculated forecast values, and real indicators. The methodology is based on the use of methods and models for forecasting time series. The study uses neural networks, as well as prediction methods such as linear regression, FB Prophet model, Holt-Winters exponential smoothing method, SARIMA, XGBoost, which are combined into a single ensemble with the use of the stacking method. The study employed such general scientific methods as analysis, synthesis, abstraction, and comparison. Also, methods of graphical and tabular presentation of materials were applied. Statistical modelling was used to determine the parameters of the model. The study found that the DCF model can potentially be improved by using ensemble methods when predicting metrics such as inflation, exchange rates, and the selling price of goods and services. These metrics have a significant impact on the final result of the model and therefore the slightest changes in these input data can lead to significant deviations in the result. It was demonstrated that the use of ensemble methods only at the selling price can increase the accuracy of the model by 5-15%. It is advisable to use the results of the study in the investment activities of companies, in mergers and acquisitions of companies, as well as in measuring the fair or investment value of companies.