Measuring Financial Forecast Accuracy and Demand Forecasting for Saudi Corporate Sectors

Abstract

Many companies are facing challenges in forecast uncertainty and how to deal with market fluctuations specially in crises. In this thesis, different kinds of Saudi companies were selected from Tadawul to evaluate their forecast accuracy of Earning Before Interest and Taxes (EBIT) with two regressors from (firm specific variables) such as Operating Cost and Firm Size, and two regressors from (macro-economic variables) such as Growth and Inflation rate. Any corporate entity can create opportunity to look to enhance their operation and performance along with customers forecasting range to improve full cycle of supply chain profitability. This research aims to find the optimum forecast efficiency based on two kinds of quantitative models which are Panel Linear Regression method and Generalized Method of Moments (GMM). In order to assess the reliability of the results, Descriptive Statistical Analysis and Unit Root Test were conducted. So, after conducting both models, it shows that both models reflect high efficiency of forecast accuracy for EBIT because of the close to actual result for the in-sample data. The data provided was as panel data for around 10 years of quarter base for most effective 18 companies in 4 varied sectors.