Volume 07 Issue 10 October 2024
1Dr. Usama M. Allam, 2Prof. DrElsayed A. Elseify
1Adjunct Professor, Eslsca University, Egypt.
2Professor of Finance & Investment and the Dean of Faculty of Business, Alexandria University, Egypt
DOI : https://doi.org/10.47191/ijsshr/v7-i10-97Google Scholar Download Pdf
ABSTRACT
In the fast-paced and fiercely competitive banking industry and increasing demand for high forecasting accuracy for effective lending decision making, mastering the art of sales revenue forecasting is crucial for banking professionals who are concerned with efficient credit assessment and investment evaluation. This study delves deep into the main independent variables that impact sales revenue forecasting, moving beyond the widely used historical sales lagging approach. Through a comprehensive qualitative analysis utilizing semi-structured in-depth interviews technique with industry experts whereby study model is developed and subsequently validated using Multiple Linear Regression (MLR) analysis. Spanning a period from 2013 to 2022, this empirical research leverages secondary data from sample of 12 selected companies who manufacture primary aluminiumfrom different economics, accordingly with related macro and micro economic figures on quarterly basis. The findings revealed that interviews claimed six independent variables have significant impact on sales revenues forecasting including (Historical Trading metal price at the metal exchange, company strategy) while only four variables were (quantities sold, historical market selling prices, and historical producer price index and historical foreign exchange rates) have significant impact on sales revenue forecasting, empowering banking credit and investment teams with the critical knowledge necessary to make informed lending and investment decisions
KEYWORDS:Banking,Sales forecasting,Budgeting, Time series model, Aluminiumindustry, MLR
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