InÀuence factors for enterprise free cash Àow: correlation and regression analysis

• Main Author: Rohanova H.
• Format: Article
• Language: English
• Published: BilaTserkva National Agrarian University 2020-05-01
• Series: Економіка та управління АПК
• Subjects: free cash flow; nopat; financial result; cash flow report; confectionary enterprise; correlation analysis; regression analysis; model

A specific informative indicator allowing judging enterprise movement through its life cycle stages is a Free Cash Flow (FCF). Differences in FCF determining considerably influence its calculation and further analysis methods. Thus the issue of FCF size determination algorithm formalization arises. The present time need in practical usage of certain applied aspects regarding the enterprise FCF management stipulates the research issues and thesis relevance as well. The aim – identification of both negative and positive influence factors for the confectionary enterprises FCF by means of correlation and regression analysis; checking of the selected influence factors statistical meaning and model adequacy. The article refers to the correlation and regression analysis, liner regression. It allows transferring from factors functional relation and effective indicator to scholastic dependence. The correlation and regression analysis allows the task solving: to determine the analytical form of relation between the effective and factor indicators as well as define their density relation level. The correlation analysis is held for 15 confectionary enterprises FCF size in 2002-2018 and 85 indicators of No1-3 financial accounting forms indicators in the first case. According to the correlation analysis results 10 influence factors possessing the close relation with FCF size are chosen. Pre-requisite availability of independent and not related factors in the regression modelresults in their reduction to 5. The greatest reverse influence makes the enterprise income from the capital assets sell and financial investments; its correlation coefficient is equal to -0.76. The other factors possess the correlation coefficient meaning at the visible and high level. The received empiric linear regression equation possesses multiple correlation effect on 0.9 level. The hypothesis on heteroscedastic model absence is confirmed. The model received according to the correlation and regression analysis results is adequate and statistically meaningful. The offered model application allows forecasting the general FCF indicator meaning for confectionary industry enterprises as well as determining tendencies in the future and managing it in general.