A preliminary approach to identify the best function that fits the growth of company total assets

Research output: Chapter in Book/InformChapterResearch

Abstract

Total Assets define companies; it is relevant in determining the size of the companies, ranking them by economic relevance and making predictions about their future. That makes necessary to analyze how companies growth, according to Total Assets, so this research problem focuses on identifying what the trend in Total Assets growth is across industries and company size categories, to find similarities and differences. Accordingly, the purpose of the study is to analyze the Total Assets company growth finding a function that fits across industries and company size categories. The method is analytical, deductive and empirical; it is a cross-sectional analysis with two industries and four different company sizes, based on Total Assets grouped into the categories of micro, small, medium or big enterprise. Every combination of industry-company size is analyzed to see which function draws the best fit. The functions are: 1) Linear, 2) Logarithmic, 3) Inverse, 4) Quadratic, 5) Cubic, 6) Compound, 7) Power, 8) S, 9) Growth, 10) Exponential, and 11) Logistic. The test consists of a regression analysis. ANOVA significance test and explained variance allow identifying the best function fit. Results show that cubic function gives the best results in all industry-company size combination. Other functions are relevant in some, but not all, combinations of categories. The conclusion is that cubic function provides the best fit to total assets company growth across several industry-company size combinations. However, it requires more in deep analysis and replication of the research in other industries to confirm the results.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 International Conference on Applied Mathematics and Computer Science, ICAMCS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages23-27
Number of pages5
ISBN (Electronic)978-1-7281-3258-7
ISBN (Print)978-1-7281-3258-7
DOIs
StatePublished - Apr 2018
Event2018 International Conference on Applied Mathematics and Computer Science, ICAMCS 2018 - Paris, France
Duration: Apr 13 2018Apr 15 2018

Publication series

NameProceedings - 2018 International Conference on Applied Mathematics and Computer Science, ICAMCS 2018

Conference

Conference2018 International Conference on Applied Mathematics and Computer Science, ICAMCS 2018
Country/TerritoryFrance
CityParis
Period4/13/184/15/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Applied Mathematics
  • Computational Mathematics
  • Modeling and Simulation

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