Some Non-Linear Problems in Accounting and Finance: Can we Apply Regression?

John Ogwang
2021 International Journal of Business, Economics and Management  
Article History Keywords Linearity Non-linear models Logarithmic transformations Regression Finance Accounting. JEL Classification: C02; M41; M10; C19. Recent studies have indicated that many decision problems in accounting and finance can be better modeled by non-linear models in practice. However, existing literatures have also shown that managers and decision makers are not very conversant with nonlinear models as compared to linear models because of the simplicity of linear models. In this
more » ... ar models. In this paper, attempts are made to transform some non-linear models in accounting and finance which conform to exponential and power functions to their equivalent linear forms. The resulting equivalent linear models are subjected to regression analysis. The paper documents interesting practical non-linear problems in accounting and finance where it is possible to apply regression, and provides technical interpretations of coefficients of resulting regression equations. Some non-linear problems which have been documented in this analysis include; depreciation of noncurrent assets, the learning curve model, life cycle costing, compounding, discounting and exponential growth bias. Although logarithmic transformation of non-linear functions is not a novel idea in literature of accounting and finance, there is no evidence in literature that scholars have proposed particular cases in finance and accounting where these linear transformations and their resulting regression equations would yield meaningful results that can enhance management decision making. This paper fills this gap by documenting practical non-linear problems in finance and accounting where linearization and subsequent application of regression analysis generates useful results for management decision making purposes. Contribution/Originality: In the literature of accounting and finance specifically; firstly, this paper originates new formulae for some non-linear problems. Secondly, it's among very few papers which examined regression of non-linear problems. Thirdly, this is the first paper to document practical cases where regression of log transformed variables generates useful results. of the number of units produced. In business, regression analysis is used for predictive purposes, for example sales forecast. Assumption of the linearity relationship may of course limit the applications of regression because many business and economic problems are non-linear in nature. In such circumstances, some form of non-linear or curve-linear models can be more suitable. Recent empirical studies in finance and accounting such as Jaisinghani and Kanjilal (2017); Vatavo (2016); Thanatawee (2016) and McMillan (2012) have indicated very strong evidence of non -linear relationships among many finance and accounting variables. There are also evidences that non-linear models in some circumstances are more accurate in practice than linear models for simulating business operations. Although these models are gaining a lot of popularity in practice and theory, papers such as Wu and Li (2017) argued that managers and other decision makers find a lot of difficulties in applying these models because of their complexities as compared to linear models. In order to ease this problem, it is advisable that the scholarly community, once again, emphasize the need to transform some non-linear models to their approximate linear forms. Logarithmic transformation of non-linear
doi:10.18488/journal.62.2021.82.81.99 fatcat:44kpx2wg7jaj3iqtwuoiu46nmy