A Study on Enterprise Performance Prediction Model by Reducing Financial Ratio
Financial ratio is an important indicator to represent the operation performance of an enterprise. Operating performance is defined as some important financial ratios which can indicate enterprise financial with positive operation and growth. The strength and weakness of financial health can not only show the results of operation performance of the organization, but also indicate its growth and potential in the future. However, how to select a set of representative financial ratios is an important issue for evaluating the operating performance of an enterprise. Traditionally, more related researchers have long used statistical methods for handling these problems. However, these conventional methods become more complex when relationships in the input/output dataset are nonlinear. Nevertheless, statistical techniques always rely on the restrictive assumptions on linear separability for the predictive variables, multivariate normality, and many of the models of financial performance violate these assumptions. Therefore, to overcome these existing shortcomings, the proposal proposed attribute selection method to extract financial ratio attributes and OWA based multiple attribute decision making (MADM) to analyze enterprise operating performance situation for stakeholders (i.e., management, investors, employees, shareholders and other interested parties). The financial ratios are collected from the open source information retrieval systems of dataset for publicly traded enterprises in Taiwan stock market from 2008. The proposed model use singular value decomposition (SVD) + ordered weight averaging (OWA) for evaluating enterprise operating performance. At last, the results indicate that the proposed selection attribute can explain enterprise financial situation, and proposed model can objectively evaluate the performance of enterprise.
C. H. Cheng et al., "A Study on Enterprise Performance Prediction Model by Reducing Financial Ratio", Advanced Materials Research, Vols. 211-212, pp. 1221-1225, 2011