標題: A comparison of neural network and multiple regression analysis in modeling capital structure
作者: Pao, Hsiao-Tien
管理科學系
Department of Management Science
關鍵字: capital structure;multiple regression model;artificial neural network model
公開日期: 1-Oct-2008
摘要: Empirical studies of the variation in debt ratios across firms have used statistical models singularly to analyze the important determinants of capital structure. Researchers, however, rarely employ non-linear models to examine the determinants and make little effort to identify a superior prediction model. This study adopts multiple linear regressions and artificial neural networks (ANN) models with seven explanatory variables of corporation's feature and three external macro-economic control variables to analyze the important determinants of capital structures of the high-tech and traditional industries in Taiwan, respectively. Results of this study show that the determinants of capital structure are different in both industries. The major different determinants are business-risk and growth opportunities. Based on the values of RMSE, the ANN models achieve a better fit and forecast than the regression models for debt ratio, and ANNs are cable of catching sophisticated non-linear integrating effects in both industries. It seems that the relationships between debt ratio and independent variables are not linear. Managers can apply these results for their dynamic adjustment of capital structure in achieving optimality and maximizing firm's value. (c) 2007 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2007.07.018
http://hdl.handle.net/11536/8331
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2007.07.018
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 35
Issue: 3
起始頁: 720
結束頁: 727
Appears in Collections:Articles


Files in This Item:

  1. 000257993700016.pdf