TY - JOUR
T1 - A comparison of neural network and multiple regression analysis in modeling capital structure
AU - Pao, Hsiao-Tien
PY - 2008/10/1
Y1 - 2008/10/1
N2 - 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.
AB - 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.
KW - Artificial neural network model
KW - Capital structure
KW - Multiple regression model
UR - http://www.scopus.com/inward/record.url?scp=44949102299&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.07.018
DO - 10.1016/j.eswa.2007.07.018
M3 - Article
AN - SCOPUS:44949102299
SN - 0957-4174
VL - 35
SP - 720
EP - 727
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -