Default Prediction with Macroeconomic and Industrial Variables: A Case Study for Taiwan’s Semiconductor Industry

• Main Authors: Wei-Jen Chang, 張瑋真
• Other Authors: 林修葳
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/42276336734060593425

碩士 === 國立臺灣大學 === 國際企業學研究所 === 96 === Indicative roles of macroeconomic and industrial attributes in default probability prediction have been reported by many studies. However, studies conducted to identify the best indicators for individual industries outside US are scant. Therefore, the purpose of this paper is to identify macroeconomic and industrial variables and to build default prediction models for Taiwan’s semiconductor firms. This study selects variables through demand, supply, competitive conditions, and macroeconomic aspect. The samples included are 84 sets of monthly observations over the period from January 2000 to December 2006 covering Taiwan registered IC manufacturers and IC design houses. After analyzing the relationship between dependent and independent variables, the significant variables identified in simple regressions are included to build models for three-month and one-year predictions. Results of this study showed that several variables have significant explanatory power and the models which perform better in three-month and one-year prediction are different. To conclude, four findings may add to our understanding of default prediction for Taiwan’s semiconductor firms. (1) The percentage change in the semiconductor equipment booking value and the change in composite index of leading indicators of OECD appear to be the most pronounced variables to all default probabilities. (2) Dummy slope coefficients for several variables are significant in the one-year default prediction models, implying that the effects of these variables change as their level increase. (3) Several variables included in models for IC manufacturers only and IC design houses only are different and diverse relations with the same indicators exist. (4) The model for the whole industry performs better in predicting defaults in three-month prediction, while the models for separated groups should be employed in one-year prediction.