A Knowledge-Based Decision Support System for Audit Pricing

• Main Authors: Yong-Ling CHE, 車詠淩
• Other Authors: Wei-Kang Wang
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/11678895803245436528

碩士 === 元智大學 === 會計學系 === 93 === The audit fee market in Taiwan has changed dramatically over the past few years in both supply and demand sides due to the changing economic conditions. Theoretically, the supply side (accounting firms) and demand side (clients) behavior determines the reasonable audit fee. However, in practice, there are numerous factors that are known to affect audit fee. Previous research on determinants of audit fee is based mostly on linear regression model. This provides a foundation for using the ordinary least square (OLS) regression model to forecast audit fee. In additional to the well-known OLS regression model, this study incorporated the support vector regression (SVR) model into a decision support system (DSS) to build a framework for audit fee pricing system. The system integrates a database for known audit fee records, a model library and a knowledge-base to facilitate a transparent and convenient process in deciding audit fee. This study has also used audit fee data from 2002 to 2003 to validate the system. Preliminary findings indicate that using leave one out cross validation, among the 363 audit fee records, SVR outperformed OLS regression in 200 cases. For those cases when OLS are better, the difference between predictions of both models is not significant. In addition, the results are robust against using different kernels in SVR. Therefore, one conclusion can be made from this research is that non-linear models are better in describing the audit fee process as opposed to the widely adopted linear models.