中小企業經營績效評估模型之建立與比較

The Study of SMEs Business Performance Rating Model Construction and Comparative Analysis

古文潔(Wen-Jie Ku) ； 丁台怡(Tai-I Ting) ； 江志民(Chih-Ming Chiang)

《Journal of Data Analysis》 7卷2期 (2012/04) Pp. 53-65

https://doi.org/10.6338/JDA.201204_7(2).0003

摘要

過去在經濟發展中扮演重要角色的中小企業，隨著經濟全球化之開放而面臨了強大的競爭。隨著2006年底新巴賽爾協定的正式實施，金融機構為符合新協定規範，亦需將中小企業信用評分程序，納入其徵、授信管理系統，以求信用風險評估皆可量化處理。故本研究為國內首次使用經營績效評等模型，針對中小企業的生產、營利、資金流動、勞動力需求等方面的營運績效以較客觀的角度評量，以作為未來對中小企業之管理參考依據。本研究將以中小企業中建築產業公司為主要的研究對象，分別利用比較隨機森林、SVM支持向量機、Boosting等三種方法建立模型並加以評估比較其預測能力。研究結果發現，表現最佳的是SVM支持向量機模型，整體正確率都達到八成以上，而敏感度、特異度的正確率也很高，顯示此模型可以準確的公司做正確的判斷。

關鍵字

中小企業 ； 隨機森林 ； 支持向量機

並列摘要

SMEs play the strong character from the past economic development until now and have faced the formidable competition with the economic globalization. Along with new Pakistan Syre agreement's official implementation at the end of 2006, the financial institutions had to integrate credit grading procedure into management system and conform to the new agreement standard in order to quantitative process the credit risks appraisal. Hence the research compared the predictive power of three models: random forest, SVM, Boosting by production profit, cash flow, labor demand and other aspects of operating, and got a future reference for the management of SMEs. The results showed that SVM model achieve the best performance and the overall correct-rate reach over 80% of sensitivity. It meant the SVM model can help company making the right judgment mostly.

並列關鍵字

Small and Medium Enterprises (SMEs) ； Random Forest ； SVM ； Boosting

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