標題: 比較多變量分析與類神經網路績效評估模型Comparing Multivariate Analysis and Neural Network Analysis on Performance Evaluation 作者: 陳國彰虞孝成管理學院科技管理學程 關鍵字: 因子分析;群落分析;判別分析;類神經網路;Factor Analysis;Cluster Analysis;Discriminant Analysis;Neural Network Analysis 公開日期: 2002 摘要: 近20年來台灣電子資訊產業蓬勃發展，在全球電子資訊產業中具有舉足輕重的地位 ; 由於技術不斷進步加速公司競爭導致產業變化快速，每一家公司都必需暸解自已的定位與屬性以作為公司策略規劃之基礎。本研究利用投入產出之財務指標(財務比率)共15項進行屬性及定位分析，並利用多變量因子分析、群落分析、判別分析以及類神經網路進行研究比較分析。研究結果顯示15項財務指標可收斂成5項因子，243家上市公司可分為9群，再進行判別分析，其中多變量方法的判中率分析結果為92.1%、類神經網路方法判中率為100%，最後每次隨機選取70%的公司建立預測模型，另外30%的公司為實證對照，進行三次模擬預測，多變量方法三次預測結果準確度偏低，僅22.3%、21.4%、20.9%，類神經網路方法三次預測結果準確度分別可達到73.9%、75.3%、70.4%。經由上述分析結果可以得證類神經網路的方法可求得較精確的結果，建議未來處理類似問題採用類神經網路方法。In the past two decades, Taiwan electronics and information industries have played important roles in the global market. In order to remain competitive, each company has to know its positioning and competitiveness. This research analyzed the performance of companies based on their financial indexes. Multivariable analysis models such as Factor Analysis, Cluster Analysis, and Discriminant Analysis were used to formulate a model to forecast a company’s future performance. The results were compared with those generated by a Neural Network Analysis Model. This thesis used 243 Taiwanese companies in the electronic and information industries as research subjects. Financial information from 70% of these companies were used for model building, while the rest 30% of companies were used to verify the forecast ability of the two models. Our analysis demonstrated that the Neural Network Analysis Model outperformed the multivariable models. URI: http://140.113.39.130/cdrfb3/record/nctu/#NT910685009http://hdl.handle.net/11536/71196 Appears in Collections: Thesis