臺灣博碩士論文加值系統

企業發生財務危機，不僅僅是傷害公司利害關係人（stakeholder）之權益，也讓社會付出成本；隨著資訊化的時代來臨，企業面對的環境愈顯複雜，伴隨而來的是知識經濟時代，而帳面價值與市值的差異即為智慧資本，由資策會（MIC)經濟部技術處智慧資本專案所建構的智慧資本專案，亦指出智慧資本是企業所擁有的核心能力與知識資源所建構的動態能力。
由於產業別的不同，因此對於各項指標之代表意義亦有所不同；傳統的財務報表顯示的僅是歷史的資訊，而財務危機的預警，牽涉到的是對於未來的預測，而智慧資本正顯示了企業將來的發展潛力。
本研究應用類神經網路模型中之倒傳遞類神經與存活分析中的Cox等比例危險模型以建構預警模式，其中在倒傳遞類神經網路模型加入基因演算法的概念以解決網路不易收斂的問題，且非固定模型中的轉換率與學習率藉以增加預測的擊中率。
以倒傳遞類神經網路模型分別對季資料及年資料建立模型，發現加入智慧資本指標之擊中率較僅運用財務指標建立之模型為佳。Cox等比例危險模型方面，對季資料而言，加入智慧資本指標之區別正確力較僅運用財務指標建立模型為佳；年資料方面，失敗前一年為財務與智慧資本指標建立之模型較佳，且能完全正確區別正常與失敗公司；失敗前二年為僅財務指標建立之模型較佳；失敗前三年則無差異。整體而言，倒傳遞類神經網路模型的效果較佳。

When the business happens financial distress, it hurts not only the equity on stakeholders but also brings about cost for society. By information age is coming, the environment which business faced is more complex, and the knowledge-based economy would come along with it . The difference between market value and book value is intellectual capital. MIC also indicates that intellectual capital is company’s dynamic ability which construct by core ability and knowledge resource .
The different kind of industry, different meaning of index exists. The financial report only shows one company’s history information, but the distress diagnostic forecasts the future, and the intellectual capital just shows the future potential development for a company.
This study adapts one of neural network model---back-propagation neural net and one of survival analysis---Cox proportional hazard model to develop diagnostic model. To solve the question on net is not easy to be convergence , the genetic algorithms concept is involved in back-propagation neural net , on the other hand, the transfer rate and learning rate is un-fixed to improve hits ratio.
Using season data and year data to develop financial distress diagnostic model by back-propagation neural net, found hits ratio is higher when the intellectual capital index is included and the result is the same as when using season data to develop by Cox proportional hazard model. Regarding year data, using Cox proportional hazard model in prediction, one year before failure, could totally discriminate between normal and failure company when intellectual capital index is added. Two year before failure, using only financial index in prediction is better, three year before failure, there is no difference even intellectual capital index included. To sum up, the effect on back-propagation neural net is better than Cox proportional hazard model.

第 壹 章 緒論 1
第一節 研究背景與研究動機 1
第二節 研究目的 4
第三節 論文架構 5
第 貳 章 文獻探討 6
第一節 企業失敗 6
第二節 不同統計方法對企業危機預測 8
第三節 智慧資本 24
第四節 小結 33
第 參 章 研究設計與研究方法 35
第一節 研究流程 36
第二節 研究架構與假說 37
第三節 存活分析與Cox等比例危險模型 40
第四節 類神經網路模型 48
第五節 研究樣本及變數選擇 59
第 肆 章 實証分析 70
第一節 敘述統計分析 70
第二節 Cox等比例危險模型---季資料 73
第三節 Cox等比例危險模型---年資料 85
第四節 Cox等比例危險模型小結 96
第五節 倒傳遞類神經網路分析─季資料 100
第六節 倒傳遞類神經網路分析─年資料 106
第七節 倒傳遞類神經網路模型小結 112
第八節 Cox等比例危險模型與倒傳遞類神經網路模型小結 115
第 伍 章 結論與建議 118
第一節 研究結論 118
第二節 研究建議 126
參考文獻 129
附錄 A 我國法律上對企業為機定義的相關法條 138
附錄 B 斯勘地亞智慧資本報告 139
附錄 C Stewart 智慧資本衡量指標 140
附錄 D 加拿大管理會計人員協會提出之智慧資本衡量指標 140
附錄 E 美國訓練與發展協會智慧資本衡量指標 141
附錄 F Battery 公司智慧資本衡量系統 142
附錄 G 我國智慧資本衡量指標重要性調查 143
附錄 H 本研究納入樣本 144

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