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考慮景氣循環趨勢應用資料探勘於企業財務危機預測 = Using data...

國立高雄大學資訊管理學系碩士班

考慮景氣循環趨勢應用資料探勘於企業財務危機預測 = Using data mining techniques with the trends of business cycle for financial crisis prediction

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Using data mining techniques with the trends of business cycle for financial crisis prediction
作者:	林裕晟,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	民99[2010]
面頁冊數:	117面圖，表 : 30公分;
標題:	景氣循環
標題:	Trends of Business Cycle
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/69725115114809192102
摘要註:	在不同的景氣循環趨勢下，企業所面臨的財務危機通常不盡相同，當景氣復甦時，企業因為過於樂觀市場上的需求，進而提高產能、擴建廠房等，財務危機的發生可能因為從事高財務槓桿所致；而當景氣反轉衰退時，企業可能因存貨上變現不易、銀行抽銀根等現象，造成資金週轉不靈而演變為財務危機。　　財務危機的發生，往往付出高額的社會成本，因此本論文將運用資料探勘技術，揭露企業發生財務危機之風險，目的是從不同的景氣循環趨勢中，找出重要的財務比率，以提昇預測之準確率及變數之解釋能力。　　本研究以1992年至2009年2月台灣電子產業為樣本，主要原因是該產業占台灣總體上市櫃家數一半以上，對於總體經濟而言具有舉足輕重之影響力，樣本集為正常公司115家及財務危機115家，以資產規模相近進行配對，共選擇了57個財務變數。實證結果顯示：一、加入景氣循環趨勢因素後，預測準確率高於未考量景氣循環趨勢的預測方法。二、資料分割的方法以財務危機發生日較佳。三、景氣循環的基準日若以月、季為資料分割之基準，其季的分割的解釋能力較佳。最後本論文就所得之結果加以探討，以解釋不同景氣循環趨勢下預測模型之差異。　　Under different trends of business cycle, enterprises may face diffent finical crisis with different factors. It is therefore essential for us to discover and understand the factors for financial crisis prediction. In the thesis we take business cycle into consideration and use data mining as the main technique to predict firms that have potential crisis. We hypothesize that financial crisis is closely related to business cycle trends.Thus, the business cycle has been discriminated to expansion and recession trends.The ojective of the thesis has been set to find out important financial crisis variables and by this to improve the accuracy of finacial crisis prediction.　　In the thesis, companies from electronic industry has been selected as the main focus, due to this is a very important industry in Taiwan. Five experiment methods have been designed for empirical study as well as a optimal model has been established. From the experiment results : 1.we find out some important financial variables that cause financial crisis in different business cycle trends 2.in considering the business cycle ,the model will be better 3. support vector machine has higher prediction accuracy.

考慮景氣循環趨勢應用資料探勘於企業財務危機預測 = Using data mining techniques with the trends of business cycle for financial crisis prediction
林, 裕晟

考慮景氣循環趨勢應用資料探勘於企業財務危機預測 = Using data mining techniques with the trends of business cycle for financial crisis prediction / 林裕晟撰 - [高雄市] : 撰者, 民99[2010]. - 117面 ; 圖，表 ; 30公分.
參考書目：面.
景氣循環Trends of Business Cycle

考慮景氣循環趨勢應用資料探勘於企業財務危機預測 = Using data mining techniques with the trends of business cycle for financial crisis prediction
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330 $a 　　在不同的景氣循環趨勢下，企業所面臨的財務危機通常不盡相同，當景氣復甦時，企業因為過於樂觀市場上的需求，進而提高產能、擴建廠房等，財務危機的發生可能因為從事高財務槓桿所致；而當景氣反轉衰退時，企業可能因存貨上變現不易、銀行抽銀根等現象，造成資金週轉不靈而演變為財務危機。　　財務危機的發生，往往付出高額的社會成本，因此本論文將運用資料探勘技術，揭露企業發生財務危機之風險，目的是從不同的景氣循環趨勢中，找出重要的財務比率，以提昇預測之準確率及變數之解釋能力。　　本研究以1992年至2009年2月台灣電子產業為樣本，主要原因是該產業占台灣總體上市櫃家數一半以上，對於總體經濟而言具有舉足輕重之影響力，樣本集為正常公司115家及財務危機115家，以資產規模相近進行配對，共選擇了57個財務變數。實證結果顯示：一、加入景氣循環趨勢因素後，預測準確率高於未考量景氣循環趨勢的預測方法。二、資料分割的方法以財務危機發生日較佳。三、景氣循環的基準日若以月、季為資料分割之基準，其季的分割的解釋能力較佳。最後本論文就所得之結果加以探討，以解釋不同景氣循環趨勢下預測模型之差異。　　Under different trends of business cycle, enterprises may face diffent finical crisis with different factors. It is therefore essential for us to discover and understand the factors for financial crisis prediction. In the thesis we take business cycle into consideration and use data mining as the main technique to predict firms that have potential crisis. We hypothesize that financial crisis is closely related to business cycle trends.Thus, the business cycle has been discriminated to expansion and recession trends.The ojective of the thesis has been set to find out important financial crisis variables and by this to improve the accuracy of finacial crisis prediction.　　In the thesis, companies from electronic industry has been selected as the main focus, due to this is a very important industry in Taiwan. Five experiment methods have been designed for empirical study as well as a optimal model has been established. From the experiment results : 1.we find out some important financial variables that cause financial crisis in different business cycle trends 2.in considering the business cycle ,the model will be better 3. support vector machine has higher prediction accuracy.
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