應用支向量機於公司資產減損之預測

Assets Write-off Prediction with Support Vector Machine Model

吳思音(Szu-Yin Wu)

指導教授 ： 陳慶隆 ； 武季蔚

朝陽科技大學/管理學院/會計所/碩士(2009年)

摘要

目前關於三十五號公報之研究，多數著重於探討宣告資產減損公司之動機、對財務或經營績效之影響、減損的決定因素、會計報導誘因與對盈餘品質的影響等。然對公司是否應認列資產減損？資產減損認列的幅度是否合理？皆少有觸及。本研究以不同模型進行資產減損的預測，透過不同預測模型之建立與比較，企求能提供較合理的預測結果，以豐富此一研究領域。本文將先分別比較Logit及SVM分類模型，探討何者對是否應認列資產減損的預測較準確。其次，本研究將針對資產減損類型進行分類後，分別以傳統線性迴歸與SVM的迴歸模型(Support Vector Regression，SVR)預測個別資產的減損幅度，提出適當的模型供未來預測參考。因資產減損的樣本異質性廣且變異很大，本研究將進一步結合案例取樣的集成模型袋法(Bagging)，以求取準確的預測。
本文分別分析2005年至2007年的資料。實證結果顯示：是否選擇資產減損的分類預測績效，SVM分類模型略優於Logit模型；經由傳統線性迴歸模型與SVR模型預測結果的比較，亦發現SVR的預測績效在某些情境設定下優於傳統線性迴歸模式的預測績效。進一步比較逐年的實證結果顯示，經由資產減損分類後的SVR與傳統線性迴歸模式對資產減損的預測績效(平均均方偏誤)逐年降低外，本研究亦發現減損幅度預測亦確實與減損類型有關。在不同類型的減損設定下，長期投資減損預測績效優於其他資產類型的預測績效，且SVR模式對資產減損幅度的預測可藉由公司成長率(M/B)的區分而更準確。

關鍵字

資產減損 ； 預測模型 ； 支向量機(SVM) ； 集成模型袋法

並列摘要

Most of current researches about Statement of Financial Accounting Standards No. 35 (SFAS 35) focused on the motivations of assets write-off claims from companies, the effects on financial or managerial performance, the critical elements of losses, the incentives of accounting reports, the influences on revenue quality, etc. However, there hasn’t been any investigation on how the companies made their decisions on whether to declare write-offs and how to predict reasonable amount for declared ones. The present study enriches the reasonable predictions of decision and amount of assets write-off by the comparison of results from different predicting models. First, the classification performances on decision of write-off are studied with respect to Logit and SVM models. Second, reasonable magnitude of assets write-off prediction models for future reference is provided by the study of both linear regression model and Support Vector Regression model (SVR) for each write-off type respectively. In order to overcome the heterogeneity of write-off samples, the ensemble bagging approach is integrated into present prediction models.
The data from year 2005 to 2007 are used for analysis. The empirical results show: the SVM model is mildly accurate to Logit model on the write-off decision prediction; the magnitude of assets write-off prediction performance by SVR model is better than traditional linear regression model under certain conditions; and the mean square errors are decreased year by year. The present study also displays that prediction performances of models relate with different write-off types, especially, the long-term investment write-off prediction is more feasible than others. Besides, the prediction performance could be more obvious by grouping samples with company growth options.

並列關鍵字

SVM ； Predict model ； Assets Write-off ； Bagging

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