Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction
Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In this paper, the problem of corporate bankruptcy prediction is faced by means o...
保存先:
| 第一著者: | |
|---|---|
| その他の著者: | , , |
| フォーマット: | Artículo |
| 言語: | English |
| 出版事項: |
2019
|
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/s10614-017-9783-4 https://doi.org/10.1007/s10614-017-9783-4 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In this paper, the problem of corporate bankruptcy prediction is faced by means of four linear classifiers (Fisher’s linear discriminant, linear discriminant classifier, support vector machine and logistic regression), which are designed on the dissimilarity space instead of the classical feature space. Experimental results indicate that the prediction methods implemented with the dissimilarity representation perform considerably better than the same techniques when applied onto the feature space, in terms of overall accuracy, true-positive rate and true-negative rate. |
|---|