Selecting bankruptcy predictors using a Support Vector Machine approach

被引：41

作者：

Fan, A ^{[1
]}

Palaniswami, M ^{[1
]}

机构：

[1] Univ Melbourne, Dept EEE, Melbourne, Vic 3010, Australia

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859421

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.

引用

页码：354 / 359

页数：6

共 17 条

[1] Altman E., 1993, Corporate financial distress and bankruptcy: A complete guide to predicting and avoiding distress and profiting from bankruptcy
[2] FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND PREDICTION OF CORPORATE BANKRUPTCY
ALTMAN, EI
[J]. JOURNAL OF FINANCE, 1968, 23 (04) : 589 - 609
[3] CORPORATE DISTRESS DIAGNOSIS - COMPARISONS USING LINEAR DISCRIMINANT-ANALYSIS AND NEURAL NETWORKS (THE ITALIAN EXPERIENCE)
ALTMAN, EI
MARCO, G
VARETTO, F
[J]. JOURNAL OF BANKING & FINANCE, 1994, 18 (03) : 505 - 529
[4] BEAVER W, 1966, EMPIRICAL RES ACCOUN
[5] BORITZ JE, 1995, EXPERT SYST APPL, V9, P503, DOI 10.1016/0957-4174(95)00020-8
[6] Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
[7] Etheridge H, 1996, ADV ACC INFORM SYST, V4, P201
[8] Haykin S., 1999, Neural Networks: A Comprehensive Foundation, V2nd ed
[9] Klersey G. F., 1995, Advances in Accounting Information Systems, V3, P137
[10] KOHAVI R, 1995, THESIS STANDFORD U

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