A comparison of the ability of neural networks and logit regression models to predict levels of financial distress

被引：0
|
作者：
Zurada, JM [1 ]
Foster, BP [1 ]
Ward, TJ [1 ]
Barker, RM [1 ]
机构：
[1] Univ Louisville, Coll Business & Publ Adm, Dept Comp Informat Syst, Louisville, KY 40292 USA
来源：
SYSTEMS DEVELOPMENT METHODS FOR THE NEXT CENTURY | 1997年
关键词：
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this study we compared the classification accuracy rates of neural networks to those from ordinal legit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal legit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial distress.
引用
收藏
页码：291 / 295
页数：5
相关论文
共 50 条
  • [31] Comparison of Generalized Profile Function Models Based on Linear Regression and Neural Networks
    Radonja, Pero
    [J]. ELEVENTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING (NEUREL 2012), 2012,
  • [32] Prediction of functional characteristics of ecosystems: A comparison of artificial neural networks and regression models
    Paruelo, JM
    Tomasel, F
    [J]. ECOLOGICAL MODELLING, 1997, 98 (2-3) : 173 - 186
  • [33] Effectiveness of neural networks for prediction of corporate financial distress in China
    Xie, JG
    Wang, J
    Qiu, ZD
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2, 2004, 3174 : 994 - 999
  • [34] Predicting the Distress of Financial Intermediaries using Convolutional Neural Networks
    Taylor, Stacey
    Keselj, Vlado
    [J]. 2021 IEEE 23RD CONFERENCE ON BUSINESS INFORMATICS, CBI 2021, VOL 2, 2021, : 71 - 77
  • [35] Forecasting economic growth using financial variables - Comparison of linear regression and neural network models
    Marijana, Curak
    Poposki, Klime
    Ivan, Curak
    [J]. MICBE '09: PROCEEDINGS OF THE 10TH WSEAS INTERNATIONAL CONFERENCE ON MATHEMATICS AND COMPUTERS IN BUSINESS AND ECONOMICS, 2009, : 255 - +
  • [36] Comparison between logistic regression and neural networks to predict death in systemic inflammatory response syndrome
    Jaimes, FA
    Farbiarz, J
    Alvarez, D
    Martinez, C
    [J]. CRITICAL CARE MEDICINE, 2001, 29 (12) : A113 - A113
  • [37] External validity of predictive models: A comparison of logistic regression, classification trees, and neural networks
    Terrin, N
    Schmid, CH
    Griffith, JL
    D'Agostino, RB
    Selker, HP
    [J]. JOURNAL OF CLINICAL EPIDEMIOLOGY, 2003, 56 (08) : 721 - 729
  • [38] A comparison of artificial neural network and multinomial logit models in predicting mergers
    Fescioglu-Unver, Nilgun
    Tanyeri, Basak
    [J]. JOURNAL OF APPLIED STATISTICS, 2013, 40 (04) : 712 - 720
  • [39] A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending
    Liu, Yuan Y.
    Yang, Min
    Ramsay, Malcolm
    Li, Xiao S.
    Coid, Jeremy W.
    [J]. JOURNAL OF QUANTITATIVE CRIMINOLOGY, 2011, 27 (04) : 547 - 573
  • [40] A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending
    Yuan Y. Liu
    Min Yang
    Malcolm Ramsay
    Xiao S. Li
    Jeremy W. Coid
    [J]. Journal of Quantitative Criminology, 2011, 27 : 547 - 573
12345