Nonparametric Quantile Regression-Based Classifiers for Bankruptcy Forecasting-Reference-Cited by-同舟云学术

Nonparametric Quantile Regression-Based Classifiers for Bankruptcy Forecasting

Published:2013-12-19 Issue:2 Volume:33 Page:124-133
ISSN:0277-6693
Container-title:Journal of Forecasting
language:en
Short-container-title:J. Forecast.

Author:

Lorca Pedro¹,Landajo Manuel²,Andrés Javier De³

Affiliation:

1. Department of Accounting; University of Oviedo; Spain

2. Department of Applied Economics; University of Oviedo; Spain

3. School of Computing; University of Oviedo; Spain

Publisher

Wiley

Subject

Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation

Reference48 articles.

1. Comparing the performance of market-based and accounting-based bankruptcy prediction models;Agarwal;Journal of Banking and Finance,2008

2. Explaining returns on venture capital backed companies: evidence from Belgium;Alperovych;Research in International Business and Finance,2011

3. Financial ratios, discriminant analysis and the prediction of the corporate bankruptcy;Altman;Journal of Finance,1968

4. Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience);Altman;Journal of Banking and Finance,1994

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