6 research outputs found
Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy
We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction
Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy
We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction
Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure
This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many
companies are interested in forecasting their incoming nancial status in order to adapt to the current
nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep
Learning methods with respect to classi cation tasks, we compare the performance of three well-known
Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model
of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest
Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in
companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three
extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in
this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling)
and one clustering-based balancing technique have been applied to avoid data inconsistency
problem. Considering the real nancial data complexity level and type, the results show that the Multilayer
Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best
performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term
Memory and ensemble methods obtained also very good results, outperforming several classi ers used in
previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P
PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18
P18-RT-483