scholarly journals Predictive potential of the bankruptcy global models in the tourism industry

2021 ◽  
Vol 17 (4) ◽  
pp. 23-31
Author(s):  
Agustín Del Castillo García ◽  
◽  
Sergio Manuel Fernández Miguélez ◽  
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Sample Selection ◽  
Bankruptcy Prediction ◽  
Tourism Industry ◽  
Global Model ◽  
Global Models ◽  
Cost Minimisation ◽  
Travel Agencies ◽  
Industry Sectors

The globalisation process and the recent economic crises have increased the development of models to identify the factors related to business bankruptcy. The tourism industry is not immune to this concern, and in the previous literature, bankruptcy prediction models are generally focused on hotels or restaurants. However, there are no experiences of global models for tourism companies. This study develops a global bankruptcy prediction model capable of predicting any activities carried out in the tourism industry with high precision. To this end, a sample of 406 Spanish companies that have developed their activity in three tourism industry sectors (hotels, restaurants, and travel agencies) in the period 2017-2019 has been used. This sample includes bankrupt and non-bankrupt corporations and has allowed the comparison between a global model and various focused models applying artificial neural network techniques. The results have confirmed the superiority of the global model and provide different sample selection and cost minimisation solutions for bankruptcy prediction modelling in the tourism industry

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

scholarly journals A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Yajiao Tang ◽  
Junkai Ji ◽  
Yulin Zhu ◽  
Shangce Gao ◽  
Zheng Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Financial Institutions ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Differential Evolution Algorithm ◽  
Dendritic Structure ◽  
Bankruptcy Prediction ◽  
Operating Characteristics ◽  
Lending Decisions

Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending decisions. In recent decades, various bankruptcy prediction models have been developed for academics and practitioners to predict the likelihood that a loan customer will go bankrupt. Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. Inspired by the mechanism of biological neurons, we propose an evolutionary pruning neural network (EPNN) model to conduct financial bankruptcy analysis. The EPNN possesses a dynamic dendritic structure that is trained by a global optimization learning algorithm: the Adaptive Differential Evolution algorithm with Optional External Archive (JADE). The EPNN can reduce the computational complexity by removing the superfluous and ineffective synapses and dendrites in the structure and is simultaneously able to achieve a competitive classification accuracy. After simplifying the structure, the EPNN can be entirely replaced by a logic circuit containing the comparators and the logic NOT, AND, and OR gates. This mechanism makes it feasible to apply the EPNN to bankruptcy analysis in hardware implementations. To verify the effectiveness of the EPNN, we adopt two benchmark datasets in our experiments. The experimental results reveal that the EPNN outperforms the Multilayer Perceptron (MLP) model and our previously developed preliminary pruning neural network (PNN) model in terms of accuracy, convergence speed, and Area Under the Receiver Operating Characteristics (ROC) curve (AUC). In addition, the EPNN also provides competitive and satisfactory classification performances in contrast with other commonly used classification methods.

scholarly journals The Development and Evaluation of a Statistical–Dynamical Tropical Cyclone Genesis Guidance Tool

2016 ◽  
Vol 32 (1) ◽  
pp. 27-46 ◽  
Author(s):  
Daniel J. Halperin ◽  
Robert E. Hart ◽  
Henry E. Fuelberg ◽  
Joshua H. Cossuth
Keyword(s):  
Tropical Cyclone ◽  
Prediction Models ◽  
Weather Prediction ◽  
Multiscale Model ◽  
Global Model ◽  
Tropical Cyclone Genesis ◽  
Regression Equations ◽  
Global Models ◽  
Tropical Weather ◽  
Cyclone Genesis

Abstract The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.

Comparison of Logistic Regression and Artificial Neural Network based Bankruptcy Prediction Models

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Easwaran Iyer ◽  
Vinod Kumar Murti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Manufacturing Companies ◽  
Recent Developments ◽  
Artificial Neural

Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context, however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

scholarly journals Dynamic Bankruptcy Prediction Models for European Enterprises

2019 ◽  
Vol 12 (4) ◽  
pp. 185 ◽  
Author(s):  
Tomasz Korol
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Research Approach ◽  
Bankruptcy Risk ◽  
Testing Sample ◽  
Forecasting Models ◽  
Two Samples

This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms.

scholarly journals Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry

2020 ◽  
Vol 12 (12) ◽  
pp. 5180 ◽  
Author(s):  
Rafael Becerra-Vicario ◽  
David Alaminos ◽  
Eva Aranda ◽  
Manuel A. Fernández-Gámez
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Restaurant Industry ◽  
Sufficient Time ◽  
Predictive Capacity ◽  
Financial Variables ◽  
A Chain

Using logistic regression technique and Deep Recurrent Convolutional Neural Network, this study seeks to improve the capacity of existing bankruptcy prediction models for the restaurant industry. In addition, we have verified, in the review of existing literature, the gap in the research of restaurant bankruptcy models with sufficient time in advance and that only companies in the restaurant sector in the same country are considered. Our goal is to build a restaurant bankruptcy prediction model that provides high accuracy, using information distant from the bankruptcy situation. We had a sample of Spanish restaurants corresponding to the 2008–2017 period, composed of 460 solvent and bankrupt companies, for which a total of 28 variables were analyzed, including some of a non-financial nature, such as age of restaurant, quality, and belonging to a chain. The results indicate that the best bankruptcy predictors are financial variables related to profitability and indebtedness and that Deep Recurrent Convolutional Neural Network exceeds logistic regression in predictive capacity.

scholarly journals Comparative Analysis of Bankruption Prediction Models in Property and Real Estate Sector Companies Listed on the IDX 2017-2019

2021 ◽  
Vol 6 (1) ◽  
pp. 170-173
Author(s):  
Fika Andriani ◽  
Pardomuan Sihombing
Keyword(s):  
Comparative Analysis ◽  
Real Estate ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sample Selection ◽  
Sampling Technique ◽  
Bankruptcy Prediction ◽  
Real Estate Sector ◽  
Score Model ◽  
The Right

This study takes the topic of comparative analysis of bankruptcy prediction models in the Property and Real Estate Sector companies listed on the IDX in 2017-2019. In this case, the test was conducted to compare the accuracy between the Altman Z-Score, Springate S-Score and Zmijewski X-Score models in predicting the bankruptcy of property and real estate companies. The sample consisted of 20 companies after the sample selection was carried out using purposive sampling technique. To obtain the right level of accuracy, a comparison of the distress or non-distress score categories of each bankruptcy model with the earnings per share (EPS) value of each company was carried out through a dummy variable. Through this test, it is known that the Zmijewski X-Score model is the most accurate model in predicting bankruptcy in the property and real estate sectors listed on the Indonesia Stock Exchange (IDX) by 90%.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

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