scholarly journals Neural Networks Versus Logit Regression Model For Predicting Financial Distress Response Variables

2011 ◽  
Vol 15 (1) ◽  
pp. 21 ◽  
Author(s):  
Jozef M. Zurada ◽  
Benjamin P. Foster ◽  
Terry J. Ward ◽  
Robert M. Barker
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Financial Distress ◽  
Response Variable ◽  
Logistic Regression Models ◽  
State Response ◽  
Distressed Firms ◽  
Complex Relationships ◽  
Logit Regression Model ◽  
Better Than

<span>Neural networks are designed to detect complex relationships among variables better than traditional statistical methods. Our study examined whether the complexity of the response measure impacts whether logistic regression or a neural network produces the highest classification accuracy for financially distressed firms. We compared results obtained from the two methods for a four state response variable and a dichotomous response variable. Our results suggest that neural networks are not superior to logistic regression models for the traditional dichotomous response variable, but are superior for the more complex financial distress response variable.</span>

scholarly journals An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 200
Author(s):  
Youssef Zizi ◽  
Amine Jamali-Alaoui ◽  
Badreddine El Goumi ◽  
Mohamed Oudgou ◽  
Abdeslam El Moudden
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Financial Distress ◽  
Prediction Models ◽  
Type I ◽  
Return On Assets ◽  
Logistic Regression Models ◽  
The Face ◽  
One Year ◽  
Type Ii Errors

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors.

scholarly journals An Empirical Test Of The View Of Inventory As A Liability IN Explaining Financial Distress

2011 ◽  
Vol 14 (2) ◽  
pp. 83 ◽  
Author(s):  
Benjamin P. Foster ◽  
M. Cathy Sullivan ◽  
Terry J. Ward
Keyword(s):  
Logistic Regression ◽  
Financial Distress ◽  
Regression Models ◽  
Empirical Test ◽  
Explanatory Power ◽  
Manufacturing Firms ◽  
Logistic Regression Models ◽  
Financially Distressed ◽  
Accounting Ratios ◽  
Extreme View

<span>This study reports a first attempt in a financial distress context to test the extreme JIT and TOC view that inventory is a liability. We compared inventory levels and the change in inventory for healthy and financially distressed manufacturing firms. We also compared the explanatory power of logistic regression models including traditional accounting ratios to that of models including accounting ratios created by viewing inventory as a liability. We found some support for the extreme view of some JIT and TOC proponents that traditional inventory should be considered a liability.</span>

scholarly journals Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment

1997 ◽  
Vol 87 (1) ◽  
pp. 83-87 ◽  
Author(s):  
E. D. De Wolf ◽  
L. J. Franel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Tan Spot ◽  
Environmental Data ◽  
Outdoor Environment ◽  
Stepwise Logistic Regression ◽  
Data Set ◽  
Logistic Regression Models ◽  
Processing Elements

Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting.

scholarly journals Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Navid Feroze ◽  
Muhammad Ajmal Ziad ◽  
Rabia Fayyaz ◽  
Yaé Ulrich Gaba
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Healthcare Services ◽  
Maternal Healthcare ◽  
Logistic Regression Models ◽  
Socioeconomic Groups ◽  
Bayesian Logistic Regression ◽  
Skilled Health ◽  
Better Than

Objectives. This study is aimed at investigating the time trends and disparities in access to maternal healthcare in Pakistan using Bayesian models. Study Design. A longitudinal study from 2006 to 2018. Methods. The detailed analysis is based on the data from Pakistan Demographic and Health Survey (PDHS) conducted during 2006-2018. We have proposed Bayesian logistic regression models (BLRM) to investigate the trends of maternal healthcare in the country. Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (CLRM). Results. The results from the analysis suggested that BLRM perform better than CLRM. The access to antenatal healthcare increased from 61% to 86% during years 2006-18. The utilization of medication also improved from 44% in 2006 to 60% in 2018. Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018. Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery. Additionally, the likelihood of MHS provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of Sindh and Punjab. Conclusions. The gaps in utilization of MHS in different socioeconomic groups of the society have not decreased significantly during 2006-2018. Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

scholarly journals PREDICTING FINANCIAL DISTRESS IN THE MALAYSIAN MARKET: HAZARD MODEL VERSUS THE LOGIT MODEL

2021 ◽  
Vol 3 (7) ◽  
pp. 01-19
Author(s):  
Ahmad Harith Ashrofie Hanafi ◽  
Rohani Md-Rus ◽  
Kamarun Nisham Taufil Mohd
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Financial Distress ◽  
Logit Model ◽  
Hazard Model ◽  
Financial Ratios ◽  
Logit Models ◽  
Listed Firms ◽  
Distressed Firms ◽  
Model Versus

The increasing numbers of financially distressed firms in the Malaysian market demonstrate the importance of predicting financial distress among firms in Malaysia. Using firm financial ratios, this study focuses on predicting financial distress using the hazard model and logistic regression (logit model) based on the Malaysian market. This study used listed firms on the Malaysian stock market from 2000 to 2018 to create two sets of data comprising the main sample and holdout sample in order to compare the predictability between hazard and logit models. The results clearly show that the hazard model is better compared to the logit model in predicting financial distress for the Malaysian market since more variables were found to be significant in addition to the model being more consistent in terms of accuracy.

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