scholarly journals COVID-19 and firms' financial health in Brescia: a simulation with Logistic regression and neural networks

2021 ◽  
Vol 3 (3) ◽  
pp. 293-309
Author(s):  
Alberto Bernardi ◽  
◽  
Daniela Bragoli ◽  
Davide Fedreghini ◽  
Tommaso Ganugi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Global Economy ◽  
Financial Statements ◽  
Financial Health ◽  
Demand And Supply ◽  
Industrial Areas ◽  
Economic Health

<abstract><p>COVID-19 has generated an unprecedented shock to the global economy causing both the decrease in demand and supply. The purpose of this paper is to simulate the effect of COVID-19 on firms' financial statements in Brescia. The shocked information is then fed into two bankruptcy models with the aim of providing an up-to-date picture of firms' economic health in one of the most prosperous industrial areas in Italy and Europe.</p></abstract>

scholarly journals Feature Selection for Bankruptcy Prediction

2012 ◽  
pp. 158-178
Author(s):  
A. Gaspar-Cunha ◽  
F. Mendes ◽  
J. Duarte ◽  
A. Vieira ◽  
B. Ribeiro ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Support Vector ◽  
Financial Health ◽  
Vector Machines ◽  
Feature Selection Approach ◽  
A Company

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and the validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests, it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The proposed method can provide useful information for decision makers in characterizing the financial health of a company.

scholarly journals Catalan family companies with high growth during the crisis period

2016 ◽  
Vol 12 (5) ◽  
pp. 1255
Author(s):  
Nuria Arimany-Serrat ◽  
Elisenda Tarrats-Pons
Keyword(s):  
Economic Analysis ◽  
Small Businesses ◽  
High Growth ◽  
Financial Statements ◽  
Family Businesses ◽  
Descriptive Statistics ◽  
Financial Health ◽  
Crisis Period ◽  
Economic Health ◽  
The Impact

Purpose: This study aims to analyze the financial and economic health of the Catalan family companies with high growth during the crisis period (2008-2013), as an engine of job creation and economic development of the territory, identifying the economic and financial characteristics.Design/methodology/approach: The data used comes from the financial statements of the high growth Catalan companies during the crisis period of 2008-2013, in particular it collects a sample of the 140 high growth Catalan companies where a 70% of them are family businesses, there is a financial and economic analysis using descriptive statistics of these family businesses with a regression model to achieve results and conclusions.Findings: During the 2008-2013 crisis, Catalan family companies with high growth that are mostly small businesses have a good economic and financial health, with moderate innovation and a great potential for survival.Research limitations/implications: It would be convenient to make the same study in similar territories where most of the companies are family businesses, in order to compare the financial and economic analysis, valuing the impact of these companies in the business.Practical implications: It allows valuing the projection of these kinds of family companies, mostly in Catalonia, in a financial and economic level, identifying if they have a proper financial strategy.Social implications: The results show that these companies have great financial and economic health and have a better response during a crisis period.Originality / value: The study shows that family businesses have moderate indebtedness and acceptable rendibility in the crisis period, although, since they are small businesses the lack of innovation and patents can lead to future problems.

Impact of Coronavirus Pandemic on the Global Economy: Demand and Supply Shock

2020 ◽  
Author(s):  
Chinenye Ifeoma Nwokolo ◽  
Matthew Ikechukwu Ogbuagu ◽  
Onyebuchi Iwegbu
Keyword(s):  
Global Economy ◽  
Demand And Supply ◽  
Supply Shock

scholarly journals Statutory audit in Slovakia – significant phenomenon of global European changes

2020 ◽  
Vol 74 ◽  
pp. 06006
Author(s):  
Denisa Domaracká ◽  
Veronika Kňažková
Keyword(s):  
Financial Reporting ◽  
Global Economy ◽  
Financial Statements ◽  
Legislative Regulation ◽  
Significant Phenomenon ◽  
The Subject ◽  
The Impact ◽  
Regulation Changes ◽  
Audit Performance

The changing global economy environment also affected the area of statutory audit. Nowadays, statutory audit faces the significant changes not only because of the processes of digitization and automation in accounting and auditing, but because of increased and tightened legislative regulation, too. The most important aspects of financial reporting and auditing are subject to EU Regulations and EU Directives. For this reason, the issue of legislative regulation changes in field of statutory audit in Slovakia has become the subject of our article. Currently, the proposal of amending and supplementing Act. No 431/2002 Coll. on Accounting, as amended underwent an interdepartmental comment procedure. The proposal includes the changes on requirements for statutory audit. This article examines the current proposal to change (mainly increase) the conditions for performing the mandatory statutory audit of financial statements in Slovak audit environment. Our goal is to clarify the reasons and implications behind the changes of Slovak legislation as well as the impact of these changes on audit performance in Slovakia. We believe conducting statutory audits in accordance with the applicable legislation accepted and implemented at international European level can contribute to transparency and improve the quality of audit performance. In order to achieve the goal, it was necessary to choose a purposeful work methodology and research methods.

Are Disasters Disastrous to Nonprofit Organizations? Investigating the Financial Implications of Hurricane Sandy for Nonprofits

2021 ◽  
pp. 089976402199845
Author(s):  
Xintong Chen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Nonprofit Organizations ◽  
Financial Distress ◽  
Logistic Regression Model ◽  
Hurricane Sandy ◽  
Financial Health ◽  
External Funds

Nonprofit organizations are sensitive to external disasters due to their high reliance on external funds and volunteers. In this study, I investigate how disasters affect the financial health of nonprofits and what factors make them more vulnerable within the context of disaster. The sample in this study includes nonprofits directly and indirectly affected by Hurricane Sandy. Using a logistic regression model, I explore if the disaster contributed to the likelihood of a nonprofit experiencing financial distress. Disaster, as an external shock, increases risks of nonprofits experiencing financial distress, especially for smaller nonprofits and nonprofits not relying on commercial revenue.

Impact of Covid-19 on women entrepreneurship in India

2021 ◽  
Vol 13 (1) ◽  
pp. 127-135
Author(s):  
Hiren Rana ◽  
◽  
Dr. Ninad Jhala
Keyword(s):  
Supply Chain ◽  
Business Model ◽  
Small Businesses ◽  
Financial Crises ◽  
Global Economy ◽  
Demand And Supply ◽  
The Government ◽  
Government Rules ◽  
Start Ups ◽  
Roll Back

The current pandemic of COVID 19 proliferated from China since December 2019 over the globe. Since then it has a significant effect visible on the global economy and living pattern of life. India is the fifth richest country abruptly affected after China and America. India is known for innovative start-ups and the business model collapsed due to the reduction in demand and supply chain because the sudden outbreak of COVID 19 resulted in complete lockdown. During COVID 19 pandemic, the government has taken new initiatives to reborn the entrepreneurs of India. However, many industries, small businesses, start-ups were rolling behind due to financial crises. There were no options for entrepreneurs to rely on the government rules, regulations to roll back in the market.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

2017 ◽  
Vol 56 (05) ◽  
pp. 377-389 ◽  
Author(s):  
Xingyu Zhang ◽  
Joyce Kim ◽  
Rachel E. Patzer ◽  
Stephen R. Pitts ◽  
Aaron Patzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emergency Department ◽  
Logistic Regression ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hospital Admission ◽  
Language Processing ◽  
Predictive Accuracy ◽  
Free Text

SummaryObjective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements.Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model.Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

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