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.

The Timescale Effects of Corporate Governance Measure on Predicting Financial Distress

2008 ◽  
Vol 11 (01) ◽  
pp. 35-46 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
Corporate Governance ◽  
Logistic Regression ◽  
Regression Model ◽  
Financial Distress ◽  
Logistic Regression Model ◽  
Historical Data ◽  
Stock Exchange ◽  
Taiwan Stock Exchange ◽  
Prediction Horizon ◽  
The Empirical Analysis

This study aims to investigate the timescale effects of the corporate governance measure on predicting financial distress of corporations. A new corporate governance measure is adopted in the logistic regression model. Historical data of the companies listed on the Taiwan Stock Exchange Corporation (TSEC) were used in the empirical analysis. The analysis was based on three different prediction horizons comprising one-, two- and three-year horizons. The results confirmed that the accuracy of the logistic regression model for predicting corporate financial distress can be improved by incorporating the corporate governance measure. Moreover, the improvements of the correct rate for classification by incorporating the corporate governance measure increased as the prediction horizon was raised. The improvements of the correct rate for classification by incorporating the corporate governance measure are 2.9%, 4.4% and 5.8% for "Year 1", "Year 2" and "Year 3" models respectively.

Study on Early Warning of Enterprise Financial Distress — Based on Partial Least-squares Logistic Regression

2015 ◽  
Vol 65 (s2) ◽  
pp. 3-16 ◽  
Author(s):  
Kun Xu ◽  
Qilan Zhao ◽  
Xinzhong Bao
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Early Warning ◽  
Financial Distress ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Warning System ◽  
Study Results

Establishment of an effective early warning system can make the company operators make relevant decisions as soon as possible when finding the crisis, improve the operating results and financial condition of enterprise, and can also make investors avoid or reduce investment losses. This paper applies the partial least-squares logistic regression model for the analysis on early warning of enterprise financial distress in consideration of quite sensitive characteristics of common logistic model for the multicollinearity. The data of real estate industry listed companies in China are used to compare and analyze the early warning of financial distress by using the logistic model and the partial least-squares logistic model, respectively. The study results show that compared with the common logistic regression model, the applicability of partial least-squares logistic model is stronger due to its eliminating multicollinearity problem among various early warning indicators.

scholarly journals FINANCIAL DISTRESS DENGAN LOGISTIC REGRESSION MODEL

2018 ◽  
Author(s):  
Hasrul Siregar
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Financial Distress ◽  
Logistic Regression Model ◽  
Financial Leverage ◽  
Purposive Sampling

Financial distress adalah merupakan suatu periode dimana perusahaan mengalami gangguan keuangan yang terjadi sebelum perusahaan mengalami kebangkrutan. Analisis terhadap financial distress sangat perlu dilakukan sejak awal untuk mengantisipasi bangkrutnya perusahaan. Perlu dikembangkan suatu model yang menggambarkan prediksi kebangkrutan suatu perusahaan melalui analisis financial distress sebuah perusahaan. Dengan mengetahui penyebab financial distress perusahaan akan lebih bersikap lebih hati-hati dalam pengambilan keputusan keuangan. Penelitian ini bertujuan untuk menganalisis apakah-apakah faktor-faktor likuidity, financial leverage, asset utilization dan profitability dapat memprediksi financial distress perusahaan. Populasi dalam penelitian ini adalah seluruh perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia. Metode pengambilan sampel dilakukan dengan metode purposive sampling dengan beberapa kriteria yang ditetapkan.Model yang digunakan dalam penelitian ini adalah model regressi logistic yang bertujuan untuk menguji probabilitas terjadinya variabel terikat financial distress dapat diprediksi dengan variabel bebasnya yaitu likuidity, financial leverage, asset utilization dan profitabilility. Persamaan model yang dikembangkan dalam penelitian ini adalah :Y = ( FD/1-FD ) = β0 + β1X1+β2X2+β3X3+β4X4Populasi dalam penelitian ini adalah seluruh perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia berjumlah 136 perusahaan. Dengan criteria tertentu terpilih 50 perusahaan yang digunakan sebagai sampel. Periode penelitian tahun 2010 sd 2012.Hasil penelitian ini menunjukkan bahwa secara simultan likuidity, financial leverage, asset utilization, profitability dapat memprediksi kondisi financial distress perusahaan. Namun secara parsial hanya variabel asset utilization dan profitability yang mampu memprediksi kondisi kebangkrutan perusahaan.

scholarly journals Predicting Egyptian Banks Distress

2018 ◽  
Vol 8 (3) ◽  
pp. 39 ◽  
Author(s):  
Osama EL-Ansary ◽  
Mohamed Saleh
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Data Base ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Logistic Regression Model ◽  
Predictive Accuracy ◽  
Prediction Method ◽  
Accurate Prediction

Purpose – the main purpose of the study is to investigate an accurate prediction method for banking distress applied on a set of Egyptian banks.Methodology - the researchers have compared the prediction accuracy of the discriminant analysis and logistic regression model, to choose the most appropriate one. The data has been collected from the “Bank scope” data base and for the period of 2002–2016.Findings – the results of the study revealed that the predictive accuracy of discriminant analysis outperformed that of the logistic regression model.Originality - The study adds value to the literature as it is one of the few studies that is concerned with predicating the banking financial distress especially in Egypt.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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