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Predicting the financial distress of companies using logistic regression and its impact on earnings per share in companies listed on the Iraqi Stock Exchange: Predicting the financial distress of companies using logistic regression and its impact on earnings per share in companies listed on the Iraqi Stock Exchange
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Abstract

The prevention of bankruptcy not only prolongs the economic life of the company and increases its financial performance, but also helps to improve the general economic well-being of the country. Therefore, forecasting the financial shortfall can affect various factors and affect different aspects of the company, including dividends. In this regard, this study examines the prediction of the financial deficit of companies that use the logistic regression method and its impact on the earnings per share of companies listed on the Iraqi Stock Exchange. The time period of the research is from 2015 to 2020, where 33 companies that were accepted in the Iraqi Stock Exchange were selected as a sample, and the research hypotheses were tested using normal least squares regression and logistic regression. The results of testing the first hypothesis of the research indicated that the results of the unidirectional logistic regression from Baytree indicated the confirmation of this hypothesis and it can be said with confidence that by combining accounting and market information, a suitable model can be used to predict the financial distress of accepted companies offered on the Iraqi Stock Exchange. The results of the second hypothesis of the research also showed that earnings per share is a predictor of financial deficit. Firms that predict fiscal deficit try to show lower earnings per share and try to be honest and by accurate prediction of earnings per share according to signal theory, they gain the trust of shareholders and creditors and assure that the company is trying to find a suitable solution for the current issue and other issues that may take place in the future.

Keywords: Predicting financial distress, logistic regression, earnings per share

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Publication Date
Sun Apr 26 2026
Journal Name
International Journal Of Dental Hygiene
Antimicrobial Efficacy of Pelargonium Graveolens Gold Nanoparticles: A Novel Mouthwash for Children
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ABSTRACT<sec> <title>Background

Nowadays, there is a trend to promote the use of natural materials in dentistry. One of these compounds that has lately grown significantly in relevance in clinical research is Pelargonium Graveolens .

Objectives

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Wed Dec 25 2019
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Sat Jun 10 2023
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Buildings
Structural Performance of Internally Stiffened Double-Skinned Profiled Composite Walls with Openings
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Journal Name
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Objectives: The objective of present study was estimate lipid profiles among control and uncontrolled type 2 diabetic patients.

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Tue Feb 01 2022
Journal Name
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Publication Date
Sat Jun 15 2024
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Validity of digital interceptive orthodontic/therapeutic protocols post global pandemics: A review
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Background: Aggressive global pandemics such as COVID-19 can disrupt societies tragically, imposing a suspension to almost every field throughout the world; the orthodontic treatment and follow-up is no exemption. Objectives: To provide practical recommendations about resuming treatment for orthodontic patients during the “Return-to-Practice” phase and emphasize the validity of certain digital interceptive measures post global pandemics to minify the risk of infection spread. Materials and Methods: Sources of information pertaining to orthodontic/therapeutic implications during the COVID-19 pandemic were searched using electronic databases including COVID-19 Open Research Dataset (CORD-19 2020), Google Scholar, Scopus, PubMed, M

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Publication Date
Mon Nov 20 2028
Journal Name
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Publication Date
Thu Feb 15 2024
Journal Name
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A Comparison of Traditional and Optimized Multiple Grey Regression Models with Water Data Application
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Grey system theory is a multidisciplinary scientific approach, which deals with systems that have partially unknown information (small sample and uncertain information). Grey modeling as an important component of such theory gives successful results with limited amount of data. Grey Models are divided into two types; univariate and multivariate grey models. The univariate grey model with one order derivative equation GM (1,1) is the base stone of the theory, it is considered the time series prediction model but it doesn’t take the relative factors in account. The traditional multivariate grey models GM(1,M) takes those factor in account but it has a complex structure and some defects in " modeling mechanism", "parameter estimation "and "m

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