FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3185637639> ?p ?o ?g. }

• W3185637639 endingPage "168" @default.
• W3185637639 startingPage "153" @default.
• W3185637639 abstract "Objective:  Predictive models for diagnosing bankruptcy or financial crisis have been widely discussed in studies and articles in the fields of economics and accounting and have been considered by financial institutions. One of the methods that can be used to help take advantage of investment opportunities and better allocation of resources is to predict financial distress or bankruptcy of companies. So, by providing the necessary warnings, can be alerted companies to the occurrence of financial distress so that according to these warnings they can take appropriate action, Secondly, investors and creditors can identify distinguish investment opportunities from unfavorable opportunities and invest in the right opportunities. Timely foresight can help decision-makers find solutions and prevent bankruptcy. The main aim of the current study is to express, determine and explain the predictive power of bankruptcy and profitability models of Tehran Stock Exchange companies to evaluate their performance and financial status by logistic regression using financial ratios selected by artificial neural network and Fulmer models. Method: The method of the present study is applied in terms of purpose and descriptive in nature. Logistic regression technique was used to test the hypotheses. The results are presented in two parts: descriptive and inferential statistics. Collection of information from the financial statements of 132 companies of Tehran Stock Exchange during the years 2012 to 2018. Firstly, the initial classification and processing of information was performed and then Eviews software was used to fit the Fulmer model and Spss26 software was used for the neural network model. Suitable indicators based on the research background in the models include debt-to-equity ratio of shareholders, profit before interest and taxes, total liabilities to assets, receivable accounts ration to sale, net return on assets, long-term debt to assets, working capital, net profit to to sale. Results: The research results indicates that both artificial neural network and Fulmer models have the ability to detect bankruptcy prediction with different accuracy, but the predictive accuracy of artificial neural network model is higher and has better performance compared to Fulmer model. In the artificial neural network model, the variables of working capital, receivable accounts on sales, net profit on assets, net profit on sales and long-term debt to assets are significant at high level in predicting corporate bankruptcy. Also, among the financial ratios used, the ratio of receivable accounts on sales had the most impact and the debt-to-equity ratio had the least impact on determining bankruptcy among the available variables. Conclusion: The best way is to take preventive measures before the occurrence of financial incapability of companies and in this regard, the result of the present study confirms the use of artificial neural network method to predict the bankruptcy of listed companies. And also, the crtiteria of working capital, net profit on assets, ratio of total debt to total assets and net profit on sales are related to transactions with bankruptcy. That is, the higher the ratio of these ratios, the probability of bankruptcy is lower. Therefore, by issuing the necessary warnings to decision makers and as a result of their actions, companies can be guided in the right direction in order to avoid wasting resources." @default.
• W3185637639 created "2021-08-02" @default.
• W3185637639 creator A5005113662 @default.
• W3185637639 creator A5030152512 @default.
• W3185637639 date "2021-02-19" @default.
• W3185637639 modified "2023-09-23" @default.
• W3185637639 title "Bankruptcy Prediction of listed Companies in Tehran’s Stock Exchange by Artificial Neural Network (ANN) and Fulmer Model" @default.
• W3185637639 cites W1566103283 @default.
• W3185637639 cites W2302289441 @default.
• W3185637639 cites W2606658888 @default.
• W3185637639 cites W2919606717 @default.
• W3185637639 cites W2924051899 @default.
• W3185637639 cites W3129796690 @default.
• W3185637639 cites W3205091966 @default.
• W3185637639 doi "https://doi.org/10.22103/jdc.2020.16422.1102" @default.
• W3185637639 hasPublicationYear "2021" @default.
• W3185637639 type Work @default.
• W3185637639 sameAs 3185637639 @default.
• W3185637639 citedByCount "0" @default.
• W3185637639 crossrefType "journal-article" @default.
• W3185637639 hasAuthorship W3185637639A5005113662 @default.
• W3185637639 hasAuthorship W3185637639A5030152512 @default.
• W3185637639 hasConcept C10138342 @default.
• W3185637639 hasConcept C119857082 @default.
• W3185637639 hasConcept C129361004 @default.
• W3185637639 hasConcept C144133560 @default.
• W3185637639 hasConcept C151956035 @default.
• W3185637639 hasConcept C154945302 @default.
• W3185637639 hasConcept C162118730 @default.
• W3185637639 hasConcept C200870193 @default.
• W3185637639 hasConcept C2777388754 @default.
• W3185637639 hasConcept C41008148 @default.
• W3185637639 hasConcept C504631918 @default.
• W3185637639 hasConcept C50644808 @default.
• W3185637639 hasConcept C98014903 @default.
• W3185637639 hasConceptScore W3185637639C10138342 @default.
• W3185637639 hasConceptScore W3185637639C119857082 @default.
• W3185637639 hasConceptScore W3185637639C129361004 @default.
• W3185637639 hasConceptScore W3185637639C144133560 @default.
• W3185637639 hasConceptScore W3185637639C151956035 @default.
• W3185637639 hasConceptScore W3185637639C154945302 @default.
• W3185637639 hasConceptScore W3185637639C162118730 @default.
• W3185637639 hasConceptScore W3185637639C200870193 @default.
• W3185637639 hasConceptScore W3185637639C2777388754 @default.
• W3185637639 hasConceptScore W3185637639C41008148 @default.
• W3185637639 hasConceptScore W3185637639C504631918 @default.
• W3185637639 hasConceptScore W3185637639C50644808 @default.
• W3185637639 hasConceptScore W3185637639C98014903 @default.
• W3185637639 hasIssue "2" @default.
• W3185637639 hasLocation W31856376391 @default.
• W3185637639 hasOpenAccess W3185637639 @default.
• W3185637639 hasPrimaryLocation W31856376391 @default.
• W3185637639 hasRelatedWork W2161625377 @default.
• W3185637639 hasRelatedWork W2187728341 @default.
• W3185637639 hasRelatedWork W2196298281 @default.
• W3185637639 hasRelatedWork W2265724515 @default.
• W3185637639 hasRelatedWork W2290924541 @default.
• W3185637639 hasRelatedWork W2888881127 @default.
• W3185637639 hasRelatedWork W2936991489 @default.
• W3185637639 hasRelatedWork W2940834140 @default.
• W3185637639 hasRelatedWork W3009270246 @default.
• W3185637639 hasRelatedWork W3011780364 @default.
• W3185637639 hasRelatedWork W3118419000 @default.
• W3185637639 hasRelatedWork W3125799784 @default.
• W3185637639 hasRelatedWork W3129419194 @default.
• W3185637639 hasRelatedWork W3161308705 @default.
• W3185637639 hasRelatedWork W3162035899 @default.
• W3185637639 hasRelatedWork W3183886972 @default.
• W3185637639 hasRelatedWork W3196134121 @default.
• W3185637639 hasRelatedWork W2183242760 @default.
• W3185637639 hasRelatedWork W3111423051 @default.
• W3185637639 hasRelatedWork W3133044537 @default.
• W3185637639 hasVolume "5" @default.
• W3185637639 isParatext "false" @default.
• W3185637639 isRetracted "false" @default.
• W3185637639 magId "3185637639" @default.
• W3185637639 workType "article" @default.