پيش‌ بيني ورشكستگي شركت‌هاي پذيرفته شده در بورس اوراق بهادار تهران با روش‌هاي شبكه عصبي مصنوعي و مدل فولمر

Bankruptcy Prediction of listed Companies in Tehran’s Stock Exchange by Artificial Neural Network (ANN) and Fulmer Model

هدف: درماندگي مالي و ورشكستگي، هزينه‌هاي زيادي داشته و به اقتصاد كشورها صدمه وارد مي‌كند و پيش‌بيني آن جهت جلوگيري از ورشكستگي كمك شايان توجهي مي‌كند. هدف پژوهش پيش‌بيني ورشكستگي و سودآوري شركت‌ها جهت ارزيابي عملكرد و وضعيت مالي با استفاده از رگرسيون لجستيك و نسبت‌هاي مالي بامدل‌هاي شبكه عصبي مصنوعي و فولمر براساس دوره زماني 1391 الي 1397 براي 132 شركت بورس هست. روش: براي برازش مدل فولمر از نرم افزار EViews و براي برازش مدل شبكه عصبي از نرم افزار Spss26 استفاده شده است. شاخص‌هاي استفاده شده در مدل‌ها شامل نسبت بدهي به حقوق صاحبان سهام، سود قبل از بهره و ماليات، جمع بدهي‌ها به مجموع دارايي‌ها، حساب‌هاي دريافتني به فروش، سود خالص بر دارايي، بدهي بلندمدت به دارايي، سرمايه در گردش، سود خالص به فروش هستند. يافته‌ها: با استفاده از نتايج و مدل‌هاي ارائه شده در پژوهش مي‌توان از مبتلا شدن شركت‌ها به بحران مالي، ورشكستگي و همچنين پيامدهاي آن، به‌ طور مناسبي جلوگيري كرد. البته توجه اين نكته نيز ضروري است كه پس از پيش‌بيني مي‌بايستي به ريشه‌يابي مساله و پيگيري علل پرداخته شود. نتيجه‌گيري: نتايج پژوهش نشان داد ميزان قدرت و دقت پيش‌بيني ورشكستگي مدل شبكه عصبي مصنوعي در مقايسه با مدل فولمر از دقت بالاتري برخوردار است و همچنين حساب‌هاي دريافتني بر فروش بيشترين و نسبت بدهي به حقوق صاحبان سهام كمترين نسبت‌هاي مالي مؤثر بر ورشكستگي در مدل شبكه عصبي مصنوعي هست.

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.