پیش‌بینی ورشکستگی بنگاه‌های اقتصادی قابل پذیرش در بورس برق و انرژی با استفاده از اتوماتای یادگیر

نویسندگان

1 دانشگاه صنعتی امیرکبیر، دانشکدة مهندسی برق، آزمایشگاه آنالیز سیستم‌های قدرت، تهران

2 دانشگاه تهران، پردیس دانشکده‌های فنی، دانشکدة مهندسی برق و کامپیوتر، آزمایشگاه تحقیقاتیِ مطالعات بهره‌برداری و برنامه‌ریزی سیستم‌های قدرت، تهران

چکیده

با توجه به آغاز به کار بورس برق و انرژی در سال 1391، ارائة مشاوره‌های جانبی به سرمایه‌گذاران یکی از اولویت‌های توسعه و پیشرفت این بورس تازه‌ تاسیس‌، می‌باشد. پیش‌بینی ورشکستگی بنگاه‌های اقتصادی، نه تنها به سرمایه‌گذاران در اولویت‌دهی و جلوگیری از دست رفتن اصل و فرع سرمایه کمک می‌کند، بلکه تاثیر بسزایی در نحوة اعتباردهی و در نتیجه جلوگیری از نابودی بنگاه اقتصادی خواهد داشت. در این مقاله، مسألة پیش‌بینی ورشکستگی بنگاه‌های اقتصادی مرتبط با حوزة برق و انرژی، در محیط شرکت‌های ایران، بررسی می‌گردد. برای این منظور از اطلاعات 200 سال-شرکت، از بین شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران، در سال‌های 1380 تا 1388، استفاده شده است. در کلیة مطالعات تعداد شرکت‌های ورشکسته و غیرورشکسته مساوی در نظر گرفته شده و شرکت‌های ورشکسته بر مبنای مادة 141 قانون تجارت انتخاب شده‌اند. به منظور ایجاد یک رابطة پیشنهادی برای پیش‌بینی ورشکستگی مالی شرکت‌های مرتبط با حوزة برق و انرژی، از یک الگوریتم هوشمند مبتنی بر اتوماتای یادگیر استفاده شده است. مطابق نتایج ارائه شده، دقت مدل پیشنهادی برای داده‌های آموزش حدود 91% و بر روی داده‌های آزمون تقریباً 88% می‌باشد. با توجه آنالیز حساسیت‌های انجام‌شده، می‌توان نتیجه گرفت که مدل پیشنهادی نیازهای فنی و اقتصادی مسأله را ارضاء نموده و می‌تواند به عنوان ابزاری برای پیش‌بینی ورشکستگی شرکت‌ها مورد استفاده قرار گیرد.
 

کلیدواژه‌ها


عنوان مقاله [English]

A Learning Automaton Based Algorithm for Bankruptcy Prediction of Acceptable Firms within Power and Energy Exchange

نویسندگان [English]

  • Seyed Mahdi Mazhari 1
  • Hassan Monsef 2
  • Hooman Mirzaei 1
1 Amirkabir University of Technology, School of EE, Power System Analysis Laboratory, Tehran, Iran
2 University of Tehran, University College of Engineering, School of ECE, Research Laboratory of Power System Operation and Planning Studies, Tehran, Iran
چکیده [English]

In today's world, insurance of productive capital investment and reducing economic risk causes more fundraising and therefore the greatest economic boom cycle. One way to arrive capital investment security is to predict bankruptcy of a business unit. As the Iranian power and energy stock is going to start working by 2012, providing suitable bits of advice to investors would be a priority. This paper proposes a new solution approach for bankruptcy prediction of the Iranian power and energy industries. To do so, an evolutionary algorithm premised on Learning Automata is employed and adapted to the problem. Two sets of firms related to power and energy industries that are listed on the Tehran Stock Exchange (TSE) are selected as the training and test data, respectively. The developed algorithm is conducted on both train and test data, and the efficiency of the proposed method is evaluated via several scenarios. It was practically seen in simulations that the learning automata-based algorithm could achieve an accuracy of 91% and 88% over the train and test data, respectively. Besides these, the same data sets are also conducted by other methods such as MDA and Logit, and the obtained results are compared with reality. The yielded results prove the accuracy as well as the efficiency of the proposed solution technique
 

کلیدواژه‌ها [English]

  • Bankruptcy Prediction
  • Financial ratios
  • Firms Related to Power and Energy Industry
  • Learning Automata

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