P/E Modeling and Prediction of Firms Listed on the Tehran Stock Exchange; a New Approach to Harmony Search Algorithm and Neural Network Hybridization

Document Type: Research Paper

Authors

Department of Accounting, Islamic Azad University, Kashan Branch, Kashan, Iran

Abstract

Investors and other contributors to stock exchange need a variety of tools, measures, and information in order to make decisions. One of the most common tools and criteria of decision makers is price-to earnings per share ratio. As a result, investors are in pursuit of ways to have a better assessment and forecast of price and dividends and get the highest returns on their investment. Previous research shows that neural networks have better predictability than statistical models. Thus, Harmony Search algorithm and neural network have been used in this work, since achieving the best forecast is more likely. For this purpose, a sample consisting of 87 companies has been selected from those listed at the Tehran Stock Exchange over a 10-year period (2006-2015). The results show the high accuracy of the designed model that predicts the price-to-earnings ratio at the stock exchange by hybridizing the balanced search algorithm with neural network.

Keywords

Main Subjects


Article Title [Persian]

مدلسازی و پیش بینی P/E شرکت‎های پذیرفته شده در بورس اوراق بهادار تهران؛ رویکرد نوین ترکیب الگوریتم هارمونی سرچ و شبکه عصبی

Authors [Persian]

  • مژگان صفا
  • حسین پناهیان
گروه حسابداری، واحد کاشان، دانشگاه آزاد اسلامی، کاشان ، ایران
Abstract [Persian]

سرمایه‌گذاران و سایر مشارکت‌کنندگان در بورس اوراق بهادار به‌منظور تصمیم‌گیری نیازمند ابزارها، معیارها و اطلاعات متنوع و متعددی می‌باشند یکی از رایج‌ترین ابزارها و معیارهای تصمیم‌گذران، ضریب قیمت‌به‌سود هر سهم می‌باشد. در نتیجه سرمایه‌گذاران به دنبال روش‌هایی هستند تا ارزیابی و پیش‌بینی بهتری را از قیمت و سود سهام داشته باشند و بالاترین بازدهی را از سرمایه‌گذاری خود کسب نمایند. تحقیقات قبلی نشان می‌دهد که شبکه‌های عصبی نسبت به مدل‌های آماری قابلیت پیش‌بینی بیشتری دارند. در این تحقیق از الگوریتم‌ هارمونی سرچ و شبکه عصبی استفاده شده است که احتمال دست یافتن به بهترین پیش‌بینی افزایش یابد. بدین منظور نمونه‌ای متشکل از 87 شرکت در طی یک‌دوره 10 ساله (1385-1394) از شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران انتخاب شده است. نتایج، نشان‌دهنده دقت بالای مدل‌‌ طراحی شده پیش‌بینی نسبت قیمت‌به‌سود سهام در بورس اوراق بهادار به وسیله ترکیب الگوریتم جستجوی متوازن با شبکه عصبی می‌باشد.

Keywords [Persian]

  • جستجوی هماهنگ
  • پیش‌بینی نسبت قیمت به سود
  • تحلیل بنیادی
  • اقتصادسنجی داده‌های ترکیبی
  • شبکه عصبی RBF
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