الگوسازی و پیش‌بینی قیمت سهام شرکت‌های صنایع دارویی و شیمیایی پذیرفته‌شده در بورس اوراق بهادار تهران با استفاده از الگوها و روش‌های نوین

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس‌ارشد حسابداری از دانشگاه فردوسی مشهد

2 استادیار حسابداری دانشگاه فردوسی مشهد

3 استاد اقتصاد دانشگاه فردوسی مشهد

چکیده

مقدمه: در این پژوهش از الگوی اقتصادسنجی و شبکه عصبی پایه شعاعی برای افزایش اثربخشی، کاهش هزینه و زمان روش تحلیل بنیادی در پیش‌بینی قیمت سهام شرکت‌های صنایع مواد و محصولات دارویی، محصولات شیمیایی و وسایل اندازه‌گیری پزشکی و اپتیکی استفاده شده است.
روش پژوهش: پژوهش حاضر کاربردی و طرح آن از نوع شبه‌تجربی است. جامعه آماری این پژوهش متشکل از 30 شرکت پذیرفته‌شده در بورس اوراق بهادار تهران در بازه زمانی 1390-1384 است. ساخت الگو و تجزیه و تحلیل داده‌ها با استفاده از نرم‌افزار Eviews نسخه 7 و Clementine نسخه 12 انجام شده است.
یافته‌ها: نتایج پژوهش نشان‌دهنده آن است که الگوی انتخابی شامل PC1 (جمع دارایی‌های جاری و جمع بدهی‌ها)، PC2 (نسبت جاری، نسبت آنی، نسبت گردش دارایی‌های ثابت مشهود، حاشیه سود ناخالص، حاشیه سود عملیاتی و حاشیه سود خالص)، بازده سهام و سود هر سهم قدرت توضیح‌دهندگی بالایی برای پیش‌بینی قیمت سهام دارد.
نتیجه‌گیری: شبکه عصبی در پیش‌بینی قیمت سهام از دقت خوبی برخوردار است. هم‌چنین، مقایسه دقت دو الگو بیانگر دقت بیش‌تر شبکه عصبی پایه شعاعی نسبت به الگوی اقتصادسنجی داده‌های تابلویی در پیش‌بینی قیمت سهام است.

کلیدواژه‌ها


عنوان مقاله [English]

Modelling and Predicting the Stock Price of the Pharmaceutical and Chemical Companies Listed on the Stock Exchange via New Methods and Models

نویسندگان [English]

  • O. Aman Dad 1
  • M. Salehi 2
  • M. Fallahi 3
1 M. A. in Accounting, Ferdowsi University of Mashhad
2 Assistant Professor, Department Reportment of Accounting, Ferdowsi University of Mashhad
3 Professor of Economics, Ferdowsi University of Mashhad
چکیده [English]

Introduction: In this research, econometrics and Radial Base Function neural networks have been used to increase the effectiveness, decrease time and costs for predicting the stock price of the material industries and pharmaceutical products, and the medical and optical measuring instruments companies by the method of fundamental analysis.
Method: The current research is an applied one and it has a quasi-empirical design. The statistical population of the research consist of 30 companies listed on the Tehran Stock Exchange from 2005 to 2011. Designing the model and analyzing data have been done through Eviews Software Version 7, and Clementine Version 12.
Results: The results of the research indicate that the selected model includes PC1 (the sum of current assets, and the sum of liabilities), PC2 (current ratio, quick ratio, the ratio of tangible fixed assets turnover, gross profit margin, operational profit margin, and net profit margin), return on equity and earnings per share has a high explanatory ability to predict the stock price.
Conclusion: Neural network has a good accuracy in predicting the stock price. Moreover, the comparison of these two models state that the Radial Base Function neural network is more accurate than the model of econometrics of panel data in predicting the stock price.

کلیدواژه‌ها [English]

  • Fundamental Analysis
  • Pharmaceutical and Chemical Industries
  • Predicting the Stock Price
  • Radial Basis Function Neural Network
 

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