محاسبه احتمال سقوط سهام با استفاده از شبکه های عصبی پیچیده و بررسی رابطه بین احتمال سقوط سهام و بازده انتظاری سهام در بازار سرمایه ایران(1400-1386)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد، دانشکده اقتصاد، دانشگاه تهران

2 دانشیار، دانشکده اقتصاد، دانشگاه تهران

3 استاد اقتصاد دانشگاه تهران

چکیده

یکی از مخرب ترین نوسانات بازار سهام، سقوط قیمت سهام می باشد، محاسبه دقیق احتمال سقوط  قیمت سهام می تواند کمک شایانی به سرمایه گذاران بازار سهام جهت انتخاب پرتفوی مناسب سرمایه گذاری نماید. در این مقاله تکنیک شبکه های عصبی پیچیده یک بعدی جهت پیش بینی  احتمال سقوط سهام و مدل  فاما و فرنچ سه عاملی و قیمت گذاری دارایی سرمایه ای جهت محاسبه بازده سهام مورد استفاده قرار گرفته است. نمونه مورد استفاده در تحقیق شامل 80 شرکت بورسی صادرات محور و واردات محور ایرانی در بازه زمانی1400-1386 می باشد. طبق نتایج بدست آمده شبکه های عصبی پیچیده با دقت بالایی احتمال سقوط سهام را پیش بینی می نمایند و نیز طبق نتایج پژوهش بین احتمال سقوط سهام و بازده انتظاری آن رابطه معکوس وجود دارد که نتیجه مذکور بیانگر آن است که با محاسبه احتمال سقوط سهام می توان تقاضای آتی برای آن و لذا قیمت آتی آن را پیش بینی کرد .محاسبه احتمال سقوط  قیمت سهام با استفاده از شبکه های عصبی پیچیده روش جدیدی در بدست آوردن پرتفولیو های با احتمال سقوط کمتر می باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Calculating the stock crash probability using complex neural networks and investigating the relationship between the stock crash probability and the expected stock return in the Iranian capital market (2007-2022)

نویسندگان [English]

  • Najibeh Najafi Kangarlui 1
  • Farkhondeh Jebel Ameli 2
  • Mohsen Mehrara 3
1 PhD Student, Faculty of Economics, Department of Economics. University of Tehran
2 Associate professor, Faculty of Economics, University of Tehran
3 Professor, Faculty of Economics, Department of Economics, University of Tehran
چکیده [English]

One of the most destructive fluctuations in the stock market is the crash in stock prices. The accurate calculation of the probability of the stock price crash can greatly help investors choose the right investment portfolio. In this article, the one-dimensional convolutional neural network technique is used to calculate the probability of the stock price crash, and the three-factor Fama and French model and Capital asset pricing model are used to calculate stock returns.The sample used in this paper includes 80 import- and export-oriented exchange companies in the period 2008-2021 According to the obtained results, convolutional neural networks predict the probability of the stock crash with high accuracy, and there is an inverse relationship between the probability of the stock price crash and its expected return, which means that by calculating the probability of a stock price crash, it is possible to predict the future demand for it and the direction of its future price. Calculating the probability of stock price crashes, using convolutional neural networks is a new method for calculating the risk of stock price crash and can help us measure portfolio risk.

کلیدواژه‌ها [English]

  • Crash risk
  • Convolutional neural network
  • Expected return
  • Portfolio

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