پیش بینی سقوط بازار سهام با استفاده از شبکه های عصبی نگاشت خود سازمان ده

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دکتری مدیریت مالی، دانشگاه تهران، تهران، ایران

2 استاد گروه مدیریت مالی، دانشگاه تهران، تهران، ایران

3 دانشیار گروه مدیریت مالی، دانشگاه تهران، تهران، ایران

چکیده

سقوط بازار پدیده­ای است که سبب از دست رفتن ثروت و دارایی سرمایه‎گذاران در بازۀ زمانی نسبتاً کوتاهی می­شود، از این رو تلاش برای پیش­بینی آن از اهمیت زیادی برای سرمایه­گذاران، سیاست‎گذاران، نهادهای مالی و دولت برخوردار است. بررسی اجمالی تئوری­ها و مدل‎های ارائه‎شدۀ پیش­بینی سقوط در بازار سهام نشان می­دهد میان پژوهشگران دربارۀ الگوهای مشاهده‎شدۀ متغیرها، مانند حجم معامله، بازده‎ها، نوسان‎پذیری، عوامل بنیادی، شاخص­های رفتاری و غیره در بازارهای سهام پیش از وقوع سقوط، اتفاق نظری وجود ندارد. یکی از روش‎های بسیار مناسب پیشنهادشده برای یافتن الگوهایی که در داده­های شبکه­های عصبی وجود دارد، نگاشت خودسازمان‎ده است که روشی ناپارامتریک و غیرخطی محسوب می‎شود. در این پژوهش با استفاده از شبکه­های عصبی نگاشت خوسازمان‎ده، روشی برای پیش‎بینی سقوط در بازار سهام ایران ارائه شده است. نتایج اجرای مدل و پیش‎بینی برون‎نمونه‎ای حاکی از این است که مدل عملکرد به‎نسبت قابل قبولی را در پیش‎بینی دوره­های پیش از سقوط در بازار سهام به‎دست آورده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of stock market crash using self-organizing maps

نویسندگان [English]

  • Arash Mohamad Alizadeh 1
  • Reza Raei 2
  • Shapour Mohammadi 3
1 Ph.D. in Finance, University of Tehran, Tehran, Iran
2 Associate Prof., Finance Department, University of Tehran, Tehran, Iran
3 Associate Prof., Finance Department, University of Tehran, Tehran, Iran
چکیده [English]

Market crash is a phenomenon which occurs in stock markets occasionally and leads to loss of the investors’ wealth and assets in a relatively short period of time. Therefore, attempts for prediction of this phenomenon are of much importance for the investors, financial institutions and government. To this date, numerous and varied studies have been carried out for predicting and modeling  stock markets and their crash. Each of these studies has tried to fulfill this important task more precisely from a different point of view. A brief review of the theories and models presented for prediction of stock market crash indicates that there is no agreement among the researchers in relation to the observed patterns of variables such as trading volume, returns, volatility, fundamental factors, behavioral indicators, etc. in the stock markets in the pre-crash period. One of the very suitable methods proposed for finding the existing patterns in the data is the self-organizing map neural networks method which is considered as a non-parametric and non-linear method. In this study, a method is proposed for prediction of the crash in the Iranian stock market using the self-organizing map neural networks. The results of implementation of the model and out-of-sample prediction indicate that the model has a relatively acceptable performance in prediction of the pre-crash periods in the stock market.

کلیدواژه‌ها [English]

  • neural networks
  • Prediction
  • Self-organizing Maps
  • stock market crash

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تحقیقات مالی
دوره 17، شماره 1 - شماره پیاپی 1
بهار و تابستان
فروردین 1394
صفحه 159-178

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