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پیش بینی سقوط بازار سهام با استفاده از شبکه های عصبی نگاشت خود سازمان ده | ||
| تحقیقات مالی | ||
| مقاله 10، دوره 17، شماره 1، فروردین 1394، صفحه 159-178 اصل مقاله (675.61 K) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jfr.2015.52861 | ||
| نویسندگان | ||
| آرش محمد علی زاده* 1؛ رضا راعی2؛ شاپور محمدی3 | ||
| 1دکتری مدیریت مالی، دانشگاه تهران، تهران، ایران | ||
| 2استاد گروه مدیریت مالی، دانشگاه تهران، تهران، ایران | ||
| 3دانشیار 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 | ||
| پیش بینی؛ سقوط بازار سهام؛ شبکه های عصبی؛ نگاشت خودسازمانده | ||
| عنوان مقاله [English] | ||
| Prediction of stock market crash using self-organizing maps | ||
| نویسندگان [English] | ||
| Arash Mohamad Alizadeh1؛ Reza Raei2؛ Shapour Mohammadi3 | ||
| 1Ph.D. in Finance, University of Tehran, Tehran, Iran | ||
| 2Associate Prof., Finance Department, University of Tehran, Tehran, Iran | ||
| 3Associate 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 | ||
| مراجع | ||
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