استفاده از تکنیک های یادگیری ماشین برای پیش بینی پیامدهای بحران بازار سهام
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استفاده از تکنیک های یادگیری ماشین برای پیش بینی پیامدهای بحران بازار سهام

عنوان فارسی مقاله: پیش بینی پیامدهای بحران بازار سهام با استفاده از تکنیک های آماری و عمیق یادگیری ماشین
عنوان انگلیسی مقاله: Forecasting stock market crisis events using deep and statistical machine learning techniques
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems with Applications
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، اقتصاد
گرایش های تحصیلی مرتبط: هوش مصنوعی، اقتصاد پولی، اقتصاد مالی، مهندسی الگوریتم ها و محاسبات
کلمات کلیدی فارسی: سقوط بازار سهام، پیش بینی، جنگل های تصادفی، ماشین بردار های پشتیبانی، یادگیری عمیق، XGBoost
کلمات کلیدی انگلیسی: Stock market crashes، Forecasting، Random forests، Support vector machines، Deep learning، XGBoost
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2018.06.032
دانشگاه: Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 5/891 در سال 2018
شاخص H_index: 162 در سال 2019
شاخص SJR: 1/190 در سال 2018
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 45
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11366
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Literature review


3- Data collection and processing


4- Model development


5- Experimental evaluation


6- Conclusions and future work


References

نمونه متن انگلیسی مقاله

Abstract


This work contributes to this ongoing debate on the nature and the characteristics of propagation channels of crash events in international stock markets. Specifically, we investigate transmission mechanisms across stock markets along with effects from bond and currency markets. Our approach comprises a solid forecasting mechanism of the probability of a stock market crash event in various time frames. The developed approach combines different machine learning algorithms which are presented with daily stock, bond and currency data from 39 countries that cover a large spectrum of economies. Specifically, we leverage the merits of a series of techniques including Classification Trees, Support Vector Machines, Random Forests, Neural Networks, Extreme Gradient Boosting, and Deep Neural Networks. To the best of our knowledge, this is the first time that Deep Learning and Boosting approaches are considered in the literature as a means of predicting stock market crisis episodes. The independent variables included in our data contain information regarding both the two fundamental linkage channels through which financial contagion can be initiated: returns and volatility. We apply a suite of machine learning algorithms for selecting the most relevant variables out of a large set of proposed ones. Finally, we employ bootstrap sampling for adjusting the imbalanced nature of the available fitting dataset. Our experimental results provide strong evidence that stock market crises tend to exhibit persistence. We also find significant evidence of interdependence and cross-contagion effects among stock, bond and currency markets. Finally, we show that the use of Deep Neural Networks significantly increases the classification accuracy, while offering a robust way to create a global systemic early warning tool that is more efficient and risk-sensitive than the currently established ones. Thus, central banks may use these tools to early adjust their monetary policy, so as to ensure financial stability.


Introduction


A global financial crisis can emerge from a series of local or/and regional market shocks, which evolve into a worldwide economic crisis due to the interconnectedness of the financial markets. For example, the Asian crisis in 1997 initially originated in Thailand; subsequently, it propagated to other Asian countries, and eventually made it to the financial markets of the United States of America and Europe (Kaminsky et al., 1998). In other cases, a crisis may start from a single economy whose size is large enough to generate turbulence in other countries. This is the case, for instance, with the subprime crisis that started in the United States and evolved into a sovereign debt crisis in several European countries. The observation that an economic crisis is manifested by a subsequent recession (Bluedorn et al.. (2013), Barro and Ursua (2009), Estrella and Mishkin (1998), Farmer (2012)) renders reliable Early Warning Systems (EWSs) valuable tools for policymakers, in their effort to curtail contagion risk and, in extreme cases, even preempt a global economic crisis. An EWS must be capable of producing clear signals as to whether an economic crisis is imminent, complementing the expert judgment of policymakers. Hence, EWS systems facilitate policy makers in unveiling vulnerabilities of the economy and taking precautionary actions to diminish the risks that can trigger a crisis. Certainly, there is always a trade-off between developing EWSs that are capable of predicting a lot of alarms for an imminent crisis, at the expense of some of them being wrong (false-alarms), and EWSs that predict rather too few signals of impending crises, at the expense of missing a major crisis event. Optimally, an EWS should let no crisis events go unnoticed, while minimizing the number of generated false-alarms. It goes without saying that the cost of not signaling a global crisis is significantly higher than that of an incorrect alarm. At the same time, the incorporation of the probability of a worldwide crisis in decisions related to asset al.location (Kole et al.., 2006) can substantially benefit investors. Indeed, this is the case since a global crisis significantly curtails diversification benefits, as worldwide markets move in the same direction. In addition, any hedging strategies may become ineffective (Ibragimov and Walden, 2007) due to the structural changes in the observed correlations among asset classes. Indeed, during periods of high volatility in bear markets, correlations increase across assets (Longin and Solnik, 2001). Thus, as the markets cannot quickly correct any disruptions in their function, it becomes even more imperative for regulators to intervene so as to restore financial stability.

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