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Τίτλος: Forecasting stock market crisis events using deep and statistical machine learning techniques
Συγγραφείς: Chatzis, Sotirios P. 
Siakoulis, Vassilis 
Petropoulos, Anastasios 
Stavroulakis, Evangelos 
Vlachogiannakis, Nikos 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Deep learning;Forecasting;Random forests;Stock market crashes;Support vector machines;Support vector machines
Ημερομηνία Έκδοσης: 1-Δεκ-2018
Πηγή: Expert Systems with Applications, 2018, vol. 112, pp. 353-371
Volume: 112
Start page: 353
End page: 371
Περιοδικό: Expert systems with applications 
Περίληψη: 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.
ISSN: 09574174
DOI: 10.1016/j.eswa.2018.06.032
Rights: © Elsevier
Type: Article
Affiliation: Cyprus University of Technology 
Bank of Greece 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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