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Frontiers in Signal Processing
FSP > Volume 1, Number 2, October 2017

Neural Networks for Financial Market Risk Classification

Download PDF  (154.2 KB)PP. 62-66,  Pub. Date:August 31, 2017
DOI: 10.22606/fsp.2017.12002

Author(s)
Narek Abroyan
Affiliation(s)
Division of Computer Systems and Informatics, National Polytechnic University of Armenia, Yerevan, Armenia
Abstract
During the last several years machine learning started to revolutionize many industrial fields by replacing human intellectual work with recent technologies. Machine learning has started to be used in financial sphere as well for predicting stock prices, detecting fraud actions etc. In this work, we are focusing on financial market risk classification, which is a part of fraud action detection problem. Although artificial intelligence researchers and specialists achieved notable results in visual, voice signal and natural language processing tasks by using new methods and approaches of deep learning, such as convolutional and recurrent neural networks, not many results are in the sphere of elaboration of real-time non-stationary data, such as financial data. Moreover, methods which are used in industry usually are not published. The goal of this work is exploring, experimenting and providing new and more effective methods of classification of financial non-stationary risk data by using neural networks.
Keywords
Deep learning, convolutional neural networks, recurrent neural networks, financial data, risky transaction, classification.
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