Using a Combination of Recurrent and Convolutional Neural Networks to Forecast the Direction of Financial Instrument Price Movement

DOI

10.2991/aisr.k.201029.040How to use a DOI?

Keywords

recurrent neural networks, convolutional neural networks, open interest, forecasting, financial market

Abstract

Securities market forecasting has long been of interest to analysts and mathematicians due to the obvious opportunity to monetize the research if it proves to be successful. The work of these researchers has led to the creation of various trading algorithms; however, their effectiveness has not yet been proven. With the development of computing technologies that allow implementing complex mathematical machine learning systems, the attention to this direction has increased considerably, in particular because of the introduction of neural networks. The present paper focuses on describing the initial data (pairs of price and the number of transactions available at this price) and the process of data collection and preparation for the neural network training. Moreover, the reasons for choosing the combination of recurrent and convolutional neural networks and its scheme are given, and the training results and insights are presented.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - V. A. Melnikov
AU  - N. D. Kharchenko
PY  - 2020
DA  - 2020/11/10
TI  - Using a Combination of Recurrent and Convolutional Neural Networks to Forecast the Direction of Financial Instrument Price Movement
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 209
EP  - 211
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.040
DO  - 10.2991/aisr.k.201029.040
ID  - Melnikov2020
ER  -