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Applying Neural Networks for Modeling of Financial Assets

Applying Neural Networks for Modeling of Financial Assets

Dmitry Averchenko, Artem Aldyrev
Copyright: © 2018 |Pages: 33
ISBN13: 9781522537670|ISBN10: 1522537678|EISBN13: 9781522537687
DOI: 10.4018/978-1-5225-3767-0.ch010
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MLA

Averchenko, Dmitry, and Artem Aldyrev. "Applying Neural Networks for Modeling of Financial Assets." Fractal Approaches for Modeling Financial Assets and Predicting Crises, edited by Inna Nekrasova, et al., IGI Global, 2018, pp. 172-204. https://doi.org/10.4018/978-1-5225-3767-0.ch010

APA

Averchenko, D. & Aldyrev, A. (2018). Applying Neural Networks for Modeling of Financial Assets. In I. Nekrasova, O. Karnaukhova, & B. Christiansen (Eds.), Fractal Approaches for Modeling Financial Assets and Predicting Crises (pp. 172-204). IGI Global. https://doi.org/10.4018/978-1-5225-3767-0.ch010

Chicago

Averchenko, Dmitry, and Artem Aldyrev. "Applying Neural Networks for Modeling of Financial Assets." In Fractal Approaches for Modeling Financial Assets and Predicting Crises, edited by Inna Nekrasova, Oxana Karnaukhova, and Bryan Christiansen, 172-204. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3767-0.ch010

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Abstract

The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key parameters of forecast for this analytical system are the indicators presented by the terminal MetaTrader 4-broker Forex Club: Average Directional и Movement Index; Bollinger Bands; Envelopes; Ichimoku Kinko Hyo; Moving Average; Parabolic SAR; Standard Deviation; Average True Range; and others.

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