FOREX rate prediction improved by Elliott waves patterns based on neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302C3J" target="_blank" >RIV/61988987:17310/22:A2302C3J - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0893608021004251?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0893608021004251?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neunet.2021.10.024" target="_blank" >10.1016/j.neunet.2021.10.024</a>

Result language

angličtina

Original language name

FOREX rate prediction improved by Elliott waves patterns based on neural networks

Original language description

Financial market predictions represent a complex problem. Most prediction systems work with the term time window, which is represented by exchange rate values of a real financial commodity. Such values (time window) provide the base for prediction of future values. Real situations, however, prove that prediction of only a single time-series trend is insufficient. This article aims at suggesting a novelty and unconventional approach based on the use of several neural networks predicting probable courses of a future trend defined in a prediction time window. The basis of the proposed approach is a suitable representation of the training-set input data into the neural networks. It uses selected FFT coefficients as well as robust output indicators based on a histogram of the predicted course of the selected currency pair. At the same time, the given currency pair enters the prediction in a combination with another three mutually interconnected currency pairs. A significant output of the articles is, apart from the proposed methodology, confirmation that the Elliott wave theory is beneficial in the trading environment and provides a substantial profit compared with conventional prediction techniques. That was proved in the performed experimental study.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

NEURAL NETWORKS

ISSN

0893-6080

e-ISSN

1879-2782

Volume of the periodical

—

Issue of the periodical within the volume

January 2022

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

342-355

UT code for WoS article

000729932000007

EID of the result in the Scopus database

2-s2.0-85119286314