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

• Published in: Neural networks 2022-01, Vol.145, p.342-355
• Main Authors: Jarusek, Robert, Volna, Eva, Kotyrba, Martin
• Format: Article
• Language: English
• Subjects: Elliott wave; Fast Fourier Transform (FFT); Forecasting; FOREX; Neural networks; Neural Networks, Computer; Prediction; Probability
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2021.10.024

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. •It was proved the benefits of the Elliott Wave theory in trading simulation.•It was proved a real financial profit on a sufficiently large test samples.•It was proposed an innovative approach for suitable representation of the input data.•The proposed approach successfully minimizes the pattern offset problem.•The successfulness of the presented trading system achieves 77% on average.