Machine learning approaches for financial time series forecasting

- Derbentsev, Vasily (orcid.org/0000-0002-8988-2526), Matviychuk, Andriy (orcid.org/0000-0002-8911-5677), Datsenko, Nataliia (orcid.org/0000-0002-8239-5303), Bezkorovainyi, Vitalii (orcid.org/0000-0002-4998-8385) and Azaryan, A.A. (orcid.org/0000-0003-0892-8332) (2020) Machine learning approaches for financial time series forecasting Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) Odessa, Ukraine, July 13-18, 2020. pp. 434-450. ISSN 1613-0073

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Abstract

This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.

Item Type:	Article
Keywords:	financial time series, short-term forecasting, machine learning, support vector machine, random forest, gradient boosting, multilayer perceptron
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.94 Simulation Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 33 Economics. Economic science
Divisions:	Institute for Digitalisation of Education > Joint laboratory with SIHE “Kryvyi Rih National University”
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	25 Nov 2021 21:54
Last Modified:	25 Nov 2021 21:54
URI:	https://lib.iitta.gov.ua/id/eprint/727244

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