Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 31, 07 March 2024
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Application of ARIMA agorithm and genetic algorithm based on MATLAB language in finance
* Author to whom correspondence should be addressed.
Abstract
The price changes of bitcoin and gold are frequent, as observed in previous studies, and market participants can base their daily investment plans on historical data. Market traders can maximise their investment returns by buying low and selling high on volatile assets based on the day's market conditions, as markets fluctuate every day. Gold and bitcoin are two of the most common volatile investments. In this project, we are trying create an algorithm of high degree of integration which can provide advice to traders before they made their decisions. We used ARIMA algorithm based on the Genetic Algorithm to predict the data and let the algorithm to make based on the predicted data. The complete code could be seen in appendix. The codes were mostly based on MATLAB language, for which it integrated the features of “simple and easy to get started”; “dispatchable sensitivity”; “adaptable and reliable”. Which allows the traders to use simple operations to get reliable and visualizable results. Which can let the traders to have the opportunity to get suggestions on when they should trade.
Keywords
ARIMA Model, Profit-Position, Deficit-Position, Sensitivity, MATLAB
References
1. Bhuiyan, R. A., Husain, A., & Zhang, C. (2023). Diversification evidence of bitcoin and gold from wavelet analysis. Financial Innovation, 9(1), 100.
2. Jareño, F., de la O González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 101666.
3. Vantuch, T., & Zelinka, I. (2015). Evolutionary based ARIMA models for stock price forecasting. In ISCS 2014: Interdisciplinary Symposium on Complex Systems (pp. 239-247). Springer International Publishing.
4. Hřebík, R., & Sekničková, J. ARIMA model selection in Matlab.
5. Pedregal, D. J., Contreras, J., & de la Nieta, A. A. S. (2012). ECOTOOL: A general MATLAB forecasting toolbox with applications to electricity markets. Handbook of networks in power systems I, 151-171.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-317-3
- ISBN (Online)
- 978-1-83558-318-0
- Published Date
- 07 March 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/31/20241156
- Copyright
- 07 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited