Forecasting and Categorizing Euro-To-Dollar Exchange Rates Using Machine Learning Algorithms with Voting Classifier
Keywords:
Forecasting, Euro-to-Dollar Exchange Rates, Investment Strategies, Economic PlanningAbstract
Forecasting and categorizing exchange rates is a critical task in financial markets, impacting both investment strategies and economic planning. This study explores various machine learning algorithms for predicting and classifying Euro-to-Dollar exchange rates, focusing on existing models such as AdaBoost, Gradient Boosting, Bagging, Extreme Gradient Boosting (XGBoost) Classifier, and Decision Tree Classifier. These algorithms are evaluated for their performance in handling the complexities of exchange rate movements. In addition to analyzing traditional methods, this study proposes a novel approach by combining three advanced machine learning models: Logistic Regression, Random Forest Classifier, and Gaussian Naive Bayes. This ensemble model aims to leverage the strengths of each algorithm, potentially improving prediction accuracy and classification precision. By integrating these models, we seek to enhance the robustness and reliability of exchange rate forecasts, providing a more comprehensive tool for financial analysts and decision-makers. The results of this comparative study offer insights into the efficacy of various machine learning techniques and their application to currency exchange rate forecasting.
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References
Chen, J., & Li, J. (2019). "A deep learning approach for forecasting foreign exchange rates." Neuro computing, 338, 113-123. doi:10.1016/j.neucom.2019.01.042
Huang, C., & Wang, S. (2020). "Combining machine learning and technical analysis for foreign exchange market prediction." Expert Systems with Applications, 139, 112855. doi:10.1016/j.eswa.2019.112855
Krauss, C., Do, X. A., & Huck, N. (2017). "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500." European Journal of Operational Research, 259(2), 689-702. doi:10.1016/j.ejor.2016.10.016
Li, C., & Wu, Y. (2021). "Exchange rate forecasting using hybrid deep learning and attention mechanisms." Journal of Forecasting, 40(7), 1033-1054. doi:10.1002/for.2843
Liu, Y., & Zhang, X. (2019). "Hybrid model for forecasting exchange rates based on deep learning and wavelet transform." Journal of Computational and Applied Mathematics, 355, 123-135. doi:10.1016/j.cam.2018.12.005
Ozturk, S., & Ozturk, H. (2020). "Application of recurrent neural networks in forecasting foreign exchange rates." Procedia Computer Science, 176, 261-270. doi:10.1016/j.procs.2020.09.019
Ritter, J., & Wang, Q. (2021). "Time-series forecasting of exchange rates with attention-based recurrent neural networks." International Journal of Forecasting, 37(1), 116-129. doi:10.1016/j.ijforecast.2020.04.001
Saeed, M., & Arshad, M. (2018). "Forecasting foreign exchange rates using a novel hybrid model based on deep learning and statistical techniques." Applied Soft Computing, 68, 351-362. doi:10.1016/j.asoc.2018.03.011
Sikdar, S., & Saha, S. (2019). "Predicting currency exchange rates with machine learning algorithms: An empirical study." Computers & Industrial Engineering, 128, 726-740. doi:10.1016/j.cie.2018.12.008
Wang, C., & Xu, H. (2017). "A comparative study of machine learning algorithms for forecasting currency exchange rates." Knowledge-Based Systems, 123, 1-12. doi:10.1016/j.knosys.2017.01.023
Wang, L., & Li, Y. (2020). "Hybrid machine learning models for foreign exchange rate prediction." Mathematical Methods in the Applied Sciences, 43(14), 7715-7729. doi:10.1002/mma.6753
Wang, Z., & Yu, X. (2019). "Ensemble methods for predicting exchange rates: A study of machine learning techniques." Journal of Financial Markets, 43, 1-14. doi:10.1016/j.finmar.2018.10.002
Xia, Y., & Zhang, J. (2021). "Machine learning for foreign exchange rate prediction: A survey." International Journal of Data Science and Analytics, 12(3), 201-222. doi:10.1007/s41060-021-00253-8
Yang, T., & Wang, H. (2020). "Forecasting exchange rates using machine learning and neural network models." Computers & Mathematics with Applications, 79(7), 2025-2041. doi:10.1016/j.camwa.2019.12.032
Yao, J., & Liu, L. (2018). "Deep learning for exchange rate forecasting: A case study of the USD/CNY exchange rate." Economic Modelling, 73, 45-55. doi:10.1016/j.econmod.2018.04.021
Zhang, J., & Liu, Z. (2019). "Exploring the potential of machine learning techniques for predicting currency exchange rates." Journal of Computational Finance, 23(4), 1-23. doi:10.21314/JCF.2019.451
Zhang, S., & Chen, Y. (2020). "A hybrid approach combining deep learning and sentiment analysis for foreign exchange rate forecasting." Decision Support Systems, 131, 113268. doi:10.1016/j.dss.2020.113268
Zhu, X., & Li, H. (2021). "Adaptive forecasting of exchange rates using machine learning methods." Applied Intelligence, 51(7), 4584-4598. Doi :10.1007/s10489-021-02009-w
Zhu, Y., & Zhang, W. (2018). "Application of machine learning techniques to foreign exchange rate prediction." Mathematical Finance, 28(2), 257-282. doi:10.1111/1467-9965.12217
Zou, H., & Yang, X. (2019). "Predicting currency exchange rates with deep neural networks: An empirical analysis." Journal of Computational Economics, 53(2), 317-339. Doi :10.1007/s10614-018-9812-8
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