Machine Learning Technique to Predict the Right Buying and Selling for EUR_USD

Authors

  • Mohamed EL Mahjouby Student, Faculty of Sciences, USMBA, Fez-30000, Morocco
  • Younes Manzali Student, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed Taj Bennani Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed Lamrini Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco
  • Mohamed EL FAR Professor, Faculty of Sciences, USMBA, Fez–30000, Morocco

Keywords:

Technical indicators, Machine learning, Classification, Adaptive boosting classifier, Decision tree classifier

Abstract

Predicting foreign exchange movements is an extensively studied and widely notable domain in finance. They have many studies using machine learning for the exchange market. This research explores and uses machine-learning techniques such as gradient boosting, random forest, bagging, extreme gradient boosting classifier, adaptive boosting, gaussian naïve, decision tree, and logistic regression and combines the adaptive boosting classifier with a base estimator decision tree. The goal of this combination is to forecast the optimal moments for purchasing and selling the euro against the dollar currency pair. This method entails suggesting the inclusion of 21 technical indicators into the training dataset to enhance the precision of the methodologies and our approach. The objective of this enhancement is to predict upcoming instances of buying and selling the currency pair euro against the dollar. The set of four metrics involves accuracy and measurements within the area under the receiver-operating characteristic curve, utilized for comparing multiple machine-learning models and assessing the effectiveness of various classification models. Analysis of the experiment demonstrates that our method achieves higher accuracy when compared to the decision tree classifier and other models, which obtained an accuracy of 0.763.

Downloads

Download data is not yet available.

References

B. Labiad, A. Berrado, and L. Benabbou, “Machine learning techniques for short term stock movements classification for Moroccan stock exchange,” SITA 2016 - 11th Int. Conf. Intell. Syst. Theor. Appl., pp. 1–6, 2016, doi: 10.1109/SITA.2016.7772259.

R. K. Nayak, D. Mishra, and A. K. Rath, “A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices,” Appl. Soft Comput. J., vol. 35, no. C, pp. 670–680, 2015, doi: 10.1016/j.asoc.2015.06.040.

N. S. Arunraj and D. Ahrens, “A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting,” Int. J. Prod. Econ., vol. 170, no. PA, pp. 321–335, 2015, doi: 10.1016/j.ijpe.2015.09.039.

K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, pp. 2823–2824, 2015, doi: 10.1109/BigData.2015.7364089.

S. Mehtab and J. Sen, “Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries,” 2020, doi: 10.36227/techrxiv.15088734.v1.

W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-based model to forecast stock prices,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6622927.

A. A. Adebiyi, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,” Proc. - UKSim-AMSS 16th Int. Conf. Comput. Model. Simulation, UKSim 2014, pp. 106–112, 2014, doi: 10.1109/UKSim.2014.67.

L. A. Teixeira and A. L. I. De Oliveira, “A method for automatic stock trading combining technical analysis and nearest neighbor classification,” Expert Syst. Appl., vol. 37, no. 10, pp. 6885–6890, 2010, doi: 10.1016/j.eswa.2010.03.033.

G. Rekha, B. D Sravanthi, S. Ramasubbareddy, and K. Govinda, “Prediction of stock market using neural network strategies,” J. Comput. Theor. Nanosci., vol. 16, no. 5–6, pp. 2333–2336, 2019, doi: 10.1166/jctn.2019.7895.

A. Kelotra and P. Pandey, “Stock Market Prediction Using Optimized Deep-ConvLSTM Model,” Big Data, vol. 8, no. 1, pp. 5–24, 2020, doi: 10.1089/big.2018.0143.

H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustain., vol. 10, no. 10, 2018, doi: 10.3390/su10103765.

B. Weng et al., “Macroeconomic indicators alone can predict the monthly closing price of major U.S. indices: Insights from artificial intelligence, time-series analysis and hybrid models,” Appl. Soft Comput., vol. 71, no. October 2019, pp. 685–697, 2018, doi: 10.1016/j.asoc.2018.07.024.

R. Ali, M. M. Yusro, M. S. Hitam, and M. Ikhwanuddin, “Machine Learning With Multistage Classifiers For Identification Of Of Ectoparasite Infected Mud Crab Genus Scylla,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 406–413, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16724.

H. Takci, “Improvement of heart attack prediction by the feature selection methods,” Turkish J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 1–10, 2018, doi: 10.3906/elk-1611-235.

T. Mantoro, M. A. Permana, and M. A. Ayu, “Crime index based on text mining on social media using multi classifier neural-net algorithm,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 3, pp. 570–579, 2022, doi: 10.12928/TELKOMNIKA.v20i3.23321.

S. Misra and H. Li, Noninvasive fracture characterization based on the classification of sonic wave travel times. Elsevier Inc., 2019. doi: 10.1016/B978-0-12-817736-5.00009-0.

Y. P. Huang and M. F. Yen, “A new perspective of performance comparison among machine learning algorithms for financial distress prediction,” Appl. Soft Comput., vol. 83, p. 105663, Oct. 2019, doi: 10.1016/J.ASOC.2019.105663.

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17-Augu, pp. 785–794, 2016, doi: 10.1145/2939672.2939785.

Z. Jin, J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang, “RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12343 LNCS, pp. 503–515, 2020, doi: 10.1007/978-3-030-62008-0_35.

H. A. Saleh, R. A. Sattar, E. M. H. Saeed, and D. S. Abdul-Zahra, “Hybrid features selection method using random forest and meerkat clan algorithm,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 5, pp. 1046–1054, 2022, doi: 10.12928/TELKOMNIKA.v20i5.23515.

M. Nabipour, P. Nayyeri, H. Jabani, S. Shahab, and A. Mosavi, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; A Comparative Analysis,” IEEE Access, vol. 8, pp. 150199–150212, 2020, doi: 10.1109/ACCESS.2020.3015966.

G. Pattnaik and K. Parvathi, “Machine learning-based approaches for tomato pest classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 2, pp. 321–328, 2022, doi: 10.12928/TELKOMNIKA.v20i2.19740.

J. K. Mandal, Emerging Technologies in Data Mining and Information Security Proceedings of IEMIS 2018, Volume 2 by Ajith Abraham, Paramartha Dutta, Jyotsna Kumar Mandal, Abhishek Bhattacharya, Soumi Dutta (z-lib.org).pdf, vol. 2. 2018. doi: 10.1007/978-981-13-1498-8.

P. Venkata and V. Pandya, “Data mining model and Gaussian Naive Bayes based fault diagnostic analysis of modern power system networks,” Mater. Today Proc., vol. 62, no. P13, pp. 7156–7161, Jan. 2022, doi: 10.1016/J.MATPR.2022.03.035.

M. Shohel Rana, C. Gudla, and A. H. Sung, “Evaluating machine learning models for android malware detection - A comparison study,” ACM Int. Conf. Proceeding Ser., no. March 2019, pp. 17–21, 2018, doi: 10.1145/3301326.3301390.

Downloads

Published

23.02.2024

How to Cite

Mahjouby, M. E. ., Manzali, Y. ., Bennani, M. T. ., Lamrini, M. ., & FAR, M. E. . (2024). Machine Learning Technique to Predict the Right Buying and Selling for EUR_USD. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 439–445. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4856

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.