Machine Learning for Euro-to-Dollar Exchange Rate Forecasting: Buy/Sell Decision System
DOI:
https://doi.org/10.70162/mijarcse/2025/v11/i1/v11i106Keywords:
EUR/USD forecasting, machine learning, LSTM, Forex trading, technical indicators, decision support system.Abstract
Accurate forecasting of foreign exchange (Forex) rates, particularly the Euro-to-US Dollar (EUR/USD) pair, remains a complex challenge due to the market’s non-linear behavior, high volatility, and sensitivity to macroeconomic factors. Traditional statistical models often fail to capture these dynamic patterns, limiting their effectiveness in real-world trading environments. This study aims to develop a machine learning-driven forecasting framework integrated with a decision-making system to generate actionable Buy/Sell signals for EUR/USD trading. The proposed system utilizes a hybrid approach, combining traditional models (Linear Regression, Random Forest) and a deep learning-based Long Short-Term Memory (LSTM) network. Historical EUR/USD data spanning from 2010 to 2024 was used, enriched with technical indicators such as RSI, MACD, and Bollinger Bands. A rule-based decision engine translates predicted rate movements into Buy/Sell/Hold signals, and the system’s performance is validated through backtesting and statistical evaluation. The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 0.94% and Root Mean Squared Error (RMSE) of 0.0210, outperforming both traditional models. The decision engine produced a 65.4% trading signal accuracy and a Sharpe Ratio of 1.42, indicating strong risk-adjusted returns. This research contributes a scalable, interpretable, and data-driven Forex forecasting system that bridges predictive analytics and trading execution, offering significant potential for deployment in algorithmic trading and decision support applications.
References
B. King, Global Foreign Exchange Market Report, Bank for International Settlements, 2022.
M. Taylor and D. Peel, “Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals,” Journal of International Money and Finance, vol. 19, no. 1, pp. 33–53, 2000.
R. Meese and K. Rogoff, “Empirical exchange rate models of the seventies: Do they fit out of sample?” Journal of International Economics, vol. 14, no. 1, pp. 3–24, 1983.
A. Neely and P. Weller, “Technical analysis in the foreign exchange market: A layman’s guide,” Federal Reserve Bank of St. Louis Review, vol. 83, pp. 23–38, 2001.
T. Andersen, T. Bollerslev, F. Diebold, and C. Vega, “Real-time price discovery in global stock, bond and foreign exchange markets,” Journal of International Economics, vol. 73, no. 2, pp. 251–277, 2007.
G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986.
C. Sims, “Macroeconomics and reality,” Econometrica, vol. 48, no. 1, pp. 1–48, 1980.
M. Pindyck and D. Rubinfeld, Econometric Models and Economic Forecasts, McGraw-Hill, 1998.
J. Kilian and L. Vega, “Do energy prices respond to U.S. macroeconomic news?” The Energy Journal, vol. 32, no. 3, pp. 1–31, 2011.
J. Hamilton, Time Series Analysis, Princeton University Press, 1994.
G. Kim, H. Kim, and C. Ahn, “Forecasting the volatility of stock price index: A hybrid model integrating LSTM with SVM,” Expert Systems with Applications, vol. 142, p. 112987, 2020.
J. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
D. Nelson, A. Pereira, and R. Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” in Proc. IEEE IJCNN, 2017, pp. 1419–1426.
A. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018.
H. Atsalakis and K. Valavanis, “Surveying stock market forecasting techniques – Part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941, 2009.
G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986.
M. Pindyck and D. Rubinfeld, Econometric Models and Economic Forecasts, McGraw-Hill, 1998.
Y. Huang, Q. Zeng, and B. Wang, “A novel forecasting system for currency exchange rates using machine learning algorithms,” Physica A, vol. 512, pp. 333–344, 2018.
J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and Forex markets using machine learning: A survey,” Procedia Computer Science, vol. 132, pp. 1351–1362, 2018.
D. Nelson, A. Pereira, and R. Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” in Proc. IEEE IJCNN, 2017, pp. 1419–1426.
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018.
X. Zhang, H. Zhang, and Z. Zhang, “Forex forecasting using CNN and hybrid indicators,” Expert Systems with Applications, vol. 145, p. 113103, 2020.
H. Atsalakis and K. Valavanis, “Surveying stock market forecasting techniques – Part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941, 2009.
J. Liu and L. Wang, “Deep reinforcement learning for foreign exchange trading,” in Proc. IEEE ICMLA, 2020, pp. 401–406.
S. Chappidi and R. Anitha, “A survey of machine learning techniques on speech-based emotion recognition and post-traumatic stress disorder detection,” NeuroQuantology, vol. 20, no. 14, pp. 69–79, Oct. 2022. doi: 10.4704/nq.2022.20.14.NQ88010
S. Chappidi and R. Anitha, “Enhanced speech emotion recognition using the cognitive emotion fusion network for PTSD detection with a novel hybrid approach,” J. Electr. Syst., 2024. https://doi.org/10.52783/jes.644
S. Chappidi and R. Anitha, “Advancements in speech-based emotion recognition and PTSD detection through machine and deep learning techniques: A comprehensive survey,” SSRG Int. J. Electron. Commun. Eng., vol. 11, no. 5, 2024. doi: 10.14445/23488549/IJECE-V11I5P121
S. Chappidi and R. Anitha, “Speech-based emotion recognition by using a faster region-based convolutional neural network,” Multimedia Tools Appl., 2024. https://doi.org/10.1007/s11042-024-19004-2
Naresh Kumar Bhagavatham et al., “Autonomic Resilience in Cybersecurity: Designing the Self-Healing Network Protocol for Next-Generation Software-Defined Networking,” International Journal of Computational and Experimental Science and Engineering, vol. 10, no. 4, Nov. 2024, doi: 10.22399/ijcesen.640.
B. D. D. . Nayomi, S. S. . Mallika, S. . T., J. . G., P. . Laxmikanth, and M. . Bhavsingh, “A Cloud-Assisted Framework Utilizing Blockchain, Machine Learning, and Artificial Intelligence to Countermeasure Phishing Attacks in Smart Cities”, Int J Intell Syst Appl Eng, vol. 12, no. 1s, pp. 313–327, Sep. 2023
L. P, V. V, M. M, P. Swetha, A. J, and B. M, “AquaPredict: Deploying Data-Driven Aquatic Models for Optimizing Sustainable Agriculture Practices,” International Journal of Electrical and Electronics Engineering, vol. 11, no. 6, pp. 76–90, Jun. 2024, doi: 10.14445/23488379/ijeee-v11i6p109.
K. V. Ramana, B. Ramesh, R. Changala, T. A. S. Srinivas, K. Praveen Kumar, and M. Bhavsingh, "Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management," Int. Res. J. Multidiscip. Technovation, vol. 6, no. 4, pp. 325–340, 2024. doi: 10.54392/irjmt24324.
P. SumanPrakash et al., "Learning-driven Continuous Diagnostics and Mitigation Program for Secure Edge Management through Zero-Trust Architecture," Computer Communications, pp. 94–107, 2024. doi: 10.1016/j.comcom.2024.04.007.
U. V. Krishna, G. S. Rao, L. Addepalli, M. Bhavsingh, V. S. S. D., and L. M. Jaime, "Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation," Int. Res. J. Multidiscip. Technovation, vol. 6, no. 2, pp. 45–60, 2024. doi: 10.54392/irjmt2425.
V. S. Saranya, G. Subbarao, D. Balakotaiah, and M. Bhavsingh, "Real-Time Traffic Flow Optimization using Adaptive IoT and Data Analytics: A Novel DeepStreamNet Model," Int. J. Adv. Res. Comput. Sci., vol. 15, no. 10, pp. 45–52, 2024.
K. V. Kumar Reddy, C. Madhava Rao, M. Archana, Z. Begum, M.Bhavsingh, and H. Ravikumar, “VisiDriveNet: A Deep Learning Model for Enhanced Autonomous Navigation in Urban Environments,” 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1294–1300, Oct. 2024, doi: 10.1109/i-smac61858.2024.10714627.
K. Dasari, M. A. Ali, S. N.B, K. D. Reddy, M. Bhavsingh, and K. Samunnisa, “A Novel IoT-Driven Model for Real-Time Urban Wildlife Health and Safety Monitoring in Smart Cities,” 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 122–129, Oct. 2024, doi: 10.1109/i-smac61858.2024.10714601.
V. R. . K., H. K. . Yadav G., H. . Basha P., L. V. . Sambasivarao, 5Balarama K. . Rao Y. V., and M. . Bhavsingh, “Secure and Efficient Energy Trading using Homomorphic Encryption on the Green Trade Platform”, Int J Intell Syst Appl Eng, vol. 12, no. 1s, pp. 345–360, Sep. 2023.
P. Kumar, M. K. Gupta, C. R. S. Rao, M. Bhavsingh, and M. Srilakshmi, “A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 3s, pp. 184–192, Mar. 2023, doi: 10.17762/ijritcc.v11i3s.6180.
M. S. Lakshmi, G. Rajavikram, V. Dattatreya, B. S. Jyothi, S. Patil, and M. Bhavsingh, "Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security," Journal of Electrical Systems, vol. 19, no. 4, pp. 279–297, 2023. doi: 10.52783/jes.639.
E. V. N. Jyothi, G. S. Rao, D. S. Mani, C. Anusha, M. Harshini, M. Bhavsingh, and A. Lavanya, "A Graph Neural Network-Based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency," Journal of Electrical Systems, vol. 19, no. 4, pp. 336–349, 2023. doi: 10.52783/jes.642.
K. V. Ramana, A. Muralidhar, B. C. Balusa, M. Bhavsingh, and S. Majeti, “An Approach for Mining Top-k High Utility Item Sets (HUI),” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 2s, pp. 198–203, Jan. 2023, doi: 10.17762/ijritcc.v11i2s.6045.
M. Jahir Pasha, M. Pingili, K. Sreenivasulu, M. Bhavsingh, S. I. Saheb, and A. Saleh, “Bug2 algorithm-based data fusion using mobile element for IoT-enabled wireless sensor networks,” Measurement: Sensors, vol. 24, p. 100548, Dec. 2022, doi: 10.1016/j.measen.2022.100548.
G. Ravikumar, Z. Begum, A. S. Kumar, V. Kiranmai, M. Bhavsingh, and O. K. Kumar, “Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 1s, pp. 247–253, Dec. 2022, doi: 10.17762/ijritcc.v10i1s.5846.
G. Yedukondalu, K. Samunnisa, M. Bhavsingh, I. S. Raghuram, and A. Lavanya, “MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 10, pp. 143–154, Oct. 2022, doi: 10.17762/ijritcc.v10i10.5743.
M. S. Lakshmi, K. S. Ramana, M. J. Pasha, K. Lakshmi, N. Parashuram, and M. Bhavsingh, “Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 2s, pp. 306–312, Dec. 2022, doi: 10.17762/ijritcc.v10i2s.5948.
P. S. Prakash, M. Janardhan, K. Sreenivasulu, S. I. Saheb, S. Neeha, and M. Bhavsingh, “Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs,” Journal of Sensors, vol. 2022, pp. 1–13, Sep. 2022, doi: 10.1155/2022/8373343.
B. Swathi, S. Veerabomma, M. Archana, D. Bhadru, N. L. Somu, and M. Bhavsingh, “Edge-Centric IoT Health Monitoring: Optimizing Real-Time Responsiveness, Data Privacy, and Energy Efficiency,” 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), pp. 354–361, Jan. 2025, doi: 10.1109/icmcsi64620.2025.10883456.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You may share and adapt the work for non-commercial purposes with appropriate attribution. For more details, visit https://creativecommons.org/licenses/by-nc/4.0/.
