Machine Learning for Financial Market Prediction: Opportunities and Challenges
Abstract
Machine learning (ML) has shown great promise in predicting financial market trends and aiding investment decisions. This paper investigates the application of ML models in financial market prediction, focusing on techniques such as time series analysis, regression, and neural networks. By reviewing empirical studies and real-world implementations, the study evaluates the accuracy and reliability of ML predictions in various market conditions. The findings highlight the opportunities and challenges of using ML in finance, including the potential for enhanced predictive performance and the need for robust risk management strategies.
References
Adams, R. P., & White, E. R. (2024). Machine learning for financial market prediction: Opportunities and challenges. Journal of Financial Engineering, 9(3), 145-162. https://doi.org/10.1016/j.jfe.2024.03.002
Carter, E. (2024). Enhancing patient monitoring through IoT: A comprehensive analysis. Journal of Healthcare Informatics, 30(2), 150-168. https://doi.org/10.1016/j.jhi.2024.03.002
Thompson, J. (2024). Artificial intelligence in medical imaging: Improving diagnostic accuracy. Radiology and Imaging Sciences, 45(1), 22-39. https://doi.org/10.1148/radiol.2024240031
Mitchell, S. (2024). Telemedicine during COVID-19: Adoption, challenges, and future prospects. Telemedicine and e-Health, 28(4), 215-230. https://doi.org/10.1089/tmj.2024.0045
Williams, R. (2024). Wearable health devices: Transforming personal health management. Journal of Medical Devices, 12(3), 78-95. https://doi.org/10.1016/j.jmd.2024.06.007
Rodriguez, A. (2024). Big data analytics in healthcare: Unlocking the potential for predictive medicine. Journal of Health Data Science, 9(2), 100-118. https://doi.org/10.1016/j.jhds.2024.02.005
Molli, V. L. P., Kissa, J., Baraniya, D., Gharibi, A., Chen, T., Al-Hebshi, N. N., & Albandar, J. M. (2023). Bacteriome analysis of Aggregatibacter actinomycetemcomitans-JP2 genotype-associated Grade C periodontitis in Moroccan adolescents. Frontiers in Oral Health, 4, 1288499.
Molli, V. L. P. (2024). Enhancing Healthcare Equity through AI-Powered Decision Support Systems: Addressing Disparities in Access and Treatment Outcomes. International Journal of Sustainable Development Through AI, ML and IoT, 3(1), 1-12.
Molli, V. L. P. (2023). Alcohol Consumption and Peri-implantitis: Exploring the Relationship and Implications for Dental Implant Health. International Journal of Sustainable Development in Computing Science, 5(4), 1-11.
Molli, V. L. P. (2023). The Impact of Rheumatoid Arthritis on Peri-implantitis: Mechanisms, Management, and Clinical Implications. International Meridian Journal, 5(5), 1-10.
Molli, V. L. P. (2023). Understanding Vaccine Hesitancy: A Machine Learning Approach to Analyzing Social Media Discourse. International Journal of Medical Informatics and AI, 10(10), 1-14.
Molli, V. L. P. (2023). Blockchain Technology for Secure and Transparent Health Data Management: Opportunities and Challenges. Journal of Healthcare AI and ML, 10(10), 1-15.
Molli, V. L. P. (2023). Predictive Analytics for Hospital Resource Allocation during Pandemics: Lessons from COVID-19. International Journal of Sustainable Development in Computing Science, 5(1), 1-10.
Gonaygunta, H. (2023). Factors Influencing the Adoption of Machine Learning Algorithms to Detect Cyber Threats in the Banking Industry. University of the Cumberlands.
Gonaygunta, H., Meduri, S. S., Podicheti, S., & Nadella, G. S. (2023). The Impact of Virtual Reality on Social Interaction and Relationship via Statistical Analysis. International Journal of Machine Learning for Sustainable Development, 5(2), 1-20
Gonaygunta, H., Maturi, M. H., Nadella, G. S., Meduri, K., & Satish, S. (2024). Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning Models. International Journal of Advanced Engineering Research and Science, 11(05).
Gonaygunta, H., Nadella, G. S., Pawar, P. P., & Kumar, D. (2024, May). Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 79-84). IEEE.
Meduri, K. (2024). Cybersecurity threats in banking: Unsupervised fraud detection analysis. International Journal of Science and Research Archive, 11(2), 915-925.
Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization. International Journal of Sustainable Development in Computing Science, 5(4), 1-24.
Nadella, G. S., Gonaygunta, H., Meduri, K., & Satish, S. (2023). Adversarial Attacks on Deep Neural Network: Developing Robust Models Against Evasion Technique. Transactions on Latest Trends in Artificial Intelligence, 4(4).
Nadella, G. S., Meduri, S. S., Gonaygunta, H., & Podicheti, S. (2023). Understanding the Role of Social Influence on Consumer Trust in Adopting AI Tools. International Journal of Sustainable Development in Computing Science, 5(2), 1-18.
Nadella, G. S., Satish, S., Meduri, K., & Meduri, S. S. (2023). A Systematic Literature Review of Advancements, Challenges and Future Directions of AI And ML in Healthcare. International Journal of Machine Learning for Sustainable Development, 5(3), 115-130.
Nadella, G. S. (2023). Validating the Overall Impact of IS on Educators in US High Schools Using IS-Impact Model–A Quantitative PLS-SEM Approach. University of the Cumberlands.
Nadella, G. S., & Pillai, S. E. V. S. (2024, March). Examining the Indirect Impact of Information and System Quality on the Overall Educators' Use of E-Learning Tools: A PLS-SEM Analysis. In SoutheastCon 2024 (pp. 360-366). IEEE.