Financial Engineering and AI: Developing Predictive Models for Market Volatility
DOI:
https://doi.org/10.18034/abr.v14i1.724Keywords:
Financial Engineering, Artificial Intelligence, Predictive Models, Market Volatility, Risk Management, Algorithmic Trading, Machine Learning, Quantitative FinanceAbstract
This paper examines financial engineering's use of AI to anticipate market volatility. To determine their efficacy, machine learning and deep learning are compared to ARCH and GARCH models. The study reviews secondary data and empirical experiments to assess AI-based model performance, strengths, and weaknesses. AI approaches outperform conventional methods in complex and turbulent markets because of their improved forecasting accuracy, adaptability, and capacity to capture non-linear market dynamics. AI models' interpretability, processing costs, and dependence on massive datasets restrict their acceptance. Policy implications underline the need for transparent, accountable, and ethical AI regulation in financial markets. The research also shows hybrid models that mix conventional and AI methods may improve volatility predictions while resolving interpretability issues. Overall, AI in financial modeling improves knowledge of market volatility and management.
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Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145
Baffour, A. A., Feng, J., Fan, L., Buanya, B. A. (2019). Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach. Journal of Time Series Econometrics, 11(2). https://doi.org/10.1515/jtse-2017-0022
Chen, Y-s., Cheng, C-h., Tsai, W-l. (2014). Modeling Fitting-function-based Fuzzy Time Series Patterns for Evolving Stock Index Forecasting. Applied Intelligence, 41(2), 327-347. https://doi.org/10.1007/s10489-014-0520-6
Chung, H., Shin, K-s. (2018). Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction. Sustainability, 10(10), 3765. https://doi.org/10.3390/su10103765
Deming, C., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Natakam, V. M., & Karanam, R. K. (2023). Sustainable Digitization: How U.S. Tech Leaders are Shaping the Global Future. Digitalization & Sustainability Review, 3(1), 35-47. https://upright.pub/index.php/dsr/article/view/153
Deming, C., Pasam, P., Allam, A. R., Mohammed, R., Venkata, S. G. N., & Kothapalli, K. R. V. (2021). Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy. Asia Pacific Journal of Energy and Environment, 8(2), 77-88. https://doi.org/10.18034/apjee.v8i2.762
Dupuis, D., Gauthier, G., Godin, F. (2016). Short-term Hedging for an Electricity Retailer. The Energy Journal, 37(2). https://doi.org/10.5547/01956574.37.2.ddup
Fadziso, T., Mohammed, R., Kothapalli, K. R. V., Mohammed, M. A., Karanam, R. K. (2022). Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions. Silicon Valley Tech Review, 1(1), 14-34.
Hammer, P. L., Kogan, A., Lejeune, M. A. (2011). Reverse-engineering Country Risk Ratings: A Combinatorial Non-recursive Model. Annals of Operations Research, 188(1), 185-213. https://doi.org/10.1007/s10479-009-0529-0
Han, L., Ge, R. (2017). Wavelets Analysis on Structural Model for Default Prediction. Computational Economics, 50(1), 111-140. https://doi.org/10.1007/s10614-016-9584-1
Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
Kothapalli, K. R. V. (2019). Enhancing DevOps with Azure Cloud Continuous Integration and Deployment Solutions. Engineering International, 7(2), 179-192.
Kothapalli, K. R. V. (2022). Exploring the Impact of Digital Transformation on Business Operations and Customer Experience. Global Disclosure of Economics and Business, 11(2), 103-114. https://doi.org/10.18034/gdeb.v11i2.760
Kothapalli, K. R. V. (2023). Java in Robotics: Bridging Software Development and Hardware Control. ABC Journal of Advanced Research, 12(1), 17-30. https://doi.org/10.18034/abcjar.v12i1.761
Kothapalli, K. R. V., Tejani, J. G., Rajani Pydipalli, R. (2021). Artificial Intelligence for Microbial Rubber Modification: Bridging IT and Biotechnology. Journal of Fareast International University, 4(1), 7-16.
Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663
Lotti, L. (2018). Fundamentals of Algorithmic Markets: Liquidity, Contingency, and the Incomputability of Exchange. Philosophy & Technology, 31(1), 43-58. https://doi.org/10.1007/s13347-016-0249-8
Miletic, M., Miletic, S. (2015). Performance of Value at Risk Models in the Midst of the Global Financial Crisis in Selected CEE Emerging Capital Markets. Ekonomska Istrazivanja: Znanstveno-Strucni Casopis, 28(1), 132-166. https://doi.org/10.1080/1331677X.2015.1028243
Mohammed, M. A., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R. (2023). Economic Modeling with Brain-Computer Interface Controlled Data Systems. American Digits: Journal of Computing and Digital Technologies, 1(1), 76-89.
Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93
Nizamuddin, M., Natakam, V. N., Kothapalli, K. R. V., Raghunath Kashyap Karanam, R. K., Addimulam, S. (2020). AI in Marketing Analytics: Revolutionizing the Way Businesses Understand Consumers. NEXG AI Review of America, 1(1), 54-69.
Pasam, P., Kothapalli, K. R. V., Mohammed, R., Ying, D. (2023). Integrating Data Remediation Strategies in Robotic Data Processing. American Digits: Journal of Computing and Digital Technologies, 1(1), 90-104.
Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151
Thompson, C. R., Talla, R. R., Gummadi, J. C. S., Kamisetty, A (2019). Reinforcement Learning Techniques for Autonomous Robotics. Asian Journal of Applied Science and Engineering, 8(1), 85-96. https://ajase.net/article/view/94
Vilela, L. F. S., Leme, R. C., Pinheiro, C. A. M., Carpinteiro, O. A. S. (2019). Forecasting Financial Series Using Clustering Methods and Support Vector Regression. The Artificial Intelligence Review, 52(2), 743-773. https://doi.org/10.1007/s10462-018-9663-x
Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149
Zhang, Q., Wu, K-J., Ming-Lang, T. (2019). Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method. Sustainability, 11(12). https://doi.org/10.3390/su11123240
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Copyright (c) 2024 Prasanna Pasam; Kanaka Rakesh Varma Kothapalli; Rahimoddin Mohammed; Md. Shelim Miah; Srinivas Addimulam
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