Hybrid AI Models for Real-Time Stock Management and Market Price Prediction

Efficient stock management and accurate market price prediction are essential in dynamic and volatile business environments, where traditional forecasting and inventory control methods often lack adaptability and real-time responsiveness. This paper proposes a hybrid artificial intelligence (AI) framework for real-time stock management and market price prediction, integrating machine learning and deep learning models to capture both linear trends and nonlinear market patterns. The system processes real-time transactional data, historical stock prices, and relevant market indicators to continuously update inventory levels and forecast future price movements. Feature engineering, data normalization, and model fusion techniques are employed to enhance prediction accuracy and robustness. A decision-support module utilizes predicted demand and price trends to optimize inventory replenishment, reduce stockouts, and minimize overstocking costs. Experimental evaluation using real-world market datasets demonstrates that the proposed hybrid model outperforms individual predictive approaches in terms of forecasting accuracy, adaptability, and inventory efficiency, as reflected by improved MAE and RMSE values. The results confirm the effectiveness of the proposed approach as a scalable and intelligent solution for real-time stock management and market price prediction applications. Experimental results show that the proposed RT-HAF model reduces RMSE by approximately 23% compared to LSTM and improves inventory service level by over 10%.

Hybrid AI, Real-Time Stock Management, Market Price Prediction, Machine Learning, Deep Learning, Time-Series Forecasting, Predictive Analytics, Inventory Optimization, Decision Support Systems

1. P. Chachra and D. S. Bawa, "Training LSTM on Historical Market Data for Predicting Stock Prices on Real-Time Basis," 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), Delhi, India, 2025, pp. 1-6, doi: 10.23919/INDIACom66777.2025.11115302. [Google Scholar] [Crossref]

2. V. Alagdeve, P. Karule and P. Palsodkar, "Stock Prediction Using Dual Analysis of Candlestick Chart Pattern," 2024 2nd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), Nagpur, India, 2024, pp. 497-502, doi: 10.1109/ICETEMS64039.2024.10965039. [Google Scholar] [Crossref]

3. S. T. Surulivel, M. Ashfaq Sultan, K. Ajithkumar and S. Selvabaskar, "Sentiment Analysis in Financial Markets: Using NLP to Predict Stock Price from News and Social Media Data," 2025 International Conference on Visual Analytics and Data Visualization (ICVADV), Tirunelveli, India, 2025, pp. 25-29, doi: 10.1109/ICVADV63329.2025.10961060. [Google Scholar] [Crossref]

4. A. Sharma, S. Kumar, C. Shyamsunder, B. Devender, V. S. Nishok and K. S. Sidhu, "Optimizing Stock Market Prediction Through Integration of Market News and Stock Prices with Machine Learning Models," 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Bahrain, Bahrain, 2024, pp. 1-6, doi: 10.1109/ICETAS62372.2024.11120199. [Google Scholar] [Crossref]

5. S. Yazhinian, Y. Hanumanthu, U. A. Srinivas, P. Srikrishna, M. L. Prasanna and K. T. Kumar, "ProStock: Professional Stock Market Navigation and Analysis Suite," 2025 International Conference on Advanced Computing Technologies (ICoACT), Sivalasi, India, 2025, pp. 1-5, doi: 10.1109/ICoACT63339.2025.11004817. [Google Scholar] [Crossref]

6. M. Joseph, D. P. Kumar and J. K. Keerthana, "Stock Market Analysis and Portfolio Management," 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2024, pp. 1-6, doi: 10.1109/ICRITO61523.2024.10522179. [Google Scholar] [Crossref]

7. S. K. Inani, H. Pradhan, G. Nagpal, V. Pagaria and X. -H. Nghiem, "Applications of Deep Learning Techniques in Stock Market Forecasting: A Bibliometric Analysis," 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2023, pp. 01-06, doi: 10.1109/ICCAKM58659.2023.10449584. [Google Scholar] [Crossref]

8. K. Singhal, V. Singh and A. Kaul, "Smart Retail: Utilizing Machine Learning for Demand Prediction, Price Strategy, and Inventory Management," 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN), Indore, India, 2024, pp. 485-489, doi: 10.1109/CICN63059.2024.10847534. [Google Scholar] [Crossref]

9. K. R. Ahmed, M. M. H. Sizan, A. Chouksey, M. A. A. Montaser, M. R. Islam and M. S. Islam, "AI for Financial Market Prediction: Machine Learning Models for Stock Price Forecasting," 2025 International Conference on Networks and Cryptology (NETCRYPT), New Delhi, India, 2025, pp. 739-744, doi: 10.1109/NETCRYPT65877.2025.11102142. [Google Scholar] [Crossref]

10. S. Das, D. K. Mohanta, G. K. Mahato and M. Siddique, "Improved Forecasting Model for Stock Market Prediction Using Machine Learning Techniques," 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi Campus, Centurion University of Technology and Management, Odisha., India, 2024, pp. 1-6, doi:10.1109/SCOPES64467.2024.10991322. [Google Scholar] [Crossref]

11. S. K. Roy, N. Narayan Das, Savita, N. Kumar Bhasin, S. Kadyan and A. R. K, "Empowering Robo-Advisors: Data-Driven Mutual Fund and Stock Market Price Prediction with Deep Learning Techniques," 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichirappalli, India, 2024, pp. 1-6, doi: 10.1109/ICEEICT61591.2024.10718557. [Google Scholar] [Crossref]

12. M. Fairuzzaky, R. Jayadi and E. Miranda, "Utilization of Sentiment Analysis to Predict Stock Price Movement," 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML), Surakarta, Indonesia, 2025, pp. 1-6, doi: 10.1109/SIML65326.2025.11081041. [Google Scholar] [Crossref]

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