Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
Feature Engineering; Financial Volatility Prediction; International Stock Market; Long Short-Term Memory (LSTM); Root Mean Squared Error (RMSE); Unmanned AI
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE.
Boggavarapu S.; Ramkumar G.; Gedamkar P.R.; Kaneria A.; Pundir S.; Selvameena R.
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 16-20.
Institute of Electrical and Electronics Engineers Inc.
Boggavarapu S., University of Cumberlands, Prospect Heights, IL, United States; Ramkumar G., Christ University, School of Commerce, Finance and Accountancy, Department of Commerce, Bengaluru, India; Gedamkar P.R., Dr. VishwanathKarad MIT World Peace University, Department of MBA, Maharashtra, Pune, India; Kaneria A., Sri Balaji University, Balaji Institute of Technology & Management, Maharashtra, Pune, India; Pundir S., Graphic Era Deemed to be University, Department of Computer Science Engineering, Uttrakhand, Dehradun, India; Selvameena R., Dr.M.G.R Educational and Research Institute, Department of Computer Science and Engineering, Maduravoyal, Chennai, 600095, India