Utilizing stochastic differential equations and random forest for precision forecasting in stock market dynamics
*Nisha VasudevaCorresponding authorvasudeva.nisha1@gmail.comDepartment of Computer Science and Engineering Koneru Lakshmaiah Education FoundationSchool of Computing Science and Engineering Galgotias UniversityGreater Noida, Uttar Pradesh, 522302, IndiaView full profile → , M. Rajyalaxmirajyalaxmi.m@sru.edu.inSchool of Business S R UniversityWarangal, Telangana, 506371, IndiaView full profile → , A.V.V.S. Subbalakshmisubbusravani76@gmail.comDepartment of Commerce School of Social Sciences and Languages VIT UniversityVellore, Tamil Nadu, 632014, IndiaView full profile → , Sudhakar Sengansudhasengan@gmail.comDepartment of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli, Tamil Nadu, 627152, IndiaView full profile → , Ravi Kumar Bommisettiravi9949418650@yahoo.comP G Department of Business Administration and Dean - Research and Development Maris Stella CollegeDepartment of Commerce Akal University Talwandi SaboBathinda, Punjab, 151302, IndiaView full profile → , Pankaj Dadheechpankajdadheech777@gmail.comDepartment of Computer Science and Engineering Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT) Department of Computer Science & Engineering Swami Keshvanand Institute of Technology, Management & GramothanJaipur, Rajasthan, 302017, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 19 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-1822
- Pages:
- 285–298
Abstract
Keywords
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References
[1] Bai, Y., et al., A hybrid stochastic differential reinsurance and investment game with bounded memory. European Journal of Operational Research, 296(2), 717-737 (2022).
[2] Duan, H., et al., A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting. Energy, 251, 123968 (2022).
[3] Wang, B., et al., Intelligent parameter identification and prediction of variable time fractional derivative and application in a symmetric chaotic financial system. Chaos, Solitons & Fractals, 154, 111590 (2022).
[4] E. Kolářová and L. Brančík, Noise Influenced Transmission Line Model via Partial Stochastic Differential Equations, 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, pp. 492-495 (2019).
[5] R. Jia et al., Stochastic Recursive Zero-Sum Differential Game and Mixed Zero-Sum Differential Game Problem with Payoff Functional in BDSDES, IEEE 3rd International Conference of Safe Production and Informatization, Chongqing City, China, pp. 337-342 (2020).
[6] P. H. A. Ngoc, A New Approach to Mean Square Exponential Stability of Stochastic Functional Differential Equations, IEEE Control Systems Letters, vol. 5, no. 5, pp. 1645-1650 (2021).
[7] Aheer, A. K., et al., Application of Feedforward Neural Network in Portfolio Optimization and Geometric Brownian Motion in Stock Price Prediction. 4th International Conference on Electronics and Sustainable Communication Systems, pp. 1494-1503 (2023).
[8] E.T. Mensah et al., Simulating stock prices using the geometric Brownian motion model under normal and convoluted distributional assumptions. Scientific African, 19, e01556 (2023).
[9] W. Lu et al., A CNN-BiLSTM-AM method for stock price prediction, Neural Computing and Applications, vol. 33, pp. 4741-4753 (2021).
[10] Y. Ma, et al., Portfolio optimization with return prediction using deep learning and machine learning, Expert Systems with Applications, vol. 165, pp. 113973 (2021).
[11] S. Mukherjee et al., Stock market prediction using deep learning algorithms, CAAI Transactions on Intelligence Technology, vol. 8, no. 1, pp. 82-94 (2023).
[12] G. Sui and Y. Yu, Bayesian Contextual Bandits for Hyper Parameter Optimization, IEEE Access, vol. 8, pp. 42971-42979 (2020).
[13] S. Huckemann, T. Hotz and A. Munk, Intrinsic MANOVA for Riemannian Manifolds with an Application to Kendall’s Space of Planar Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 593-603 (2010).
[14] F. B. Mahmud et al., A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection, 7th International Conference on Data Science and Machine Learning Applications, Riyadh, Saudi Arabia, pp. 97-102 (2022).




