Real-Time Forecasting of Stock Trends using Particle Swarm Optimization
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Abstract
The traditional neural network algorithm for stock price forecasting is prone to local optima. To enhance the accuracy of stock price forecasting and reduce forecasting time, this paper introduces an improved Particle Optimisation Neural Network Algorithm. By integrating neural networks and particle swarm optimisation algorithms, a more effective forecasting model is constructed that better reflects the dynamic changes in stock prices. Meanwhile, introducing chaos-interference and mutation factors can enhance the algorithm's diversity, thereby further improving forecast accuracy and stability. This method presents a novel solution for research and application in stock price forecasting, offering a valuable reference for relevant practitioners.
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Fischer, T., & Krauss, C. 2023. Long short-term memory networks for financial market prediction. Journal of Economic Dynamics and Control, 139, DOI: https://doi.org/10.1016/j.jedc.2022.104559
Li, F., & Xu, Y. 2023. Real-time high-frequency data analysis using the Kalman Filter. Quantitative
Finance, 21(4), 567-580. DOI: https://doi.org/10.1155/2024/4418858
Kumar, R., Singh, S., & Verma, P. 2022. PSO-based trading strategy optimization. Journal of Trading Strategies, 17(2), 117-136.
DOI: https://doi.org/10.1145/3696952.3696996
Zheng J, Zhang Z, Zou J, et al.2022. A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighbourhood evolution[J]. Swarm and Evolutionary Computation, 69: 100987. DOI: https://doi.org/10.1016/j.swevo.2021.100987
Bas, Eren, Erol Egrioglu, and Emine Kolemen. 2022. Training a simple recurrent deep artificial neural network for forecasting using particle swarm. Optimization. Granular Computing 7.2:411-420. DOI: http://doi.org/10.1007/s41066-021-00274-2
Adamu A, Abdullahi M, Junaidu S B, et al. 2021. A hybrid particle swarm optimization with crow search algorithm for feature selection[J]. Machine Learning with Applications, 6: 100108. DOI: https://doi.org/10.1016/j.mlwa.2021.100108
Bootkrajang J, Kabán .2021. A. Label-noise robust logistic regression and its applications[M]. Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg. DOI: http://doi.org/10.1007/978-3-030-86523-8_11
Bento M E C. 2021. A hybrid particle swarm optimization algorithm for the wide-area damping control design[J]. IEEE Transactions on Industrial Informatics, 18(1): 592-599. DOI: http://dx.doi.org/10.1016/j.ijepes.2019.03.001
Lee, S., Kim, H., & Park, J. 2020. Support vector machines for stock market prediction. Expert Systems with Applications, 160, 113-129. DOI: http://dx.doi.org/10.1016/j.eswa.2009.02.038
Sahu, Suraj Kumar, and Sandeep Kumar Gonnade. 2013. “QR Code and Application in India.” International Journal of Recent Technology and Engineering 2 (3): 26–28.
https://www.ijrte.org/wp-content/uploads/papers/v2i3/C0692072313.pdf
Lohrmann C,Luukka P .2019. Classification of intraday S&P500returnswitharandomforest[J] International Journal of Forecasting,35(1):390-407. DOI: https://doi.org/10.1016/j.eswa.2015.07.002
Kwon O, Tseng K, Tjung L.2017. Time-series and neural network forecasts of daily stock prices [J]. Investment Management & Financial Innovations, 9(1):32-54. DOI: https://doi.org/10.1016/j.ijforecast.2018.01.002
Strobelt H, Gehrmann S, Huber B, et al. 2016. Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks[J]. IEEE Transactions on Visualization &Computer Graphics, PP (99):1-1. DOI: http://dx.doi.org/10.1109/TVCG.2017.2744158
Kim M, Sayama H. Predicting stock market movements using network science: an information theoretic approach[J]. Applied Network Science, 2017, 2(1):35. DOI: https://doi.org/10.1109/TVCG.2016.2598498
Fama, E.F. 1970."Efficient capital markets: A review of theory and empirical work", Journal of Finance, 25:383-417.
Rather, A. M., Agarwal, A., & Sastry, V. 2015. A recurrent neural network and a hybrid model for the prediction of stock returns. Expert Systems with Applications, 42(6),3234–3241. DOI: https://doi.org/10.2307/2325486
Rani, R., Gupta, S., & Kumar, V. 2020. A hybrid genetic algorithm and support vector machine for stock market prediction. Expert Systems with Applications, 44(5), 234-245. DOI: https://doi.org/10.1016/j.eswa.2014.09.016