PREDICTION OF STOCK PRICES USING GENETIC ALGORITHM FEATURE SELECTION AND NEURAL NETWORK
Abstract
A trustworthy data base for our stock price forecast may be found in the financial market's abundance of indicators that seek to explain changes in stock price. Due to their distinct industry sectors and geographical locations, individual stocks are cluttered with a variety of factors. As a result, it is vital to find a multi-element aggregate it really is ideal for a given inventory to evaluate its value. For function selection, the Long STM (LSTM) neural community inventory prediction version defined on these studies will appoint a green Genetic Algorithm (GA). First, we use the GA to rank the relevance of the aspects. The excellent aggregate of objects is then derived from this rating the usage of a trial-and-mistakes methodology. Finally, we integrate the excellent elements with the LSTM version to are expecting stocks. The CSI three hundred inventory dataset and in-intensity empirical investigations the usage of the China Construction Bank dataset display that the GA-LSTM version can forecast statistics better than the baseline methods.
Keyword : Genetic Algorithms (GA), Neural network, LSTM, financial market, Stock prediction, Neural Network.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1] E. F. Fama and K. R. French, ``Common risk factors in the returns on stocks and bonds,'' J. Financial Econ., vol. 33, no. 1, pp. 3_56, Feb. 1993. [2] P. Ding, Quantitative Investment: Strategy and Technology. Beijing, China:Electronics Industry Press, 2012, pp. 122_127. [3] N. Bowden and J. E. Payne, ``Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models,'' Energy Econ., vol. 30, no. 6, pp. 3186_3197, Nov. 2008. [4] B. B. Nair, V. P. Mohandas, and N. R. Sakthivel, ``A decision tree-rough set hybrid system for stock market trend prediction,'' Int. J. Comput. Appl., vol. 6, no. 9, pp. 1_6, Sep. 2010. [5] H. Penman, ``Application study of BP neural network and logistic regression for stock investment,'' Expert Syst., vol. 5, no. 12, pp. 21_22, Dec. 1989. [6] F. Li and C. Liu, ``Application study of BP neural network on stock market prediction,'' in Proc. 9th Int. Conf. Hybrid Intell. Syst., Beijing, China, 2009, pp. 2_5. [7] L. J. Cao and F. H. Tay, ``Support vector machine with adaptive parameters in Financial time series forecasting,'' IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 5_10 Nov. 2003. [8] J. Deng and L. Li, ``Application of parameter optimization stochastic forest in stock prediction,'' Software, vol. 41, no. 1, pp. 178_182, Jan. 2020. [9] M. R. Costa-jussà, A. Allauzen, L. Barrault, K. Cho, and H. Schwenk, ``Introduction to the special issue on deep learning approaches for machine translation,'' Comput. Speech Lang., vol. 46, pp. 367_373, Nov. 2017. [10] H. M. Fayek, M. Lech, and L. Cavedon, ``Evaluating deep learning architectures for speech emotion recognition,'' Neural Netw., vol. 5, no. 3, [11] J. Xing, K. Li, W. Hu, C. Yuan, and H. Ling, ``Diagnosing deep learning models for high accuracy age estimation from a single image,'' PatternRecognit., vol. 66, no. 1, pp. 106_116, Jun. 2017. [12] D. Lv, D. Wang, M. Li, and Y. Xiang, ``DNN models based on dimensionality reduction for stock trading,'' Intell. Data Anal., vol. 24, no. 1, pp. 19_45, Feb. 2020. [13] A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosi_dis, ``Forecasting stock prices from the limit order book using convolutional neural networks,'' in Proc. IEEE 19th Conf. Bus.Informat. (CBI), Beijing, China, Jul. 2017, pp. 10_15. [14] S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, andK. P. Soman, ``Stock price prediction using LSTM, RNN and CNN-sliding window model,'' in Proc. Int. Conf. Adv. Comput., Commun. Informat. (ICACCI), Beijing, China, Sep. 2017, pp. 234_239. [15] U. F. Siddiqi, S. M. Sait, and O. Kaynak, ``Genetic algorithm for the mutualinformation-based feature selection in univariate time series data,'' IEEE Access, vol. 8, pp. 9597_9609, 2020.