Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions based on Sentiment Analysis

Author(s): K. Kalaiselvi* and Vasantha Kalyani David

Volume 16, Issue 8, 2023

Published on: 18 September, 2023

Article ID: e150823219709 Pages: 11

DOI: 10.2174/2666255816666230815121119

Price: $65

Abstract

Background: A significant problem in economics is stock market prediction. Due to the noise and volatility, however, timely prediction is typically regarded as one of the most difficult challenges. A sentiment-based stock price prediction that takes investors' emotional trends into account to overcome these difficulties is essential.

Objective: This study aims to enhance the ELM's generalization performance and prediction accuracy.

Methods: This article presents a new sentiment analysis based-stock prediction method using a modified extreme learning machine (ELM) with deterministic weight modification (DWM) called S-DELM. First, investor sentiment is used in stock prediction, which can considerably increase the model's predictive power. Hence, a convolutional neural network (CNN) is used to classify the user comments. Second, DWM is applied to optimize the weights and biases of ELM.

Results: The results of the experiments demonstrate that the S-DELM may not only increase prediction accuracy but also shorten prediction time, and investors' emotional tendencies are proven to help them achieve the expected results.

Conclusion: The performance of S-DELM is compared with different variants of ELM and some conventional method.

Keywords: Stock market prediction, Convolutional neural network, extreme learning machine, sentiment analysis, deterministic weight modification, machine learning.

Graphical Abstract
[1]
K. Velusamy, and R. Amalraj, Performance of the cascade correlation neural network for predicting the stock price. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2017, pp. 1-6.
Coimbatore, India [http://dx.doi.org/10.1109/ICECCT.2017.8117824]
[2]
K. Kalaiselvi, K. Velusamy, and C. Gomathi, "Financial prediction using back propagation neural networks with opposition based learning", J. Phys. Conf. Ser., vol. 1142, no. 1, p. 012008, 2018.
[http://dx.doi.org/10.1088/1742-6596/1142/1/012008]
[3]
R. Efendi, N. Arbaiy, and M.M. Deris, "A new procedure in stock market forecasting based on fuzzy random auto-regression time series model", Inf. Sci., vol. 441, pp. 113-132, 2018.
[http://dx.doi.org/10.1016/j.ins.2018.02.016]
[4]
L.Y. Wei, C.H. Cheng, and H.H. Wu, "A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock", Appl. Soft Comput., vol. 19, pp. 86-92, 2014.
[http://dx.doi.org/10.1016/j.asoc.2014.01.022]
[5]
Y.S. Lee, and L.I. Tong, "Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming", Knowl. Base. Syst., vol. 24, no. 1, pp. 66-72, 2011.
[http://dx.doi.org/10.1016/j.knosys.2010.07.006]
[6]
C-F. Chen, W-H. Ho, H-Y. Chou, S-M. Yang, I-T. Chen, and H-Y. Shi, "Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model", Comput Math Methods Med, vol. 2011, 2011.
[http://dx.doi.org/10.1155/2011/395690]
[7]
R. Dash, P.K. Dash, and R. Bisoi, "A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction", Swarm Evol. Comput., vol. 19, pp. 25-42, 2014.
[http://dx.doi.org/10.1016/j.swevo.2014.07.003]
[8]
A.H. Moghaddam, M.H. Moghaddam, and M. Esfandyari, "Stock market index prediction using artificial neural network", J. Econ. Finance Adm. Sci., vol. 21, no. 41, pp. 89-93, 2016.
[http://dx.doi.org/10.1016/j.jefas.2016.07.002]
[9]
W. Huang, Y. Nakamori, and S.Y. Wang, "Forecasting stock market movement direction with support vector machine", Comput. Oper. Res., vol. 32, no. 10, pp. 2513-2522, 2005.
[http://dx.doi.org/10.1016/j.cor.2004.03.016]
[10]
J. Wang, S. Lu, S.H. Wang, and Y.D. Zhang, "A review on extreme learning machine", Multimedia Tools Appl., vol. 81, no. 29, pp. 41611-41660, 2022.
[http://dx.doi.org/10.1007/s11042-021-11007-7]
[11]
P. Vigneshvaran, and A.V. Kathiravan, "Heart disease prediction using an optimized extreme learning machine with bacterial colony optimization", In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022.
Trichy, India [http://dx.doi.org/10.1109/ICOSEC54921.2022.9952051]
[12]
X. Tang, and L. Chen, "Artificial bee colony optimization-based weighted extreme learning machine for imbalanced data learning", Cluster Computing, vol. 22, no. Suppl 3, pp. 6937-6952, 2019.
[http://dx.doi.org/10.1007/s10586-018-1808-9]
[13]
J. Hu, and Han A.A., "Heidari, Y. Shou, H. Ye, L. Wang, X. Huang, H. Chen, Y. Chen, and P. Wu, “Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine”", Comput. Biol. Med., vol. 142, p. 105166, 2022.
[http://dx.doi.org/10.1016/j.compbiomed.2021.105166] [PMID: 35077935]
[14]
H. Chen, Q. Zhang, J. Luo, Y. Xu, and X. Zhang, "An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine", Appl. Soft Comput., vol. 86, p. 105884, 2020.
[http://dx.doi.org/10.1016/j.asoc.2019.105884]
[15]
F. Weng, Y. Chen, Z. Wang, M. Hou, J. Luo, and Z. Tian, "Gold price forecasting research based on an improved online extreme learning machine algorithm", J. Ambient Intell. Humaniz. Comput., vol. 11, no. 10, pp. 4101-4111, 2020.
[http://dx.doi.org/10.1007/s12652-020-01682-z]
[16]
X. Xue, M. Yao, Z. Wu, and J. Yang, "Genetic ensemble of extreme learning machine", Neurocomputing, vol. 129, pp. 175-184, 2014.
[http://dx.doi.org/10.1016/j.neucom.2013.09.042]
[17]
K. Kalaiselvi, and V.K. David, "Enhanced extreme learning machine algorithm with deterministic weight modification for investment decision on indian stocks", In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022, pp. 1409-1414
Trichy, India [http://dx.doi.org/10.1109/ICOSEC54921.2022.9951899]
[18]
Z. Jin, Y. Yang, and Y. Liu, "Stock closing price prediction based on sentiment analysis and LSTM", Neural Comput. Appl., vol. 32, no. 13, pp. 9713-9729, 2020.
[http://dx.doi.org/10.1007/s00521-019-04504-2]
[19]
F. Wang, Y. Zhang, Q. Rao, K. Li, and H. Zhang, "Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction", Soft Comput., vol. 21, no. 12, pp. 3193-3205, 2017.
[http://dx.doi.org/10.1007/s00500-015-2003-z]
[20]
J. Shobana, and M. Murali, "Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis", J. Intell. Fuzzy Syst., vol. 40, no. 6, pp. 10703-10719, 2021.
[http://dx.doi.org/10.3233/JIFS-201644]
[21]
D. Londhe, and A. Kumari, "Multilingual sentiment analysis using the social eagle-based bidirectional long short-term memory", Int. J. Intell, vol. 15, no. 2, pp. 479-493, 2022.
[22]
S. Wu, Y. Liu, Z. Zou, and T.H. Weng, "SI-LSTM: Stock price prediction based on multiple data sources and sentiment analysis", Connect. Sci., vol. 34, no. 1, pp. 44-62, 2022.
[http://dx.doi.org/10.1080/09540091.2021.1940101]
[23]
Y.L. Lin, C.J. Lai, and P.F. Pai, "Using deep learning techniques in forecasting stock markets by hybrid data with multilingual sentiment analysis", Electronics, vol. 11, no. 21, p. 3513, 2022.
[http://dx.doi.org/10.3390/electronics11213513]
[24]
B. Ahuja, and V.P. Vishwakarma, "Deterministic Multi-kernel based extreme learning machine for pattern classification", Expert Syst. Appl., vol. 183, p. 115308, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.115308]
[25]
D. Tripathi, D.R. Edla, V. Kuppili, and A. Bablani, "Evolutionary extreme learning machine with novel activation function for credit scoring", Eng. Appl. Artif. Intell., vol. 96, p. 103980, 2020.
[http://dx.doi.org/10.1016/j.engappai.2020.103980]
[26]
S. Priya, and R. Manavalan, "Optimum parameters selection using bacterial foraging optimization for weighted extreme learning machine", J. Soft Comput, vol. 8, no. 4, 2018.
[27]
X. Li, H. Xie, R. Wang, Y. Cai, J. Cao, F. Wang, H. Min, and X. Deng, "Empirical analysis: Stock market prediction via extreme learning machine", Neural Comput. Appl., vol. 27, no. 1, pp. 67-78, 2016.
[http://dx.doi.org/10.1007/s00521-014-1550-z]
[28]
P. Khuwaja, S.A. Khowaja, I. Khoso, and I.A. Lashari, "Prediction of stock movement using phase space reconstruction and extreme learning machines", J. Exp. Theor. Artif. Intell., vol. 32, no. 1, pp. 59-79, 2020.
[http://dx.doi.org/10.1080/0952813X.2019.1620870]
[29]
S. Das, T.P. Sahu, R.R. Janghel, and B.K. Sahu, "Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm", Neural Comput. Appl., vol. 34, no. 1, pp. 555-591, 2022.
[http://dx.doi.org/10.1007/s00521-021-06403-x] [PMID: 34413575]
[30]
F. Zhang, "Extreme learning machine for stock price prediction", Int. J. Electr. Eng. Educ., 2021.
[http://dx.doi.org/10.1177/0020720920984675]
[31]
G.B. Huang, Q.Y. Zhu, and C.K. Siew, "Extreme learning machine: Theory and applications", Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
[http://dx.doi.org/10.1016/j.neucom.2005.12.126]
[32]
S.C. Ng, C.C. Cheung, and S.H. Leung, "Magnified gradient function with deterministic weight modification in adaptive learning", IEEE Trans. Neural Netw., vol. 15, no. 6, pp. 1411-1423, 2004.
[http://dx.doi.org/10.1109/TNN.2004.836237] [PMID: 15565769]
[33]
C. Wang, and D.J. Hill, Deterministic learning theory for identification, recognition, and control., CRC Press, 2009.
[34]
K. Velusamy, and R. Amalraj, Cascade correlation neural network with deterministic weight modification for predicting stock market price. IOP Conference Series: Materials Science and Engineering, vol. 1110. 2021, p. 012005.
[http://dx.doi.org/10.1088/1757-899X/1110/1/012005]
[35]
K. O’Shea, and R. Nash, "An introduction to convolutional neural networks", arXiv:1511.08458, 2015.
[36]
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, "Recent advances in convolutional neural networks", Pattern Recognit., vol. 77, pp. 354-377, 2018.
[http://dx.doi.org/10.1016/j.patcog.2017.10.013]
[37]
W. Antweiler, and M.Z. Frank, "Is all that talk just noise? The information content of internet stock message boards", J. Finance, vol. 59, no. 3, pp. 1259-1294, 2004.
[http://dx.doi.org/10.1111/j.1540-6261.2004.00662.x]
[38]
"Yahoo finance", https://finance.yahoo.com/

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy