ANN based modeling for stock market prediction
*Niranjan PandaCorresponding authorniranjanpanda@soa.ac.inDepartment of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be Univesity) Bhubaneswar Odisha IndiaView full profile → , Twinkle Singhtwinklesingh0101@gmail.comDepartment of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be Univesity) Bhubaneswar Odisha IndiaView full profile → , Shrabanee Swagatikashrabaneeswagatika@soa.ac.inDepartment of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be Univesity) Bhubaneswar Odisha IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 31 Dec 2022
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-949
- Pages:
- 87–100
Abstract
Keywords
Subject Classifications
References
[1] Wang, X. (2021). Rigorous Modeling of Solubility of Acid in Supercritical Carbon Dioxide Using Connectionist approach: Comparison between ANN and density based modeling. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 1-14.
[2] Tambe, S., Pawar, A., & Yadav, S. K. (2021). Deep fake videos identification using ANN and LSTM. Journal of Discrete Mathematical Sciences and Cryptography, 24(8), 2353-2364.
[3] O’Connor, Niall and Michael G. Madden. “A neural network approach to predicting stock exchange movements using external factors.” International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer, London, 2005.
[4] Guresen, Erkam, Gulgun Kayakutlu and Tugrul U. Daim. “Using artificial neural network models in stock market index prediction.” Expert Systems with Applications 38.8 (2011): 10389-10397.
[5] Kara, Yakup, Melek Acar Boyacioglu and Ömer Kaan Baykan. “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange.” Expert Systems with Applications 38.5 (2011): 5311-5319.
[6] Niaki, Seyed Taghi Akhavan and Saeid Hoseinzade. “Forecasting S&P 500 index using artificial neural networks and design of experiments.” Journal of Industrial Engineering International 9.1 (2013): 1-9.
[7] Adebiyi, Ayodele Ariyo, Aderemi Oluyinka Adewumi and Charles Korede Ayo. “Comparison of ARIMA and artificial neural networks models for stock price prediction.” Journal of Applied Mathematics 2014 (2014).
[8] Patel, Jigar, et. al. “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques.” Expert Systems with Applications 42.1 (2015): 259-268.
[9] Sheta, Alaa F., Sara Elsir M. Ahmed and Hossam Faris. “A comparison between regression, artificial neural networks and support vector machines for predicting stock market index.” Soft Computing 7.8 (2015): 2.
[10] Dash, Rajashree and Pradipta Kishore Dash. “A hybrid stock trading framework integrating technical analysis with machine learning techniques.” The Journal of Finance and Data Science 2.1 (2016): 42-57.
[11] Chiang, Wen-Chyuan, et. al. “An adaptive stock index trading decision support system.” Expert Systems with Applications 59 (2016): 195-207.
[12] Zhong, Xiao and David Enke. “Forecasting daily stock market return using dimensionality reduction.” Expert Systems with Applications 67 (2017): 126-139.
[13] Weng, Bin, et. al. “Predicting short-term stock prices using ensemble methods and online data sources.” Expert Systems with Applications 112 (2018): 258-273.
[14] Hu, Hongping, et. al. “Predicting the direction of stock markets using optimized neural networks with Google Trends.” Neurocomputing 285 (2018): 188-195.
[15] Naik, Nagaraj and Biju R. Mohan. “Optimal feature selection of technical indicator and stock prediction using machine learning technique.” International Conference on Emerging Technologies in Computer Engineering. Springer, Singapore, 2019.
[16] Naik, Nagaraj and Biju R. Mohan. “Stock price movements classification using machine and deep learning techniques-the case study of indian stock market.” International Conference on Engineering Applications of Neural Networks. Springer, Cham, 2019.
[17] Zhou, Feng, et. al. “EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction.” Expert Systems with Applications 115 (2019): 136-151.
[18] Chopra, S., D. Yadav and A. N. Chopra. “Artificial neural networks based indian stock market price prediction: before and after demonetization.” J Swarm Intel Evol Comput 8.174 (2019): 2.
[19] Zhang, Y., & Wu, L., Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36(5), 8849-8854, (2009)..
[20] Nikfarjam, A., Emadzadeh, E., & Muthaiyah, S., Text mining approaches for stock market prediction. In 2010 The 2nd international conference on computer and automation engineering (ICCAE) (Vol. 4, pp. 256-260). IEEE, (2010, February).
[21] Yang, H., Chan, L., & King, I., Support vector machine regression for volatile stock market prediction. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 391-396). Springer, Berlin, Heidelberg.. (2002, August).
[22] Jeswal, S. K., & Chakraverty, S. (2020). Connectionist based models for solving Diophantine equation. Journal of Interdisciplinary Mathematics, 23(4), 825-841.



