TARU PUBLICATIONS
 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
Powered by:Powered by

The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

ANN based modeling for stock market prediction

* , ,

* Corresponding author · click or hover a name for details

pp. 87–100Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-949XML
Published Online:
31 Dec 2022
Article type:
Research Article
Language:
EN
Article no.:
JSMS-949
Pages:
87–100

Abstract

Analyzing stock market data and using recent developed algorithms for predicting the changes and forecasting the results is a difficult task nowaday. An efficient and faster method for selecting appropriate stock price improves market value as well as helps investors for benefits. In this paper new method implementing state of the art Artificial Neural Networks (ANNs) known as Legendre Neural Network (LENN) and Chebyshev Functional Link based Artificial Neural Network (CHFLANN) has been described and used for the analysis of stock market data. The process has been efficiently implemented in MATLAB language for representing results by evaluating accuracy and F-measure for the processed data. Prior to it the database collected is passed through a series of processes starting from normalization, expansion and then dividing it into train and test datasets to get the processed data used for implementing the learning algorithm for the stock market price evaluation and prediction purpose. In the results second order expansion systems have been used showing accuracy results as well as Mean Square Error (MSE) rates to find best method among proposed schemes. Also, our proposed models have been compared with the statistical model AutoRegressive Integrated Moving Average (ARIMA) and the basic Functional Link based Artificial Neural Network (FLANN) to analyze their performance based on the metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-Squared (R2).

Keywords

Subject Classifications

68 Computer science

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.

Views: 124Downloads: 6Citations: 0