Stock Market Forecasting Using ant Colony Optimization Based Algorithm

Muhammed Kabir Ahmed; Gregory Maksha Wajiga; Nachamada Vachaku Blamah; Bala Modi

doi:doi:10.11648/j.ajmcm.20190403.11

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Stock Market Forecasting Using ant Colony Optimization Based Algorithm

Muhammed Kabir Ahmed, Gregory Maksha Wajiga, Nachamada Vachaku Blamah, Bala Modi

Published in American Journal of Mathematical and Computer Modelling (Volume 4, Issue 3)

Received: 30 May 2019 Accepted: 10 July 2019 Published: 10 August 2019

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Abstract

Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.

Published in	American Journal of Mathematical and Computer Modelling (Volume 4, Issue 3)
DOI	10.11648/j.ajmcm.20190403.11
Page(s)	52-57
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Stock Market, Technical Analysis, ACO, Forecasting

References

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[3]	Osazevbaru, H. O. (2014). Measuring Nigerian Stock Market Volatility. SINGAPOREAN Journal of Business Economics, and management studies Vol. 2, no 8.
[4]	Ghoshal, S. and Roberts, S (2015). Forecasting Time Series from heterogeneous Data Streams using Adaptive Automatic Relevance Determination Gaussian Process Regression. University of Oxford.
[5]	Milosevic, N. (2016). Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning. Journal of Economics Library Volume 3 (2).
[6]	Tsai, C. F. and Wang, S. P., (2009). Stock Price Forecasting by Hybrid Machine Learning Techniques: Proceedings of the International Multi-Conference of Engineers and Computer Scientists IMECS 2009, Hong Kong. Vol. 1.
[7]	Shen, S., Jiang, H. and Zhang, T. (2016). Stock Market Forecasting Using Machine Learning Algorithms. Citeseer.
[8]	Pang. X, Zhou. Y, Wang. P, Lin, W and Chang, V. Stock Market Prediction based on Deep Long Short Term Memory Neural Network In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2018), pages 102-108.
[9]	Dorigo, M and Stutzle, T. (2004). Ant Colony Optimizatio:. London: A Bradford Book, The MIT Press, Cambridge, Massachusetts.
[10]	Blum, C. (2005). Ant Colony Optimization: Introduction and Recent Trends: Science Direct, Physics of Life Review 353-373.
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[12]	Katiyar, S, Nasiruddin, I and Ansari, A.(2015). Ant Colony Optimization: A Tutorial Review. National Conference on Advances in Power and Control, At Faculty of Engineering and Technology, Manav Rachna International University, Faridabad, Haryana.
[13]	Ramalingam. S and Sujatha. P (2018). An Extensive Work on Stock Price Prediction Using Ant Colony Optimization Algorithm (ACO-SPP). International Journal of Intelligent Engineering and Systems, Vol. 11, No. 6.
[14]	Stock Basics Tutorials (2010). Investopedia, A Division of Value Click, Inc.

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Muhammed Kabir Ahmed, Gregory Maksha Wajiga, Nachamada Vachaku Blamah, Bala Modi. (2019). Stock Market Forecasting Using ant Colony Optimization Based Algorithm. American Journal of Mathematical and Computer Modelling, 4(3), 52-57. https://doi.org/10.11648/j.ajmcm.20190403.11

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ACS Style

Muhammed Kabir Ahmed; Gregory Maksha Wajiga; Nachamada Vachaku Blamah; Bala Modi. Stock Market Forecasting Using ant Colony Optimization Based Algorithm. Am. J. Math. Comput. Model. 2019, 4(3), 52-57. doi: 10.11648/j.ajmcm.20190403.11

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AMA Style

Muhammed Kabir Ahmed, Gregory Maksha Wajiga, Nachamada Vachaku Blamah, Bala Modi. Stock Market Forecasting Using ant Colony Optimization Based Algorithm. Am J Math Comput Model. 2019;4(3):52-57. doi: 10.11648/j.ajmcm.20190403.11

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@article{10.11648/j.ajmcm.20190403.11,
  author = {Muhammed Kabir Ahmed and Gregory Maksha Wajiga and Nachamada Vachaku Blamah and Bala Modi},
  title = {Stock Market Forecasting Using ant Colony Optimization Based Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {4},
  number = {3},
  pages = {52-57},
  doi = {10.11648/j.ajmcm.20190403.11},
  url = {https://doi.org/10.11648/j.ajmcm.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20190403.11},
  abstract = {Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.},
 year = {2019}
}

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TY  - JOUR
T1  - Stock Market Forecasting Using ant Colony Optimization Based Algorithm
AU  - Muhammed Kabir Ahmed
AU  - Gregory Maksha Wajiga
AU  - Nachamada Vachaku Blamah
AU  - Bala Modi
Y1  - 2019/08/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmcm.20190403.11
DO  - 10.11648/j.ajmcm.20190403.11
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 52
EP  - 57
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20190403.11
AB  - Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.
VL  - 4
IS  - 3
ER  -

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Author Information

Muhammed Kabir Ahmed

Department of Mathematics, Gombe State University, Gombe, Nigeria
Gregory Maksha Wajiga

Department of Computer Science, Modibbo Adama University of Technology, Yola, Nigria
Nachamada Vachaku Blamah

Department of Computer Science, University of Jos, Jos, Nigeria
Bala Modi

Department of Mathematics, Gombe State University, Gombe, Nigeria

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APA Style

Muhammed Kabir Ahmed, Gregory Maksha Wajiga, Nachamada Vachaku Blamah, Bala Modi. (2019). Stock Market Forecasting Using ant Colony Optimization Based Algorithm. American Journal of Mathematical and Computer Modelling, 4(3), 52-57. https://doi.org/10.11648/j.ajmcm.20190403.11

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ACS Style

Muhammed Kabir Ahmed; Gregory Maksha Wajiga; Nachamada Vachaku Blamah; Bala Modi. Stock Market Forecasting Using ant Colony Optimization Based Algorithm. Am. J. Math. Comput. Model. 2019, 4(3), 52-57. doi: 10.11648/j.ajmcm.20190403.11

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AMA Style

Muhammed Kabir Ahmed, Gregory Maksha Wajiga, Nachamada Vachaku Blamah, Bala Modi. Stock Market Forecasting Using ant Colony Optimization Based Algorithm. Am J Math Comput Model. 2019;4(3):52-57. doi: 10.11648/j.ajmcm.20190403.11

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@article{10.11648/j.ajmcm.20190403.11,
  author = {Muhammed Kabir Ahmed and Gregory Maksha Wajiga and Nachamada Vachaku Blamah and Bala Modi},
  title = {Stock Market Forecasting Using ant Colony Optimization Based Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {4},
  number = {3},
  pages = {52-57},
  doi = {10.11648/j.ajmcm.20190403.11},
  url = {https://doi.org/10.11648/j.ajmcm.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20190403.11},
  abstract = {Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Stock Market Forecasting Using ant Colony Optimization Based Algorithm
AU  - Muhammed Kabir Ahmed
AU  - Gregory Maksha Wajiga
AU  - Nachamada Vachaku Blamah
AU  - Bala Modi
Y1  - 2019/08/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmcm.20190403.11
DO  - 10.11648/j.ajmcm.20190403.11
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 52
EP  - 57
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20190403.11
AB  - Due to the importance of forecasting the capital market earnings in finance, recently the aspect of stock market prediction has been a major research area that has generated a lot of attention involving various machine learning algorithms. In the recent presentations, it has been indicated that neural networks have some drawbacks in learning the data patterns or that they may perform inconsistently and unpredictable because of the complexity of the stock market data. However, due to the distributive nature of the capital market, a computational intelligence technique called Ant Colony Optimization (ACO) which is suitable for solving distributed control problem was applied in this paper, to get the most optimal solution from three technical analysis strategies. The obtained optimal prediction of the next day closing stock price the ACO algorithm performs better than the other three approaches (Price Momentum Oscillator, Stochastic and Moving Average). Our algorithm (ACO based) was evaluated to have the accuracy of 0.812500, Sensitivity of 0.907407 and Specificity of 0.690476. The ACO based technique have the highest accuracy, Sensitivity and Specificity than the other three (3) technical indicators in predicting the next day closing stock price. Therefore, the optimal prediction of our ACO Agent provides a better forecast than the three initial strategies.
VL  - 4
IS  - 3
ER  -

Copy | Download