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Reseach Article

Cloud based Financial Market Prediction through Genetic Algorithms: A Review

by Nitasha Soni, Tapas Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 8
Year of Publication: 2015
Authors: Nitasha Soni, Tapas Kumar
10.5120/ijca2015905413

Nitasha Soni, Tapas Kumar . Cloud based Financial Market Prediction through Genetic Algorithms: A Review. International Journal of Computer Applications. 123, 8 ( August 2015), 18-20. DOI=10.5120/ijca2015905413

@article{ 10.5120/ijca2015905413,
author = { Nitasha Soni, Tapas Kumar },
title = { Cloud based Financial Market Prediction through Genetic Algorithms: A Review },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 8 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number8/21979-2015905413/ },
doi = { 10.5120/ijca2015905413 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:07.854311+05:30
%A Nitasha Soni
%A Tapas Kumar
%T Cloud based Financial Market Prediction through Genetic Algorithms: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 8
%P 18-20
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper surveys recent literature in the area of stock market forecasting using advanced engineering based methods like Neural Network, fractal theory, Data Mining, Hidden Markov Model and Neuro-Fuzzy system. Neural Networks and Neuro-Fuzzy systems are emerging as an effective tool to be used in the forecasting of stock market especially in machine learning techniques. Due to chaotic behavior of the market, traditional techniques are insufficient to cover all the possible relation of the stock price fluctuations. Neural Network and Markov Model is being used exclusively in the forecasting of finance markets but in third world countries. In this paper, we will discuss the relevance of existing methods based on neural network and discussed gaps between these methods. We also propose a forecasting method to provide better an accuracy rather traditional method.

References
  1. Abdulsalam sulaiman olaniyi, adewole, kayoed, Jimoh R.G, “Stock Trend Prediction using Regression Analysis – A Data Mining Approach”, AJSS journal, ISSN 2222-9833, 2010.
  2. Akinwale Adio T, Arogundade O.T and Adekoya Adebayo F, “Translated Nigeria stock market price using artificial neural network for effective prediction”, Journal of theoretical and Applied Information technology, 2009.
  3. Ching-Hsue cheng, Tai-Liang Chen, Liang-Ying Wei, “A hybrid model based on rough set theory and genetic algorithms for stock price forecasting”, pp. 1610 – 1629, 2010.
  4. Dase R.K. and Pawar D.D., “Application of Artificial Neural Network for stock market predictions: A review of literature” International Journal of Machine Intelligence, ISSN: 0975–2927, Volume 2, Issue 2, pp-14-17, 2010.
  5. David Enke and Suraphan Thawornwong, “The use of data mining and neural networks for forecasting stock market returns, 2005.
  6. E. E. Peters, Fractal Market Analysis. Applying Chaos Theory to Investment & Economics, Wiley, NewYork, 1994.
  7. Halbert White, “Economic prediction using neural networks: the case of IBM daily stock returns” Department of Economics University of California, San Diego, 2010.
  8. JingTao YAO and Chew Lim TAN, “Guidelines for Financial Prediction with Artificial neural networks”, 2011.
  9. K. Senthamarai Kannan, P. Sailapathi Sekar, M. Mohamed Sathik and P. Arumugam, “Financial stock market forecast using data mining Techniques", Proceedings of the international multiconference of engineers and computer scientists, 2010.
  10. Krishna Kumar Singh, Dr. Priti Dimri and Madhu Rawat, “Green Data Base for Stock Market: A case study of Indian Stock Market”, IEEE Xplore digital library, pp. 848 – 853, September 2014, ISBN Number: 978-1-4799-4236-7, 25 – 26, DOI: 10.1109/CONFLUENCE.2014.6949306.
  11. Krishna Kumar Singh, Dr. Priti Dimri and J. N. Singh, “Green Data Base Management System for intermediaries of the Indian stock market”, IEEE Xplore digital library, pp. 1 - 5, March 2014, ISBN number: 978-1-4799-3063-0, DOI: 10.1109/CSIBIG.2014.7056996
  12. Kuang Yu Huang, Chuen-Jiuan Jane, “A hybrid model stock market forecasting and portfolio selection based on ARX, grey system and RS theories”, Expert systems with Applications, pp. 5387 - 5392, 2009 .
  13. M. Suresh babu, N.Geethanjali and B. Sathyanarayana, “Forecasting of Indian Stock Market Index Using Data Mining & Artificial Neural Nework”, International journal of advance engineering & application, 2011.
  14. Md. Rafiul Hassan and Baikunth Nath, “Stock Market forecasting using Hidden Markov Model: A New Approach”, Proceeding of the 2005 5th international conference on intelligent Systems Design and Application 0-7695 - 2286 –6/05, IEEE 2005.
  15. Ovunc Polat, Tulay Yıldırım. Genetic optimization of GRNN for pattern recognition without feature extraction, Expert Systems with Applications, vol. 34, pp: 2444 –2448, 2008.
  16. Tiffany Hui-Kuang yu and Kun-Huang Huarng, “A Neural network-based fuzzy time series model to improve forecasting”, Elsevier, pp: 3366-3372, 2010.
  17. Y. L. Hsieh, Don-Lin Yang and Jungpin Wu, “Using Data Mining to study Upstream and Downstream causal relationship in stock Market”.
  18. Yi - Fan Wang, Shihmin Cheng and Mei-Hua Hsu, “Incorporating the Markov chain concepts into fuzzy stochastic prediction of stock indexes”, Applied Soft Computing, pp. 613-617, 2010.
  19. Youngohc Yoon and George Swales, Proceedings of the IEEE International Conference on Neural networks, 156 – 162, 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Data Mining Stock Market Prediction Markov Model Neuro-Fuzzy Systems Forecasting Techniques and Time Series Analysis.