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Tracking the Trends of Financial Applications Using Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 48 - Number 16 |
| Year of Publication: 2012 |
| Authors: B. Manjula, R. Lakshman Naik, S.s.v.n. Sarma |
10.5120/7434-0440
|
B. Manjula, R. Lakshman Naik, S.s.v.n. Sarma . Tracking the Trends of Financial Applications Using Genetic Algorithm. International Journal of Computer Applications. 48, 16 ( June 2012), 36-40. DOI=10.5120/7434-0440
Abstract
The generation of profitable trading rules for stock market investments is a difficult task but admired problem. First stage is classifying the prone direction of the price for India cements stock price index (ICSPI) futures with several technical indicators using artificial intelligence techniques. And second stage is mining the trading rules to determined conflict among the outputs of the first stage using the evolve learning. We have found trading rule which would have yield the highest return over a certain time period using historical data. These groundwork results suggest that genetic algorithms are promising model yields highest profit than other comparable models and buy-and-sell strategy. Experimental results of buying and selling of trading rules were outstanding.
References
- Arun K Punjari "Data mining techniques" universities press (India) Pvt. Ltd. 2001
- Ahmadi, H. , "Testability of the arbitrage pricing theory by neural networks," Proceedings of the IEEE International Conference on Neural Networks, 1990, pp. 1385-1393.
- Davis, L. , Handbook of genetic algorithms, Van Nostrand Reinhold, NY, 1991.
- Hiemstra, Y. , "Modeling structured nonlinear knowledge to predict stock market returns", In Trippi, R. R. (Eds. ), Chaos & Nonlinear Dynamics in the financial Markets: Theory, Evidence and Applications, Irwin, 1995, pp. 163-175.
- Kohara, K. , Ishikawa, T. , Fukuhara, Y. and Nakamura, Y. , "Stock price prediction using prior knowledge and neural networks", International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 6, 1997, pp. 11-22.
- Kamijo, K. and Tanigawa, T. , "Stock price pattern recognition: A recurrent neural network approach'" Proceedings of the IEEE International Joint Conference on Neural Networks, 1990, pp. 1215-1221.
- Kimoto, T. , Asakawa, K. , Yoda, M. and Takeoka, M. , "Stock market prediction system with modular neural networks," Proceedings of the IEEE International Joint Conference on Neural Networks, 1990, pp. 11-16.
- Tsaih, R. , Hsu, Y. and Lai, C. C. , " Forecasting S&P 500 Stock index futures with a hybrid AI system", Decision support Systems, 1998, pp. 161-174.
- Waksman, G. , Sandler, M. , Ward, M. and Firer, C. , "Market timing on the Johannesburg Stock Exchange using derivative instruments", Omega, International Journal of Management Science, Vol. 25, No. 1, 1997, pp. 81-91.
- Trippi, R. R. and DeSieno D. , "Trading equity index futures with a neural network," The Journal of Portfolio Management, 1992.
- Duke, L. S. and Long, J. , "Neural network futures trading - A feasibility study," Adaptive Intelligent Systems, Elsevier Science Publishers, 1993.
- David de la Fuente, Alejandro Garrido, Jaime Laviada, Alberto Gomez, "Genetic Algorithms to Optimize the Time to Make Stock Market Investment". GECCO 2006, Vol. 2, pp. 1857- 1858, 2006
- Cyril Schoreels, Brian Logan, Jonathan M. Garibaldi, "Agent based Genetic Algorithm Employing Financial Technical Analysis for Making Trading Decisions Using Historical Equity Market Data", IAT04, 2004.
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