Abstract

Predicting the return of a financial product is a very risky task. It involves subjectivity and experts knowledge. In the development of an expert system, domain knowledge is one of the important component. For a software to be artificial intelligent, some heuristics are required, which can help in decision making. It is admitted by the technical experts of financial sectors that in predicting the support or resistance backtracking is required when prediction of support or resistance fails. In this paper ,an attempt has been made to restrict the back tracking of support and resistance to a maximum of two attempts. Proposed model can be further used in machine learning to remove the subjectivity.

References

• P. C Biswal ,2009,Discrete mathematics and graph theory, 2nd edition, EEE, PHI learning private limited.
• Darren George, Paul Mallery,2009,SPSS for windows step by step a simple guide and reference 15. 0 update, 8th edition, Pearson.
• I. T. Jollif, Principal Component Analysis, 2nd edition Springer, Springer series in statistics.
• K. Senthamarai Kannan, P. Sailapathi Sekar, M. Mohamed Sathik and P. Arumugam, march 17-19,2010, Financial Stock Market Forecast using Data Mining Techniques, proceedings of the international multi conference of Engineers and computer scientists ,volume 1, Hongkong.
• N. K Liu ,K. K Lee,1997,An intelligent business advisor system for stock investment, expert systems Vol. 14 number 3,page 129-139.
• Werner F. M,De Bondt, Richard Thaler, 1985,Does the stock market over react ?,The Journal of finance, Volume XL. Number 3, pp 793-805.
• Md. Rafiul Hassan and Baikunth Nath, 2005,StockMarket Forecasting Using Hidden Markov Model:A New Approach, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
• N. G Mankiw,David Romer, M. D. Shapiro,1985,An unbiased reexamination of stock market volatility,The journal of finance, Volume. XL, Number 3.
• T. G Anderson, T Bollerslev, F. X. Diebold, 2007,Roughing it up: including jump components in the measurement, Modeling and forecasting of return volatility, The review of economics and statistics, pp 701-720.
• National stock exchange of India, 2013,NCFM,Technical analysis module.
• Dongsong Zhang and Lina Zhou,2004, Discovering Golden Nuggets: Data Mining in Financial application, IEEE Transactions on systems ,man and cybernetics part C:Application and reviews ,vol 34,no 4.
• A. N. Refenes, A. D. Zapranis, and Y. Bentz, 1993,"Modeling stock returns with neural networks," presented at the Workshop on Neural Network Applications and Tools, London, U. K.
• J. Roman and A. Jameel,19966,Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns, presented at the 29th HICSS, Wailea, HI.
• S. Walczak, 1999,Gaining competitive advantage for trading in emerging capital markets with neural networks, J. Manag. Inform. Syst. , vol. 16, pp. 177–192.