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Neural Networks for Financial Applications

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Neural Networks for Financial Applications
Neural Networks for Financial Application
J.T Gunasekara Index Number:09002006 Registration Number:2009cs200 Email: jgtharindu@gmail.com Phone:0714771759 Supervisor: H.D Premarathne University of Colombo School of Computing September 6, 2012

Declaration I hear by declare that this literature survey report has been prepared by J.T Gunasekara based on mainly the reference material listed under the bibliography of this report. No major components (sentences/paragraphs etc. ) of other publications are directly into this report without being duly cited. Name of Candidate: J.T Gunasekara Signature of Candidate :............................... Date :..............

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Abstract The survey is on Neural Networks for Financial Application. Artificial Neural Network usage for finance has broadly discussed on three major areas of Financial Applications. Financial fraud Detection, Financial Forecasting and Financial Risk Management are three main sections considered here. Credit card fraud Detection, Tax Fraud Detection, Stock Market Predictions, Cash Forecasting Bankruptcy Predictions, Risk Management of finance and Foreign Exchange Rate forecasting are the financial applications considered on this survey. Other options available in solving applications are discussed and accuracy and efficiency compare with Neural Network approach models. How to further develop designed model are address in this survey and what is best Neural Network architecture and best input parameters selection has considered in this review.

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Acknowledgement I would like to express my deepest gratitude to my thesis supervisor Dr. H.D Premarathne for his valuable guidance, motivation and support throughout this thesis study. My special thanks goes out to all wonderful people who have committed their lives for great research on these areas and always trying to build a better world for others with science, without whom I would be in trouble writing this survey.

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Bibliography: [1] Z. Arisa and D. Mohamadb, “Application of artificial neural networks using hijri lunar transaction as extracted variables to predict stock trend direction,” Labuan e-Journal of Muamalat and Society, vol. 2, pp. 9–16, 2008. [2] A. Vahedi, “The predicting stock price using artificial neural network,” Journal of Basic and Applied Scientific Research, pp. 2325–2328, 2012. [3] S. Soni, “Applications of anns in stock market prediction,” International Journal of Computer Science and Engineering Technology, vol. 2. [4] A. Ayodele and A. Marion, “Stock price prediction using neural network with hybridized market indicators,” Journal of Emerging Trends in Computing and Information Sciences, vol. 3, 2012. [5] Z. Arisa and D. Mohamadb, “Modeling stock market exchange prices using artificial neural network: A study of amman stock exchange,” Jordan Journal of Mechanical and Industrial Engineering, vol. 5, pp. 439–446, 2011. [6] S. Agrawal and M. Jindal, “Artificial neural networks an application to stock market volatility,” International Journal of Engineering Science and Technology, vol. 2, pp. 1451–1460, 2010. [7] M. P. N. andHamidreza Taremian, “Stock market value prediction using neural networks,” 978-1-4244-7818-7/10/@ 2010 IEEE, vol. 2, pp. 9–16, 2010. [8] Y. Kara and M. A. Boyacioglu, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange,” journal homepage: www.elsevier.com/locate/eswa, vol. 38, pp. 5311–5319, 2011. [9] D. R.K. and P. D.D., “Application of artificial neural network for stock market predictions:a review of literature,” International Journal of Machine Intelligence, vol. 2, pp. 14–17, 2010. [10] A. F. Atiya, “Bankruptcy prediction for credit risk using neural networks: A survey and new results,” IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 12, pp. 929–935, 2001. [11] G. Zhang and M. Y. Hu, “Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis,” European Journal of Operational Research, vol. 116, pp. 16–32, 1999. 16 [12] R. Patidar and L. Sharma, “Credit card fraud detection using neural network,” International Journal of Soft Computing and Engineering, vol. 1, pp. 33–38, 2011. [13] P. Kumar and E. Walia, “Cash forecasting: An application of artificial neural networks in finance,” International Journal of Computer Science and Applications, vol. 3, pp. 61–77, 2006. [14] R. C.-F. Wu, “Integrating neurocomputing and auditing expertise,” Managerial Auditing Journal, vol. 9, pp. 20–26, 1994. [15] R. P. Pradhan and R. Kumar, “Forecasting exchange rate in india: An application of artificial neural network model,” Journal of Mathematics Research, vol. 2, pp. 111–117, 2010. [16] W. HUANG, “Forecasting foreign exchange rates with artificial neural networks: A review,” International Journal of Information Technology and Decision Making, vol. 3, pp. 145–165, 2004. [17] J. Kamruzzaman and R. A. Sarker, “Ann-based forecasting of foreign currency exchange rates,” Neural Information Processing - Letters and Reviews, vol. 3, pp. 49–58, 2004. [18] P. Tenti, “Forcasting forieng exchnage rates using recurrent neural networks,” pp. 567–581, 1996. [19] S. Ghosh and D. L. Reilly, “Credit card fraud detection with a neural-network,” Neural Information Processing - Letters and Reviews, pp. 621–630, 1994. 17

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