IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 1    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 1,January 2012
Comparative study of Financial Time Series Prediction By Artificial Neural Network with Gradient Descent Learning
Full Text(PDF, )  PP.206-212  
Author(s)
Arka Ghosh
KEYWORDS
Financial Forecasting,Financial Timeseries Feedforward Multilayer Artificial Neural Netw ork,Recurrent Timedelay Neural Netw ork,Backpropagation,Gradient descent.
ABSTRACT
Financial forecasting is an example of a signal processing problem which is challenging due to Small sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net ,learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries
References
[1].Artificial Neural Networks By Dr.B.Yegnanarayana.

[2]. Neural Networks – A Comprehensive Foundation By Simon Haykin.

[3].White, H. (1988). Economic prediction using neural networks: the case of IBM daily stock returns. In Proceedings of the second IEEE annual conference on neural networks, II (pp. 451–458).

[4].Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). Stock market prediction system with modular neural networks. In Proceeding of the international joint conference on neural networks (IJCNN) (Vol. 1, pp. 1–6.) San Diego.

[5].Chiang, W.-C., Urban, T. L., & Baldridge, G. W. (1996). A neural network approach to mutual fund net asset value forecasting. Omega International Journal of Management Science, 24(2), 205–215.

[6].Trafalis, T. B. (1999). Artificial neural networks applied to financial forecasting. In C. H. Dagi Dagli, A. L. Buczak, J. Ghosh, M. J. Embrechts, & O. Ersoy (Eds.), Smart engineering systems:neural networks, fuzzy logic, data mining, and evolutionary programming. Proceedings of the artificial neural networks in engineering conference (ANNIE’99) (pp. 1049–1054). New York: ASME Press.

[7].Choi, J. H., Lee, M. K., & Rhee, M. W. (1995). Trading S&P 500 stock index futures using a neural network. In Proceedings of the 3rd annual international conference on artificial intelligence applications on wall street (pp. 63–72). New York.

[8].White, H. (1989). Learning in artificial neural networks: a statistical perspective. Neural Computation, 1, 425–464.

[9].Ripley, B. D. (1993). Statistical aspects of neural networks. In O. E. Brandorff-Nielsen, J. L. Jensen, & W. S. Kendall (Eds.), Networks and chaos-statisticalandprobabilistic aspects (pp. 40–123). London: Chapmanand Hall.

[10].Cheng, B., & Titterington, D. M. (1994). Neural networks: a review from statistical perspective. Statistical Science, 9(1), 2–54.

[11].Zhang, G., Patuwo, B. E., & Hu, M. H. (1998). Forecasting with artificialneural networks: the state of the art. International Journal ofForecasting, 14, 35–62.

[12].Kim, K.-J., & Han, I. (2000). Genetic algorithms approach to featurediscretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, 125–132.

[13].Romahi, Y., & Shen, Q. (2000). Dynamic financial forecasting withautomatically induced fuzzy associations. In Proceedings of the 9th international conference on fuzzy systems (pp. 493–498).

[14].A fusion model of HMM, ANN and GA for stock market forecasting Md. Rafiul Hassan *, Baikunth Nath, Michael Kirley Computer Science and Software Engineering, The University of Melbourne, Carlton 3010, Australia 2006.

[15] MATLAB-by MathWorks MATLAB Version 7.12.0.635 (R2011a) .

Untitled Page