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Reseach Article

Financial Forecasting using Neural Networks: A Review

Published on November 2011 by Punam Varghade, Prof. Rahila Sheikh
2nd National Conference on Information and Communication Technology
Foundation of Computer Science USA
NCICT - Number 1
November 2011
Authors: Punam Varghade, Prof. Rahila Sheikh
00353913-c3e9-4677-91b1-e90a440f9646

Punam Varghade, Prof. Rahila Sheikh . Financial Forecasting using Neural Networks: A Review. 2nd National Conference on Information and Communication Technology. NCICT, 1 (November 2011), 1-6.

@article{
author = { Punam Varghade, Prof. Rahila Sheikh },
title = { Financial Forecasting using Neural Networks: A Review },
journal = { 2nd National Conference on Information and Communication Technology },
issue_date = { November 2011 },
volume = { NCICT },
number = { 1 },
month = { November },
year = { 2011 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /proceedings/ncict/number1/4196-ncict001/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Information and Communication Technology
%A Punam Varghade
%A Prof. Rahila Sheikh
%T Financial Forecasting using Neural Networks: A Review
%J 2nd National Conference on Information and Communication Technology
%@ 0975-8887
%V NCICT
%N 1
%P 1-6
%D 2011
%I International Journal of Computer Applications
Abstract

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this article. Pre and post data processing/analysis skills, data sampling, training criteria and model recommendation will also be covered in this article.

References
  1. E. B. Baum, D. Hassler, “What Size Net Gives Valid Generalization?”, Neural Computation, 1, (1989), 151-160.
  2. J. M. Benitez, J. L. Castro & I Requena, “Are artificial neural networks black boxes?” IEEE Transactions on Neural Networks, 8, 1997, 1156-1164.
  3. J. L. Callen, C. C.Y. Kwan, P. C.Y. Yip, Y.F. Yuan, “Neural network forecasting of quarterly accounting earnings”, International Journal of Forecasting, (12)4, 1996 pp 475-482.
  4. A. J. Chapman, “Stock Market Trading Systems Through Neural Networks: Developing a Model”, International Journal of Applied Expert Systems, Vol. 2, no. 2, 1994, pp88-100.
  5. P. R. Cohen, “A Survey of the Eighth National Conference on Artificial Intelligence: Pulling Together or Pulling Apart? ”, AI Magazine, Vol. 12, No. 1, 1992, pp17-41.
  6. R. G. Donaldson, M. Kamstra, “An artificial neural network-GARCH model for international stock return volatility”, Journal of Empirical Finance, 4(1), 1997, pp 17-46.
  7. R. Fildes, ``Quantitative forecasting --- the state of the art: Causal models'', Journal of the operational Research Society, vol. 36, 1985, pp691-710
  8. R. Hecht-Nielsen, Neurocomputing, Addison-Wesley, 1990.
  9. R. Heinkel, A. Kraus, ``Measuring Event Impacts in Thinly Traded Stocks'', Journal of Financial and Quantitative Analysis, March 1988.
  10. K. Hornik, M. Stinchcombe, White, “Multilayer feedforward networks are universal approximators”,Neural Networks, 2(5), 1989, 359-366.
  11. T. Kolarik, G. Rudorfer, “Time series forecasting using neural networks,” APL Quote Quad, 25(1), 1994, 86-94.
  12. M.C. Mozer, P. Smolensky , “Using relevance to reduce network size automatically”, Connection Science, 1(1), 1989, pp3-16.
  13. H.-L. Poh, J. T. Yao, T. Jasic, “Neural Networks for the Analysis and Forecasting of Advertising and Promotion Impact”, International Journal of Intelligent Systems in Accounting, Finance and Management, Vol. 7, No. 4, 1998, pp253-268.
  14. L. Prechelt, “A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice”, Neural Networks, 9(3), 1996, pp457-462.
  15. A. N. Refenes, M. Azema-Barac, L. Chen and S. A. Karoussos, “Currency Exchange Rate Prediction and Neural Network Design Strategies”, Neural Computing & Applications, No. 1, 1993, pp46-58.
  16. W. Remus, M. O'connor, “Neural Networks For Time Series Forecasting”, in Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong, editor, Norwell, MA: Kluwer Academic Publishers, 2001.
  17. D. E. Rumelhart, D.E., G.E. Hinton, R.J. Williams, “Learning Internal Representations by Error propagation”, in: Parallel Distributed Processing, Volume 1, D.E. Rumelhart, J.L. McClelland (Eds.), MIT Press, Cambridge, MA, 1986, pp318-362.
  18. R. Setiono, W.K. Leow and J.Y-L. Thong. “Opening the neural network blackbox: An algorithm for extracting rules from function approximating neural networks”, In Proceedings of International Conference on Information Systems 2000, Brisbane, Australia, December 10 - 13, pp176-186
  19. R. S. Sexton, R. E. Dorsey, J. D. Johnson, “Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation”, Decision Support Systems, vol. 22, no. 2, pp. 171--185, 1998.
  20. W. F. Tichy, P. Lukowicz, L. Prechelt, A. Heinz, “Experimental Evaluation in Computer Science: AQuantitative Study”, Journal of Systems and Software, 28(1):9-18. 1995.
  21. W. F. Tichy, “Should Computer Scientists Experiment More? 16 Reasons to Avoid Experimentation”, IEEE Computer, 31(5), 1998, pp32- 44.
  22. J. T. Yao, C. L. Tan and H.-L. Poh, “Neural Networks for Technical Analysis: A Study on KLCI”, International Journal of Theoretical and Applied Finance, Vol. 2, No.2, 1999, pp221-241.
  23. J. T. Yao, C. L. Tan, “Time dependent Directional Profit Model for Financial Time Series Forecasting”,Proceedings of The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, 24- 27 July 2000, Volume V, pp291-296.
  24. J. T. Yao, C. L. Tan, “A case study on using neuralnetworks to perform technical forecasting of forex”, Neurocomputing, Vol. 34, No. 1-4, 2000, pp79-98.
  25. J. T. Yao, C. L. Tan, Y. L. Li, “Option Prices Forecasting Using Neural Networks”, Omega: The International Journal of Management Science, Vol. 28, No. 4 2000, pp455-466.
  26. S. Wang, “The Unpredictability of Standard Back Propagation Neural Networks in Classification Applications,” Management Science 41(3), March 1995, 555-559.
  27. D. H. Wolpert, W.G. Macready, “No Free Lunch Theorems for Search”, Technical Report of The Santa Fe Institute, SFI-TR-95-02-010, 1996.
  28. B. Zhou, “Estimatinzg the Variance Parameter From Noisy High Frequency Financial Data”, MIT Sloan School Working Paper, No. 3739, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Networks Finance Time Series Analysis Forecasting Artificial Intelligence