Abstract
This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.
This is a preview of subscription content, log in via an institution to check access.
References
Y. Abu-Mostafa, “Learning from hints,” Journal of Complexity, Academic Press, vol. 10, pp. 165–178, 1994.
H. Akaike, “Fitting autoregressive models for prediction,” Ann. Inst. Statist. Math., vol. 21, pp. 243–247, 1969.
E. Fama, “Efficient capital markets: II,” Journal of Finance, vol. 46, no. 5, pp. 1575–1618, 1991.
A. Hoptroff, “The principles and practice of time series forecasting and business modeling using neural nets,” Neural Computing and Applications, vol. 1, no. 1, pp. 59–66, 1993.
W. Hsu, L. Hsu, and M. Tenorio, “Feature subset selection with application to financial prediction tasks,” in Financial Applications and Neural Networks, edited by A. Refenes, Cambridge Press, 1993.
P. Kaufman, The New Commodity Trading Systems and Methods, John Wiley & Sons, 1987.
T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka, “Stock market prediction system with modular neural networks,” Proc. International Joint Conference on Neural Networks, San Diego, CA, 1990.
B. Malkiel, A Random Walk Down Wall Street, W.W. Norton & Co.: New York, 1985.
L. Mendelsohn and J. Stein, “Fundamental analysis meets the neural network,” Futures, September 1991.
D. Montgomery, L. Johnson, and J. Gardiner, Forecasting and Time Series Analysis, McGraw-Hill, Inc., 1990.
A. Refenes (Ed.), Proc. of the First International Workshop on Neural Networks in Capital Markets, London, U.K., November 1993.
A. Refenes and M. Azema-Barac, “Neural network applications in financial asset management,” Neural Computing and Applications, vol. 2, pp. 13–39, 1994.
D. Rumelhart, G. Hinton, and R. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing, MIT Press, vol. 1, pp. 318–362, 1986.
E. Schoenenburg, “Stock price prediction using neural networks: A project report,” Neurocomputing, vol. 2, pp. 17–27, 1990.
E. Turban and R. Trippi, Neural Network Applications in Investment and Financial Services, Probus Publishing, 1992.
H. White, “Economic prediction using neural networks: The case of IBM daily returns,” Proc. IEEE Internat. Conf. on Neural Networks, San Diego, CA, 1988, vol. 2, pp. 451–458.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Abu-Mostafa, Y.S., Atiya, A.F. Introduction to financial forecasting. Appl Intell 6, 205–213 (1996). https://doi.org/10.1007/BF00126626
Issue Date:
DOI: https://doi.org/10.1007/BF00126626