Abstract
This paper presents a method for predicting nonlinear time series. It is based on the multiscale filtering, fundamental and technical model and artificial neural networks. In the technical model we used wavelet transform for disjoin the time series trends then to smooth the economic time series by multiscale filtering. We used too the fundamental analysis, that is, financial and macroeconomics variables to improve the network forecasting. The results were compared with the technical analysis showing that the multiscale filtering and addition of the fundamental variables increase the network forecasting ability.
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References
Dhatt, M.S., Kim, Y.H., Mukherji, S.: Relations between stock returns and fundamental variables: Evidence from a segmented market. Asia-Pacific Financial Markets 6(3), 221–233 (1999)
Donoho, D.L.: De-noising by soft-thresholding. IEEE Transactions on Information Theory 41(3), 613–627 (1995)
Downie, T.R., Silverman, B.W.: The discret multiple wavelet transform and thresholding methods. IEEE Transactions on Information Theory 46(9), 2558–2561 (1998)
Gencay, R., Whitcher, B., Gengay, R., Selguk, F.: An introduction to wavelets and other filtering methods in finance and economics. Academic Press, London (2001)
Jang, G.S.: An intelligent stock portfolio management system based on short-term trend prediction using dual-module neural networks. In: International Conference on Artificial Neural Networks, vol. 1, pp. 447–452 (1991)
Kim, K.-J.: Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures. Intelligent Systems in Accounting, Finance and Management 3(12), 167–176 (2004)
Lam, M.: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems 37(4), 567–581 (2004)
Percival, D.B., Percial, A.T.W.: Wavelet methods for time series analysis. Cambridge University Press, Cambridge (2000)
Qi, M.: Nonlinear predictability of stock returns using financial and economic variables. Journal of Business and Economic Statistics 17(4), 419–429 (1999)
Racine, J.: On the nonlinear predictability of stock returns using financial and economic variables. Journal of Business and Economic Statistics 19(3), 380–382 (2001)
Renaud, O., Starck, J.L., Murtagh, F.: Wavelet-based combined signal filtering and prediction. IEEE Transaction on Systems, Man and Cybernetics 35(6), 1241–1251 (2005)
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing - Vol. 1: Foundations. MIT Press, Cambridge (1986)
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da Silva Soares, A., Veludo de Paiva, M.S., José Coelho, C. (2007). Technical and Fundamental Analysis for the Forecast of Financial Scrip Quotation: An Approach Employing Artificial Neural Networks and Wavelet Transform. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_125
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DOI: https://doi.org/10.1007/978-3-540-72395-0_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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