CONSTRAINED FORMULATIONS AND ALGORITHMS FOR PREDICTING STOCK PRICES BY RECURRENT FIR NEURAL NETWORKS

Benjamin W. Wah () and Ming-Lun Qian
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Benjamin W. Wah: Department of Electrical and Computer Engineering, and the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign, 1308 West Main Street, Urbana, IL 61801, USA
Ming-Lun Qian: Department of Computer Science, University of Illinois, Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA

International Journal of Information Technology & Decision Making (IJITDM), 2006, vol. 05, issue 04, 639-658

Abstract: In this paper, we develop a new constrained artificial-neural-network (ANN) formulation and the associated learning algorithm for predicting stock prices, a difficult time-series prediction problem. We characterize daily stock prices as a noisy non-stationary time series and identify its predictable low-frequency components. Using a recurrent finite-impulse-response ANN, we formulate the learning problem as a constrained optimization problem, develop constraints for incorporating cross validations, and solve the learning problem using algorithms based on the theory of extended saddle points for nonlinear constrained optimization. Finally, we illustrate our prediction results on ten stock-price time series. Our main contributions in this paper are the channel-specific low-pass filtering of noisy time series obtained by wavelet decomposition, the transformation of the low-pass signals to improve their stationarity, and the incorporation of constraints on cross validation that can improve the accuracy of predictions. Our experimental results demonstrate good prediction accuracy and annual returns.

Keywords: Channel-specific low-pass filtering; edge effects; nonlinear constrained optimization; non-stationarity; recurrent FIR neural networks; stock prices; time-series predictions; wavelet decomposition (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1142/S0219622006002209

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