NDLI: Computational Intelligence Approaches for Stock Price Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Jui-Yu Wu Chi-Jie Lu
Copyright Year	2012
Abstract	Computational intelligence (CI) approaches such as neural networks (NNs) and neuro-fuzzy approaches have been used for stock price forecasting. Robust and efficient stock market models can achieve more accurate predictions and decision making for individual investors or stock fund managers. This work thus surveys individual and hybrid CI methods, including a self-organizing polynomial neural network (SOPNN) based on statistical learning algorithm, cerebellar model articulation controller NN, standard back propagation NN (BPNN) with the steepest descent method (BPNN-GD), BPNN with scaled conjugate gradient (SCG) method, artificial immune algorithm-based BPNN (AIA-BPNN), advanced simulated annealing-based BPNN (ASA-BPNN) and adaptive network based fuzzy inference system (ANFIS) method. The performances of these methods are evaluated by using the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) dataset collected from the Taipei Stock Exchange, and root mean square error (RMSE), mean absolute difference (MAD) and mean absolute percent error (MAPE) are used as the performance indices. Experimental results show that the best SOPNN, CMAC NN, BPNN-SCG, AIA-BPNN, ASA-BPNN and ANFIS obtain identical training and test accuracies. Particularly, hybrid CI approaches such as AIA-BPNN and ASA-BPNN are recommended for stock price forecasting, since these methods have the lowest test RMSE, MAD and MAPE.
Starting Page	52
Ending Page	55
File Size	237754
Page Count	4
File Format	PDF
ISBN	9781467307673
DOI	10.1109/IS3C.2012.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-04
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Time series analysis Neural networks Artificial neural networks Back-propagation neural network Inference algorithms Forecasting Stock markets Computational intelligence Stock price forecasting
Content Type	Text
Resource Type	Article

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