Complex-Valued Neural Network Model and Its Application to Stock Prediction

In this paper, a novel complex-valued neural network (CVNN) algorithm is proposed to predict stock index. In a CVNN, input layer, weights, threshold values and output layer are all complex numbers. Cuckoo search (CS) is proposed to optimize the complex parameters. NIFTY stock market indices and Shan...

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Bibliographic Details
Published inProceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) Vol. 552; pp. 21 - 28
Main Authors Wang, Haifeng, Yang, Bin, Lv, Jiaguo
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesAdvances in Intelligent Systems and Computing
Subjects
Artificial intelligence
Complex-valued
Cuckoo search
Neural network
Prediction
Stock index
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Summary:In this paper, a novel complex-valued neural network (CVNN) algorithm is proposed to predict stock index. In a CVNN, input layer, weights, threshold values and output layer are all complex numbers. Cuckoo search (CS) is proposed to optimize the complex parameters. NIFTY stock market indices and Shanghai stock exchange composite index are used to evaluate the performance of CVNN. The results reveal that CVNN performs better than the classical real neural networks.
ISBN:9783319529400
3319529404
ISSN:2194-5357
2194-5365
DOI:10.1007/978-3-319-52941-7_3