Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection

Over the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this p...

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Bibliographic Details
Published in:Neural computing & applications 2019-02, Vol.31 (2), p.577-592
Main Authors: Göçken, Mustafa, Özçalıcı, Mehmet, Boru, Aslı, Dosdoğru, Ayşe Tuğba
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Gaussian process
Generalized linear models
Image Processing and Computer Vision
Investment
Investments
Markets
Model accuracy
Neural networks
Original Article
Probability and Statistics in Computer Science
Recurrent neural networks
Regression analysis
Regression models
Soft computing
Statistical models
Stock exchanges
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Summary:Over the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this point, soft computing models have empowered them to capture the data patterns and characteristics of stock markets. However, one of the open problems in soft computing models is how to systematically determine architecture of models for given applications. In this study, Harmony Search is utilized to optimize the architecture of Neural Network, Jordan Recurrent Neural Network, Extreme Learning Machine, Recurrent Extreme Learning Machine, Generalized Linear Model, Regression Tree, and Gaussian Process Regression for 1-, 2-, 3-, 5-, 7-, and 10-day-ahead stock price prediction. The experimental results show worthy findings of stock market behavior over different prediction terms and stocks. This study also helps researchers understand which prediction model performed the best and how different conditions affect the prediction accuracy of the models. Proposed hybrid models can be successfully used by speculators and investors to make the investment or to hedge against potential risk in stock markets.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3089-2