Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm

Authors

DOI:

https://doi.org/10.26636/jtit.2016.2.718

Keywords:

Artificial Bee Colony algorithm, Artificial Neural Network, back-propagation algorithm, stock price forecasting, wavelet transform

Abstract

Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.

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References

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Submitted

2023-06-22

Published

2016-06-30

Issue

No. 2 (2016)

Section

ARTICLES FROM THIS ISSUE

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Copyright (c) 2016 Journal of Telecommunications and Information Technology

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
T. T. Khuat, Q. C. Le, B. L. Nguyen, and M. H. Le, “Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm”, JTIT, vol. 64, no. 2, pp. 43–52, Jun. 2016, doi: 10.26636/jtit.2016.2.718.

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