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Prediction of Stock Market Index Using a Hybrid Technique of Artificial Neural Networks and Particle Swarm Optimization

Author

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  • Farnaz Ghashami
  • Kamyar Kamyar
  • S. Ali Riazi

Abstract

In this paper we examine the ability of Artificial Neural Network methods (ANN) for predicting the stock market index. We first conduct an ANN analysis and then optimize the ANN model using Particle Swarm Optimization algorithm (PSO) to improve the prediction accuracy. In terms of data, we use NASDAQ index which is one of the most widely followed indices in the United States. Empirical results show that by determining the optimal set of biases and weights using PSO, we can augment the accuracy of the ANN model for this stock market data set.

Suggested Citation

  • Farnaz Ghashami & Kamyar Kamyar & S. Ali Riazi, 2021. "Prediction of Stock Market Index Using a Hybrid Technique of Artificial Neural Networks and Particle Swarm Optimization," Applied Economics and Finance, Redfame publishing, vol. 8(3), pages 1-8, December.
    • Handle: RePEc:rfa:aefjnl:v:8:y:2021:i:3:p:1-8
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    References listed on IDEAS

    as
    1. Ren-Raw Chen & Jeffrey Huang & William Huang & Robert Yu, 2021. "An Artificial Intelligence Approach to the Valuation of American-Style Derivatives: A Use of Particle Swarm Optimization," JRFM, MDPI, vol. 14(2), pages 1-22, February.
    2. Ru Zhang & Chenyu Huang & Shaozhen Chen, 2018. "Futures Trend Strategy Model Based on Recurrent Neural Network," Applied Economics and Finance, Redfame publishing, vol. 5(4), pages 95-101, July.
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    Cited by:

    1. Sina E. Charandabi & Farnaz Ghashami & Kamyar Kamyar, 2021. "US-China Tariff War: A Gravity Approach," Business and Economic Research, Macrothink Institute, vol. 11(3), pages 69-77, December.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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