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Improved market prediction using meta-heuristic algorithms and time series model and testing market efficiency

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Abstract

This study aims to evaluate the efficiency of using technical indicators such as closing price, lowest price, highest price, and exponential moving average in the prediction of stock prices. We use a genetic algorithm (GA) and a hybrid of grey wolf optimization and particle swarm optimization binary algorithm (GWO-PSO) as feature selection methods. In addition, we train our neural network by using some metaheuristic algorithms such as harmony search algorithm (HS), particle swarm optimization algorithm (PSO), modified particle swarm optimization algorithm (MPSO), modified particle swarm optimization algorithm with time-varying acceleration coefficients (MPSO-TVAC), moth flame optimization (MFO), wolf optimization algorithm (WOA) and chimp optimization algorithm (ChOA). The experimental results show that using metaheuristic algorithms to fortify neural networks may increase their ability in finding optimal solutions. We also compare the results of our proposed algorithms with the results of the autoregressive integrated moving average (ARIMA) model. To compare the performance of the proposed algorithms and select the best one, we introduce eight estimation criteria for error assessment. Moreover, market efficiency is another important factor that is checked in this paper to avoid abnormal returns. Briefly speaking, it is the first time that ChOA and MFO algorithms have been used for the prediction of stock prices and to improve ANN. In addition, we use two algorithms (i.e., GA and GWO-PSO) for improving the feature selection process. Finally, experimental results show that WOA has the best performance among applied algorithms.

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Notes

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This research paper is not supported by any organization. Any organization or employment will not gain or loss financially through publication of this manuscript. The authors declare they have no financial interests.

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Authors and Affiliations

  1. Department of Finance, Faculty of Management, Khatam University, Tehran, Iran

    Milad Shahvaroughi Farahani

  2. Sheldon B. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Hamed Farrokhi-Asl

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  1. Milad Shahvaroughi Farahani
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This paper is handled and done by Milad Shahvaroughi Farahani. In this paper, Hamed Farrokhi-asl who is added as an author to the paper helped us in improving the paper and doing revisions.

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Correspondence to Milad Shahvaroughi Farahani.

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The data were obtained through two sites which are called TSETMC and CODAL sites, respectively Which are at the following address: http://tsetmc.ir/, https://www.codal.ir/. On the other hand, there is a financial data software which is called TSECLIENT 2.0 and you can download data easily according to symbol, date, different variables and etc.

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Appendix

Appendix

See Tables 33, 34 and Fig. 23.

Table 33 Input and target correlation
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Table 34 Training strategy
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Fig. 23
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The best validation performance(Khodro)

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Shahvaroughi Farahani, M., Farrokhi-Asl, H. Improved market prediction using meta-heuristic algorithms and time series model and testing market efficiency. Iran J Comput Sci 6, 29–61 (2023). https://doi.org/10.1007/s42044-022-00120-x

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