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Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends

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Abstract

The stock market prices are dynamic, thus remaining a major challenge in forecasting future stock trends. The Competitive Feedback Particle Swarm Optimization-based Deep Recurrent Neural Network (CFPSO-based Deep RNN) is created to ensure an efficient forecast of the stock market. The forecasting is done concerning the precedent and up to date status of the market. First, the input is submitted to the features extraction phase to extract technical indicators, and then the extracted practical indicators are used to forecast stock market movements. In addition, feature fusion and the data augmentation process effectively enhance the prediction quality. Finally, the Deep RNN classifier is accomplished in the forecast module, where the preparation method of the Deep RNN is performed using a developed optimization algorithm named CFPSO. The developed CFPSO is planned by hybridizing the Competitive Swarm Feedback Algorithm (CSFA) and Particle Swarm Optimization (PSO). The implementation of the proposed work is done in PYTHON. The developed CFPSO-based Deep RNN exhibits superior performance based on MAE, MSE, RMSE, accuracy, sensitivity and specificity with values of 0.136, 0.107, 0.246, 0.963, 0.957 and 0.980, respectively.

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Availability of data and materials

The datasets analyzed during the current study are available in the Stock Market Data repository, https://www.moneycontrol.com/stocks/histstock.php?ex=N&sc_id=AMF&mycomp=Apple%20Mutual%20Fund.

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Authors and Affiliations

  1. Annamalai University, Chidambaram, Tamil Nadu, India

    Nagarjun Yadav Vanguri & S. Pazhanirajan

  2. Anurag University, Telangana, India

    T. Anil Kumar

Authors
  1. Nagarjun Yadav Vanguri
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  2. S. Pazhanirajan
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  3. T. Anil Kumar
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Contributions

NYV conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from SP and TAK. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Nagarjun Yadav Vanguri.

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Vanguri, N.Y., Pazhanirajan, S. & Kumar, T.A. Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends. Int J Intell Robot Appl 7, 385–405 (2023). https://doi.org/10.1007/s41315-022-00250-2

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  • DOI: https://doi.org/10.1007/s41315-022-00250-2

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