Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting

Gourav Kumar, Uday Pratap Singh () and Sanjeev Jain
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Gourav Kumar: Shri Mata Vaishno Devi University
Uday Pratap Singh: Shri Mata Vaishno Devi University
Sanjeev Jain: Indian Institute of Information Technology, Design and Manufacturing

Computational Economics, 2022, vol. 60, issue 3, No 9, 1039 pages

Abstract: Abstract In this paper, a two-stage swarm intelligence based hybrid feed-forward neural network approach is designed for optimal feature selection and joint optimization of trainable parameters of neural networks in order to forecast the close price of Nifty 50, Sensex, S&P 500, DAX and SSE Composite Index for multiple-horizon (1-day ahead, 5-days-ahead and 10-days ahead) forecasting. Although the neural network can deal with complex non-linear and uncertain data but defining its architecture in terms of number of input features in the input layer, the number of neurons in the hidden layer and optimizing the weights is a challenging problem. The back-propagation algorithm is frequently used in the neural network and has a drawback to getting stuck in local minima and overfitting the data. Motivated by this, we introduce a swarm intelligence based hybrid neural network model for automatic search of features and other hlearnable neural networks' parameters. The proposed model is a combination of discrete particle swarm optimization (DPSO), particle swarm optimization (PSO) and Levenberg–Marquardt algorithm (LM) for training the feed-forward neural networks. The DPSO attempts to search automatically the optimum number of features and the optimum number of neurons in the hidden layer of FFNN whereas PSO, simultaneously tune the weights and bias in different layers of FFNN. This paper also compares the forecasting efficiency of proposed model with another hybrid model obtained by integrating binary coded genetic algorithm and real coded genetic algorithm with FFNN. Simulation results indicate that the proposed model is effective for obtaining the optimized feature subset and network structure and also shows superior forecasting accuracy.

Keywords: Discrete particle swarm optimization; Genetic algorithm; Hybrid neural network; Particle swarm optimization; Stock price time series; Swarm intelligence; Technical analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-021-10176-9

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