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Grey Wolf optimization-Elman neural network model for stock price prediction

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Abstract

Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1 day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman, CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1 day ahead prediction and outperforms the benchmark models taken for comparison.

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Abbreviations

ACO:

Ant colony optimization

ANN:

Artificial neural network

ARV:

Average relative variance

BPNN:

Back propagation neural network

ELM:

Extreme learning machine

EMA:

Exponential moving average

ENN:

Elman neural network

ERNN:

Elman recurrent neural network

FLANN:

Functional link artificial neural network

FPA:

Flower pollination algorithm

GA:

Genetic algorithm

GWO:

Grey Wolf optimization

LSTM:

Long short-term memory

MA:

Moving average

MACD:

Moving average convergence/divergence

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MFI:

Money flow index

MLP:

Multi-layer perceptron

MSE:

Mean square error

OBV:

On balance volume

PCA:

Principal component analysis

PMO:

Price momentum oscillator

PMRE:

Percentage mean relative error

RBFN:

Radial basis function network

RMSE:

Root mean square error

RNN:

Recurrent neural network

ROC:

Rate of change

RSI:

Relative strength index

SI:

Swarm intelligence

SMAPE:

Symmetric mean absolute percentage error

SVM:

Support vector machine

WNN:

Wavelet neural network

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Authors and Affiliations

  1. School of Business and Management, CHRIST (Deemed to be University), Bangalore, India

    S. Kumar Chandar

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  1. S. Kumar Chandar
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Correspondence to S. Kumar Chandar.

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Communicated by V. Loia.

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Kumar Chandar, S. Grey Wolf optimization-Elman neural network model for stock price prediction. Soft Comput 25, 649–658 (2021). https://doi.org/10.1007/s00500-020-05174-2

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