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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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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DOI: https://doi.org/10.1007/s00500-020-05174-2