Abstract
Due to the large amounts of risks and potential financial benefits involved, the ability to achieve accurate prediction on stock market prices is of great interest to investors. However, the non-stationarity, high level of volatility, frequent fluctuations and stochastic properties that the data possesses, have made it difficult to accurately predict share prices, even by recently developed deep learning methods. This can be attributed to the outputs trained that are not responsive enough to capture the rapid adjustments in real data, hence affecting prediction accuracy. To solve these difficulties, this paper proposes a wavelet neural network model by using Gaussian wavelet as activation function and decomposing share prices data into finer precision with wavelet to account for the sensitivity, and further optimising the neural network mapping and learning process with detailed time-frequency analysis of outputs, leading to higher prediction accuracy and faster learning speed. The proposed model with two training processes has been validated using the dataset from London stock market, and the results have demonstrated that the wavelet neural network model-based predictions are distinctly superior to that of current deep learning methods, which corresponds to a significant reduction in mean squared error.
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References
Lahmiri, S.: Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks. J. King Saud Univ. Comput. Inf. Sci. 26, 25–37 (2014)
Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Wang, Y.: Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach. Expert Syst. Appl. 36, 564–570 (2009)
Hiransha, M., Gopalakrishnan, E.A., Vijay Krishna, M., Soman, K.P.: NSE stock market prediction using deep-learning models. Procedia Comput. Sci. 132, 1351–1362 (2018)
Moghaddam, A., Moghaddam, M., Esfandyari, M.: Stock market index prediction using artificial neural network. J. Econ. Finan. Adm. Sci. 21, 89–93 (2016)
Kim, H., Won, C.: Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst. Appl. 103, 25–37 (2018)
Li, J., Shi, Z., Li, X.: Genetic programming with wavelet-based indicators for financial forecasting. Trans. Inst. Meas. Control 28, 285–297 (2006)
Huang, S.C., Wu, T.K.: Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting. Expert Syst. 25, 133–149 (2008)
Huang, S.C., Wu, T.K.: Integrating recurrent SOM with wavelet-based kernel partial least square regressions for financial forecasting. Expert Syst. Appl. 37, 5698–5705 (2010)
Nourani, V., Alami, M.T., Aminfar, M.H.: A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 22, 466–472 (2009)
Okkan, U.: Wavelet neural network model for reservoir inflow prediction. Sci. Iranica A 19(6), 1445–1455 (2012)
Nourani, V., Kisi, O., Komasi, M.: Two hybrid Artificial Intelligence approaches for modelling rainfall-runoff process. J. Hydrol. 402, 41–59 (2011)
Li, R., Xu, J., Hu, S.: Real-time traffic flow forecasting based on wavelet neural network. Int. J. Online Eng. 9(3), 72–76 (2013)
Li, G.: An improved wavelet neural network model for evaluation of corporate performance. Inf. Technol. J. 12(22), 6756–6762 (2013)
Khan, M.A.S.K., Azizur Rahman, M.: A novel neuro-wavelet-based self-tuned wavelet controller for IPM motor drives. IEEE Trans. Ind. Appl. 46(3), 1194–1203 (2010)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
Daubechies, I.: The wavelet transform: time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36, 961–1005 (1990)
Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Pennsylvania (1992)
Shah, F.A., Debnath, L.: Wavelet neural network model for yield spread forecasting. Mathematics (Basel) 5(4), 72 (2017)
Ozkurt, N., AcarSavaci, F.: The implementation of nonlinear dynamical systems with wavelet network. Int. J. Electron. Commun. 60, 338–344 (2006)
Nielson, M.: Neural Networks and Deep Learning. Determination Press (2015)
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Luo, Y. (2021). Wavelet Neural Network Model with Time-Frequency Analysis for Accurate Share Prices Prediction. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_21
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DOI: https://doi.org/10.1007/978-3-030-80129-8_21
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