This work aims to utilize deep learning methods CNN and LSTM to predict
the adjusted close price of eight technical companies. The proposed model is a
CNN-LSTM hybrid model, which combined CNN and LSTM layers in the model. It was
compared with the single LSTM model and double LSTM model to evaluate its
performance. The results showed the CNN-LSTM made a great prediction and can
predict a more accurate value than the other two models, but it still can be
improved to reduce overfitting problems.
References
[1]
Fama, E.F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25, 383-417. https://doi.org/10.2307/2325486
[2]
Bouktif, S., Fiaz, A., Ouni, A. and Serhani, M.A. (2018) Optimal Deep Learning LSTM Model for Electric Load Forecasting Using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies, 11, Article ID: 1636. https://doi.org/10.3390/en11071636
[3]
Edwards, R.D. and Magee, J. (1948) Technical Analysis of Stock Trends. CRC Press, Boca Raton.
[4]
Pal, A. and Prakash, P.K.S. (2017) Practical Time Series Analysis: Master Time Series Data Processing, Visualization, and Modeling Using Python. Packt Publishing Ltd.
[5]
Ariyo, A.A., Adewumi, A.O. and Ayo, C.K. (2014) Stock Price Prediction Using the ARIMA Model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, 26-28 March 2014, 106-112. https://doi.org/10.1109/UKSim.2014.67
[6]
Ma, Y., Han, R. and Wang, W. (2021) Portfolio Optimization with Return Prediction Using Deep Learning and Machine Learning. Expert Systems with Applications, 165, Article ID: 113973. https://doi.org/10.1016/j.eswa.2020.113973
[7]
Chowdhury, R., Mahdy, M.R.C., Alam, T.N., Al Quaderi, G.D. and Rahman, M.A. (2020) Predicting the Stock Price of Frontier Markets Using Machine Learning and Modified Black–Scholes Option Pricing Model. Physica A: Statistical Mechanics and its Applications, 555, Article ID: 124444. https://doi.org/10.1016/j.physa.2020.124444
[8]
Chen, Y. and Hao, Y. (2017) A Feature Weighted Support Vector Machine and K-Nearest Neighbor Algorithm for Stock Market Indices Prediction. Expert Systems with Applications, 80, 340-355. https://doi.org/10.1016/j.eswa.2017.02.044
[9]
Allen, D.E., Powell, R.J. and Singh, A.K. (2012) Machine Learning and Short Positions in Stock Trading Strategies. In Handbook of Short Selling. Academic Press, 467-478. https://doi.org/10.1016/B978-0-12-387724-6.00032-5
[10]
Samitas, A., Kampouris, E. and Kenourgios, D. (2020) Machine Learning as an Early Warning System to Predict Financial Crisis. International Review of Financial Analysis, 71, Article ID: 101507. https://doi.org/10.1016/j.irfa.2020.101507
[11]
Vijh, M., Chandola, D., Tikkiwal, V.A. and Kumar, A. (2020) Stock Closing Price Prediction Using Machine Learning Techniques. Procedia Computer Science, 167, 599-606. https://doi.org/10.1016/j.procs.2020.03.326
[12]
Göçken, M., Özçalıcı, M., Boru, A. and Dosdoğru, A.T. (2016) Integrating Metaheuristics and Artificial Neural Networks for Improved Stock Price Prediction. Expert Systems with Applications, 44, 320-331. https://doi.org/10.1016/j.eswa.2015.09.029
[13]
Pal, A. and Prakash, P.K.S. (2017) Practical Time Series Analysis: Master Time Series Data Processing, Visualization, and Modeling Using Python. Packt Publishing Ltd, Birmingham
[14]
Zeroual, A., Harrou, F., Dairi, A. and Sun, Y. (2020) Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study. Chaos, Solitons & Fractals, 140, Article ID: 110121. https://doi.org/10.1016/j.chaos.2020.110121
[15]
Zhu, X., Wang, H., Xu, L. and Li, H. (2008) Predicting Stock Index Increments by Neural Networks: The Role of Trading Volume under Different Horizons. Expert Systems with Applications, 34, 3043-3054. https://doi.org/10.1016/j.eswa.2007.06.023
[16]
Cerchiello, P., Nicola, G., Ronnqvist, S. and Sarlin, P. (2017) Deep Learning Bank Distress from News and Numerical Financial Data. arXiv: 1706.09627. https://doi.org/10.2139/ssrn.3292485
[17]
Huang, J., Chai, J. and Cho, S. (2020) Deep Learning in Finance and Banking: A Literature Review and Classification. Frontiers of Business Research in China, 14, Article No. 13. https://doi.org/10.1186/s11782-020-00082-6
