Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..

[1]  Wenjie Lu,et al.  A CNN-LSTM-Based Model to Forecast Stock Prices , 2020, Complex..

[2]  D. Hakim,et al.  The Effect of Crude Oil Price Shocks on Indonesia Stock Market Performance , 2020 .

[3]  Masud Rana,et al.  Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks , 2019, CSAI.

[4]  Xin Zhu,et al.  A Machine Learning Based Method for Customer Behavior Prediction , 2019, Tehnicki vjesnik - Technical Gazette.

[5]  Yang Yang,et al.  Stock closing price prediction based on sentiment analysis and LSTM , 2019, Neural Computing and Applications.

[6]  Sheng Chen,et al.  Stock Prediction Using Convolutional Neural Network , 2018, IOP Conference Series: Materials Science and Engineering.

[7]  El-Sayed M. El-Alfy,et al.  Evaluation of bidirectional LSTM for short-and long-term stock market prediction , 2018, 2018 9th International Conference on Information and Communication Systems (ICICS).

[8]  Fathi M. Salem,et al.  Gate-variants of Gated Recurrent Unit (GRU) neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[9]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[10]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[11]  Thomas Oberlechner,et al.  Work stress and performance among financial traders , 2005 .

[12]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[13]  Thomas G. Dietterich.  Machine Learning for Sequential Data: A Review , 2002, SSPR/SPR.

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  Eberhard Schöneburg,et al.  Stock price prediction using neural networks : A project report , 2003 .

[16]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[17]  J. Horák,et al.  Innovations in Management Forecast: Time Development of Stock Prices with Neural Networks , 2020, Marketing and Management of Innovations.

[18]  Sung Wook Baik,et al.  A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting , 2020, IEEE Access.

[19]  Harshal Patel,et al.  Predicting Stock Prices Using LSTM , 2017 .