A Gaussian Process Regression Model for Forecasting Stock Exchange of Thailand
DOI:
https://doi.org/10.48048/tis.2022.3045Keywords:
Stock price index, Gaussian process regression, Artificial neural network, Recurrent neural networkAbstract
A stock price index measures the change in several share prices, which can describe the market and assist investors in deciding on a specific investment. Thus, foreseeing the stock price index benefits investors in creating a better investment strategy. However, forecasting the stock price index can be challenging due to its non-linearity, non-stationary and high uncertainty. Gaussian process regression (GPR) is an attractive and powerful approach for prediction, especially when the data fluctuates over time with fewer restrictions. Besides, the GPR gains advantages over other forecasting techniques as it can offer predictions with uncertainty to provide margin errors. In this study, we evaluate the use of GPR to predict the stock price of Thailand (SET). The SET data are divided into 2 datasets; the data in the year 2015 - 2020 and the data in the year 2020 due to the massive change during the COVID-19 pandemic. The prediction results from the GPR are then compared to the machine learning approaches, artificial neural network (ANN) and recurrent neural network (RNN) using evaluation scores; the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency (NSE). The results indicate that the GPR is superior to the ANN and RNN for both datasets as it provides a high prediction accuracy. Moreover, the results suggest that the GPR is less sensitive to the number of input lags in the model. Therefore, the GPR is more favorable for the prediction of SET than the ANN and RNN.
HIGHLIGHTS
- The Gaussian process regression (GPR) was applied to predict the stock price index of Thailand (SET)
- The predictive performance of the GPR was compared to artificial neural networks (ANNs) and recurrent neural networks (RNNs)
- The results indicate that GPR outperformed the other methods as it provided a high prediction accuracy along with prediction intervals
GRAPHICAL ABSTRACT
Downloads
References
Y Kara, MA Boyacioglu and OK Baykan. Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Syst. Appl. 2011; 38, 5311-9.
J Patel, S Shah, P Thakkar P and K Kotecha. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst. Appl. 2015; 42, 259-68.
W Li and J Liao. A comparative study on trend forecasting approach for stock price time series. In: Proceedings of the 11th IEEE International Conference on Anti-counterfeiting, Security, and Identification, Xiamen, China. 2017, p. 74-8.
W Long, Z Lu and L Cui. Deep learning-based feature engineering for stock price movement prediction. Knowl. Based Syst. 2019; 164, 163-73.
J Jang, S Shin, H Lee and IC Moon. Forecasting the concentration of particulate matter in the Seoul Metropolitan Area using a Gaussian process model. Sensors 2020; 20, 3845.
P Ghasemi, M Karbasi, AZ Nouri, MS Tabrizi and HM Azamathulla. Application of Gaussian process regression to forecast multi-step ahead SPEI drought index. Alex. Eng. J. 2021; 60, 5375-92.
Q Fu, W Shen, X Wei, P Zheng, H Xin and C Zhao. Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression. Inf. Process. Agric. 2019; 6, 396-406.
Q Wu, R Law and X Xu. A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong. Expert Syst. Appl. 2012; 39, 4769-74.
N Dkhili, S Thil, J Eynard and S Grieu. Comparative study between Gaussian process regression and long short-term memory neural networks for intraday grid load forecasting. In: Proceedings of the IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe, Madrid, Spain. 2020, p. 232-7.
AA Ariyo, AO Adewumi and CK Ayo. Stock price prediction using the ARIMA model. In: Proceedings of the UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge. 2014, p. 106-12.
S Selvin, R Vinayakumar, EA Gopalakrishnan, VK Menon and KP Soman. Stock price prediction using LSTM, RNN and CNN-sliding window model. In: Proceedings of the International conference on Advances in Computing, Communications and Informatics, Karnataka State, India. 2017, p. 1643-7.
I Jahan and S Sajal. Stock price prediction using Recurrent Neural Network (RNN) algorithm on time-series data. In: Proceedings of the Midwest Instruction and Computing Symposium, Minnesota. 2018, p. 1-6.
B Liu, I Kiskin and S Roberts. An overview of Gaussian process regression for volatility forecasting. In: Proceedings of the International Conference on Artificial Intelligence in Information and Communication, Fukuoka, Japan. 2020, p. 681-6.
CE Rasmussen and CK Williams. Gaussian processes for machine learning. The Massachusetts Institute of Technology press, Cambridge, Massachusetts, 2006.
MK Titsias, N Lawrence and M Rattray. Markov chain Monte Carlo algorithms for Gaussian processes. In: Proceedings of the Inference and Estimation in Probabilistic Time-Series Models, Cambridge. 2008, p. 98-106.
M Heinonen, H Mannerstrom, J Rousu, S Kaski and H Lahdesmaki. Non-stationary Gaussian process regression with Hamiltonian Monte Carlo. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, Cadiz, Spain. 2016, p. 732-40.
C Huang, Z Zhang and A Bensoussan. Forecasting of daily global solar radiation using wavelet transform-coupled Gaussian process regression: Case study in Spain. In: Proceedings of the IEEE Innovative Smart Grid Technologies - Asia, Melbourne, Australia. 2016, p. 799-804.
S Zhu, X Luo, X Yuan and Z Xu. An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stoch. Environ. Res. Risk Assess. 2020; 34, 1313-29.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
