Stock Price Forecasting Based on ARIMA Model an Example of Cheung Kong Hutchison Industrial Co.
DOI:
https://doi.org/10.54097/hbem.v10i.8134Keywords:
ARIMA model; Hongkong stock; Time series prediction.Abstract
Previous studies in stock prediction mainly researched the ARIMA model, however, scant studies take Hong Kong stock price prediction as a data source for this model. The present study takes Cheung Kong Hutchison Industrial Co. as an example. With conducting descriptive analysis and the ARIMA model, this study goes through four steps: 1) Stationarity Processing: the time series data are pre-processed with differential operations to be stationary, and the series are tested for stationarity by the unit root (ADF) test. 2) Order determination: the order can be observed by drawing autocorrelation and partial autocorrelation plots. The ARIMA model's p and q values can then be calculated through SPSS. 3) Validity tests: A Q-value Table is set to determine whether the residual term conforms to the white noise distribution. Moreover, a Ljung-Box test is performed to determine whether the residual series correlated. 4) Prediction: The stock prices in the next few trading days are predicted and analyzed according to the model. The main finding of this research suggests that the price of the stock will decline at a relatively gradual pace from 48.55 to 48.51 over the next five trading days. Moreover, the ARIMA model predicts preciously this time with an error of no more than 3%. The findings can inspire researchers with the application of the ARIMA model, which can also offer suggestions for traders to obtain returns in the HK stock market.
Downloads
References
Wang Yan. Applied Time Series Analysis [M]. Beijing: Renmin University of China Press, 2015.
Wu Yuxia, Wen Xin. Short-term Stock Price Forecast Based on ARIMA Model[J]. Statistics and Decision Making, 2016(23):83-86.
Alfarra A N K, Hagag A. Forecasting of the American Digital Economy Using ARIMA Model[C]. 2021 International Conference on Electronic Engineering IEEE,2021: 1-5.
Qian Long. Research and application of financial time series long-term and short-term forecasting model based on ARIMA and recurrent neural network[D]. Xihua University, 2022.
Box G E P, Jenkins G M. Time Series Analysis, revised edition[J]. San Francisco, 1976.
Wang Ying. Analysis and Forecast of Stock Price Based on ARMA Model[J]. Productivity Research,2021, No.350(09):124-127.
Wang Yan. Research on stock price prediction based on ARFIMA-GARCH-LSTM hybrid model[D]. Shanghai Normal University, 2022.
Liu Song, Zhang Shuai. An Empirical Study on Stock Price Prediction Using ARIMA Model[J]. Economic Research Guide, 2021(25):76-78.
Xiong Zheng, Che Wengang. Application of ARIMA-GARCH-M Model in Short-term Stock Forecasting[J]. Journal of Shaanxi University of Technology (Natural Science Edition),2022,38(04):69-74.
Lapedes A, Farber R. Nonlinear signal processing using neural networks: Prediction and system modelling[R]. 1987.
Huang Shimin. Analysis and Forecast of Stock Price Based on ARIMA Model——Taking China Merchants Bank as an Example[J]. SME Management and Technology,2022(11):184-187.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
