Time series forecasting of stock price of AirAsia Berhad using ARIMA model during COVID- 19

Rakesh Kumar Singh, Vijay Kumar Verma, Nitendra Kumar, Priyanka Agarwal, Sadhana Tiwari
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Abstract

Since the beginning of time, scholars have been interested in developing more precise predictive models because of the significance of stock price prediction in banking and economics. In this study, the price of AirAsia’s shares is predicted using the ARIMA model, a time-series analytic tool included in prediction algorithms. The technique of AirAsia’s stock price forecasting utilising the ARIMA model is demonstrated in the current study paper. Stock price forecasting using the ARIMA model has been done using historical stock data (July 2021) and it run them under Monte Carlo Simulation from Covid-19. It demonstrates that the ARIMA model’s results are more accurate for short-term forecasting and may be supported by current strategies for stock price redaction during COVID-19.
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基于ARIMA模型的新冠肺炎期间亚航股价时间序列预测
由于股票价格预测在银行业和经济学中的重要意义,学者们一直对开发更精确的预测模型感兴趣。本研究采用时序分析工具ARIMA模型对亚航股价进行预测。本文运用ARIMA模型对亚航股价进行预测。使用ARIMA模型的股票价格预测使用了历史股票数据(2021年7月),并在Covid-19的蒙特卡罗模拟下运行。这表明,ARIMA模型的结果对于短期预测更为准确,并且可能得到COVID-19期间当前股价下调策略的支持。
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