Sains Malaysiana 54(7)(2025): 1859-1873

http://doi.org/10.17576/jsm-2025-5407-18

 

Comparing Various Methods of Forecasting Stock Index Prices in a Shock-Affected Market: Based on Data Covering the COVID-19 Pandemic

(Membandingkan Pelbagai Kaedah Ramalan Harga Indeks Saham dalam Pasaran Terjejas Kejutan: Berdasarkan Data Meliputi Pandemik COVID-19)

 

REVATHI GANESAN¹ & R. NUR-FIRYAL^1,2,*

 

¹Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

²Center for Modelling and Data Analysis (DELTA), Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

 

Diserahkan: 12 November 2024/Diterima: 28 April 2025

 

Abstract

The recent years have been economically challenging, with financial markets worldwide facing turmoil. From late 2020 to mid-2022, global economies were heavily impacted by the COVID-19 pandemic due to the implementation of lockdowns and austere quarantine measures that crippled world trade. As the pandemic abated with increased vaccination rates, global economic growth was expected to return to normalcy. However, commodities (especially oil), exchange rates, and stocks remain greatly devalued, continuing to restrict financial progress. The primary goal is to identify the most effective models for predicting the impact of international trade during COVID-19 pandemic. Accurate stock index forecasting is crucial in such uncertain economic conditions. Using Malaysia, Indonesia, and Singapore as the research targets, this study compares time series linear regression (TSLR), Bayesian regression, and support vector regression (SVR) in predicting major stock indices during pre- and post-vaccination periods. The models are evaluated based on root mean square error (RMSE), mean absolute error (MAE), and adjusted R² to determine their effectiveness. Results show that Bayesian regression outperforms other models in the pre-vaccination period due to its ability to incorporate prior information, whereas SVR performs better in the post-vaccination period, capturing complex market dynamics more effectively. These findings suggest that Bayesian regression is particularly useful during high-uncertainty periods, while SVR is better suited for stable market conditions. This method should be utilized extensively in future research with other machine learning methods to enhance forecasting accuracy, while additional macroeconomic variables such as inflation, interest rates, and geopolitical factors should also be considered. Furthermore, the findings of this study, shows that by incorporating Bayesian regression and machine learning can provide valuable insights for policymakers, investors, and financial analysts in navigating financial risks during economic crises.

Keywords: Bayesian regression; COVID-19; SVR; TSLR; vaccination

 

Abstrak

Ekonomi global pada beberapa tahun kebelakangan telah menghadapi cabaran ekonomi yang kritikal. Ekonomi global menghadapi kesan yang teruk akibat pandemik COVID-19 disebabkan oleh pelaksanaan sekatan dan kuarantin yang ketat yang merencatkan perdagangan dunia dari penghujung Disember 2020 hingga pertengahan 2022. Pertumbuhan ekonomi global dijangka kembali pulih dengan peningkatan kadar vaksinasi semasa pandemik. Walau bagaimanapun, nilai komoditi (terutamanya minyak), kadar pertukaran mata wang dan saham kekal merosot, yang terus mengehadkan kemajuan ekonomi. Model regresi telah menunjukkan prestasi ramalan yang baik dalam meramalkan harga indeks saham, tetapi objektif utama adalah untuk mengenal pasti model yang paling berkesan untuk menghadapi krisis ekonomi ini. Dengan menggunakan indeks dari Malaysia, Indonesia dan Singapura sebagai sasaran kajian, kajian ini bertujuan untuk membandingkan ketepatan ramalan model regresi linear siri masa, regresi Bayesian dan regresi vektor sokongan (SVR) untuk tempoh pra- dan pasca-vaksinasi bagi menentukan prestasi terbaik berdasarkan punca min ralat kuasa dua (RMSE), ralat min kuasa dua (MAE) dan nilai terlaras R². Model regresi Bayes dan SVR didapati berprestasi baik kerana kedua-duanya membenarkan anggaran parameter berdasarkan maklumat terdahulu, manakala kaedah klasik hanya boleh menggunakan data sedia ada. Model regresi Bayes dikenal pasti sebagai model yang paling berkesan dalam kajian ini kerana ia menunjukkan prestasi yang baik dalam data ujian. Kaedah ini harus digunakan secara meluas dalam penyelidikan masa depan dengan kaedah pembelajaran mesin yang lain untuk meningkatkan ketepatan ramalan dan faktor makroekonomi lain seperti inflasi, kadar faedah serta faktor geopolitik juga boleh dikaji.

Kata kunci: COVID-19; regresi Bayes; regresi linear siri masa; regresi vektor sokongan; vaksinasi

 

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*Pengarang untuk surat-menyurat; email: nurfiryal@ukm.edu.my