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Understanding and Forecasting Selected Philippine Stock Market Prices from Different Companies Before and During Covid-19 Pandemic using Multiple Linear Regression and Arima Model

Eduardo C. Dulay Jr.1, Jeremiah A. Candelaria2, Michael Adrian G. Yambao 3
Department of Mathematics and Physics, College of Science,
University of Sto. Tomas, Manila, Philippines1, 2



COVID-19 became one of the most devastating pandemics in history. The loss of jobs, businesses, and even life had a huge impact on the world and at the same time, the economy of every country was jeopardized. Finding different ways to earn during the pandemic might be hard, but a reliable way could be investing. In the pandemic setting, the study aims to answer the question: is investing in the stock market an effective source of income with all the negative effects causing a financial crisis? On the other hand, out of the many companies in the stock market, a cluster sampling method was used to get a company from each of the clusters or sectors in the Philippine Stock Exchange Index (PSEi). From the chosen companies namely: PLDT, JFC, MPI, BPI, and MEG, each company’s stock prices before the pandemic (2016-2019) and during the pandemic (2020-2021) were correlated using multiple linear regression to show the relationship of the stock prices to economic indicators such as GDP, Inflation Rate, and Unemployment. Furthermore, the study uses ARIMA modeling to forecast 30-day stock prices for December 2019 and March 2021. To supplement the forecast of the data, performance indicators of each company for the 5-year range were obtained to further give analysis on the better investment. The study showed significant returns for the stock prices of each corporation based on MAPE, before and during the pandemic. The forecasted models are below 10% MAPE except for MPIs before the pandemic model. Forecasting the stock prices of these companies can help investors to buy low and sell high, which is taking advantage of the situation to earn higher returns while alleviating businesses as well as the economy to recover.

Keywords: COVID-19, ARIMA, Multiple Linear Regression, Stock Market, PSEi

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