COVID-19's influence on Karachi stock exchange: A comparative machine learning algorithms study for forecasting

Tahir Munir; Rabia Emhamed Al Mamlook; Abdu R Rahman; Afaf Alrashidi; Aqsa Muhammad Yaseen

doi:10.1016/j.heliyon.2024.e33190

COVID-19's influence on Karachi stock exchange: A comparative machine learning algorithms study for forecasting

Heliyon. 2024 Jun 17;10(13):e33190. doi: 10.1016/j.heliyon.2024.e33190. eCollection 2024 Jul 15.

Authors

Tahir Munir¹, Rabia Emhamed Al Mamlook^{2

3}, Abdu R Rahman⁴, Afaf Alrashidi⁵, Aqsa Muhammad Yaseen⁶

Affiliations

¹ Department of Anaesthesiology, The Aga Khan University, Karachi, 74800, Pakistan.
² Department of Business Administration, Trine University, Angola, IN, 49008, USA.
³ Department of Mechanical and Industrial Engineering, University of Zawia, Al Zawiya City, P.O. Box 16418, Libya.
⁴ Institute for Global Health and Development, The Aga Khan University, Karachi, 74800, Pakistan.
⁵ Department of Statistics, College of Science, University of Tabuk, Saudi Arabia.
⁶ Department of Sociology, University of Karachi, Pakistan.

Abstract

The COVID-19 pandemic has great effects for economies internationally. This study studies the interconnection between COVID-19 metrics and Pakistan's premier stock exchange, the Karachi Stock Exchange (KSE) with the object of identifying the most effective machine learning (ML) model for predicting KSE developments in the pandemic. Our investigation periods the peak COVID-19 period from March 1, 2020, to November 26, 2021, applying data from both the KSE 100 index and COVID-19 associated variables. Five various ML methods were applied involving Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Regression Tree (Rtree), and Support Vector Machine (SVM) and measured their performance employing critical accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The outcomes discover that the RF model outperformed its equivalents realizing an R² of 0.91 with k = 5. These results conflict with a previous study that supported a negative impact of COVID-19 on improved stock markets. The visions from this study can assist investors in managing strategic investment decisions and assist policymakers in making measures to reduce the pandemic's effects on the stock market.

Keywords: COVID-19; KSE-100 index; Karachi stock exchange; Machine learning; Performance metrics.