Forecasting the Pakistan’s Leading Stock Exchange During Covid-19 Using Machine learning (ML) Algorithms: Model Development and Validation

Abstract

Abstract During COVID-19, marketing shows sharp fluctuation in upward and downward trends. Forecasting price actions is one of the most challenging problems in this situation. It is challenging to build an accurate model, which integrates economic and Covid-19 variables as input for KSE index prediction. To tackle this problem, our proposal comprises applying machine learning (ML) techniques to predict the KSE during Covid-19. The principal aim of this study is to examine accuracy of combined models with individual models to forecast the Karachi Stock Exchange during COVID-19. This study has analyzed the indices of KSE from March 1st, 2020, to November 26th, 2021. Therefore, this study is keen to find the best-fitted model that forecasts more accurately during the pandemic. To select the most suitable machine learning technique, the six inferred models (i.e., Linear regression (LR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forests (RF), (KNN), and Support Vector Regression (SVR)) are selected to forecast the Karachi Stock Exchange During Covid-19. Performance metrics (i.e., MAE, MSE, MAPE, and R2) are applied to measure and compare accuracy. The modeling outputs presented the RF model provided the best performance of 0.98 versus the other models in predicting the KSE100 index. Thus, the addition of ML methods improves the exchange indications and the competitiveness of future trading guidelines. These projections helped the government to make strategies for the stock exchange KSE-100 and fight against a pandemic disease. The results suggest that the performance of the KSE-100 index can be predicted with machine-learning techniques.