Predicting Stock Market Trends Based on Macroeconomic Indicators through Machine Learning Approach: A Case Study of KSE 100 INDEX

Abstract

The purpose of our research is to model the monthly price of the KSE 100 index based on Pakistan's macroeconomic indicators using a Machine Learning (ML) approach. The novelty of the study is forecasting the future value of the stock market using ML. Monthly data was collected for the period from Feb 2004 to Dec 2020. The output layer of our study is the closing price of the KSE 100 index, and the input layer consists of 16 macroeconomic variables of the Pakistan economy, which are the industrial production index (IPI), the exchange rate (EX-RATE), money supply (M2), consumer price index (CPI), foreign direct investment (FDI), Treasury bill on 3-months treasury, interest rate as KIBOR – Month Average (1 Month), Foreign Exchange reserves (FXR), Consumer Financing for house building (house financing), Balance of Trade (BOT), crude oil, Gold, Labor force participation rate, GDP growth (annual %), Households and NPISHs Final consumption expenditure (household consumption) (current US$), and Domestic savings. The prediction uses the Artificial Neural Network (ANN) Backpropagation algorithm. The model developed in this research achieved 99% accuracy using macroeconomic indicators. The accuracy level indicates that the model of the KSE 100 index can predict future trends. This study also forecasts the future monthly values of the KSE 100 index from Jan 21 to Jun 23 and daily future values from Oct 1, 2022, to Dec 31, 2020.