Systematic literature review: forecasting the relationship between stock market and macroeconomic variables

Investing in the stock market is a significant and well-liked kind of investment worldwide while considering macroeconomic factors might have an impact on innovators' investment decisions and shock share returns. This study was applying PRISMA model in Systematic Literature Review (SLR) by locating numerous pertinent and acceptable forms of literature to establish the connection between macroeconomic variables and stock market future value. As SLR performed searches on articles collected from these databases, data search was conducted using criteria established by studies from several search platforms, including Google Scholar, Science Direct, Springer Link, Research Gate, and others. A total of 91 publications were found, 37 of which were relevant to the goal of this work. Only 17 of the 37 papers were pertinent to this investigation. Moreover, a qualitative method to data analysis was used to describe the research findings. The aim of study, types of data and methodology were identified to be the three key research themes, according to the findings. Roughly, the study of these papers' findings revealed that the there are two methodologies, Auto Regressive Integrated Moving Average (ARIMA) and Back Propagation Neural Network (BPNN) were often utilised techniques but not directly compared for accuracy to forecast stock market and macroeconomic factors, gross domestic product (GDP), unemployment rate and money supply specifically. The results of SLR gave understanding into the field's main contributions, opportunities, and gaps, which sparked a discussion regarding crucial study areas in the future.