Decision Support System for Stock Trading: Systematic Literature Review using PRISMA

Abstract

So many traders rely on algorithm-based utilities with indicators taken from historical data and running trade data on the exchange. However, applied research on decision support systems (DSS) for short-term stock trading interests is generally carried out by methods that are difficult to implement. Therefore, for researchers to understand more deeply trends in this scope, it is necessary to conduct a literature review so that the following research is no longer in vain and gets a novelty. Based on the initial analysis, the following research questions were obtained: (1) what trends are the main concerns of researchers in the scope of DSS for stock trading, and (2) what are the research’s gaps in the context. A Systematic Literature Review (SLR) was carried out to answer this question, and the PRISMA method was used. The initial selection resulted in a total of 136 articles since 2017. The final result of this stage makes a total of 36. The answer to the first question is Machine Learning and Neural Networks. As for the answer to the second, there are many algorithms and methods that have not been applied within the scope.