In this work, we systematically reviewed the research on machine learning's potential for predicting the stock market and other financial markets. This literature review primarily focuses on the financial markets and the different kinds of input utilized by the machines learning algorithms used to predict the financial markets. For this, we combed over 138 papers published between 2000 and 2019. The primary contributions of this review are (1) a thorough analysis of the data used for the prognostications (including that of the markets and stock indices in the preconceptions and the 2173 exceptional relevant factors used for share price predictions, which can include candlestick patterns, macro - economic factors, and basic indicators) and (2) a thorough analysis of the machine learning techniques and variants used for the predictions. Included as well is a bibliometric study of these journal papers, which ranks articles according to how often they have been cited in other publications.

Financial market, Data mining, Predictive performance, Classification, Regression, Stock market prediction