Stock market trading is an activity in which investors need fast and accurate information to make effective
decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making
process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock
price prediction is an important process and a challenging one. This leads to the research of finding the most
effective prediction model that generates the most accurate prediction with the lowest error percentage. This
paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of
stock price prediction.