Author(s): Alamir Labib Awad, Saleh Mesbah Elkafas, Mohammed Waleed Fakhr
The stock value plays a vital factor in maximizing the companies profit. It has a direct impact on the economy by affecting it positively or negatively. Many studies have been made in order to help investors make the right decisions while buying or selling the stock. Most of the recent researches relies on machine learning and deep learning algorithms as they proved the best performance the most reliable techniques in solving the prediction problems. Stock price prediction is a very challenging task due to its non-linearity nature and wide range of sudden changes. Stock price prediction is considered a classical problem in which many efforts are made to solve it across the years. This literature review paper is conducted to explore the latest machine learning and deep learning techniques used in predicting the stock price values. In order to achieve the maximum benefit from this literature review, a comparative study is conducted in order to summarize the strength and weaknesses behind each technique briefly. The comparison is made to compare between different techniques in terms of performance and accuracy.