Stock Market Price Prediction using Machine Learning Techniques: A Review

An ordinary person is showing interest in stock trading as it is a great way to make money if done correctly. Although it's a tough task as stock price forecasting requires a lot of knowledge about market share values and trends. Stock market is irregular and dynamic in nature. It is highly uncertain and difficult to detect the behaviour of stock prices. Earlier stock prediction took place with the help of technical, fundamental and econometric models. The sheer volume and complexity of data gathered in today's market makes it impossible for traditional methods of analysis to be effective. Machine learning is increasingly becoming a useful tool for stock estimation. By using sophisticated algorithms to analyse large data sets, machine learning can help identify pattern and trends that may signal future changes in stock prices. Machine learning can also be used to find correlation between stock prices and external environment. This can also be used to make decision about when it might be a favourable time to buy, sell or hold the stock. Stock price estimation can be accurately made using a machine learning algorithm. It is also used to detect anomalies in the market such as insider trading and market manipulation. Stock market price prediction has become a burning research problem among the traders, investors and stock brokers. In this prediction activity investors need fast and real time information for making quick and accurate decisions. In recent past, majority of researchers have designed algorithms for prediction of average movement of stocks and its price. This research paper provide an insight to various modern prediction techniques specially machine learning and sentiment analysis highlighting data set, prediction types, metrics used which are helpful in predicting the stock prices. There is also discussion on outcome, result, research gap and future scope of all these used techniques.