Stock Price Prediction Using Artificial Intelligence: A Survey

The stock market and the investments into it are two aspects which have gained quite some popularity in the previous decade. Although a lot of people are involved in trading and analysis of the stock market yet stock price prediction remains an exigent task. Scientists and researchers have tried their hands on different methodologies performing the same. A variety of techniques ranging from basic statistical models to stat of the art Deep Learning models have been used in performing stock price prediction. The beginning of the previous decade began with people using trivial machine learning regression models to determine stock prices, then some tried incurring sentiment analysis along with the stock regression. And finally toward the end of the decade a bunch of authors have shown very promising results using advanced Neural Networks like Generative Adversarial Networks. The aim and the purpose of this paper is to summarise the recent progress in the stock price prediction domain, comparing the recent works in the same domain. Comparison has been done on parameters like data set used, sentiments, models / algorithms, evaluation metrics and the conclusion laid down by different authors.