A Review on Contemporary Machine Learning Based Techniques for Stock Market Prediction

  • Er. Amanpreet Singh, Dr. R. K. Bathla

Abstract

Investing in stock markets is inherently prone to risks owing to the fact that stock process are extremely volatile in nature and depend on several numeric and non-numeric parameters. Previously, stock market prediction was primarily done based on statistical techniques, however with the advent of machine learning, stock market prediction employing machine learning has become extremely popular due to the fast growth and improvements in machine learning algorithms improving accuracy of prediction. It is challenging to design algorithms to predict stock markets with high accuracy since the nature of the stock prices are a function of political and socio economic variables in addition to previous or historical data. This paper aims at introducing the most important and relevant machine learning techniques used for stock market prediction along with its salient features. Related work in the domain is also cited to throw light into the recent trends in the domain. The evaluation parameters have also been discussed and future directions of research are also proposed.

Published
2021-11-10
How to Cite
Er. Amanpreet Singh, Dr. R. K. Bathla. (2021). A Review on Contemporary Machine Learning Based Techniques for Stock Market Prediction. Design Engineering, 11216- 11229. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/6182
Section
Articles