Empirical Study on Stock Market Prediction Using Machine Learning

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurat...

Full description

Saved in:
Bibliographic Details
Published in2019 International Conference on Advances in Computing, Communication and Control (ICAC3) pp. 1 - 5
Main Authors Sable, Rachna, Goel, Shivani, Chatterjee, Pradeep
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
ARIMA
DAN2 Naïve Bayes
Data models
KNN
Machine learning
Neural networks
Prediction algorithms
Predictive models
RBF
Stock market prediction
Stock markets
Support vector machines
SVM
Online AccessGet full text
DOI10.1109/ICAC347590.2019.9036786

Cover

More Information
Summary:Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. This survey will assist the readers & researchers in selecting algorithms that can be useful for a predicting the stock market. A survey of various algorithms and its parameters for stock market prediction is presented in this paper.
DOI:10.1109/ICAC347590.2019.9036786