Open AccessStock Prediction Using Machine Learning
Author(s) -
Shubha Singh,
Sreedevi Gutta,
Ahmad Hadaegh
Publication year - 2021
Publication title -
wseas transactions on computer research
Language(s) - English
Resource type - Journals
eISSN - 2415-1521
pISSN - 1991-8755
DOI - 10.37394/232018.2021.9.17
Subject(s) - machine learning , artificial intelligence , computer science , artificial neural network , support vector machine , stock market , recurrent neural network , stock market prediction , stock (firearms) , profit (economics) , engineering , economics , mechanical engineering , paleontology , horse , biology , microeconomics
The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.