Intelligent Monitoring and Forecasting Using Machine Learning Techniques

Abstract

Abstract Stock market prediction is a crucial task and a prominent research area in the financial domain as investing in the stock market involves greater risk. Various events may affect public sentiments and emotions differently, which may affect the trend of stock market prices. Because of dependency on numerous factors, the stock prices are dynamic, and not static, highly noisy and nonlinear time series data. Due to its great learning ability, machine learning has been applied to this research area. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors such as news in stock prediction. Machine learning algorithms have made a great impact in predicting stocks accurately. Methods based on learning for stock price prediction are found to be popular and a lot of strategies have been used to improve the performance of the learning-based predictors. However, performing successful predictions in the stock market is still a challenge. In this paper, we use machine learning algorithms and predict the stock market trade to examine if it increases or decreases. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using a Naïve Bayes classifier which is found to be consistent and gives the maximum efficiency.