Analysis and Prediction of Healthcare Sector Stock Price Using Machine Learning Techniques: Healthcare Stock Analysis

Analysis and Prediction of Healthcare Sector Stock Price Using Machine Learning Techniques: Healthcare Stock Analysis

Daiyaan Ahmed, Ronhit Neema, Nishant Viswanadha, Ramani Selvanambi
Copyright: © 2022 |Pages: 15
DOI: 10.4018/IJISMD.303131
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Abstract

Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis.
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Background

The efficiency of the stock market prediction model is constrained by the fact that the prices of stock prices are highly unpredictable, volatile and constantly changing. The accuracy of prediction is also affected by a set of other economic and social factors which cannot be digitalized as per today’s technology. Precisely predicting the value of a stock also depends on various conditions like supply and demand, psychology and behaviour of investor etc. are some of the theories that cannot be incorporated into the model right now but there are chances of technologies emerging in the future which take advantage of such factors to generate a more accurate result. However, we have included twitter sentiment analysis which improves the accuracy of prediction by incorporating twitter data about current ongoing events. (P. Ladyzynski et. al., 2013).

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