Comprehensive Study of Machine learning Algorithms for Stock Market Prediction during COVID-19

Urvashi Rahul Saxena; Parth Sharma; Gaurav Gupta

doi:10.57159/gadl.jcmm.1.2.22021

Authors

Urvashi Rahul Saxena Department of Computer Science & Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301 https://orcid.org/0000-0002-0419-4530
Parth Sharma Department of Computer Science & Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301
Gaurav Gupta Department of Computer Science & Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301

DOI:

https://doi.org/10.57159/gadl.jcmm.1.2.22021

Keywords:

Machine learning, Stock predicts, COVID-19, Market analysis, ARIMA, LSTM

Abstract

The stock market has always drawn investors' attention. Stock trend forecasting tools are in high demand because they facilitate the direct acquisition of profits. The more precise the results, the greater the likelihood of generating a greater profit. Statistically, only 3% of the Indian population invests in stocks, and at the time of COVID-19, that number was even lower, as the stock market was not based on general patterns and equations but rather on the emotional quotient of the people. Such a circumstance increased the stock market's vulnerability. The pandemic factor, such as the case of COVID-19, influences the stock market's trends. Technical analysis of market trends is a technique for interpreting past and present prices to forecast likely future prices. Several deep learning and machine learning algorithms are utilized to generate stock market forecasts, wherein LSTM and ARIMA models have been proven to produce reasonably accurate results. The prior works focused on individual models and their components to provide forecasts. Thus, this paper aims to compare the two well-established models and provide investors with models that work well with data and have appropriate parameter values. The LSTM and ARIMA models are presented because they provide appropriate results using technical analysis of the data set, and the results are to be compared. The closing values from the historic stock prices of the top three Indian hospitality industries were used as the data set. The results show that the individual models work well when the data matches the model and the appropriate parameter is used.

Author Biography

Urvashi Rahul Saxena, Department of Computer Science & Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India 201301

Ms. Urvashi Rahul Saxena is an experienced faculty member in the Department of Computer Science & Engineering at JSS Academy of Technical Education. With 16 years of teaching experience, she holds a B.E. from Bansal Institute of Science & Technology, Bhopal, Rajiv Gandhi Proudyogiki Vishwavidhyalaya, and an M.Tech. in CSE from Birla Institute of Technology, Mesra. Currently pursuing a Ph.D. from Jaypee Institute of Information Technology, her research interests lie in the field of computer science.

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