ISSN : 3049-1754 (PRINT) ISSN : 3048-8966 (ONLINE)
A COMPARATIVE STUDY OF MACHINE LEARNING TECHNIQUES TO EVALUATE THE IMPACT OF COVID-19 ON THE MSME SECTOR
Simrat Kaur
Affiliations:
Research Scholar, Amity University, Noida, Uttar Pradesh
Abstract:
The coronavirus, also known as Covid-19, severely affected the world economy. This research aims to compare the expected and real stock prices of BSE SMEs before and after the covid using 2 machine learning techniques. To utilize a methodical estimation of the data manipulation and model assessment, this research evaluates and investigates the literature on the implementation of machine learning models in the major financial domains. The most efficient deep learning model, the LSTM network of RNN is compared with the oldest and most used technique; linear regression to predict the stock prices in this paper. It shows the loss in the MSME sector by conducting a comparative study using secondary data collection between the predicted closed stock prices and actual stock prices of the BSE SME IPO index for the period of 1st January 2018 to 30 April 2021. The study offers insight and guidance, showing that COVID-19 significantly impacts the stock prices of BSE SME IPOs as differences in the prediction accuracy can be seen in both techniques. The LSTM model outperformed the linear regression model in the comparative analysis as the LSTM model predicted better in both scenarios, whether before COVID or after COVID-19. Little research is being done, especially in India, despite the LSTM model being the most recent model utilized for prediction. Compared to other countries, India uses machine learning and deep learning in the finance sector much less frequently.
Keywords:
Machine learning, MSME sector, Linear Regression, LSTM, BSE SME IPO, RNN
Publishing Chronology:
Received - 7/3/2024
Accepted - 21/7/2024
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