Price Prediction for Pharmaceutical Stocks During Covid-19 Pandemic

DOI

10.2991/978-94-6463-136-4_8How to use a DOI?

Keywords

Stock market; Price Prediction; Machine Learning; Statistical Modeling; K-nearest neighbor; Linear regression; Fbprophet

Abstract

Dramatic price changes in pharmaceutical equities reflect unexpected scientific information gained throughout the pharmaceutical R&D process, such as clinical trial results, recalls and withdrawals, and the approval of new treatments. During the Covid19 shutdown, major pharmaceutical firms were studying and producing vaccinations, pills, and other medicines to combat the coronavirus outbreak. This activity has a substantial impact on the global and Indian markets. Stock price prediction is an important issue in finance and economics that has piqued the interest of scholars throughout the years in developing better predictive algorithms. To evaluate Sun Pharma Ltd. data, we employed machine learning techniques such as K-nearest neighbor (KNN), Linear Regression, and Fbprophet during the time frame 2016 to 2020. It is the second-largest Indian pharmaceutical firm in terms of stock volume. Statistical modeling algorithm Fbprophet outperforms standard regressing algorithms like K-nearest neighbor and Linear regression on time series data.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Karan V. Padariya
AU  - Harsh T. Parikh
AU  - Ankit K. Sharma
PY  - 2023
DA  - 2023/05/01
TI  - Price Prediction for Pharmaceutical Stocks During Covid-19 Pandemic
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 61
EP  - 68
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_8
DO  - 10.2991/978-94-6463-136-4_8
ID  - Padariya2023
ER  -