Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature
2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022
; 2023.
Article
in English
| Scopus | ID: covidwho-20239957
ABSTRACT
India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.
COVID-19; Machine Learning; NIFTY50; Predictive Analytics; Stock Market; Decision trees; Deep learning; Financial markets; Gradient methods; International trade; Learning systems; Logistic regression; Stochastic systems; Asset prices; Global market; High volatility; Machine-learning; Market failures; Ridge regression; Risk assets; Stock market prediction; Uncertainty
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022
Year:
2023
Document Type:
Article
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