Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

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.

Causal Relationships of the Stock Market with other Asset Classes: An Indian Perspective

The recent decade has seen a rapid rise in risk assets. Stocks, commodities, and cryptocurrencies have exploded to the upside. Global central banks have maintained interest rates at record low levels following the COVID-19 crisis. This has further acted as tailwinds for risky assets. With asset classes being increasingly interlinked with each other, useful information can be gained by studying these inter-relationships. This paper looks at the interrelationships between the Indian stock market Nifty index and some key asset classes such as Gold, Crude oil, short-term and long-term Indian government bond yields, the USD/INR exchange rate, and the cryptocurrency Bitcoin for the period January 2011 to December 2020. Co-integration analysis suggests the absence of long-run relationships between the Nifty and the asset classes studied. Granger causality analysis reveals bi-directional causality between Nifty and USD/INR and Crude oil returns. Gold returns, Bitcoin returns, and changes in short and long-term government bond yields uni-directionally granger-caused Nifty returns. Impulse response analysis reveals that shocks in each of the independent variables caused a shock in the Nifty that persisted for 1 to 3 weeks. Traders in the Nifty can monitor these shocks and accordingly fine-tune their strategies for possible moves in the Nifty. © 2022 ACM.