PRE AND POST COVID COMPARATIVE STOCK MARKET STUDY USING NEURAL NETWORK AND RANDOM FOREST

Stocks are possibly the most popular Capital instrument invented for making wealth. From last few decades we 
have seen an enormous increase in the volume of stock daily traded. In stock market it is very important that you have very 
accurate information. With the development of computer science, machine learning is now more vastly used in Stock 
market. The main purpose of this paper is to find the effect of COVID on prediction of daily volume of stock traded. In this 
paper we have used two different machine learning models: Artificial Neural Network (ANN) and Random Forest (RF). The 
main task is to predict the volume traded for 90 days using learnings from the historical data. The dataset consists of the 
RELIANCE, TATA and SBIN taken from Yahoo Finance. The models are evaluated using standard strategic indicators: 
RMSE and MAE. It has been observed that the accuracy of Random Forest is greater than ANN in both pre and post COVID 
times.