Authors:-Madhur Narang, Kushagra Sahani,Asso. Prof. Dr. Neha Agrawal, Asst. Prof. Ms. Meenu Garg
Abstract- The stock market is the platform where anyone can buy and sell or trade shares of public companies, and for that predicting the stock price helps us to forecast the future value of the company shares, derivatives, and mutual funds. So, while doing predictions of the stock market we have to keep some key points in our mind such as No one can accurately predict the future movement of the stock market because the stock market is a composite and volatile system, and many factors can affect its performance.
To evaluate a company’s financial stability and performance, fundamental analysis is used. On the other hand, for reviewing historical price and bulk data, technical analysis has been carried out to recognize tendencies and patterns. Risk management, while investing in the stock market carries inherent risks, and to mitigate those risks, it is crucial to spread out investments and establish stop-market orders, and other techniques.
The aim of this paper is to suggest deep learning techniques in order to predict the stock prices of different companies such as AAPL(Apple), BAM (Brookfield Asset Management), and UBER and using two different models such CNN (Convolutional Neural Network) in CNN the paper uses One -Dimensional CNN (1D CNN) and LSTM (Long Short-Term Memory) uses Bidirectional LSTM.
