2 Years Impact Factor:
2.85
5 Years Impact Factor: 1.53
Author: J Haripriya , Gaddam Nandini ,Talarla Manish , K Surya Kanthi
Abstract:
Stock market prediction has been an area of interest for economists, investors, and researchers for decades. In India, the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) are among the largest stock exchanges, with daily trading volumes crossing ?50,000 crore. The primary objective of this study is to leverage Regression and Long Short-Term Memory (LSTM) models for predicting stock prices by analyzing historical data, considering factors like opening price, closing price, high, low, and volume, to improve the accuracy of investment strategies. Before the advent of machine learning or AI, traditional systems for stock market prediction primarily relied on technical analysis, fundamental analysis, and expert opinions. Investors heavily relied on brokers and financial advisors for expert opinions and recommendations, making decisions based on human. Traditional systems for stock market prediction are limited in their ability to handle large datasets and comp
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