@misc{10481/103612,
year = {2024},
month = {12},
url = {https://hdl.handle.net/10481/103612},
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 complex patterns. They often lack precision in volatile
markets due to their reliance on static assumptions and human interpretation, making them insufficient for
dynamic and real-time decision-making. The increasing complexity of stock market data, combined with
its non-linear and volatile nature, poses significant challenges for traditional systems. The proposed system
leverages the Long Short-Term Memory (LSTM) model, a specialized recurrent neural network (RNN)
designed for processing sequential data. LSTM excels at analyzing historical stock prices, identifying trends
over time, and effectively handling the time-series nature of stock market data by capturing dependencies
across various timeframes. By utilizing key input features such as opening price, closing price, high, low,
and trading volume, the system delivers accurate stock price predictions, enabling more informed and
strategic investment decisions.},
publisher = {Universidad de Granada},
keywords = {Recurrent Neural Networks},
keywords = {Sequential Data},
keywords = {Historical Data Analysis},
title = {Enhanced investment strategies through predictive analytics of stock prices},
author = {Sravani, B. and Sritha, T. Laxmi and Fatima, Shireen and Prasanna, T. Laxmi},
}