Enhanced investment strategies through predictive analytics of stock prices

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.