STOCK PRICE PREDICITON USING MACHINE LEARNIN
Abstract
- Stock price prediction is a challenging task due to the volatile and complex nature of financial markets. Various approaches, including statistical methods, machine learning, and deep learning, have been employed to improve prediction accuracy. This study explores different techniques such as time series analysis, regression models, and neural networks, including Long Short-Term Memory (LSTM) networks, to forecast stock prices based on historical data and market indicators. Factors such as trading volume, economic trends, and sentiment analysis from news and social media are also considered. The findings suggest that advanced machine learning models outperform traditional methods in capturing market trends and patterns, providing investors with valuable insights for decision-making. Stock price prediction is a complex and dynamic problem influenced by various factors such as historical price trends, market sentiment, macroeconomic indicators, and company performance. Traditional methods like statistical time series models (ARIMA, GARCH) have been widely used for forecasting but often fail to capture the non-linear and highly volatile nature of stock prices.