A Comparative Study of Machine Learning Approaches for Stock Price Forecasting

The stock market displays significant fluctuations, which are affected by numerous factors, including financial indicators, political events, environmental conditions, and a variety of internal and external forces. It has been a challenging endeavor, captivating the interest of both researchers and investors alike, due to its intricate and ever-changing nature. The objective of Stock Market Prediction is to forecast the future value of a company’s financial stocks. In contemporary times, a widely used method involves the application of machine learning techniques. These methods utilize current stock market indices and historical data for training to predict future values. Machine learning employs diverse models to enable precise and simplified predictions. Our study has overviewed the following methodologies which are - Neural Network, LSTM, SSA & SVR, SVM, Graph Construction, etc. After performing the comparative study, we identified LSTM (Long Short-Term Memory) as the most appropriate algorithm for stock value prediction, taking into account factors like open, close, low, high, and volume. The outcomes of this study contribute to the growing knowledge in the field of stock market prediction using machine learning algorithms. The LSTM-based method demonstrates significant potential in effectively predicting stock prices, empowering investors and financial analysts to make well-informed choices.