Predictive analysis comparison of energy stock prices using machine learning models - Valentino, Egan (Nanyang Technological University, 2023)
Recurrent neural networks (RNN), Long Short-Term Memory (LSTM), and Auto Regressive Integrated Moving Average (ARIMA) are some of the machine learning models that have demonstrated potential in forecasting temporal sequences, including the prices of stocks or commodities, by analyzing past data and incorporating various factors like technical indicators. As the stock market is complex and unpredictable due to several unknown factors, even experienced analysts find it challenging to predict market trends. However, there is a growing interest in applying machine learning to predict stock prices, as a model that can accurately forecast future prices could significantly impact investment firms' and traders' profits. This report compares various machine learning models for predicting the stock prices of energy companies.