Predicting oil price fluctuations: Integrating external indicators and advanced regression techniques

Abstract

The study utilizes external factors to improve the precision of predicting fluctuations in oil prices. Based on the pertinent research, the present research gathers 62 external factors from 2000 to 2021, that indicate the changes in oil need, oil supplies, oil stock, economic basics, monetary metrics, and estimations of unpredictability. The experimental findings suggest that shrinkage techniques provide better predictions for all times, the research indicates that principle component analysis (PCA) regression precisely forecasts oil price variations one month in advance. Shrinkage approaches, in contrast, surpass comparable methods when it comes to prediction for all type timeframes. Moreover, an uncontrolled learning technique known as Principal Component Analysis (PCA) demonstrates better predictive ability when oil prices are falling, while supervised learning methods such as shrinkage methods notably enhance the precision of variability estimation. These results suggest that using sophisticated regression methods will significantly improve the accuracy of oil price projections, hence supporting improved decision-making for legislators and traders.