Geopolitical and economic events had strong impact on crude oil markets for over 40 years. Oil prices steadily rose for several years and in July 2008 stood at a record high of $145 per barrel. Further, it plunged to $43 per barrel by end of 2008. There is need to identify appropriate features (factors) explaining the characteristics of oil markets during booming and downturn period. Feature selection can help in identifying the most informative and influential input variables before and after financial crisis. The study used an extended version of MI3 algorithm i.e. I2MI2 algorithm together with general regression neural network as forecasting engine to examine the explanatory power of selected features and their contribution in driving oil prices. The study used features selected from proposed methodology for one-month ahead and twelvemonth ahead forecast horizon. The forecast from the proposed methodology outperformed in comparison to EIA’s STEO estimates. Results shows that reserves and speculations were main players before the crisis and the overall mechanism was broken due to 2008 global financial crisis. The contribution of emerging economy (China) emerged as important variable in explaining the directions of oil prices. EPPI and CPI remain the building blocks before and after crisis while influence of Non-OECD consumption rises after the crisis.