A macroeconomic model for forecasting crude oil prices with Feedforward Neural Network Grid Search Experimentation

Abstract

Crude oil is one of the most important determinant of the global and national economy and important decision making factors of industrial activities. For this reason, numerous mathematical and machine learning approaches have been conducted to predict the future trend of oil market. Yet, to predict the price of oil is one of the most challenging issues out there because the high volatile nature of oil market and the dependency of price on other factors. In many approaches on predicting oil price use machine learning algorithms, the only factors considered are the opening and closing prices. Thus, the implementations did not reflect the price pattern truly and also hampered the sudden ups and downs of price because the oil market does not only depend on the daily pricing behavior. By reviewing the historical data of oil market it can clearly be seen that the oil market is heavily affected by the geopolitical, technical and macroeconomic factors. For example, geopolitical factor such as war in middle east made the oil price soared high and broke the pattern of daily fluctuations by a large margin. And also, it can easily be seen that the everyday demand of oil along with the quantity supplied affects the oil market. So, these factors along with other macroeconomic and technical issues must be addressed to successfully determine the oil price trend. To justify our claim, we approach to predict the oil price using only the opening and closing market price by ARIMA, SVR and Linear regression model. Afterwards, the macroeconomic, technical, geopolitical factors were considered to predict oil price using Feed Forward Neural Network and compared the results with the ones we have found on the previous models.