Prediction of movement direction in crude oil prices based on semi‐supervised learning

Oil price prediction has long been an important determinant in the management of most sectors of industry across the world, and has therefore consistently required detailed research. However, existing approaches to oil price prediction have sometimes made it rather difficult to implement the complex interconnected relationship between the price of oil and other global/domestic economic factors. This has been complicated by the influence of the irregular impact caused by the economic factors that affect the oil price. Recently, a machine learning algorithm, known as semi‐supervised learning (SSL) has emerged, whose strength is the ease it can bring to the network representation of entities and the explicitness of inference which is expressed through relations between different entities. Since an awareness of the network representation of complicated relations between economic factors including the oil price is natural in SSL, this method allows the effects of the impact of economic factors on the oil price to be assessed with improved accuracy. SSL has so far been exploited in dealing with the non time‐series types of entity, but not for the time‐series types. Therefore, the proposed study is to exploit the method of representing the network between these time‐series entities, and to then employ SSL to forecast the upward and downward movement of oil prices. The proposed SSL approach will be tested using one‐month‐ahead monthly crude oil price predictions between January 1992 and June 2008.