A Dynamic Level Technical Indicator Model for Oil Price Forecasting

Abstract

Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities’ prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit.