Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches

Abderrazak Dhaoui, Mohamed Audi and Raja Ouled Ahmed Ben Ali

MPRA Paper from University Library of Munich, Germany

Abstract: Over the years, the oil price has shown an impressive fluctuation and isn’t without signification impact on the evolution of stock market returns. Because of the complexity of stock market data, developing an efficient model for predicting linkages between macroeconomic data and stock price movement is very difficult. This study attempted to develop two robust and efficient models and compared their performance in predicting the direction of movement in the Canadian stock market. The proposed models are based on two classification techniques, artificial neural networks and Support Vector Machines. Considering together world oil production and world oil prices in order to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock price movement direction to the oil price shocks specifications is found. Experimental results showed that average performance of artificial neural networks model is around 96.75% that is significantly better than that of the Support Vector Machines reaching 95.67%.

Keywords: Oil price; Stock price movement; Oil supply shocks; Oil demand shocks; Artificial neural networks model, Support Vector Machines. (search for similar items in EconPapers)
JEL-codes: G12 G17 (search for similar items in EconPapers)
Date: 2015-08-07
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-for
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