Oil supply and demand shocks and stock price: Evidence for some OECD countries

This paper examines the interactive relationships between oil price shocks and stock market in 11 OECD countries using Vector Error Correction Models (VECM). Considering both world oil production and world oil prices to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock market returns to the oil price shocks specifications is found. As for impulse response functions, it is found that the impact of oil price shocks substantially differs along the different countries and that the results also differ along the various oil shock specifications. Our finding suggests that oil supply shocks have a negative effect on stock market returns in the net oil importing OECD countries. However, the stock market returns are negatively impacted by oil demand shocks in the oil importing OECD countries, and positively impacted in the oil exporting OECD countries

Oil supply and demand shocks and stock price: Empirical evidence for some OECD countries

This paper examines the interactive relationships between oil price shocks and stock market in 11 OECD countries using Vector Error Correction Models (VECM). Considering both world oil production and world oil prices to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock market returns to the oil price shocks specifications is found. As for impulse response functions, it is found that the impact of oil price shocks substantially differs along the different countries and that the results also differ along the various oil shock specifications. Our finding suggests that oil supply shocks have a negative effect on stock market returns in the net oil importing OECD countries. However, the stock market returns are negatively impacted by oil demand shocks in the oil importing OECD countries, and positively impacted in the oil exporting OECD countries

Oil supply and demand shocks and stock price: Empirical evidence for some OECD countries

This paper examines the interactive relationships between oil price shocks and stock market in 11 OECD countries using Vector Error Correction Models (VECM). Considering both world oil production and world oil prices to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock market returns to the oil price shocks specifications is found. As for impulse response functions, it is found that the impact of oil price shocks substantially differs along the different countries and that the results also differ along the various oil shock specifications. Our finding suggests that oil supply shocks have a negative effect on stock market returns in the net oil importing OECD countries. However, the stock market returns are negatively impacted by oil demand shocks in the oil importing OECD countries, and positively impacted in the oil exporting OECD countries

On the Causal Nexus of Road Transport CO2 Emissions and Macroeconomic Variables in Tunisia: Evidence from Combined Cointegration Tests

This paper investigates the causal relationship between road transportation energy consumption, fuel prices, transport sector value added and CO2 emissions in Tunisia for the period 1980-2012. We apply the newly developed combined cointegration test proposed by Bayer and Hanck (2013) and the ARDL bounds testing approach to cointegration to establish the existence of long-run relationship in presence of structural breaks. The direction of causality between these variables is determined via vector error correction model (VECM). Our empirical exercise reveals that the cointegration is present. Energy consumption adds in CO2 emissions. Fuel prices decline CO2 emissions. Road infrastructure boosts in CO2 emissions. Transport value-added also increases CO2 emissions. The causality analysis indicates the bidirectional casual relationship between energy consumption and CO2 emissions. Road infrastructure causes CO2 emissions and similar is true from opposite side in Granger sense. The bidirectional causality is also found between transport value-added and CO2 emissions. Fuel prices cause CO2 emissions, energy consumption, road infrastructure and transport value-added. This paper provides new insights to policy makers to design a comprehensive energy, transport and environment policies for sustainable economic growth in long run

On the Causal Nexus of Road Transport CO2 Emissions and Macroeconomic Variables in Tunisia: Evidence from Combined Cointegration Tests

This paper investigates the causal relationship between road transportation energy consumption, fuel prices, transport sector value added and CO2 emissions in Tunisia for the period 1980-2012. We apply the newly developed combined cointegration test proposed by Bayer and Hanck (2013) and the ARDL bounds testing approach to cointegration to establish the existence of long-run relationship in presence of structural breaks. The direction of causality between these variables is determined via vector error correction model (VECM). Our empirical exercise reveals that the cointegration is present. Energy consumption adds in CO2 emissions. Fuel prices decline CO2 emissions. Road infrastructure boosts in CO2 emissions. Transport value-added also increases CO2 emissions. The causality analysis indicates the bidirectional casual relationship between energy consumption and CO2 emissions. Road infrastructure causes CO2 emissions and similar is true from opposite side in Granger sense. The bidirectional causality is also found between transport value-added and CO2 emissions. Fuel prices cause CO2 emissions, energy consumption, road infrastructure and transport value-added. This paper provides new insights to policy makers to design a comprehensive energy, transport and environment policies for sustainable economic growth in long run

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

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%

Exploring the oil supply-demand shocks and stock market stabilities: Experience from OECD countries

This paper explores the interactive relationships between oil price shocks and the stockmarket in 11 OECD countries using traditional cointegrationtest and look at the rolling window Granger causality effects with various predictive power contents running between the variables. Taking into account both world oil production and world oil prices in order to supervise for oil supply and oil demand shocks, strong evidence of the sensitivity of stock market returns to the oil priceshock specifications is found in several sub-periods. As for rolling window causality tests, it is found that the impact of oil price shocks substantially differs along the different countries and that the results also differ among the various oil shock specifications.The overall finding suggests that oil supply shocks have a negative effect on stock market returns in the net oil importing OECD countries. Indeed, the stock market returns are negatively impacted by oil demand shocks in the oil importing OECD countries and positively impactedby the oil exporting OECD countries. Furthermore, these results will give a dimension for future undertaking studies with varying empirical findings

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

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%

What Does Matter in Economy Today: When Human Psychology Drives Financial Markets

This paper provides the first evidence for empirical tests of the impact of rational expectations as well as behavioral biases, including among other animal spirits such as defined by Akerlof and Shiller on the variability of trading. Using a daily data for five international capital markets in developed countries, strong evidence is found. The hypothesis of rationality fails to determine the investors’ trading behavior. The economy is, however, driven by behavioral biases, including more especially animal spirits summarized in investors’ sentiments and beliefs

R&D diversification in MNCs: Between earnings management and shareholders increasing wealth

This paper examines the impact of the R&D geographic diversification on the shareholders’ wealth (as measured appreciatively by the firm's market value) and on the earnings management as a mechanism of manager's entrainment. Using a sample of 460 firm‐year observations for multinational firms over the 2002–2006 period, we find that the R&D decentralization may enhance the shareholders’ wealth and increase the managers’ one. The results show that the R&D geographic diversification increases the informational asymmetry and support the emergence of the favourable conditions for the earnings management and the managers’ entrainment. It may increase the managers’ autonomy which likely allows them to manage the result in order to increase their own wealth and destruct the shareholder's one. First published online: 14 Oct 201