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First of all, Pearson's correlation between Bahrain and Saudi Arabia stock markets was calculated, which showed that these stock markets are highly correlated with a correlation coefficient of 0.82.The high correlation coefficient also implies that only one of these stock prices could be used in the regression equation, hence, the second step was to find the correlation between these stock market prices and the traffic.This could be because the majority of the traffic on this corridor is generated from Saudi Arabia for tourism and business purposes due to Bahrain's recreational attraction points and flexible banking and financial systems (Khan and Bhatti, 2008; Nuruzzaman, 2013).Therefore, itwasdecidedtousetheSaudiArabiastockmarketpricesforpredicting the weekly average traffic.
First of all, Pearson’s correlation between Bahrain and Saudi Arabia stock markets was calculated, which showed that these stock markets are highly correlated with a correlation coefficient of 0.82. The possible reason for this high correlation of Bahrain and Saudi Arabia stock market prices could be the fact that the economic conditions in Saudi Arabia also boost/degrade the economy of Bahrain. The high correlation coefficient also implies that only one of these stock prices could be used in the regression equation, hence, the second step was to find the correlation between these stock market prices and the traffic. It was found that Saudi Arabia has a higher correlation with traffic as compared to Bahrain (0.37 vs. 0.27). This could be because the majority of the traffic on this corridor is generated from Saudi Arabia for tourism and business purposes due to Bahrain’s recreational attraction points and flexible banking and financial systems (Khan and Bhatti, 2008; Nuruzzaman, 2013). This traffic would be more susceptible to changes in the economic situation of the Saudi Arabia stock market. Therefore, itwasdecidedtousetheSaudiArabiastockmarketpricesforpredicting the weekly average traffic. Correlation between fortnightly average Saudi Arabia stock market price and the weekly average traffic was calculated for the immediate week and that with a lag of one week. The correlation coefficient was relatively higher (0.37) for the weekly average traffic of the immediate week as compared to that with one week lag (0.25). This observation is consistent with the results achieved with ANN models in an earlier study on this dataset where lag in the input and output variables reduced the accuracy of the models (El-Alfy et al., 2015).
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