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The paper develops a goal programming-based multi-criteria methodology, for assessing different machine learning (ML) regression models under accuracy and time efficiency criteria. The developed methodology provides users with high flexibility in assessing the models as it allows for a fast and computationally efficient sensitivity analysis of accuracy and time significance weights as well as accuracy and time significance threshold values. Four regression models were assessed, namely the decision tree, random forest, support vector and the neural network. The developed methodology was employed to forecast the time to failures of NASA Turbofans. The results reveal that decision tree regression (DTR) seems to be preferred for low values of accuracy weights (up to 30%) and low accuracy and time efficiency threshold values. As the accuracy weights tend to increase and for higher accuracy and time efficiency threshold values, random forest regression (RFR) seems to be the best choice. The preference for the RFR model however, seems to change towards the adoption of the neural network for accuracy weights equal to and higher than 90%.

The purpose of the study is to examine the relationship that exists between tourism, money supply and construction, on the one hand, and the economic growth in Greece, using a multivariate autoregressive model VAR. The long-term relation based on the Cointegration test results has shown the existence of a long run relation despite the prolonged economic recession. The analysis was carried out for the period from 1965 to 2015. The empirical results show that the economy of Greece can recover and return to long run equilibrium with a speed of adjustment reaching 3,60 % per year. The global economic crisis has undoubtedly affected the Greek economy. Long before the onset of the economic crisis, Greece applied a model of economic growth that relied on the growth of the manufacturing sector. In particular, the development of the construction sector was the engine of the Greek economy. However, through our analysis, it turns out that the engine for the development of the Greek economy is tourism rather than construction. The relationship between construction and the supply of money in Greece’s GDP is positive. However, the dynamics of the tourism industry stand out in comparison to the other areas examined.

The capital market reputation attracts foreign investment. Corporate fraud phenomenon is one of the most crucial aspects that threaten foreign investors. This study investigates the impact of corporate fraud on foreign direct investment FDI. Using data of Chinese listed firms, over the period 2009 to 2017, the results show that corporate fraud is negatively associated with foreign direct investment. This suggests that corporate fraud declines foreign shareholders ratio, and foreign investors avoid investing in a risky environment where their wealth may be expropriated. Further, we explore the impact of having foreign shareholders on corporate fraud. We find that increasing foreign shareholders may help in curbing corporate fraud due to diversified corporate experience and risk-taking behavior. However, the findings remain robust after controlling for the potential endogeneity problem. Our findings have important implications for policymakers and governments as it shows that corporate fraud is a crucial determinant to the cause of foreign direct investment.