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Tian, J.J.; Liu, Y.J., and Yin, Z.D., 2020. Relationship between coastal foreign direct investment & industrial upgrading of China under the condition of the Belt and Road initiative. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 695–699. Coconut Creek (Florida), ISSN 0749-0208.At present, the issuance of the relationship between foreign direct investment & the transformation and upgrading of domestic industrial structures has been controversial under the condition of the construction of the “Belt and Road” of China. Based on the data from 2008 to 2018, the VAR model is applied for studying the interrelationship between foreign direct investment in sixty four countries along the “Belt and Road” and the transformation and upgrading of domestic industrial structures according to the experience since China has joined in Word Trade Organization (“WTO”); and it is found that in this kind of countries, the direct investment is without any statistical significance on the industrial upgrading of China so far; nevertheless, the industrial restructuring of China still in turn accelerates the going out intention of companies and promotes direct investment in this kind of countries.

With respect to different confidence on evaluation result from different decision-makers to the hybrid multi-attribute with risk, an approach to group decision- making based on prospect theory and projection theory is proposed. Firstly, a tuple is established to record the evaluation result and the hybrid decision information. Then the element of the tuple is changed into a single triangular fuzzy number by the transformation rule. Considering the confidence degree from single decision-maker to evaluation information, the group decision information is aggregated and the weights of attributes are calculated based on the intuitionistic fuzzy set theory. The improved projection method and prospect theory are proposed to rank the alternatives respectively. Finally, an application case is given to demonstrate the effectiveness and feasibility of the proposed approach.

Most studies have concentrated on building an accurate credit scoring model to decide whether or not to grant credit to new applicants and the efforts to build more accurate credit scoring model seems to be not significant. In this paper, we proposed a hybrid data mining technique which contains two processing stages. In the clustering stage, the samples of the accepted and new applicants are grouped into homogeneous clusters, the isolated samples are deleted and inconsistent samples are relabeled. In the classification stage, support vector machines used samples with new labels to build the scoring model. The difference from the other credit scoring model is that the samples were classified into three or four classes, rather than two the good and the bad credit classes. Experimental results based on the credit data set provided by a local bank in China showed that by choosing a proper cut-off point, super classification accuracy of the good and the bad credit is obtained. Risk management strategies are developed according to the characteristic of each class.

The standard approach to studying financial industrial agglomeration is to construct measures of the degree of agglomeration within financial industry. But such measures often fail to exploit the convergence or divergence of financial agglomeration. In this paper, we apply Markov chain approach to diagnose the convergence of financial agglomeration in China based on the location quotient coefficients across the provincial regions over 1993–2011. The estimation of Markov transition probability matrix offers more detailed insights into the mechanics of financial agglomeration evolution process in China during the research period. The results show that the spatial evolution of financial agglomeration changes faster in the period of 2003–2011 than that in the period of 1993–2002. Furthermore, there exists a very uneven financial development patterns, but there is regional convergence for financial agglomeration in China.

Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the i®through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines classification and computation of feature importance. By combining the samples of the accepted applicants and the new applicants, we obtain representative samples. To some extent, this is cost-free. Analytic results demonstrate that our method improves the predictive performance while still retaining interpretability.