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In this chapter, we propose a nonconventional methodology, the graph theory, which is especially relevant for the study of high-dimensional financial data. We illustrate the advantages of this method in the context of systemic risk in derivative markets, a main subject nowadays in finance. A key issue is that this methodology can be used in various areas. Numerous applications have now to face the challenge of analyzing gigantic financial data sets, which are more and more frequent. We offer a pedagogical introduction to the use of the graph theory in finance and to some tools provided by this method. As we focus on systemic risk, we first examine correlation-based graphs in order to investigate markets integration and inter/cross-market linkages. We then restrain the analysis to a subset of these graphs, the so-called “minimum spanning trees.” We study their topological and dynamic properties and discuss the relevance of these tools as well as the robustness of the empirical results relying on them.

One of the main lessons of the recent financial crisis is that the network structure of the banking system has to be taken into account to assess systemic risk.

In this chapter, we analyze the topological properties of the network of the Euro Area banking sector with the primary aim of assessing the importance of a bank in the financial system with respect to ownership and control of other credit institutions.

The network displays power law distributions in both binary and weighted degree metrics indicating a robust yet fragile structure and a direct nexus between an increase of control diversification and a rise in the market power. Therefore, while in good time the network is seemingly robust, in bad times many banks can go into distress simultaneously. This behavior opens a narrow for Central bank’s actions. In particular, we investigate whether the Single Supervisory Mechanism introduced by the European Central Banks and based on banks’ total asset is a good proxy to quantify their systemic importance. Results indicate that not all the financial institutions with high value of total asset are systemically important but only few of them.

In this reply, we respond to a series of substantive comments on our work by both Madhavan and Walker. In our response to Madhavan’s comments, we consider three accounts – “weak,” “semi-strong” and “strong” – that clarify how our model “explains” and offers insights that can emerge from our modeling strategy. We also explore ways in which our explanatory strategy might be extended in light of his critique. In our response to Walker’s comments, we adopt the “semi-strong” thesis, which admits variation in network-generating mechanisms, while also recognizing that information needs to be distributed and shared in order for many types of networks to function.

We commence answering the above questions first with an extension of the definition of Economy given by Gerard Debreu (1959). Choudhury (1999a) has extended Debreu's formulation by introducing the learning parameter of unity of knowledge. The ethically induced economy in the light of conscious oneness is a complex relational universe of its micro-parts. These comprise prices, quantities, incomes, resources, preferences and production menus, and technological choices. These are studied in relation to multimarkets and their agents represented by vector-variables of each of the above-mentioned categories. All of these categories of the representing variables are mutually interactive according to the interactive, integrative, and evolutionary (IIE)-learning processes (explained earlier) by the medium of knowledge-flows that emanate from the episteme of conscious oneness.¹

It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

Because clustering of organizational activities in space induces – and at the same time emerges from patterns of imperfect connectivity among interacting agents, the study of geography and strategy necessarily hinges on assumptions about how agents are linked. Spatial structure matters for the evolutionary dynamics of organizations because social systems are prime examples of connected systems, i.e. systems whose collective properties emerge from interaction among a large number of component micro-elements. Starting from this proposition, in this paper we explore the value of the claim that a wide range of interesting organizational phenomena can be represented as the outcome of processes that occur in overlapping local neighborhoods embedded in more general network structures. We document how patterns of spatial organization are sensitive to assumptions about the range of local interaction and about expectation formation mechanisms that induce temporal interdependence in agents’ choice. Within the lattice world that we define we discover a concave relation between the sensitivity of individual agents to new information (cognitive inertia) and system-level performance. These results provide experimental evidence in favor of the general claim that the evolutionary dynamics of social systems are directly affected by patterns of spatial organization induced by network-based activities.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

This article considers the contribution of social science in the teaching of finance based on personal teaching experience in the fields of both market finance and corporate finance. We show how adopting a social science lens can help to change teaching practice in the field. First, due to the social science research epistemology, we apply an inductive method based on observations of real facts, e.g., a financial scandal, a crisis, evaluation of a product, the bank credit granting process. Second, we portray objectified finance as it actually works. Third, we focus on deconstruction to offer a fresh take on the world of finance with the help of critical analysis. Depending on the finance course and the target audience, this can be done through the lens of techniques, financial organisation or an analysis of institutions. The paper offers new ideas to rejuvenate finance education. More specifically, by teaching finance through the lens of social science, we can abandon the monolithic and dogmatic framework of finance theory and instead propose a pluralism of theoretical frameworks and a continuum of complementary interpretations. In addition to developing students' open-mindedness in the field of finance, the inductive approach starting from by how finance actually works enriches the material provided by ‘seminal’ finance books that are mostly confined to mainstream theory. Social science is a developing and rich area of research, but, to our knowledge, its implications for finance teaching have yet to be analysed.