Promoting digital employment intention among students of Chinese higher education institutions

The analysis of the financial technologies introduction has proved that their application over-complicates the institutional structure of the global financial system. As a result, usual functional relationships cease to operate, new institutes and interdependencies appear, and systemic risks increase. In this context, the system instability increases, resulting in a transition to a new institutional status. The analysis of the financial technologies impact on the stability of financial system shows that the lack of institutional support for new financial technologies is the most important catalyst for the financial industry destabilization and the formation of financial bubbles in various market segments.
The ways to reduce the negative impact of financial technologies on the financial system stability (such as development of international prudential standards; revision of the licensing regime for financial companies; “regulatory sandboxes”, which test new technologies, business models and algorithms underlying the Fintech innovations; legal regulation of ownership of digital tokens; and clear definition of the blockchain technology in various areas of life, etc.) have been proposed.

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.
The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.
Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries’ positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.

This paper proposes a new model of measuring a latent variable, stock market manipulation. The model bears close resemblance with the literature on economic well-being. It interprets the manipulation of a stock as a latent variable, in the form of a multiple indicators and multiple causes (MIMIC) model. This approach exploits systematic relations between various indicators of manipulation and between manipulation and multiple causes, which allows it to identify the determinants of manipulation and an index of manipulation simultaneously. The main reason of stock market manipulation comes from the fact that information availability is not universally equal. The manipulation is thus critically linked to the creation, arrival and dissemination of information or rumors/mis-information. Thus, the immediate impact of manipulation is on the time profile of returns, or excess returns, from an asset and the excess volatility of returns in excess of the volatility explained by the fundamentals. In this basic setup, the model used these two variables as the indicators of stock market manipulation. The main intuition of the MIMIC approach is that some variables, or statistics, related to peace are indicators of manipulation, while others signify effects or outputs of causal factors, or inputs, of manipulation. In other words, distinction can be made between causes of manipulation and indicators of manipulation. The causal factors used in this model are classified into five different domains namely pure economic factors as determinants of manipulation, labor market conditions, international factors, quality of governance factors and systematic risk factors.