Different approaches to calculate the K±→π±π0e+e−K^{\pm}\to \pi^{\pm}\pi^0e^+e^- decay width

The rare $K^\pm\to\pi^\pm\pi^0 e^+e^-$ decay, currently under analysis by the NA48/2 Collaboration, is considered. We have performed two theoretical approaches to calculate the differential decay width -- in the kaon rest frame, where we use Cabibbo-Maksimovicz variables, and in the center-of-mass system of the lepton pair. The latter essentially simplifies the computations. A comparison between the two approaches has been performed. We have also found the dependencies of the differential decay rate as a function of the virtual photon and dipion system massesComment: 10 pages,5 figure

Network based scoring models to improve credit risk management in peer to peer lending platforms

Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models

EXPERT ASSESSMENT OF THE ENVIRONMENTAL RISK OF TECHNOGENIC NATURE – AN ELEMENT OF THE ENVIRONMENTAL POLLUTION LIABILITY INSURANCE OF THE INDUSTRIAL ENTERPRISES

The article deals with the issues of insuring the environmental pollution risk in economic enterprises. In addition, the research outlines the structure of the environment with reference to the operations of enterprises with hazardous waste production; it also analyses the elements of the environment and the risk factors, which determine the risk situations in the industrial enterprises. Finally, the article discusses the primary role of environmental pollution liability insurance within the concept of risk management in the industrial enterprises

What Determines Emotional Well-Being?

In this paper we use twin data from Australia to explore emotional well-being and its determinants. We aim to accomplish three things. First of all, using twin-fixed effects, and purging the estimates of common family environment and genetic similarities, we can test the robustness of previous findings in the well-being literature. We find that in the monozygotic twin-fixed effects estimations the marital status, health, years of education, and having low income preserve their significance, thus confirming the most pronounced stylized facts in the happiness literature. Second, using information about traumatic events, we test the validity of the adaptation hypothesis, according to which human beings can adapt to both positive and negative shocks and return to some setpoint level of life satisfaction. We find a strong negative effect of more recent traumatic events, such as being assaulted, being raped or being involved in an accident, which effects dissipate over time; thus, we confirm the validity of the adaptation hypothesis. Last but not least, we show that genetic dispositions are important for the within-pair variance of the emotional well-being

Celebrating protest: International Women’s Day

‘Celebrating Protest: International Women’s Day’ is a short documentary film created by Jacqueline Yip and Kristina Misheva; two former students of (IR318) at LSE (Visual International Politics)

The EU Restrictive Measures – What if the Court of Justice of European Union finds them not Being Legal: Cases in Croatia and Republic of Macedonia

The European Union’s restrictive measures or sanctions may be provided against one or more countries, international organizations, natural or legal persons (such as terrorists and terrorist group).In practice most used restrictive measures are the financial restrictions as asset freeze on of individuals or companies, assets bans and travel bans on individuals. But the ultimate objective of a sanction is determined in accordance with the individual situation or situation.The restrictive measures (Article 215 of Treaty of the Functioning of the European Union (TFEU)) are part of Common Foreign and Security Policy (CFSP) and judicial review is available under Article 275 TFEU, which prescribes that the Court has jurisdiction in reviewing the legality of decisions providing for restrictive measures against natural or legal persons adopted by the Council on the basis of Chapter 2 of Title V of the Treaty on European Union. This paper will analyse the legal aspects of restrictive measures and the legal nature of the Court of Justice of the European Union (CJEU) jurisdiction in this field. Second part of the paper will analyse how the European Union (EU) imposes sanctions and embargos among the member states (example Croatia) and how these measures impose to the non -State countries (example R. Macedonia).Key words: restrictive measures, legal instruments, law on international restrictive measures

Digital Finance: Reaching New Frontiers [version 1; peer review: 2 approved]

Digital Finance must become the center of academic research in finance if the European financial industry is to remain competitive in the future. We argue that the new interdisciplinary field of Digital Finance should be prioritized based on the strategic priorities of the European Union, the needs of the finance industry, and the academic research gaps. Digital Finance as an interdisciplinary field will contribute to the strategic priorities of the European Union, such as financing for growth and jobs, financial stability and supervision, financial education, financing for small and medium-sized enterprises, and combating exclusion and inequality in access to credit

A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods

Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhancing risk management. However, these complex models often lack transparency and interpretability, making them challenging to use in sensitive domains like finance. This has led to the rise of eXplainable Artificial Intelligence (XAI) methods aimed at creating models that are easily understood by humans. Classical XAI methods, such as LIME and SHAP, have been developed to provide explanations for complex models. While these methods have made significant contributions, they also have limitations, including computational complexity, inherent model bias, sensitivity to data sampling, and challenges in dealing with feature dependence. In this context, this paper explores good practices for deploying explainability in AI-based systems for finance, emphasising the importance of data quality, audience-specific methods, consideration of data properties, and the stability of explanations. These practices aim to address the unique challenges and requirements of the financial industry and guide the development of effective XAI tools.Comment: 11 pages, 1 figur

Network-Based Models to Improve Credit Scoring Accuracy

Technological advancements have prompted the emergence of peer-to-peer credit services which improve user experience and offer significant reductions in costs. These advantages may be offset by a higher credit risk, due to disintermediation and information asymmetries. We postulate that networkbased information can be employed as a tool for reducing risks through an improved credit scoring model that increases the accuracy of default predictions. Our research assumption is proven by means of empirical analysis that shows how including network parameters in classical scoring algorithms, such as logistic regression and CART, does indeed improve predictive accuracy