Financial econometrics, mathematics, statistics, and financial technology: an overall view

The 2023 2nd International Conference on Financial Technology and Market Management (FTMM 2023) have been conducted during May 27-28, 2023 in Tianjin, China. This conference aims to bring together leading academic scientists, researchers, and scholars to exchange and share their experiences and research results on all aspects of economics, business, and marketing management. It also provides a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of economics, business, and marketing management.
 More than 70 participants from China, Japan, Malaysia, France, and some other countries attended FTMM 2023. About 30 presentations including 6 plenary talks were presented. The presentations are grouped into 9 areas of particular interest: (1): Economic and monetary crisis, (2): Taxes and regulation, (3): Emerging markets issues, (4): Corporate finance and governance, (5): Asset pricing, investment, and portfolio management, (6): Financial markets, (7): Risk management and quantitative finance, (8) Econometric modeling and empirical issues, (9): Mathematical Finance.
 With best regards,
 Conference Organizing Chair
 Tianjin, China

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.

Strengthening the combination of science and technology finance is an important means to implement innovation driven development strategy.Since 2011, the Ministry of science and technology in conjunction with the eight ministries, the Ministry of finance, people's Bank of China and other selected 16 Beijing Zhongguancun, Chengdu hi tech Zone, Shanghai, Wuhan and other areas, to actively promote the combination of technology and finance pilot work, explore the new mechanism of scientific and technological resources and financial resources docking, and made significant progress.This paper attempts to sum up the experience of the pilot areas of scientific and technological financial innovation, in order to provide a good reference for the further development of Inner Mongolia's science and technology finance.

The study of financial technology applications in banking is highly relevant, as financial technologies serve as a primary driver of change in the banking sector, facilitating the development of new products, optimising operations, and expanding access to financial services. This article aimed to examine the role of financial technologies in the development of banking activities and to identify directions for their advancement through the application of modern financial technologies. The research employed methods of analysis, synthesis, systematisation, and comparison to explore the theoretical foundations of financial technology utilisation. Grouping, tabular, and graphical methods were used to assess the current state of financial technology adoption in the banking sector, alongside abstraction and generalisation techniques. In the digital era, banks must implement innovations to maintain competitiveness. The analysis of financial technology applications has made it possible to identify current trends and potential areas of implementation in the Ukrainian banking sector, as well as to adapt decision-making processes regarding their use to the realities of the Ukrainian market, ensuring more flexible financial services for clients. It has been identified that the growing popularity of digital platforms has been accompanied by an increase in cyber risks. Therefore, the study of fintech has also contributed to ensuring cybersecurity and customer trust, with the development of effective data protection strategies emerging as a key factor in the stability of the banking system. The study also reviewed academic research confirming the relevance of financial technologies in banking, analysed the nature of fintech, its types and role in the banking sector, and assessed the state of Ukraine’s fintech market. Recommendations for the development of the fintech market were proposed. The practical implementation of the recommendations provided in the study, particularly concerning the adoption of modern financial technologies such as blockchain, artificial intelligence, Big Data, and open interfaces, will enhance transparency, cybersecurity, and the adaptability of both the banking sector and the financial market of Ukraine as a whole to contemporary challenges

Financial mathematics is very closely related to financial markets, and in real life, financial mathematics has a wide range of applications in financial markets. This paper discusses the specific application of financial mathematics in financial markets through case analysis, entropy weight method, multivariate statistical regression analysis, etc., and measures the development index of China's financial market and evaluates the development factors of China's financial market. Finally, based on the conclusions, some suggestions were made, hoping to help promote the more coordinated development of financial mathematics and financial markets.

This paper studies the properties of a particular estimator of the fractal index of a time series with a view to applications in financial econometrics and mathematical finance. We show how measurement noise (e.g., microstructure noise) in the observations will bias the estimator, potentially resulting in the econometrician erroneously finding evidence of fractal characteristics in a time series. We propose a new estimator which is robust to such noise and construct a formal hypothesis test for the presence of noise in the observations. A number of simulation exercises are carried out, providing guidance for implementation of the theory. Finally, the methods are illustrated on two empirical data sets; one of turbulent velocity flows and one of financial prices.

In recent years, artificial intelligence has seen increasing interest and popularity in the financial services and the other areas like supply chain management. Since the late 1970s, artificial intelligence has been developed to improve human decision-making processes and productivity in businesses, due to the ability to understand patterns and businesses phenomena, to search and analyze information and automating tasks repeatedly by humans. According to Tungsten Network, that valuable time and money are wasted on trivial supply chain related-tasks that are conducted operationally by humans. So, a business could automate some tasks to reduce wasting time with the robotic process, and machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. In addition to using artificial intelligence in the supply chain, its presence in financial technology can be noted, including advanced machine learning software and expert systems that are capable of learning and performing intelligent analysis, as well as the automation of some business processes in financial. Meanwhile, given the focus on supply chain finance, which includes solutions for suppliers, manufacturers, vendors, and customers that improve financial processes in the supply chain, the industry is moving toward a revolution and a massive transformation in this approach. This paper gives an overview of artificial intelligence and the application areas of this technology and the current use in financial technology and supply chain management. This paper will also explore the benefits using Artificial Intelligence technologies in the Financial Technology and Supply Chain Management businesses.

Lutsyshyn O.O., Kravchuk N.Ya. FINTECH AS AN INDICATOR OF TRANSFORMATIONS OF THE GLOBAL FINANCIAL LANDSCAPE: TRENDS, CHALLENGES AND POTENTIAL OF UKRAINE Purpose. The aim of the article is to identify key trends in financial technology and assess their impact on the stability and adaptability of financial systems, particularly in Ukraine, in the context of global transformations. Methodology of research. The study uses an interdisciplinary approach that combines the tools of economic theory, financial analytics, innovation theory, and cybersecurity. The methodological basis consists of methods of scientific generalization and abstraction, content analysis of scientific and analytical sources, as well as the case method for analysing key FinTech trends. A systematic approach and elements of system modelling were used to identify the relationships between FinTech innovations, regulatory changes, and economic stability. The Ukrainian context was studied using comparative, analytical and descriptive methods, which made it possible to assess the specifics of FinTech development in wartime and determine its potential for post-war recovery and integration into the global financial space. Findings. Financial technologies (FinTech) were studied as a key indicator of transformations in the global financial landscape in the context of the digitalization of the world economy. It is substantiated that the dynamic development of FinTech innovations is changing traditional models of financial intermediation, the architecture of financial markets, and approaches to regulation, while creating new opportunities and risks for the economic development of states. The main global trends in the transformation of the global financial landscape are analysed, in particular the development of the Open Banking concept, the spread of central bank digital currencies (CBDCs), the introduction of artificial intelligence technologies in financial services, the growing role of ESG finance, and the strengthening of cybersecurity requirements. It is shown that FinTech is a systemic factor in increasing financial inclusion, payment infrastructure efficiency, and financial transaction transparency, while at the same time highlighting the problems of regulatory fragmentation, data protection and technological dependence. Particular attention is paid to analysing the Ukrainian context of FinTech development in the conditions of full-scale war and the prospects for post-war recovery. It is determined that Ukraine has formed a unique model of digital financial stability based on a high level of digitalization of public and financial services, the development of cashless payments, the adaptability of financial institutions, and innovative solutions in the field of cyber security. The potential of FinTech as a tool for economic recovery, attracting investment, expanding access to financial services for the population and businesses, and harmonizing financial regulation with European standards is substantiated. Originality. It has been proven that FinTech goes beyond a purely technological phenomenon and is becoming a strategic element of Ukraine's economic stability, competitiveness, and integration into global financial processes. It has been concluded that the effective realization of FinTech potential requires coordinated action by the state, the financial sector, and international partners in the areas of regulatory development, cybersecurity, innovation support, and human capital development. Practical value. The research has significant practical value, as its results can be used to shape and improve state policy in the field of financial technology development, modernization of the regulatory environment, and enhancement of the financial system's stability. The results can be used by government authorities and regulators in developing strategies for the digitalization of the financial sector, by financial market participants in implementing innovative FinTech solutions and managing risks, and in the scientific and educational process in training specialists in finance, economics, and digital technologies. Key words: financial technologies, FinTech, global financial landscape, Open Banking, central bank digital currencies (CBDC), artificial intelligence, cybersecurity, ESG finance, digital transformation, financial inclusion, economic stability.

The development of financial technologies fundamentally changes the face of the financial market, the speed of execution of financial transactions, and opportunities for attracting new customers. The growing role of digital assets (forecast 2024 AUM USD 80.08 billion, the number of users in the digital payment market will grow to 4805.00 million people by 2028) opens up a whole range of issues that arise before the regulatory authorities of different countries, and which need to be regulated, following the rules of the open free market on the one hand, and protecting both entire financial systems and individual users from possible risks arising from the development of new financial technologies. The main goal of the presented research is the analysis and systematization of existing approaches that are used to regulate the activities of economic entities such as FinTech and TechFin, including in the field of circulation of cryptocurrencies as an element of the financial technology market, as well as the substantiation of opportunities and threats for the traditional financial market, which arise as a result of the active development of FinTech and TechFin companies. As a result of a comparative analysis of the characteristics of FinTech and TechFin companies, it was established that these players in the financial market are differentiated by such features as access to capital, access to technology, access to databases, availability and the possibility of using technological infrastructure. It has been established that the absence or low level of harmonization of regulatory norms regarding the provision of financial services can limit competition, provoke unscrupulous behaviour in the market, and negatively affect consumers of financial services. It has been proven that it is the harmonization of regulatory requirements and their differentiation according to established distinctive features that will contribute to preserving the integrity of financial markets and financial inclusion.

Artificial intelligence is one of the most popular topics in today's era, and it is also an important development strategy of our country. In order to train high-level talents of artificial intelligence, the major of machine learning of financial science. China has gradually explored a set of relatively fixed teaching methods for the major of financial science and technology machine learning. However, in combination with the needs of the current era, industrial production puts forward higher requirements for the study of this major, It makes the traditional teaching method backward and unsuitable. In order to seek a more efficient teaching mode, it is urgent to reform the current teaching of financial technology machine learning. In this context, combined with the advanced teaching concept of intelligent information processing course group, this paper re plans the related courses of financial science and technology machine learning specialty, enhances the relevance between courses, enables the courses to connect and cooperate with each other, and forms a chain of excellent course group. strengthen the theoretical research, and strive to build a high-level teaching team to form a more three-dimensional and more close to the needs of the times. In order to investigate the rationality of the teaching reform, this paper carries on the verification analysis under the massive real data, obtains the reform method is scientific, is feasible through the analysis, and will play the positive role to the financial science and technology machine learning curriculum teaching reform under the intelligent information processing curriculum group.

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.

This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathematics program, this book contains all necessary theoretical and mathematical concepts and numerical methods, as well as the necessary programming code for porting algorithms onto a computer.