Market Stability Research Articles

STATE REGULATION OF REINSURANCE ACTIVITIES IN UKRAINE DURING THE WAR

This paper examines the evolution of state regulation of reinsurance activities in Ukraine in the period 2022-2024, with a particular focus on the impact of the ongoing conflict with Russia. This paper examines the manner in which the regulatory framework has been adapted to the distinctive challenges posed by the war in order to stabilise the insurance market and ensure effective risk management. The objective of this study is to analyse the regulatory measures and their implications for the insurance and reinsurance sectors, with a particular focus on the changes implemented, their practical results and the broader implications for market stability. The primary objective of this paper is to undertake a comprehensive analysis and evaluation of the alterations in state-level regulation of reinsurance in Ukraine between the years 2022 and 2024. The objective is to identify the regulatory changes implemented to address the distinctive challenges posed by wartime conditions, evaluate their impact on the reinsurance industry, and discuss the broader economic implications. The study aims to provide a comprehensive understanding of the manner in which the regulatory framework has been adjusted in order to maintain industry stability and ensure effective risk management in an unstable environment. Methodology. This study employs a qualitative research methodology, utilising a combination of document analysis and expert interviews. The data was gathered from regulatory reports, policy documents, and statements issued by the National Bank of Ukraine and other pertinent authorities. The analysis is centred on a detailed examination of the key regulatory adjustments, the market responses to these changes, and the practical implications of these modifications. Results. The analysis revealed several significant changes in the reinsurance regulation in Ukraine during the reporting period: 1) strengthening of regulatory supervision; 2) revision of coverage requirements; 3) state and international financial support measures and co-operation with international reinsurers to stabilise the market and manage increased risks. The regulatory changes have had several practical implications for the reinsurance sector: increased financial stability through stronger solvency and capital requirements has improved the financial strength of insurers and reinsurers, helping them to manage the increased risks associated with the conflict. Value / Originality. This paper presents a comprehensive analysis of the regulatory adaptations in Ukraine's reinsurance sector during a period of significant political and economic upheaval. The paper offers valuable insights into the effective adjustment of state regulation in response to extraordinary circumstances, thereby contributing to the broader understanding of risk management in conflict-affected regions. The study is of particular relevance in the context of the prevailing geopolitical circumstances and the necessity to comprehend the manner in which extreme circumstances affect financial regulations. The study addresses a gap in the existing literature on crisis management in the financial sector, with a particular focus on the context of war. The originality of this study lies in its focus on the intersection of war and regulatory adaptation, a topic that has been largely overlooked in the existing literature. By analysing the regulatory responses and their outcomes, the paper makes a contribution to the development of strategies for effective crisis management in the financial and insurance sectors.

The Application of Calculus in Credit Default Swaps: Pricing, Risk Assessment, and Market Implications

This paper aims at the investigation of one of the derivatives, namely from the group of Credit Default Swaps, with the help of calculus and, more particularly, differential equations and integrals. The plan is to show how these calculative instruments may be utilized to price and risk measure CDS and to thereby illustrate the utility of such models in mitigating financial risks. The paper is structured as follows: In Section 2, ‘The mechanics and functionality of CDS’, the focus is brought on the specifics of construction of CDS contracts, and the matter of their pricing. Valuation of CDS is described in section three where differential equations and integrals are used in the mathematical modeling of these instruments. Section 4 also focuses on the connection between CDS and market stability; this section also looks at the positive and negative effects of CDS on the financial markets. Finally, Section 5 provides conclusion and recommendation for the subsequent research.

Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability.

From Algorithms to Market Dynamics: A Literature Review on High-Frequency Trading

Abstract: High-frequency trading (HFT) uses advanced technologies and sophisticated algorithms to make transactions at unprecedented speeds, which could significantly impact modern financial markets. This literature review examines the technological foundations, primary trading strategies, market impacts, regulatory environment, risk management practices, and future research directions in HFT. The findings reveal that HFT could increase market liquidity and price discovery but also increase volatility during periods of high market stress. Regulatory frameworks such as the SECs Market Access Rule and MiFID II aim to monitor and control HFT activities, at the same time effective risk management practices are crucial for maintaining market stability. Future research should focus on emerging technologies such as AI, ML, quantum computing, and blockchain, along with a better understanding of market structure and global regulatory coordination. This review provides valuable insights for market participants, regulators and researchers, contributing to a balanced perspective on HFTs role in contemporary financial markets.

The Impact and Challenges of Digital Transformation on Supply Chain Resilience in Physical Enterprises

Abstract: In the digital era, supply chain resilience (SCR) has become a key pillar of the real economy. Enterprises face various challenges, including cybersecurity threats, market volatility, technological change and changing global conditions. A strong SCR not only ensures continued business operations but also maintains market stability and consumer well-being. Digital transformation helps businesses anticipate market changes, optimize inventory management, improve operational efficiency, and assist in management decisions when necessary by providing unprecedented data transparency and real-time insights. The purpose of this paper is to examine the extent of digital transformation in the supply chains of physical firms globally through a literature review, to identify the factors influencing the resilience of digital transformation in the supply chains of physical firms, and to discover the barriers to digital transformation in the supply chains of physical firms. The study finds that the digital transformation of physical firms' supply chains is still in its infancy, and there are barriers at both managerial and physical levels. Solutions to address these barriers include enhancing employee training, correctly introducing appropriate digital technologies, and improving information security.

Research on the Feasibility of Machine Learning Methods in Stock Price Prediction

Abstract: Stock price prediction has always been an important topic in financial research. Accurate price prediction can not only help investors make informed investment decisions but also enhance market stability and reduce systemic risk. In recent years, with advancements in computing technology and data science, machine learning methods have been increasingly applied in the financial field. Compared to traditional methods, machine learning methods can better handle high-dimensional, nonlinear, and large datasets, thus demonstrating higher prediction accuracy and applicability in stock price prediction.This paper reviews relevant literature and selects five models for empirical research: Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), LightGBM, a combination of LSTM and LightGBM, and Convolutional Neural Network (CNN). The effectiveness of these models in predicting the stock price of Meituan-W (3690) was analyzed and compared in detail. The experimental results show that the LightGBM model performs best in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), proving its significant advantages in handling large-scale, high-dimensional, and nonlinear data. By comparing the prediction results of different models, this paper explores the strengths and weaknesses of each model and their feasibility and effectiveness in practical applications. Machine learning methods have significant potential in stock price prediction, model selection needs to comprehensively consider data characteristics, computational resources, and practical application scenarios.

PUBLIC ADMINISTRATION UNDER MILITARY THREATS: UKRAINIAN EXPERIENCE OF ADAPTING TO CRISIS SITUATIONS

The article examines the Ukrainian experience of public administration under conditions of military threat as of 2022-2024. The key challenges faced by Ukraine during the full-scale war are analyzed, including the destruction of critical infrastructure and the need to ensure uninterrupted provision of public services. Special attention is paid to the digitization of public administration, which played a decisive role in maintaining the stability of state institutions. The role of the «Action» platform and other innovative solutions that enabled interaction between citizens and the government in crisis conditions is described. The impact of decentralization on the ability of local authorities to respond to the challenges of war has been studied. The interaction with international partners within the framework of the SIGMA program, which is aimed at providing support in the reform of public administration with a course towards European integration, is characterized. A comparative analysis of the Ukrainian experience with other European countries that faced large-scale crises was carried out, and unique aspects of the Ukrainian model of adaptation were revealed. The article analyzes strategies for the restoration and modernization of the state administration of Ukraine in crisis conditions. Attention is focused on the interaction between the state and corporate sectors to maintain economic activity even in conditions of active hostilities. The role of business relocation to safer regions and the influence of state support on the stabilization of the labor market and the economy are outlined. Programs aimed at restoring infrastructure and vital services in cooperation with international partners are mentioned. The experience of implementing temporary solutions to ensure the basic needs of the population is described. It examines the impact of international aid and financial support through IMF and World Bank programs that helped maintain budget stability and avoid economic collapse. The perspectives of public administration during Ukraine's integration into European structures and further strengthening of public administration are highlighted. Special attention is paid to the prospects for the development of public administration in the post-war period, the solution of socio-economic problems, the restoration of infrastructure and the attraction of investments. Strategic directions for reforming public administration for the future are proposed, taking into account the experience of the war and cooperation with international institutions.

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Assessing the influence of cryptocurrencies on financial market stability

Assessing the influence of cryptocurrencies on financial market stability

Modernization of the Financial Market in Uzbekistan and Its Development Prospects

This study examines the modernization of Uzbekistan's financial market, focusing on key gaps in securities trading and the role of investment intermediaries. Despite recent reforms, issues remain in market stability and foreign investment attraction. Addressing this, the research analyzes transaction volumes and sectoral performance on the "Tashkent" Republican Stock Exchange during the first half of 2024, combining quantitative data analysis and a literature review. Results show increased banking sector transactions but a decline in overall trading volume. The study emphasizes the need for further reforms, incorporating international best practices to enhance market development and support Uzbekistan's economic growth.

Government accounting supervision and risk of stock price crashes of listed companies: Evidence from the ministry of finance in China

The impact of accounting supervision by China's Ministry of Finance on the capital markets has grown significantly in recent years. Using data from China's Ministry of Finance random inspections from 1999 to 2022, this study examines the effects of government accounting supervision on corporate stock price crash risk. Employing a staggered difference-in-differences (DID) approach, the findings indicate that government accounting supervision significantly reduces the risk of corporate stock price crashes in supervised firms. Heterogeneity analysis reveals that the correlation between government accounting supervision and stock price crash risk is stronger when a company has a low marketization level, high equity concentration, low political affiliation, low audit quality, and low media attention. Findings contribute to the literature on determinants of crash risk and provide empirical support for the effectiveness of government accounting supervision in mitigating financial risks. This study offers valuable insights for policymakers while suggesting that strengthening accounting supervision can enhance market stability and investor protection when considering regional economic development and corporate ownership structures.

Impact of fund cliques on corporate cash dividends: Evidence from China

Utilizing 2007–2021 data on Chinese A-share listed companies, this study explores how fund cliques affect corporate cash dividends. The results reveal that fund cliques can significantly reduce corporate cash dividends. This conclusion is verified after addressing possible endogeneity concerns and conducting a series of robustness tests. Mechanism tests indicate that ownership concentration and agency cost are the primary channels. The results further show that the negative effect of fund cliques on cash dividends is more significant in larger firms, as well as in firms that have lower regional investor protection and greater media attention. Furthermore, we find that fund cliques can significantly increase corporate stock dividends, and the reduction of cash dividends promotes fund cliques’ share lessening, from which funds can obtain higher share returns. This study has some implications for protecting shareholder rights and maintaining capital market stability.

Optimization of Enterprise Financial Risk Management and Crisis Early Warning System Supported by AI

The stability of the financial market plays a key role in the overall health of the economic system, and optimizing corporate financial risk management and crisis warning systems is of great significance. Traditional financial risk management methods often face challenges when processing large-scale, resulting in a decrease in the accuracy of risk assessment. This study combining gated recurrent units (GRU), temporal convolutional networks (TCN) and attention mechanisms. Leverage diverse data sets to improve the accuracy of financial risk assessments and the effectiveness of crisis warnings. Experimental results show that the algorithm outperforms the baseline model in terms of risk prediction accuracy, efficiency and stability. For example, on the LendingClub loan dataset, FLOPs are reduced by more than 46.8%, inference time is improved by more than 48.5%. This method provides new perspectives and solutions for optimizing corporate financial risk management and crisis warning systems. This will have a positive impact on the stable operation of enterprises and other aspects.

The impact of risk factor disclosure on the initial return of IPO companies amidst a pandemic

The capital market has increasingly become a pivotal avenue for enterprises seeking additional capital for expansion or operational enhancements. In raising funds through an Initial Public Offering (IPO), the company must publish its risk disclosure in the prospectus. Therefore, this study aims to investigate the impact of risk disclosure on the initial return of Indonesian companies undergoing IPOs during the pandemic. Using data from 136 out of 164 companies that went public between 2020 and 2022, sourced from the Indonesian Stock Exchange and company websites, the study employs the ordinary least squares method to estimate the impact of risk disclosures on initial returns during the pandemic. The findings reveal that external and overall risk disclosures significantly influence IPO initial returns. Specifically, Indonesian investors were particularly attentive to external and overall risks when evaluating IPOs during the pandemic. This heightened concern suggests that comprehensive risk disclosure can affect investor behavior and financial outcomes for companies going public in uncertain times, highlighting the importance of transparency in risk communication to support investor decision-making and market stability.

Sicherheit als wirtschaftspolitische Herausforderung

Abstract This paper examines the evolving role of security as a key economic and political challenge, focusing on Germany’s response to geopolitical instability, particularly after the Russian invasion of Ukraine. It argues that security is now a central economic factor, influencing investment and market stability. Based on data on defense spending and fiscal policy, the article highlights inadequate earlier efforts to rebuild defense capacity, with rising fiscal pressures. The paper emphasises the need for reforms in Germany’s and Europe’s security strategies to address hybrid threats and financial burdens.

The Relationship Between ESG Ratings and Digital Technological Innovation in Manufacturing: Insights via Dual Machine Learning Models

In the era of the current scientific and technological revolution and industrial transformation, digital technology innovation serves as a critical driver for the high-quality development of manufacturing enterprises. The dual attributes of ESG (Environmental, Social, and Governance) ratings, encompassing "internal governance" and "external support," play a pivotal role in propelling digital technology innovation within these enterprises. This study utilizes a dual machine learning approach to empirically investigate the influence of ESG ratings on the digital technology innovation of manufacturing enterprises and explores the underlying mechanisms. Findings indicate that ESG ratings significantly boost digital technology innovation by alleviating financial market constraints, enhancing customer stability in the product market, elevating human resource levels, and increasing innovation awareness and efficiency. These improvements occur through the mechanisms of "external support" and "internal governance." Moreover, the study reveals that ESG ratings substantially enhance digital technology innovation in state-owned and high-tech manufacturing enterprises, in contrast to their limited impact on non-state-owned and non-high-tech counterparts. Conclusively, the paper proposes policy recommendations focused on heightening enterprise and societal awareness of ESG importance, intensifying supervision and enforcement, and refining the ESG rating system.

Rapid identification of Radix Achyranthis Bidentatae and Radix Cyathulae ingredients using Proofman-LMTIA method

Radix Achyranthis Bidentatae (RAB) and Radix Cyathulae (RC) share the same name “Niu-Xi”, which are two types of well-known herbal medicines in Asian countries due to their long history and extensive pharmacological activities. However, the misuse of counterfeits and confused species of RAB or RC due to the increased demands might lead to improper efficacies and serious health risks to consumers. To address the challenge, this study devises a rapid and accurate method named Proofman-LMTIA for the identification of RAB and RC. A set of species-specific primers were selected to target internal transcription spacer (ITS) gene sequences of RAB and RC. The temperatures of the Proofman-LMTIA systems were optimized, and the specificities, sensitivities and detection limits were investigated. The detection of RAB and RC ingredients based on the Proofman-LMTIA method was finished in less than 20 min. The sensitivities of RAB and RC DNA templates were 50 pg/µL and 10 pg/µL, respectively. The detection limits of RAB and RC DNA samples extracted from artificially adulterated formula granules were 0.1 %. To evaluate the applicability and effectiveness of the Proofman-LMTIA systems, 16 formula granules were collected and analyzed for RAB or RC authentication, among these, 14 out of 16 samples tested positive for RAB or RC components. Overall, the Proofman-LMTIA technique could be a reliable and suitable method for the accurate and rapid identification of RAB and RC ingredients in commercial samples. The popularization of the LMTIA technique will protect the rights of consumers and maintain the stability of the medicine market.

Navigating Ghana's economic waters: Exploring the impact of Fiscal and Monetary policies on stock market performance

This study leverages the Autoregressive Distributed Lag (ARDL) and Exponential Generalized Conditional Heteroscedasticity (EGARCH) models to conduct a thorough examination of the impact of fiscal and monetary policies on the Ghanaian stock market from 1990 to 2022. Key findings indicate that government spending and tax revenue, as components of fiscal policy, are positively associated with stock returns, contrasting with the negative influence of the industrial production index. On the monetary policy front, interest rates are found to negatively affect stock performance, while exchange rates and the money supply exert positive influences. In the short term, government spending enhances stock returns, although the effects of GDP and the industrial production index are inconsistent, with exchange rates and money supply demonstrating a negative impact. The study underscores the profound sway that policy decisions have on stock market dynamics, underscoring an urgent need for investors and policymakers to closely monitor policy shifts and their market reverberations. A pivotal policy recommendation emerging from this research is the strategic synchronization of fiscal and monetary policies by policymakers to underpin stock market stability and growth. Such harmonization can counteract the adverse effects of policy-induced volatility, cultivating an investment-friendly climate. Investors and policymakers are encouraged to draw upon a spectrum of credible sources, encompassing financial news, governmental releases, and market analyses, to remain abreast of policy evolutions. This research offers precious perspectives on the nexus between economic policies and market movements, offering value for academic inquiry and informing practical decision-making strategies.

Stock price spillovers from foreign institutional investor divestment: Evidence from BlackRock's closure of the China Flexible Equity Fund

This study investigates the impact of BlackRock's September 7, 2023, announcement to close the China Flexible Equity fund on Chinese A-share listed firms held by foreign institutional investors (FIIs). Using event study methodology, we find that the cumulative abnormal return (CAR) of these firms significantly decreases around the announcement date. The decline is attributed to investor concerns about the absence of original governance structures and pessimistic sentiments driven by representativeness bias. The effect is more pronounced in firms with overseas business, weaker investor protection, and overvalued stocks. Our findings provide insights into the influence of FIIs on capital market stability amid global financial movements.

Financial flexibility, firm performance, and financial distress: A comparative study of China and the U.S. during pandemics

This study examines the relationship between financial flexibility, financial distress, and firm performance during pandemics, using data from 26,621 North American-listed firms and 5212 China-listed firms from 2000 to 2022. We investigate whether financial flexibility influences firm performance and financial distress, with firm performance acting as a moderator in this relationship. Our findings highlight that financial flexibility is a crucial strategic tool for mitigating economic uncertainties, effectively reducing financial distress in the U.S. In contrast, its impact in China is more complex, influenced by market maturity and regulatory interventions, especially during economic turbulence. The study also reveals differing roles of profitability and firm size between the two markets. In the U.S., larger and more profitable firms prioritize growth over financial stability, whereas in China, there is a greater emphasis on liquidity and market perception. During the SARS pandemic, China's rapid regulatory response and strong government intervention made financial flexibility more effective in reducing financial distress. Conversely, during COVID-19, the U.S. market showed robust mechanisms and corporate adaptability, with financial flexibility helping firms manage liquidity crises and market volatility. In China, stricter regulations during COVID-19 limited the role of financial flexibility, but strong government support-maintained market stability. Overall, this study underscores the different impacts of financial flexibility on Chinese and U.S. markets during pandemics and the critical role of firm performance in moderating the relationship between financial flexibility and financial distress under varying economic conditions and policy environments.