Financial Market Research Articles

Legal regulation of the securities institute in the context of integration of new regions of Russia

Before joining the Russian Federation, the Donetsk and Lugansk People’s Republics already had their own, Russia-oriented, experience in legal regulation in the financial market. The Zaporozhye and Kherson regions are moving into the Russian legal field directly from the law of Ukraine. The Ukrainian legislation, focused on the law of the European Union, provides for a wider range of equity securities than the Russian one, it is also clearer and more structured, therefore, an investor in the securities market, according to the author, will feel more comfortable in the Ukrainian legal field than in the Russian one. However, Russia has a longer experience of legal regulation of modern stock market instruments and the application of relevant norms. The effective integration of new regions into the Russian legal field would be facilitated by the streamlining of legal norms in our country concerning the institution of securities.

ФОРМУВАННЯ НА СУЧАСНОМУ ЕТАПІ РИНКУ ФІНАНСОВИХ ТЕХНОЛОГІЙ В СВІТОВІЙ ЕКОНОМІЦІ

The current stage of development of the world economy is characterized by the process of increasing the role of the financial sector, as well as the transition to a new phase of development - innovation. Globalization in financial innovations is critical in international innovative development in today's conditions. The rapid spread of financial innovations under the conditions of financial globalization has become the main driver of the formation of the world economic system, contributing to the deepening of financial globalization. The formation of the financial services market is uneven because various factors influence it, including the level of development of information and communication technologies, the stability of the financial sector, and others. These factors have been studied by researchers, but the development of specific segments of the fintech market has its peculiarities. Fintech is a complex system that unites the sectors of new technologies and financial services, startups, and the relevant infrastructure. The financial services sector is increasingly actively using new technologies and tools to perform its functions and implement fundamentally new solutions in which consumers of services are interested. New technologies inherently changed not only the historically formed landscape of providing financial services, giving it freshness and dynamics, but also changed the attitude to such a concept as "money", realizing the transition from cash to virtual money in a reasonably short period. The industry provides subjects of financial relations with a wide range of innovative technologies, among which robotics, biometrics, remote banking, P2P lending, and many others have become vital. The fintech industry is expected to grow due to increased market penetration rates for existing services and their international expansion. The transition from the traditional financial services system to a digital one provides excellent opportunities for large financial companies and FinTech startups to work with banks or financial organizations. As more critical financial infrastructure projects come into production, more players will be interested in implementing digital financial technologies.

RESEARCH AGENDA OF SHARIAH-COMPLIANT INVESTMENT USING BIBLIOMETRIC ANALYSIS R STUDIO

Research in the financial industry is becoming more diverse as technology and financial investment products develop. Sharia concepts in financial investment products will be assessed in the context of current financial trends, challenges, and future market opportunities. This study aims to provide an overview of these three things using Systematic Literature Reviews of previous studies. This study's findings can inform future Sharia financial product studies and financial market stakeholders' policy and decision-making

Evolution of Compliance and Cybersecurity: A Risk Management Perspective in Financial Markets

Evolution of Compliance and Cybersecurity: A Risk Management Perspective in Financial Markets

Financial development and tax evasion: International evidence from OECD and non-OECD countries

This study investigates the nexus between financial development and tax evasion across 156 countries from 2000 to 2017. In contrast to previous research focusing solely on banks or financial markets’ development, we employ a more comprehensive financial development index introduced by the International Monetary Fund (IMF) in 2016. This index gauges the progress of financial institutions (FI) and financial markets (FM) in terms of depth, access, and efficiency. Our findings underscore a negative correlation between financial development and tax evasion. Enhanced depth, access, and efficiency in both FI and FM correspond to reduced levels of tax evasion. Nevertheless, disparities emerge between the Organization of Economic Cooperation and Development (OECD) and non-OECD countries. While non-OECD countries exhibit negative associations between FI and FM development and tax evasion, in OECD countries, the role of FI assumes greater significance in curtailing tax evasion. Notably, within OECD countries, the depth of FI and FM emerges as the sole influential factor. This contrasts starkly with non-OECD counterparts, where all dimensions − depth, access, and efficiency − negatively influence tax evasion. Our research has noteworthy implications for policymakers in both categories of countries.

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.

Asymmetric dependency among US national financial conditions and clean energy markets

This paper examines the relationship between the US National Financial Conditions Index (NFCI) and the clean energy industry using quantile and frequency connectedness, cross-quantile, and wavelet quantile correlation (WQC) techniques. Results reveal (a) a stronger dependence between the NFCI and clean energy under bullish market states. Moreover, the total connectedness between the NFCI and clean energy mostly exhibits time-varying characteristics. In particular, clean energy has a greater spillover effect than the NFCI. (b) Dynamic frequency total connectedness at extreme quantiles provided a more comprehensive view of structural shocks in financial markets, and major crises, such as COVID-19, significantly amplified this connectedness. Overall, the WilderHill Clean Energy Index and the NASDAQ OMX Renewable Energy Index demonstrate substantial potential for hedging financial conditions. (c) The cross-quantile correlation results revealed an asymmetric dependency, demonstrating a sustained significant positive relationship between the NFCI and clean energy index (CEI) across the relative higher quantiles and middle quantiles. The WQC showed that the NFCI and specific CEIs tended to exhibit the strongest positive correlations in nonextreme quantiles and lower frequencies. These results can be of considerable interest to various financial market participants.

An updated model of rural hospital financial distress.

To create a model that predicts future financial distress among rural hospitals. The sample included 14,116 yearly observations of 2311 rural hospitals recorded between 2013 and 2019. We randomly separated all sampled hospitals into a training set and test set at the start of our analysis. We used hospital financial performance, government reimbursement, organizational traits, and market characteristics to predict a given hospital's risk of experiencing one of three financial distress outcomes-negative cash flow margin, negative equity, or closure. The model's area under the receiver operating characteristic curve (AUC) equaled 0.87 within the test set, indicating good predictive ability. We classified 30.55% of the observations in our sample as lowest risk of experiencing financial distress over the next 2 years. In comparison, we classified 32.52% of observations as mid-lowest risk of distress, 26.40% of observations as mid-highest risk, and 10.52% of observations as highest risk. Among test set observations classified as lowest-risk, 5.78% experienced negative cash flow margin within 2 years, 1.50% experienced negative equity within 2 years, and zero observations experienced closure within 2 years. Within the highest-risk group, 61.57% of observations experienced negative cash flow margin, 43.02% experienced negative equity, and 3.33% experienced closure. Given the ongoing challenges and consequences of rural hospital unprofitability, there is a clear need for accurate assessments of financial distress risk. The financial distress model can be used by researchers, policymakers, and rural health advocates as a screening tool to identify at-risk rural hospitals for closer monitoring.

Развитие общего финансового рынка ЕАЭС: правовые аспекты

In the article, it was considered the areas of the development of the common financial market in the EAEU: expanding the list of countries participating in the forming of the com-mon financial market; deepening integration in those sectors and areas in which it is already taking place; inclusion of new sectors in the process of forming the common financial market. It is justified that the market of the digital currencies and financial assets will be a new sector of the common financial market in the EAEU in the future (along with banking, insurance and securities market).

Are the leading indicators really leading? Evidence from mixed-frequency spillover approach

Leading indicators, usually measured at low frequency (e.g., monthly or quarterly), are widely used as pioneering indices to foresee the future perspective of macro economy and financial markets. However, it is not clear whether these low-frequency leading indicators really lead the actual performance of financial markets, which are usually measured at high frequency (e.g., daily or weekly). This paper uses a novel mixed-frequency spillover method of to examine the spillover effects between monthly leading indicators and weekly financial market prices in China, aiming to assess the pioneering function of these leading indicators. The empirical results suggest that, first, some monthly leading indicators can lead the monthly performance of financial markets, but all of them fail to lead the weekly performance of financial markets. Second, the mixed-frequency approach yields much higher spillover measures than the common-frequency method of Diebold and Yilmaz (2012). Third, China's stock and commodity markets are spillover transmitters to other financial markets in both common and mixed frequency conditions. Finally, the China Monetary Index (CMI) and the Manufacturing Purchasing Managers' Index (CPMI) are more effective leading indicators than the China Consumer Confidence Index (CCCI). These findings help investors and policymakers to adjust risk management strategies in a timely manner and facilitate more accurate financial forecasting.

Financial Development and Foreign Direct Investment Impact on Economic Growth: Evidence from Nepal

The interplay among financial development, foreign direct investment (FDI), and economic growth has been a subject of keen interest among scholars, both from a theoretical standpoint and through empirical research. Focusing on Nepal, this study examines the distinctive connections among economic expansion, the influx of foreign capital, and the evolution of the financial sector. Given the lack of a definitive metric for financial development, this research adopts the financial development index, along with the financial institution index and financial market index, as alternative indicators. It also considers additional variables that could influence economic growth beyond financial development and FDI. Utilizing the Autoregressive Distributed Lag (ARDL) bounds testing method and the Error Correction Model (ECM), the analysis uncovers a robust positive link between financial development and FDI in enhancing Nepal's economic output. Furthermore, both the short-term and long-term analyses prove to be reliable, devoid of issues like heteroscedasticity, autocorrelation, and multicollinearity. Importantly, the findings indicate that in the face of short-term disruptions, the dynamics between these variables adjust back to a long-term equilibrium state.

The impact of the Russia-Ukraine war on the competitiveness of European airlines

Purpose – The research identifies the impact of the Russia-Ukraine war on the stock prices of six publicly traded European airlines and evaluates their ability to adapt to this situation. Research methodology – The dependence of the development of airline stock prices on the level of public and institutional stress due to the war was measured using GoogleTrends and is analysed on the basis of a Vector Autoregression model (VAR). Findings – A short-term negative relationship was confirmed between the development of stock prices and GoogleTrends; the impact of the stress related to the war was negligible about 5 months after the outbreak thereof. Those companies that were the fastest to adapt to the shock of the war in terms of share prices are identified. Research limitations – The link between GoogleTrends, as an input variable reflecting market sentiment, and the stock prices of European airlines, is considered a limitation. Practical implications – The impact of investor sentiment on the returns on the stocks of airlines is a thing of the past; which is an important finding for financial market participants and airlines alike. Originality/value – The ability of the specific airlines to adapt to the shock of war creates a competitive advantage.

The Mediating Effect Of Market Liquidity On The Relationship Between Financial Liberalization And Stock Market Returns Among East Africa Community Member Countries

The motivation for this study stems from the critical role of financial markets in economic development and the need to understand the factors influencing stock market performance in the East African Community (EAC) member countries. Financial liberalization, market liquidity, and macroeconomic stability are pivotal in shaping stock market returns. However, empirical studies examining these relationships within the EAC context are limited, prompting this comprehensive analysis. The general objective of this study was to investigate the effect of financial liberalization, market liquidity, and macroeconomic factors on stock market returns among EAC member countries. The study was anchored on the theory of financial liberalization, neoclassical theory, efficient market hypothesis, behavioral finance theory, and the general theory of employment, interest, and money. Data were collected from secondary sources, including financial reports, stock exchange databases, and relevant economic databases, covering the period from 2002 to 2021.The study employed fixed-effects regression models, chosen based on the Hausman specification test, to control for unobserved heterogeneity across countries. Baron and Kenny’s approach was used to test for mediation and moderation effects. The analysis involved examining the direct and, mediating relationships between the key variables. The findings revealed that financial liberalization significantly and positively affects stock market returns. Conversely, market liquidity was found to mediate this relationship, enhancing the positive effects of financial liberalization. The study concludes that financial liberalization is a crucial determinant of stock market performance in the EAC region. Market liquidity plays a significant role in amplifying the benefits of financial liberalization. For policymakers, the study recommends policymakers in EAC should focus on developing infrastructure that supports efficient trading systems. This recommendation is based on the finding that higher market liquidity significantly boosts the positive effects of foreign assets and liabilities on stock market returns

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Characteristics of financial capital flow circulation and the risk transmission effect in China

Financial capital is an important force driving China's economic modernisation. Examining the characteristics of financial capital flow circulation across sectors and risk transmission laws is crucial in guiding financial market operations and deepening financial system reforms. This study establishes a 2011–2020 China matrix that includes capital fund flow statements for non-financial corporations, financial institutions, the general government, households, and foreign sources and examines changes in capital flow circulation by developing a financial flow multiplier system model to measure the direct and indirect transmission effects of capital flows between sectors from the perspective of liabilities. Results show that China's overall scale of financial capital flows has increased, but fluctuations have emerged in the last decade. The sectors' financing structure has changed significantly, with financial institutions remaining the primary capital inflow and outflow sectors of the financial market; however, the proportion of liability and investment in the household sector has increased, resulting in increased risk transmission. Moreover, the sectoral fund surpluses and shortages are highly volatile and uncertain, with non-financial enterprises serving as the primary drivers of real-economy investment. The capital flow from financial services to the real economy shows a fluctuating growth trend, primarily flowing to non-financial corporations; however, the proportion exhibits a downwards trend. Finally, we find that the flow transmission relationship between different sectors and financial transactions in the financial market is complex, with both direct and indirect risk transmission effects between sectors. Changes of financial institutions' liabilities significantly affect the liabilities and investments of other sectors.

Modeling and pricing credit risk with a focus on recovery risk

Consider a defaultable bond traded in a financial market that is subject to shocks and regime shifts. Its recovery payment has a hybrid structure, comprising two components: one contingent on historical information up to the time of default, and the other an independent variable indexed by the regime at the time of default. The default intensity, interest rate, and reference rate are assumed to be general deterministic functions of certain state variables, while these state variables jointly follow a jump-diffusion process, with drift and volatility coefficients governed by the regime and with jumps induced by shocks. We construct a risk-neutral pricing measure that prices all risk sources in an integrated manner. A rigorous verification of this pricing measure reveals the corresponding time-dependent market prices of these risk sources. The resulting pricing framework is applicable to most defaultable bonds and credit derivatives.

Determining the leaders of Ukraine’s insurance market based on the adaptation of the DEA method

The escalating wartime risks in Ukraine has led to a rapid reduction in insurance coverage in life and non-life segments. The purpose of the study is to rank Ukraine’s insurance companies based on an adaptation of the DEA method to the insurance market conditions. The study utilized the Supervisory Statistics of the National Bank of Ukraine as data. The study also used ranking of insurance companies based on technical efficiency criteria. The output indicators include the profitability of total capital (Output1) and the occupied share of the insurance market (Output2). The input indicators comprise the volume of total assets of insurers (Input1), the share of equity capital in assets (Input2), the level of gross payments (Input3), and the level of payments to insurance reserves (Input4). The ranking of insurance companies is based on minimizing the distance of each from the bounds of technical efficiency by solving a set of optimization problems. Based on the modeling results, a list of market leaders (7 companies) was formed for the end of the third quarter of 2023. They served nearly 37% of the insurance market in Ukraine, accumulating 41.5% of the total assets of this financial market sector. So, the primary issue for insurance companies is the loss of solvency due to the absence of adequate levels of insurance reserves. Therefore, priority measures should include strengthening regulatory constraints in this financial market segment to facilitate its qualitative renewal.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

Tail-driven portfolios: Unveiling financial contagion and enhancing risk management

In financial markets, tail risks, representing the potential for substantial losses, bear significant implications for the formulation of effective risk management strategies. Yet, there exists a notable gap in understanding the interconnectedness within the global market, particularly when analysing time-series tail data. This study introduces a reliable method for identifying events indicative of tail transitions in financial time-series data. The investigation suggests consistent patterns governing extreme events across diverse industries and different time periods, suggestive of the financial contagion in tail risks. Importantly, time-series tail slopes in specific stocks emerge as viable predictors of price fluctuations in others. These findings offer valuable insights for portfolio diversification and risk mitigation in the interconnected financial market.

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.