Financial Market Dynamics Research Articles

The Impact of White Noise on Chaotic Behavior in a Financial Fractional System with Constant and VariableOrder: A Comparative Study

In this study, we investigated the impact of white noise on the chaotic dynamics of a financial system with Caputo fractional derivatives of constant- and variable-order. We solve thesefractional financial systems numerically. We analyze the chaotic behavior of these fractional-order hyperchaotic systems through simulations, study the effects of white noise on chaotic dynamics, and provide a comparison between fractional hyperchaotic systems of constant and variable orders. The study reveals that white noise can either amplify or suppress chaos, with significant differences observed between the constant- and variable-order cases. We obtained numerous intriguing graphical findings for the model by evaluating various scenarios. The findings provide useful information for better understanding financial market dynamics in the presence of noise.

Navigating Economic Uncertainties: The Role of Technological Innovations in Enhancing Supply Chain Resilience

This study explores the multifaceted impact of economic indicators, financial market dynamics, and technological innovations on supply chain efficiency and resilience. Through comprehensive quantitative analyses, including panel data regression, multivariate regression, and time-series analysis, we dissect the relationship between GDP growth rates, inflation, unemployment rates, interest rate fluctuations, exchange rate volatility, stock market trends, and supply chain performance metrics. Our findings highlight a significant correlation between economic growth and supply chain efficiencies, the exacerbating effect of inflation and unemployment on supply chain costs and demand, and the nuanced impacts of financial market dynamics on supply chain financing and operational strategies. Moreover, we delve into the transformative potential of digitalization, automation, blockchain technology, and advanced analytics in mitigating risks associated with economic fluctuations and financial uncertainties. The empirical evidence suggests that technological innovations not only enhance supply chain resilience but also offer strategic advantages in navigating the complexities of global economic landscapes. This study underscores the critical need for adaptive strategies that leverage technological advancements to sustain supply chain competitiveness in an era of economic and financial volatility.

Decoding Market Efficiency: A Multifractal Approach to Financial Market Dynamics

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

Synchronization and limit cycles in a simple contagion model with time delays

We examine a system of N=2 coupled non-linear delay-differential equations representing financial market dynamics. In such time delay systems, coupled oscillations have been derived. We linearize the system for small time delays and study its collective dynamics. Using analytical and numerical solutions, we obtain the bifurcation diagrams and analyze the corresponding regions of amplitude death, phase locking, limit cycles and market synchronization in terms of the system frequency-like parameters and time delays. We further numerically explore higher order systems with N>2, and demonstrate that limit cycles can be maintained for coupled N−asset models with appropriate parameterization.

Business and financial cycle across regimes: Does financial stress matter?

Identifying the business and financial cycles and their regimes is crucial for understanding the economy's state and financial market dynamics. Using a bivariate four-regime Markov-switching framework, we assess the accuracy of a model that identifies the business cycle by combining financial and real variables and compare it to a model that only uses real variables. We find that a model that incorporates information from a financial stress index outperforms a model based solely on real variables, not only during a financial crisis, but also, and more interestingly, during economic downturns. The empirical evidence provides insights for policymakers and investors to improve economic policy formulation, investment strategies, strategic decision-making, and proactive risk management. The results are robust to various model specifications.

Modeling financial market dynamics with the use of fuzzy

In the modern world, financial markets play an important role in the economy and people’s lives. They provide access to financial resources and are also a source of profit for many companies. However, instability in the financial markets can lead to serious consequences such as financial crises and loss of investor confidence. In this regard, modelling the financial market dynamics becomes increasingly relevant. This work considered the use of fuzzy mathematics for this purpose. Fuzzy mathematics is a branch of mathematics that studies methods and algorithms for dealing with fuzzy data and fuzzy objects. It allows to consider uncertainty and incompleteness of information, which is especially important in the financial markets where data is often incomplete and inaccurate. The purpose of this research is to establish the relationship between financial asset prices while using behavioural factors (investor sentiment), fundamental (market returns), and microstructural ones (company size, ratio of book and market values of the company). The application of fuzzy mathematics in financial modelling will improve the accuracy and reliability of forecasts as well as increase the stability of the model to various sources of uncertainty.

Forecasting financial market dynamics: an in-depth analysis of social media data for predicting price movements in the next day

Forecasting financial market dynamics: an in-depth analysis of social media data for predicting price movements in the next day

Strategic alliances and competitive advantage of commercial banks in Kenya

Commercial banks in Kenya operate in quite dynamic financial market environment and continue to experience stiff competition in the provision of financial services. The liberation of the financial markets has resulted into an upsurge in the number of institutions providing financial products and services, threatening the future of Kenyan commercial banks. This study aimed at determining the effect of strategic alliances on competitive advantage of commercial banks in Kenya. The objectives were to determine effect of joint ventures, equity alliances, and joint research and development on the competitiveness of Kenyan banks. The following theories informed this research; Resource Based View theory, competitive theory, and resource dependence theory. Causal research design was adopted where all the 42 Kenyan commercial banks formed the target population. Census survey sampling design was applied and semi-structured questionnaire used. Reliability and validity results were used to improve the research instrument prior to the actual data collection. Data was analyzed using descriptive and inferential statistics. Study found that joint venture (?=0.435, p=0.000) equity alliances (?=0.227, p=0.018), and joint research and development (?=0.0.612, p=0.000) had positive significant effects on competitive advantage. With a coefficient of determination (R-square) of 68.9% and overall p-value of 0.000, the study concluded strategic alliances have a statistical significant effect on competitive advantage. The study recommends that managements of commercial banks should fast-truck implementation of such alliances to for improved competitiveness.

Financial network communities and methodological insights: a case study for Borsa Istanbul Sustainability Index

This study investigates the influence of Environmental, Social, and Governance (ESG) scores on the clustering and community formation of companies within various network models. Using daily closing prices of 78 companies operating in the Borsa Istanbul Sustainability Index, we constructed correlation, mutual information (both continuous and discrete), and causality (both linear and nonlinear) networks to analyse intercompany relationships. We performed community detection using the Leading Eigenvector and Girvan–Newman methods, which revealed that companies within the same sector, particularly in the financial and manufacturing sectors, tend to form tight-knit communities. These intra-sectoral clusters reflect strong market behaviour correlations driven by sector-specific factors. Additionally, mixed-sector communities highlighted the presence of significant inter-sector dependencies. To assess the impact of ESG scores on these communities, nonparametric tests such as the Kruskal–Wallis, Conover, and log-rank were applied. Results showed that specific ESG factors, including emission, CSR strategy, innovation, and human rights, significantly influenced community formation. For instance, companies with strong performance in emission reduction and CSR strategies were found to form more cohesive communities, emphasizing the role of sustainability in shaping financial networks. Study findings underscore the critical role of ESG factors in financial market dynamics, promoting sustainable investment practices by highlighting the importance of integrating ESG considerations into investment decisions. These results suggest that sustainability metrics not only affect individual company performance but also contribute to the formation of interconnected communities with shared sustainability practices.

Exploring Market Dynamics: A Qualitative Study on Asset Price Behavior, Market Efficiency, and Information Role in Investment Decisions in the Capital Market

This qualitative study explores market dynamics, asset pricing behavior, market efficiency, and the role of information in investment decisions within the financial markets. The research aims to provide insights into the underlying motivations, perceptions, and experiences of market participants, offering a comprehensive understanding of these complex phenomena. Employing qualitative methods such as semi-structured interviews and textual analysis, data was collected from a diverse range of participants including investors, financial analysts, and market regulators. The study found that market dynamics are influenced by various factors including investor sentiment, economic indicators, regulatory changes, and technological advancements. Behavioral biases among investors, such as herd mentality and overconfidence, challenge traditional theories like the efficient market hypothesis (EMH), indicating the presence of market inefficiencies. Moreover, information plays a central role in shaping investor perceptions and driving market trends, though concerns exist regarding the reliability of information sources in the era of social media and algorithmic trading. The study underscores the importance of investor education, diversified investment strategies, transparency, and regulatory interventions in fostering fair, efficient, and resilient financial markets. Moving forward, addressing these issues will be crucial for informed investment decision-making and advancing our understanding of financial market dynamics.

Digital financial inclusion, analyst attention, and the green innovation in energy firms

The transition towards environmental sustainability in China's energy sector underscores the crucial role of digital financial inclusion and analyst attention. This study employs an empirical analysis of data from listed Chinese energy-sector firms spanning 2011 to 2022, integrating regression models to investigate the impact of digital financial inclusion on green innovation and the moderating role of analyst attention. Findings reveal that digital financial inclusion significantly facilitates green innovation in energy firms, and analyst attention further amplifies this effect. Heterogeneity analysis shows regional differences, with the eastern and central regions benefiting more from digital financial inclusion, while the western region shows no significant effect. Additionally, state-owned enterprises experience a stronger positive impact compared to non-state-owned enterprises. The study highlights the synergistic potential between digital financial mechanisms and market dynamics in driving sustainable practices. Implications extend to policymakers, financial institutions, and energy companies, suggesting that enhancing digital financial access and market visibility of green initiatives can collectively advance sustainability goals within the energy sector, contributing to broader environmental and economic objectives.

Application of the Fractal Brownian Motion to the Athens Stock Exchange

The Athens Stock Exchange (ASE) is a dynamic financial market with complex interactions and inherent volatility. Traditional models often fall short in capturing the intricate dependencies and long memory effects observed in real-world financial data. In this study, we explore the application of fractional Brownian motion (fBm) to model stock price dynamics within the ASE, specifically utilizing the Athens General Composite (ATG) index. The ATG is considered a key barometer of the overall health of the Greek stock market. Investors and analysts monitor the index to gauge investor sentiment, economic trends, and potential investment opportunities in Greek companies. We find that the Hurst exponent falls outside the range typically associated with fractal Brownian motion. This, combined with the established non-normality of increments, disfavors both geometric Brownian motion and fractal Brownian motion models for the ATG index.

The ripple effect: analyzing the contagion of the Tunisian revolution on the Egyptian stock market

Purpose This study aims to test the contagion effect of the Tunisian revolution on the Egyptian stock market. Thus, the purpose of this research is to distinguish the contagion effect from the simple interdependence between these markets. Design/methodology/approach This paper examines the contagion hypothesis between Tunisia and Egypt during the Arab Spring, using a DCC-MGARCH model to capture time-varying contagion effects and dynamic linkages in stock markets. Therefore, to identify the contagion effect from the simple interdependence, the authors apply the pure contagion test developed by Forbes and Rigobon (2002). Findings The findings indicate a contagion effect, as the EGX 30 index exhibited similar changes, positive or negative, as the Tunindex index during the period of the Tunisian revolution. Moreover, the analysis demonstrates the presence of an interdependence between the Tunisian revolution and the Egyptian market, emphasizing the interconnections between these two economies. Practical implications The findings provide investors with a better understanding of financial market dynamics in times of major political unrest, notably on the Tunisian and Egyptian markets. By understanding the contagion effect of the Tunisian revolution on the Egyptian stock market, investors can further explore the complexities of these markets in times of financial crises, which can help mitigate losses and identify strategic investment opportunities. Originality/value This study makes two significant contributions to the field. First, it addresses the scarcity of research specifically focused on the contagion effect during the Arab Spring, aiming to fill this gap by testing the contagion effect of the Tunisian revolution on a nearby market. Second, it extends the contagion test of Forbes and Rigobon (2002), which associates “pure” contagion with a significantly higher correlation between markets during a crisis.

Behavioral Finance: Investor Psychology and Market Outcomes

Purpose: The general objective of the study was to explore how psychological factors and cognitive biases influence investor decisions and market dynamics. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to investor psychology and market outcomes. Preliminary empirical review revealed that psychological factors such as cognitive biases and investor sentiment significantly impacted financial decision-making and market dynamics, leading to anomalies like market bubbles and volatility. It highlighted the need for integrating behavioral insights into financial models to better predict and understand market behavior, emphasizing the importance of financial literacy in mitigating adverse effects of these biases. Unique Contribution to Theory, Practice and Policy: The Prospect Theory, Herding Theory and Efficient Market Hypothesis (EMH) may be used to anchor future studies on behavioural finance. The study recommended further development of behavioral finance models to incorporate psychological factors, and for financial institutions to integrate behavioral insights into their services to guide better investment decisions. It also advised policymakers to design regulations enhancing market transparency and protecting investors from biases, while promoting financial literacy and implementing measures to monitor and mitigate systemic risks from irrational behavior.

Complex Network Model of Global Financial Time Series Based on Different Distance Functions

By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles (p=0.1, 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics.

Monetary policies on green financial markets: Evidence from a multi-moment connectedness network

This paper introduces a novel multi-moment connectedness network approach for analyzing the interconnectedness of green financial market. Focusing on the impact of monetary policy shocks, our study reveals that connectedness within the green bond and equity markets varies with different moments (returns, volatility, skewness, and kurtosis) and changes significantly around Federal Open Market Committee (FOMC) events. Static analysis shows a decrease in connectedness with higher moments, while dynamic analysis highlights increased sensitivity to event-driven shocks. We find that both tight and loose monetary policy shocks initially elevate connectedness within the first six months. However, the effects of tight shocks gradually fade, whereas loose shocks may reduce connectedness after one year. These results offer insight to policymakers in regulating sustainable economies and investment managers in strategizing asset allocation and risk management, especially in environmentally focused markets. Our study contributes to understanding the complex dynamics of the green financial market in response to monetary policies, helping in decision-making for sustainable economic development and financial stability.

Perspectives on Migration and Financial Markets Research

This study comprehensively analyzes the relationship between migration and financial markets. We examine existing research on this subject using a scientometric and bibliometric approach. By employing VOSviewer and Bibliometrix tools, we introduce a novel methodology that enhances comprehension of this intricate relationship. The findings underscore two significant outcomes. Firstly, the impact of migration on financial markets is evident through the substantial flow of remittances and microfinance. Secondly, this study uncovers challenges hindering the integration of migrants into formal banking systems, thereby affecting financial market dynamics. This research deepens our understanding of migration’s implications on financial markets, offering practical insights that can guide policymakers and financial institutions in their decision-making processes.

Long Run and Short Run Relationships Between Sensex Macro Economic Variables

This study delves into the intricate relationships between the Sensex, India's premier stock market index, and key macroeconomic variables, highlighting both long-term and short-term interactions. Macroeconomic variables such as GDP, inflation, interest rates, and foreign exchange reserves are analysed to understand their impact on the Sensex. Additionally, the study considers microeconomic variables, providing a comprehensive view of economic dynamics. By utilizing econometric methods like Vector Auto regression (VAR) and Johansen co integration tests, the research aims to distinguish between immediate economic shocks and sustained economic trends affecting the Sensex. The investigation focuses on Aditya Birla Money as a case study to explore financial market dynamics. The identified research gap emphasizes the need to consider the influence of emerging digital financial assets and fin tech innovations on traditional macroeconomic variables and market stability. By addressing this gap, the study aims to provide a deeper understanding of how technological advancements and digital transformation interact with established economic indicators to affect financial markets. The primary objectives include examining the long-term and short-term relationships between the Sensex and key macroeconomic indicators, identifying the most significant factors influencing Sensex performance, and assessing the predictive power of these variables. Data collection involves both primary sources, such as interviews with financial experts, and secondary sources, including financial databases. The study's findings are expected to offer valuable insights for investors, policymakers, and financial analysts, aiding in informed decision-making and enhancing strategies for risk management in the ever-evolving economic landscape.

The catalyzing role of financial inclusion in decoding environmental challenges and fostering a sustainable future in BRICS‐T

AbstractEnvironmental degradation poses a significant challenge globally, exhibiting diverse manifestations across regions and ecosystems. This study aims to decipher the factors contributing to environmental degradation, with a specific focus on Brazil, Russia, India, China, South Africa, and Türkiye countries, spanning the period of 1990–2018. This investigation revealed intricate interdependencies among financial institutions, market dynamics, energy utilization, demographic shifts, and ecological impacts. According to the findings of studies based on Durbin‐Hausman, Westerlund, CS‐ARDL cointegration, Juodis, Karavias, and Sarafidis and Dumitrescu‐Hurlin causality tests, policies that encourage financial inclusion and energy efficiency should be developed to prevent environmental degradation. On the other hand, attention has been given to the impact of population growth on environmental policy decisions. This research contributes valuable information to the ongoing discourse on the interrelationship between financial inclusion, the energy population, and environmental protection.

American option in a market generated by interactive agents

This study introduces a new financial market model inspired by Remita and Eisele's methodology, incorporating a substantial number of interacting agents denoted by “n”. Focusing on the valuation of European and American options within this vast network of agents, and more specifically on behavior when “n” tends to infinity, the research illustrates the convergence of an initially incomplete market towards completeness. In addition, the study scrutinizes the price stability of options on the underlying risky asset, St, in the context of an increasing number of agents. Taking into account a comprehensive set of internal and external factors influencing market dynamics, this study offers a holistic analysis of option price dynamics and market completeness. By examining the complex interactions between a large number of agents and option pricing, the research provides valuable insights into the complexity of financial markets. The results not only elucidate convergence phenomena within an expanding network of agents, they also shed light on the stability of option prices under ever-changing market conditions. This comprehensive analysis underlines the multifaceted nature of financial markets, highlighting the intrinsic relationship between option price dynamics and various market influences. Overall, this study makes a significant contribution to the understanding of financial market dynamics, particularly in the context of option pricing.