International Journal of Financial Studies

19 pages, 1105 KB

Open AccessEditor’s ChoiceArticle

Financial Traits and Convertible Bond Motives: China’s Evidence

by Jiaqi Chen, Xiuwen Lu and Xiongzhi Wang

Int. J. Financial Stud. 2025, 13(4), 240; https://doi.org/10.3390/ijfs13040240 - 16 Dec 2025

Viewed by 1196

Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks [...] Read more.

Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks in China’s unique institutional context. We employ a natural experiment design, constructing a binary logistic regression model to analyze data from Chinese A-share listed companies that issued convertible bonds, corporate bonds, seasoned equity offerings, or rights offerings between 2022 and 2023. Our results reveal a paradox: contrary to risk-transfer theory, firms with lower leverage exhibit a stronger propensity to issue convertible bonds. Instead, motives are driven by high profitability, operational inefficiencies, and robust operating cash flow generation—traits that align with signaling and backdoor equity theories. The study identifies China’s convertible bond market as a dual-track system where regulatory screening distorts classical motives while market frictions amplify the role of convertible bonds in resolving information asymmetry. We conclude with targeted policy implications for regulators and corporate treasurers to enhance market efficiency and governance. Full article

► Show Figures

Figure 1

17 pages, 308 KB

Open AccessEditor’s ChoiceArticle

Assessing the Impact of Digital Transformation on Manufacturing Enterprises’ Performances: An Efficiency Perspective

by Chenxi Wang, Jing Yang, Yan Lin and Biao Xue

Int. J. Financial Stud. 2025, 13(4), 241; https://doi.org/10.3390/ijfs13040241 - 16 Dec 2025

Viewed by 970

Abstract

In recent years, the impacts of the new scientific and technological revolution on the industrial system and production modes have begun to emerge. Digital transformation is gradually being integrated into the production behaviors of manufacturing enterprises and has become a component of the [...] Read more.

In recent years, the impacts of the new scientific and technological revolution on the industrial system and production modes have begun to emerge. Digital transformation is gradually being integrated into the production behaviors of manufacturing enterprises and has become a component of the micro-economy. We aim to find better methods for measuring digital transformation and to analyze its impact on both market performance and innovation performance within manufacturing enterprises. To achieve our goals, we employ an empirical study to examine the influence of digital transformation on market and innovation performance using panel data for Chinese listed manufacturing enterprises from 2012 to 2021. We emphasize digital transformation efficiency and affirm its role in relieving financing constraints. Our study shows that digital transformation effectively improves both the market and innovation performance of manufacturing enterprises. Moreover, it mitigates the financing constraint dilemma, resulting in performance enhancement. Heterogeneity analysis indicates that digital transformation has a more significant promotional effect on the market and innovation performance of large-scale and mature enterprises. Our research offers fresh perspectives for better understanding digital transformation, enriching the body of work on the impact of digital transformation in manufacturing enterprises and its underlying mechanisms. Full article

(This article belongs to the Special Issue Financial System in the Digital Age: Opportunities, Challenges and Future Directions)

22 pages, 631 KB

Open AccessEditor’s ChoiceArticle

Executive Pay-Rank Inversion and M&A Decisions: Evidence from Chinese State-Owned Enterprises

by Shaoni Zhou, Qiyue Du and Zhitian Zhou

Int. J. Financial Stud. 2025, 13(4), 239; https://doi.org/10.3390/ijfs13040239 - 15 Dec 2025

Viewed by 654

Abstract

In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank [...] Read more.

In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank inversion—where subordinates earn more than their superiors. Leveraging this anomaly as a quasi-natural experiment, this study investigates the specific impact and underlying mechanism of pay-rank inversion on mergers and acquisitions (M&A) decisions and subsequent value realization within Chinese SOEs, thereby addressing the broad academic discourse on optimal executive compensation design. Employing a difference-in-differences (DID) approach with panel data spanning from 2007 to 2022, our analysis reveals that pay-rank inversion significantly reduces firms’ M&A intentions. Mechanistic analysis suggests that this negative effect arises primarily from diminished executive risk-taking. Furthermore, we find that the adverse impact is attenuated when CEOs possess longer tenures or receive equity-based incentives, but it ultimately undermines the realization of value post-M&A. These findings highlight the unintended consequences of high-level compensation reforms and emphasize the critical role of a well-structured pay hierarchy in sustaining executive incentives for strategic decision-making. Despite providing robust evidence, this study is subject to limitations, including its focus on measuring inversion only between the first and second management tiers. Future research should extend the analysis to the pay inversion between the listed firm and its controlling SOE group and explore alternative causal pathways beyond risk-taking, such as CEO work motivation, to deepen the understanding of high-level executive behavior. Full article

(This article belongs to the Special Issue Emerging Trends in Corporate Finance: ESG, Climate Risk, and Other Contemporary Issues)

► Show Figures

Figure 1

19 pages, 484 KB

Open AccessEditor’s ChoiceArticle

The Impact of Social Trust on the Development of Digital Finance

by Fan Zeng and Benyong Hu

Int. J. Financial Stud. 2025, 13(4), 232; https://doi.org/10.3390/ijfs13040232 - 4 Dec 2025

Viewed by 832

Abstract

Social trust is a fundamental element in the evolution of digital finance, significantly influencing its development. This study presents an innovative exploration of the role and internal mechanisms of social trust in digital finance, utilizing using provincial panel data from 27 provinces in [...] Read more.

Social trust is a fundamental element in the evolution of digital finance, significantly influencing its development. This study presents an innovative exploration of the role and internal mechanisms of social trust in digital finance, utilizing using provincial panel data from 27 provinces in China spanning the period from 2012 to 2021. By focusing on trust as a core element, the study enriches the research framework on digital finance development, revealing that beyond traditional factors such as technology and the economy, social and psychological factors also affect digital finance growth. These findings provide new perspectives on understanding digital finance development. The study elucidates the complex substitution and interdependence between formal and informal institutions, offering valuable insights for optimizing institutional frameworks to promote digital finance. It also uncovers significant regional heterogeneity in the influence of social trust on digital finance, and social trust primarily enhances the depth and digitization of digital finance, while its effect on the breadth of digital finance is statistically insignificant. These insights serve as a valuable reference for policymakers aiming to ensure the sustainable expansion of the digital finance sector. Full article

► Show Figures

Figure 1

33 pages, 731 KB

Open AccessEditor’s ChoiceArticle

Does the Stock Market Encourage Sustainability? Evidence from UK Investment Announcements

by Kuburat Olayinka Lawal, Edward Jones and Lucy (Jia) Lu

Int. J. Financial Stud. 2025, 13(4), 215; https://doi.org/10.3390/ijfs13040215 - 12 Nov 2025

Viewed by 1242

Abstract

This paper examines the stock market reaction to company investment decisions with and without a sustainability objective. Abnormal returns are estimated using a standard event study methodology for a sample of 517 investment announcements for listed UK firms for the period 2013 to [...] Read more.

This paper examines the stock market reaction to company investment decisions with and without a sustainability objective. Abnormal returns are estimated using a standard event study methodology for a sample of 517 investment announcements for listed UK firms for the period 2013 to 2021. Using a sample of 90 sustainable investments and 427 non-sustainable investments, we test whether 90 announcements with a sustainability agenda are more positively viewed by market participants than 427 announcements without a sustainability agenda. This study documents significant positive stock market reactions to both sets of investments, but abnormal returns are higher for investments without a sustainability agenda. The difference in abnormal returns between both sets of investments is not statistically significant. The findings reported in this study suggest that classifying corporate investment decisions according to information content indicative of a sustainability agenda contains price-sensitive information. This has implications for information made available to the market and will therefore promote price discovery, reducing the information asymmetry between informed and uninformed investors and allowing improved market efficiency in categorizing investment decisions according to investment objectives. In a market-based system, the positive valuation of investments associated with sustainability undertakings has implications for allocative efficiency, because firms become more attractive regarding the future allocation of funds to investment projects that address sustainability concerns, indicating that new sustainable investments should be encouraged. Full article

23 pages, 1356 KB

Open AccessEditor’s ChoiceArticle

Digital Transformation in Accounting: An Assessment of Automation and AI Integration

by Carlos Sampaio and Rui Silva

Int. J. Financial Stud. 2025, 13(4), 206; https://doi.org/10.3390/ijfs13040206 - 5 Nov 2025

Cited by 3 | Viewed by 7296

Abstract

This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results [...] Read more.

This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results reveal that the scientific output within this research field has increased since 2018, emphasising the integration of artificial intelligence (AI), robotic process automation, and blockchain technologies in accounting. The findings also suggest that automation enhances efficiency, accuracy, and reliability while also raising concerns about ethics, cybersecurity, and job displacement. This study evaluates the accounting research from early discussions on information systems and automation to current topics such as digital transformation, sustainability, and intelligent decision-making. Furthermore, it contributes to the understanding of the scientific development of digital accounting and addresses future research directions involving AI and machine learning for predictive analytics and fraud detection, blockchain for secure and transparent accounting systems, sustainability through the integration of ESG reporting, and interdisciplinary collaboration between accounting, computer science, and business management to develop intelligent financial systems. The findings provide insights for academics and practitioners aiming to understand the ongoing digital transformation of accounting systems. Full article

(This article belongs to the Special Issue Technologies and Financial Innovation)

► Show Figures

Figure 1

21 pages, 512 KB

Open AccessEditor’s ChoiceArticle

Determinants of M&A Acquisition Premiums on the European Market in the Period of 2009 to 2022

by Marc Brixius, Jens Kai Perret, Jörg Schröder and Kamilė Taujanskaitė

Int. J. Financial Stud. 2025, 13(4), 204; https://doi.org/10.3390/ijfs13040204 - 3 Nov 2025

Viewed by 2692

Abstract

This study analyzes the development and determinants of control premiums in mergers and acquisitions in the European market from 2009 to 2022 (i.e., stock volatility, liquidity via money supply, sectoral growth, transaction volume, market capitalization, free cash flows, presence of a toehold, public [...] Read more.

This study analyzes the development and determinants of control premiums in mergers and acquisitions in the European market from 2009 to 2022 (i.e., stock volatility, liquidity via money supply, sectoral growth, transaction volume, market capitalization, free cash flows, presence of a toehold, public listing, cross-border transactions, payment types, and sectoral relatedness), whereby control premiums represent the premium that buyers pay above the current market value of a company to gain control. The empirical analysis implements linear as well as quantile regression analyses. Results reveal that the average and median premiums fluctuated notably between 2009 and 2022, with the lowest premiums paid in 2009 and the highest in 2022. Factors such as the volatility of the stock market, capital liquidity, and deal activity within certain sectors have a consistently significant influence on the level of premiums if a longer period of analysis is selected. Cross-border status, payment structure, stock market listing of the acquiring company, and the build-up of a toehold influence the premiums paid in shorter- and longer-term analyses. In contrast, neither the market capitalization nor the free cash flow of the target company has a significant influence on the premiums paid. Full article

► Show Figures

Figure 1

23 pages, 930 KB

Open AccessEditor’s ChoiceArticle

Stock Price Prediction Using a Stacked Heterogeneous Ensemble

by Michael Parker, Mani Ghahremani and Stavros Shiaeles

Int. J. Financial Stud. 2025, 13(4), 201; https://doi.org/10.3390/ijfs13040201 - 28 Oct 2025

Cited by 1 | Viewed by 4862

Abstract

Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous [...] Read more.

Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous set of base learners, such as statistical (ARIMA), machine learning (Random Forest), and deep learning (LSTM, GRU, Transformer) models, with an XGBoost meta-learner. Applied to several major financial indices and a single stock, the proposed framework demonstrates high predictive accuracy, achieving

R^{2}

scores between 0.9735 and 0.9905. These results highlight the efficacy of a multi-faceted stacking approach in navigating the complexities of financial forecasting. Full article

► Show Figures

Figure 1

30 pages, 659 KB

Open AccessEditor’s ChoiceArticle

Hedge Fund Activism, Voice and Value Creation

by Christos Bouras and Efstathios Karpouzis

Int. J. Financial Stud. 2025, 13(4), 200; https://doi.org/10.3390/ijfs13040200 - 24 Oct 2025

Viewed by 1166

Abstract

We construct a novel hand-collected large dataset of 205 U.S. hedge funds and 1025 activist events over the period 2005–2013, which records both the Schedule 13D filing date and the voice date, and explore the role of voice in value creation. We employ [...] Read more.

We construct a novel hand-collected large dataset of 205 U.S. hedge funds and 1025 activist events over the period 2005–2013, which records both the Schedule 13D filing date and the voice date, and explore the role of voice in value creation. We employ alternative inferential statistical approaches, including parametric, non-parametric, and heteroscedasticity-robust tests. We reveal that the voice date is important in creating short-term firm value and provide strong evidence that voice is associated with positive abnormal returns. These findings suggest that voice leads to information revelation, with implications for U.S. stock market arbitrage. Full article

► Show Figures

Figure 1

17 pages, 758 KB

Open AccessEditor’s ChoiceArticle

Impact of ESG Preferences on Investors in China’s A-Share Market

by Yihan Sun, Diyang Jiao, Yiqu Yang, Yumeng Peng and Sang Hu

Int. J. Financial Stud. 2025, 13(4), 191; https://doi.org/10.3390/ijfs13040191 - 15 Oct 2025

Viewed by 1896

Abstract

This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model [...] Read more.

This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model with seven control variables (including firm systematic risk, asset turnover ratio, and ownership concentration) to quantify ESG’s marginal effect on stock returns. Annual regressions (2017–2022) reveal distinct ESG coefficient shifts: insignificant negative coefficients in 2017–2018, significantly positive coefficients in 2019–2020, and significantly negative coefficients in 2021–2022. Heterogeneity analysis across five non-financial industries (Utilities, Properties, Conglomerates, Industrials, Commerce) shows industry-specific ESG effects. Portfolio performance tests using 2023 data (stocks divided into eight ESG groups) indicate that portfolios with medium ESG scores outperform high/low ESG portfolios and the traditional mean-variance model in risk-adjusted returns (Sharpe ratio) and volatility control, avoiding poor governance risks (low ESG) and excessive ESG resource allocation issues (high ESG). Overall, policy shocks and institutional maturation transformed the market from ESG indifference to ESG-motivated pricing within a decade, offering insights for stakeholders in emerging ESG markets. Full article

► Show Figures

Figure 1

21 pages, 498 KB

Open AccessEditor’s ChoiceArticle

Employee Tenure, Earnings Management, and the Moderating Role of Foreign Investors: Evidence from South Korea

by Dongkuk Lim and Dong Hyun Son

Int. J. Financial Stud. 2025, 13(4), 190; https://doi.org/10.3390/ijfs13040190 - 14 Oct 2025

Viewed by 1468

Abstract

This study examines the influence of employee tenure on earnings management and the moderating role of foreign investors in Korean listed firms. Drawing on agency theory and entrenchment perspectives, we argue that longer employee tenure, while fostering stability and firm-specific expertise, can entrench [...] Read more.

This study examines the influence of employee tenure on earnings management and the moderating role of foreign investors in Korean listed firms. Drawing on agency theory and entrenchment perspectives, we argue that longer employee tenure, while fostering stability and firm-specific expertise, can entrench practices that enable opportunistic reporting. In contrast, consistent with resource dependence theory, foreign investors act as effective external monitors who can mitigate such behavior, particularly in emerging markets with weaker governance institutions. Using 11,381 firm-year observations from 2011 to 2019, we estimate discretionary accruals with the modified Jones model and performance-matched model. The results indicate that employee tenure is positively associated with accrual-based earnings management, but this effect is significantly reduced in firms with higher foreign investor ownership. Robustness tests, including instrumental variable estimation, confirm the validity of these findings. This study’s main contributions are introducing employee tenure as an underexplored governance factor, integrating internal and external monitoring perspectives, and showing that foreign investors moderate workforce-related risks. Practically, it highlights that investors can use tenure as a reporting risk signal, managers should complement workforce stability with strong governance, and policymakers should promote tenure disclosure and foreign participation to enhance transparency. Full article

► Show Figures

Figure 1

22 pages, 3211 KB

Open AccessEditor’s ChoiceArticle

The Measurement and Characteristic Analysis of the Chinese Financial Cycle

by Siyuan Qiu

Int. J. Financial Stud. 2025, 13(4), 187; https://doi.org/10.3390/ijfs13040187 - 3 Oct 2025

Viewed by 989

Abstract

In this paper, based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, five financial serials are dynamically weighted, and then China’s Financial Conditions Index is synthesized to measure China’s financial cycle. After that, using the monthly data of 2000–2023 as sample space, this paper [...] Read more.

In this paper, based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, five financial serials are dynamically weighted, and then China’s Financial Conditions Index is synthesized to measure China’s financial cycle. After that, using the monthly data of 2000–2023 as sample space, this paper utilizes the Markov Switching (MS) model to analyze the characteristics of China’s financial cycle and to investigate the four-zone system. Then, the Vector Autoregression (VAR) model focuses on investigating the macroeconomic effects of China’s financial cycle. The findings are as follows: Firstly, the dynamic weighting approach based on GARCH model is more suitable for valuating China’s financial cycle. Secondly, China’s financial cycle has a strong inertia at the state of transition and the imbalance of China’s overall financial situation is very common. Additionally, China’s financial cycle is distinctly characterized by the double asymmetry of fewer contractions and more expansions, shorter expansions, and longer expansions. Thirdly, China’s financial expansion offers a nine-month short-term stimulus to output and exerts lasting upward pressure on prices. Full article

► Show Figures

Figure 1

36 pages, 1441 KB

Open AccessEditor’s ChoiceArticle

When Financial Awareness Meets Reality: Financial Literacy and Gen Z’s Entrepreneurship Interest

by Eva Kicova, Jakub Michulek, Olga Ponisciakova and Juraj Fabus

Int. J. Financial Stud. 2025, 13(3), 171; https://doi.org/10.3390/ijfs13030171 - 11 Sep 2025

Cited by 1 | Viewed by 7044

Abstract

Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied [...] Read more.

Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied factor that influences entrepreneurial behaviour. A mixed-methods approach was applied, combining a survey of 403 Slovak students with structured interviews with secondary school and university teachers. Quantitative analysis used Chi-square tests, Cramer’s V, sign schemes, and MLR. Qualitative interviews provided contextual insights into educational gaps and perceived barriers to entrepreneurship. The findings confirm that a higher financial literacy is positively related to entrepreneurial interest. Objective literacy has a slightly greater predictive value than self-assessed literacy, while calibration emerged as the strongest predictor: realistically, financially literate individuals displayed the highest entrepreneurial engagement, whereas both over- and underestimation of financial knowledge reduced it. Interviews highlighted insufficient financial education, limited practical experience, and fear of risk as major obstacles. By combining three aspects of financial literacy with business goals and offering fresh data from Slovakia, this study makes a contribution to the literature. In similar situations, it makes suggestions for enhancing financial education to support Generation Z’s entrepreneurial potential. Full article

► Show Figures

Figure 1

24 pages, 1322 KB

Open AccessEditor’s ChoiceArticle

Predictive Power of ESG Factors for DAX ESG 50 Index Forecasting Using Multivariate LSTM

by Manuel Rosinus and Jan Lansky

Int. J. Financial Stud. 2025, 13(3), 167; https://doi.org/10.3390/ijfs13030167 - 4 Sep 2025

Cited by 1 | Viewed by 2788

Abstract

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed [...] Read more.

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed firms. We employ a multivariate long short-term memory (LSTM) network, a machine learning model ideal for time series data, to test this hypothesis over two periods: an 8-year analysis with a full set of ESG scores and a 16-year analysis with a single disclosure score. The evaluation of model performance utilizes standard error metrics and directional accuracy, while statistical significance is assessed through paired statistical tests and the Diebold–Mariano test. Furthermore, we employ SHapley Additive exPlanations (SHAP) to ensure model explainability. We observe no statistically significant indication that incorporating annual ESG data enhances forecast accuracy. The 8-year study indicates that using a comprehensive ESG feature set results in a statistically significant increase in forecast error (RMSE and MAE) compared to a baseline model that utilizes solely historical returns. The ESG-enhanced model demonstrates no significant performance disparity compared to the baseline across the 16-year investigation. Our findings indicate that within the one-month-ahead projection horizon, the informative value of low-frequency ESG data is either fully incorporated into the market or is concealed by the significant forecasting capability of the historical return series. This study’s primary contribution is to demonstrate, through out-of-sample testing, that standard annual ESG information holds little practical value for generating predictive alpha, urging investors to seek more timely, alternative data sources. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: ESG Ratings and Disclosures)

► Show Figures

Figure 1

29 pages, 13156 KB

Open AccessEditor’s ChoiceArticle

Exchange Rate Forecasting: A Deep Learning Framework Combining Adaptive Signal Decomposition and Dynamic Weight Optimization

by Xi Tang and Yumei Xie

Int. J. Financial Stud. 2025, 13(3), 151; https://doi.org/10.3390/ijfs13030151 - 22 Aug 2025

Cited by 1 | Viewed by 4369

Abstract

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain [...] Read more.

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article

► Show Figures

Figure 1

24 pages, 566 KB

Open AccessEditor’s ChoiceArticle

Liquidity Drivers in Illiquid Markets: Evidence from Simulation Environments with Heterogeneous Agents

by Lars Fluri, Ahmet Ege Yilmaz, Denis Bieri, Thomas Ankenbrand and Aurelio Perucca

Int. J. Financial Stud. 2025, 13(3), 145; https://doi.org/10.3390/ijfs13030145 - 18 Aug 2025

Viewed by 1479

Abstract

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital [...] Read more.

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article

(This article belongs to the Special Issue Market Microstructure and Liquidity)

► Show Figures

Figure 1

26 pages, 333 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Financial Discrimination: Consumer Perceptions and Reactions

by Miranda Reiter, Di Qing, Kenneth White and Morgen Nations

Int. J. Financial Stud. 2025, 13(3), 136; https://doi.org/10.3390/ijfs13030136 - 24 Jul 2025

Viewed by 1566

Abstract

Access to traditional financial institutions plays a key role in enhancing positive financial outcomes. However, some consumers within the United States experience discrimination from these same institutions. In particular, discrimination based on race and gender has historically been tied to outcomes such as [...] Read more.

Access to traditional financial institutions plays a key role in enhancing positive financial outcomes. However, some consumers within the United States experience discrimination from these same institutions. In particular, discrimination based on race and gender has historically been tied to outcomes such as lower service quality and a lack of access to credit. While the previous literature has discussed some of the discriminatory practices that these groups have faced, there is a lack of research on how these groups respond to discrimination from financial institutions. Through a series of logistic regressions, the authors analyzed how race, ethnicity, and gender are related to reporting experiences of discrimination. The authors then explored how consumers react to discrimination by looking at five reported reactions. Primary results show that Black consumers were more likely than most other racial groups to experience financial discrimination. Additionally, women were less likely than men to report financial discrimination. Race was shown to be a significant factor in four of the five reactions to discrimination, while gender was a factor in two of the reactions. The findings further show that after experiencing financial discrimination, most individuals turned to non-traditional financial services as a direct result of the bias or racism. Full article

16 pages, 350 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Bitcoin Return Dynamics Volatility and Time Series Forecasting

by Punit Anand and Anand Mohan Sharan

Int. J. Financial Stud. 2025, 13(2), 108; https://doi.org/10.3390/ijfs13020108 - 9 Jun 2025

Viewed by 9296

Abstract

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time [...] Read more.

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA. Full article

► Show Figures

Figure 1

20 pages, 343 KB

Open AccessEditor’s ChoiceArticle

Is the ESG Score Part of the Set of Information Available to Investors? A Conditional Version of the Green Capital Asset Pricing Model

by Lucía Galicia-Sanguino and Rubén Lago-Balsalobre

Int. J. Financial Stud. 2025, 13(2), 88; https://doi.org/10.3390/ijfs13020088 - 21 May 2025

Cited by 2 | Viewed by 1398

Abstract

In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate [...] Read more.

In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate change considerations by investors. We use ESG scores as a part of the information set used by investors to determine the unconditional version of the conditional capital asset pricing model (CAPM). Our results show that the ESG score allows the linearized version of the conditional CAPM to greatly outperform the classic CAPM and the Fama–French three-factor model for different sorts of stock portfolios, contributing significantly to reducing pricing errors. Furthermore, we find a negative price of risk for stocks that covary positively with ESG growth, which suggests that green assets may perform better than brown ones if ESG concerns suddenly become more pressing over time. Thus, our paper constitutes a step forward in the attempt to shed light on how climate change is priced regardless of the climate risk measure used. Full article

30 pages, 635 KB

Open AccessEditor’s ChoiceArticle

Tax Compliance Determinants in a Challenging Fiscal Environment: Evidence from a Greek Experiment

by Skoura V. Angeliki and Dasaklis K. Thomas

Int. J. Financial Stud. 2025, 13(2), 83; https://doi.org/10.3390/ijfs13020083 - 10 May 2025

Cited by 1 | Viewed by 4138

Abstract

This study investigates the factors influencing tax compliance among Greek entrepreneurs functioning within a difficult fiscal landscape. Through a randomized field experiment, we analyze the effects of differing tax rates, audit likelihoods, and legal frameworks on compliance behavior. Utilizing regression analysis alongside robustness [...] Read more.

This study investigates the factors influencing tax compliance among Greek entrepreneurs functioning within a difficult fiscal landscape. Through a randomized field experiment, we analyze the effects of differing tax rates, audit likelihoods, and legal frameworks on compliance behavior. Utilizing regression analysis alongside robustness checks, our results indicate that greater transparency in audits and customized penalty systems markedly improve compliance rates. These findings highlight the critical role of cultural and regulatory elements in determining taxpayer conduct and provide valuable insights for policymakers in both national and international tax systems. This study contributes to the ongoing discourse surrounding tax evasion and compliance, positioning Greece as a potential reference point for comparable economies in the European Union. Full article

► Show Figures

Figure 1

27 pages, 1202 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Financial Sentiment Analysis and Classification: A Comparative Study of Fine-Tuned Deep Learning Models

by Dimitrios K. Nasiopoulos, Konstantinos I. Roumeliotis, Damianos P. Sakas, Kanellos Toudas and Panagiotis Reklitis

Int. J. Financial Stud. 2025, 13(2), 75; https://doi.org/10.3390/ijfs13020075 - 2 May 2025

Cited by 7 | Viewed by 11927

Abstract

Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and Logistic Regression, served as [...] Read more.

Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and Logistic Regression, served as our baseline models. While somewhat effective, these conventional approaches often struggled to capture the complexity and nuance of financial language. Recent advancements in deep learning, particularly transformer-based models like GPT and BERT, have significantly enhanced sentiment analysis by capturing intricate linguistic patterns. In this study, we explore the application of deep learning for financial sentiment analysis, focusing on fine-tuning GPT-4o, GPT-4o-mini, BERT, and FinBERT, alongside comparisons with traditional models. To ensure optimal configurations, we performed hyperparameter tuning using Bayesian optimization across 100 trials. Using a combined dataset of FiQA and Financial PhraseBank, we first apply zero-shot classification and then fine tune each model to improve performance. The results demonstrate substantial improvements in sentiment prediction accuracy post-fine-tuning, with GPT-4o-mini showing strong efficiency and performance. Our findings highlight the potential of deep learning models, particularly GPT models, in advancing financial sentiment classification, offering valuable insights for investors and financial analysts seeking to understand market sentiment and make data-driven decisions. Full article

(This article belongs to the Special Issue Modern Financial Econometrics)

► Show Figures

Figure 1

28 pages, 315 KB

Open AccessEditor’s ChoiceArticle

Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity

by Changrong Lu, Lian Liu, Fandi Yu, Jiaxiang Li and Guanghong Zheng

Int. J. Financial Stud. 2025, 13(2), 72; https://doi.org/10.3390/ijfs13020072 - 1 May 2025

Cited by 1 | Viewed by 1343

Abstract

This study aims to analyze the spatial effects triggered by dollar liquidity by constructing a multidimensional spatial matrix that modifies the traditional monetary spatial framework. We utilized a three-level spatial econometric model (Spatial Lag, Durbin, and Generalized Nested Space) to measure Gross Domestic [...] Read more.

This study aims to analyze the spatial effects triggered by dollar liquidity by constructing a multidimensional spatial matrix that modifies the traditional monetary spatial framework. We utilized a three-level spatial econometric model (Spatial Lag, Durbin, and Generalized Nested Space) to measure Gross Domestic Product (GDP), Consumer Price Index (CPI), and Asset Price Bubbles (BBL) through five spillover channels (geography, linguistics, politics, war, and economy). Our aim is to establish a systematic relationship between the conduction mechanism, means, economic indicators, and dollar externalities to examine liquidity spillover effects at varying distances in the global monetary space. We find that the spatial effects induced by the global circulation of the US dollar behave significantly differently in a single matrix space compared to in a multidimensional space. While the model verifies the existence of a positive correlation between the complexity of a single space and the spillover effect from a conduction mechanism perspective, the measure of the multidimensional matrix shows that the significance of the spillover effect weakens with an increase in abstraction level from a conduction means perspective. It suggests that spatial matrices of different dimensions reflect different economic realities. The former shows hierarchical multivariate details in independent matrices, while the variation in the level of abstraction of matrices of different dimensions in the latter enhances their interactivity and complexity. Full article

27 pages, 1863 KB

Open AccessEditor’s ChoiceArticle

The Impact of Bank Fintech on Corporate Short-Term Debt for Long-Term Use—Based on the Perspective of Financial Risk

by Weiyu Wu and Xiaoyan Lin

Int. J. Financial Stud. 2025, 13(2), 68; https://doi.org/10.3390/ijfs13020068 - 16 Apr 2025

Cited by 5 | Viewed by 3534

Abstract

Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates [...] Read more.

Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates the contagion path from individual liquidity crisis to systemic repayment crisis. In order to test whether bank financial technology (hereinafter referred to as “BankFintech”) can mitigate SDLU and reduce the possibility of financial risks, this study matched the loan data of China’s A-share listed companies with the patent data of bank-invented Fintech from 2013 to 2022 to construct the BankFintech Development Index for empirical analysis. The empirical results show that the development of BankFintech can significantly inhibit SDLU. The mechanism test reveals that BankFintech reduces bank credit risk and liquidity risk by lowering firms’ risk-weighted assets, improving capital adequacy and liquidity ratios, tilts banks’ lending preferences toward duration-matched long-term financing, and “forces” enterprises to take the initiative to improve their financial health and information transparency, enhance their ability to obtain long-term loans, and realize the active management of mismatch risk. Heterogeneity analysis finds that the effect is more significant in non-state-owned enterprises and technology-intensive industries. Further analysis shows that the level of enterprise digitization, the intensity of financial regulation, and related financial policies significantly moderate the marginal effect between the two. This study verified the “Porter’s Risk Mitigation Hypothesis” of Fintech, providing empirical evidence for effectively cracking the financial vulnerability caused by debt maturity mismatch and deepening financial supply-side reform. Full article

► Show Figures

Figure 1

29 pages, 2458 KB

Open AccessEditor’s ChoiceArticle

The Impact of Income Inequality on Energy Poverty in the European Union

by Mihaela Simionescu

Int. J. Financial Stud. 2025, 13(2), 54; https://doi.org/10.3390/ijfs13020054 - 2 Apr 2025

Cited by 5 | Viewed by 2616

Abstract

The EU has consistently tackled the challenge of energy poverty (EP) through various legislative and non-legislative measures, particularly in the context of ongoing energy crisis, but it should also support the reduction of income inequality that might accelerate EP. The aim of this [...] Read more.

The EU has consistently tackled the challenge of energy poverty (EP) through various legislative and non-legislative measures, particularly in the context of ongoing energy crisis, but it should also support the reduction of income inequality that might accelerate EP. The aim of this study is to evaluate the impact of income inequality on EP and other interconnected indicators in the EU in the period 2005–2023 using method of moments quantile (MMQ) regression and mean group (MG) estimators. The results suggest that income inequality based on Gini index enhances energy poverty, while gender pay gap, economic growth, and urban population reduce it. Foreign direct investment (FDI) and renewable energy consumption (REC) might combat EP only in the long-run. These findings suggest that macroeconomic policies should focus not only on economic growth, but also on addressing income inequalities. Policymakers must prioritize measures to reduce income inequality, such as progressive taxation or targeted social programs. Full article

► Show Figures

Figure 1

19 pages, 932 KB

Open AccessEditor’s ChoiceArticle

Digital Financial Knowledge Scale (DFKS): Insights from a Developing Economy

by Kelmara Mendes Vieira, Taiane Keila Matheis, Eliete dos Reis Lehnhart and Fernando Oliveira Tavares

Int. J. Financial Stud. 2024, 12(4), 120; https://doi.org/10.3390/ijfs12040120 - 2 Dec 2024

Cited by 3 | Viewed by 5938

Abstract

This work aims to create and validate the digital financial knowledge scale (DFKS). Three studies were carried out, including a focus group, expert validation, pre-testing, and the application of item response theory. From these procedures, two versions of the scale were constructed and [...] Read more.

This work aims to create and validate the digital financial knowledge scale (DFKS). Three studies were carried out, including a focus group, expert validation, pre-testing, and the application of item response theory. From these procedures, two versions of the scale were constructed and validated. An evaluation and classification methodology was proposed. Two versions for measuring digital financial knowledge are presented. The long version is composed of 40 items and the short version has 26 items. Applying the proposed methodology, it is possible to classify the level of digital financial knowledge as low, intermediate, or high. The DFKS can be useful for both financial system agents and governments and researchers, who can use it in different contexts. In the banking sector, identifying the level of digital financial knowledge can reduce risks, as losses suffered by clients due to an uninformed adoption of digital banking services break the relationship of trust and can lead to lower financial inclusion. Full article

(This article belongs to the Special Issue Advance in the Theory and Applications of Financial Literacy)

16 pages, 786 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Anonymity in Dealer-to-Customer Markets

by Daniela T. Di Cagno, Paola Paiardini and Emanuela Sciubba

Int. J. Financial Stud. 2024, 12(4), 119; https://doi.org/10.3390/ijfs12040119 - 29 Nov 2024

Viewed by 1972

Abstract

We use a laboratory experiment to explore the effect of a change in pre-trade anonymity in a quote-driven dealer-to-customer market, organised as a request for quote (RFQ). We consider two treatments in which dealers interact with two types of customers (informed or uninformed). [...] Read more.

We use a laboratory experiment to explore the effect of a change in pre-trade anonymity in a quote-driven dealer-to-customer market, organised as a request for quote (RFQ). We consider two treatments in which dealers interact with two types of customers (informed or uninformed). In the first treatment, there is no anonymity: dealers know whether the customer that sent them the request for quote is informed or uninformed. In the second treatment, there is complete anonymity: dealers do not know the type of customers they are interacting with. We find that anonymity improves price efficiency, whereas it does not adversely impact dealers’ trading profits. Our results contribute to the debate on transparency versus the adoption of anonymity in financial markets. Full article

(This article belongs to the Special Issue Market Microstructure and Liquidity)

► Show Figures

Figure 1

13 pages, 2376 KB

Open AccessEditor’s ChoiceArticle

Statistical Modeling of Football Players’ Transfer Fees Worldwide

by Raffaele Poli, Roger Besson and Loïc Ravenel

Int. J. Financial Stud. 2024, 12(3), 93; https://doi.org/10.3390/ijfs12030093 - 19 Sep 2024

Cited by 2 | Viewed by 45970

Abstract

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to [...] Read more.

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain up to 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices is thus highly relevant on a global scale. Full article

(This article belongs to the Special Issue Sports Finance (2nd Edition))

► Show Figures

Figure 1

23 pages, 363 KB

Open AccessEditor’s ChoiceArticle

The Influence of Women on Boards on the Relationship between Executive and Employee Remuneration

by María L. Gallén and Carlos Peraita

Int. J. Financial Stud. 2024, 12(3), 84; https://doi.org/10.3390/ijfs12030084 - 23 Aug 2024

Viewed by 1983

Abstract

The growing presence of women at the top of companies has sparked interest in examining their role in the remuneration gap between senior managers and employees. This article analyses the traditional Chief Executive Officer (CEO)-to-employee pay ratio but includes a new relation, the [...] Read more.

The growing presence of women at the top of companies has sparked interest in examining their role in the remuneration gap between senior managers and employees. This article analyses the traditional Chief Executive Officer (CEO)-to-employee pay ratio but includes a new relation, the senior-management-to-employee pay ratio, and extends the research by including six positions for women in company management: on the board of directors, executive directors, CEOs, proprietary directors, independent directors, and senior managers. The study is based on a sample of 77 listed companies in Spain from 2015 to 2022 and the panel data models have been estimated using the Generalised Method of Moments (GMM). The main findings indicate that the proportion of women in different categories of board and senior management positions has a positive effect on the CEO-to-employee pay ratio, especially in companies with higher market capitalisation. In contrast, the proportion of women in senior management positions has a negative effect on the CEO-to-employee pay ratio in all the samples analysed. Government agencies should prioritise the participation of women in non-board senior management positions in order to at least reduce the pay gap between senior managers and employees. Full article

20 pages, 320 KB

Open AccessEditor’s ChoiceArticle

Risk of Economic Violence: A New Quantification

by Federica D’Agostino, Giulia Zacchia and Marcella Corsi

Int. J. Financial Stud. 2024, 12(3), 82; https://doi.org/10.3390/ijfs12030082 - 19 Aug 2024

Cited by 3 | Viewed by 4368

Abstract

This paper defines the first internationally comparable measure of the risk of economic violence to acknowledge its prevalence in different countries and its geographical and gender heterogeneity. Thanks to the availability of micro-data from the OECD/International Network on Financial Education survey, currently used [...] Read more.

This paper defines the first internationally comparable measure of the risk of economic violence to acknowledge its prevalence in different countries and its geographical and gender heterogeneity. Thanks to the availability of micro-data from the OECD/International Network on Financial Education survey, currently used to track financial literacy in different countries, we define a measure of the risk of economic violence (REV) that takes into consideration three macro-areas: (a) the risk of being prevented from acquiring and accumulating financial resources; (b) the risk of being unaware and not having access to personal and/or household financial resources; and (c) the risk of financial dependency. The definition of the new economic violence risk measure (REV) then allows us to verify with real data the presence of women’s greater exposure to the risk of economic violence and the presence of gender differences in the determinants of economic violence risk. Finally, we verify that financial literacy protects individuals from the risk of economic violence, without gender differences. Full article

21 pages, 1783 KB

Open AccessEditor’s ChoiceArticle

Perceptions of Cryptocurrencies and Modern Money before and after the COVID-19 Pandemic in Poland and Germany

by Marta Maciejasz, Robert Poskart and Daria Wotzka

Int. J. Financial Stud. 2024, 12(3), 64; https://doi.org/10.3390/ijfs12030064 - 29 Jun 2024

Cited by 1 | Viewed by 2956

Abstract

Research background: Despite the fact that the issue of private, decentralized digital money (cryptocurrencies) is already quite extensively described in the literature dedicated to the financial system, especially its periphery, there is a deficiency in terms of research on the opinions of participants [...] Read more.

Research background: Despite the fact that the issue of private, decentralized digital money (cryptocurrencies) is already quite extensively described in the literature dedicated to the financial system, especially its periphery, there is a deficiency in terms of research on the opinions of participants in the financial system, based on trust in money and its widespread acceptance. International comparative studies are lacking, particularly those conducted before and after the COVID-19 virus pandemic. The pandemic showed that people had significantly changed their willingness to use different forms of money. Being isolated at home and avoiding direct contact with others, people started to use digital money more frequently. Purpose of the article: In response to the identified research gap, this study reports research results on the perception of cryptocurrencies by young financial market participants. It attempts to provide answers to the following research questions: (1) Has the COVID-19 pandemic and the lockdown of economies caused changes at the international level in perceptions and attitudes toward the traditional monetary system and cryptocurrencies? (2) Has the COVID-19 pandemic changed perceptions of cryptocurrencies as a potential alternative to current fiat money? Methods: To evaluate respondents’ opinions, a survey in the form of a questionnaire was conducted. The respondent groups in 2019/2020 were N = 171 (Germany = 143 and Poland = 128), while in 2021, N = 157 (Germany = 95 and Poland = 62). For analytical purposes, statistical analysis using the Z ratio test was used to capture the characteristics of the response distributions and the relationships between them. These two moments in time allowed us to determine whether there were significant changes between opinions before and after COVID-19. Findings & value added: The study’s results showed that while there are significant differences in perceptions of the traditional monetary system and cryptocurrencies due to a variety of factors, the COVID-19 pandemic and the shutdown of economies did not cause statistically significant differences in this regard. Full article

► Show Figures

Figure 1

20 pages, 389 KB

Open AccessEditor’s ChoiceArticle

Ownership Structure and Bank Dividend Policies: New Empirical Evidence from the Dual Banking Systems of MENA Countries

by Hicham Sbai, Slimane Ed-Dafali, Hicham Meghouar and Muhammad Mohiuddin

Int. J. Financial Stud. 2024, 12(3), 63; https://doi.org/10.3390/ijfs12030063 - 28 Jun 2024

Cited by 9 | Viewed by 5751

Abstract

This study investigates the relationship between ownership structures and dividend policies for 46 Islamic and 75 conventional banks from 12 MENA and Asian countries between 2012 and 2020. Logit regression is employed to estimate the regression equation, centering on the moderating impacts of [...] Read more.

This study investigates the relationship between ownership structures and dividend policies for 46 Islamic and 75 conventional banks from 12 MENA and Asian countries between 2012 and 2020. Logit regression is employed to estimate the regression equation, centering on the moderating impacts of the COVID-19 pandemic and national culture. Our findings remain robust as we tackle the endogeneity issue using probit and logistic regression models. Asset growth and GDP growth serve as proxies for investment opportunities. Additionally, dividend per share acts as a proxy for dividend policy. Our findings emphasize how the ownership structure impacts dividend payouts in both banking systems. We observed positive relationships between dividend payouts and foreign ownership, bank size, age, and performance. Conversely, concentration of ownership and leverage negatively influence dividend payouts. The COVID-19 pandemic directly boosts the dividend policy for conventional banks and alters the relationship between foreign ownership and distribution policy in Islamic banks. Specifically, COVID-19 interacts with foreign and state ownership to reduce dividend payouts, but concentration of ownership does not show this effect. This study furnishes evidence affirming the significance of the ownership structure in shaping the dividend payout policy within Islamic and conventional banking. The results maintain their reliability across various estimation approaches. Moreover, this study accounts for the crisis period as a moderating factor influencing dividend payments. Full article

35 pages, 17244 KB

Open AccessEditor’s ChoiceArticle

AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges

by Li Xian Liu, Zhiyue Sun, Kunpeng Xu and Chao Chen

Int. J. Financial Stud. 2024, 12(3), 60; https://doi.org/10.3390/ijfs12030060 - 27 Jun 2024

Cited by 12 | Viewed by 14859

Abstract

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By [...] Read more.

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By designing a series of multi-step, advanced reasoning tasks and establishing task-specific evaluation metrics, we assessed ChatGPT-4o’s performance compared to human analysts. Results indicate that while ChatGPT-4o demonstrates proficiency in basic and some complex financial tasks, it struggles with deep analytical and critical thinking tasks, especially in specialized finance areas. This study underscores the need for meticulous task formulation and robust evaluation in AI financial applications. While ChatGPT enhances efficiency, integrating it with human expertise is crucial for effective decision-making. Our findings highlight both the potential and limitations of ChatGPT-4o in financial analysis, providing valuable insights for future AI integration in the finance sector. Full article

► Show Figures

Figure 1

20 pages, 459 KB

Open AccessEditor’s ChoiceArticle

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

by Apostolos Ampountolas

Int. J. Financial Stud. 2024, 12(3), 59; https://doi.org/10.3390/ijfs12030059 - 26 Jun 2024

Cited by 16 | Viewed by 5866

Abstract

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, [...] Read more.

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets. Full article

► Show Figures

Figure 1

28 pages, 367 KB

Open AccessEditor’s ChoiceArticle

Financial Decisions Based on Zero-Sum Games: New Conceptual and Mathematical Outcomes

by Pierpaolo Angelini

Int. J. Financial Stud. 2024, 12(2), 56; https://doi.org/10.3390/ijfs12020056 - 14 Jun 2024

Cited by 5 | Viewed by 2215

Abstract

All the n possible returns on a financial asset are the components of an element of a linear space over

R

. This paper shows how to transfer all these n possible returns on a one-dimensional straight line. In this research work, two [...] Read more.

All the n possible returns on a financial asset are the components of an element of a linear space over

R

. This paper shows how to transfer all these n possible returns on a one-dimensional straight line. In this research work, two or more than two financial assets are studied. More than two financial assets are always studied in pairs, so they are treated inside the budget set of a given decision-maker. Two univariate financial assets give rise to a bivariate financial asset characterized by a bivariate (two-dimensional) distribution of probability. This research work shows how constrained choices being made by a given decision-maker under conditions of uncertainty and riskiness maximize his utility of an ordinal nature. For this reason, prevision bundles are dealt with. Furthermore, every choice identifies a zero-sum game. Since a specific kind of choice associated with two or more than two objects is investigated, new conceptual and mathematical outcomes related to financial decisions are shown. Full article

20 pages, 2575 KB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions

by Qiang Liu, Chen Xu and Jane Xie

Int. J. Financial Stud. 2024, 12(2), 53; https://doi.org/10.3390/ijfs12020053 - 30 May 2024

Cited by 6 | Viewed by 5193

Abstract

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to [...] Read more.

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes. Full article

► Show Figures

Figure 1

16 pages, 699 KB

Open AccessEditor’s ChoiceArticle

The Impact of Political Risks on Financial Markets: Evidence from a Stock Price Crash Perspective

by Yanping Ma, Qian Wei and Xiang Gao

Int. J. Financial Stud. 2024, 12(2), 51; https://doi.org/10.3390/ijfs12020051 - 27 May 2024

Cited by 7 | Viewed by 13427

Abstract

Political risk, one of the most significant uncertainty shocks, affects firms’ future attitudes toward risks and plays a crucial role in their decision making. A stock price crash risk is a classical topic in financial markets; therefore, this paper probes the relationship between [...] Read more.

Political risk, one of the most significant uncertainty shocks, affects firms’ future attitudes toward risks and plays a crucial role in their decision making. A stock price crash risk is a classical topic in financial markets; therefore, this paper probes the relationship between firm-level political risk and stock price crash risk based on a sample of Chinese listed firms from 2011 to 2020. This paper collects the MD&A textual material of Chinese listed firms and calculates the firm-level political risk of Chinese listed firms. Our results show that a firm’s stock price crash risk is positively associated with its firm-level political risk exposure. Our findings hold after conducting various robustness tests, including instrument variable regression and altering the measurement of stock price crash risk. Further discussion reveals that political involvement mitigates the positive effect of firm-level political risk on the risk of a stock price jump. Full article

► Show Figures

Figure 1

18 pages, 457 KB

Open AccessEditor’s ChoiceArticle

Sovereign Green Bond Market: Drivers of Yields and Liquidity

by Kamila Tomczak

Int. J. Financial Stud. 2024, 12(2), 48; https://doi.org/10.3390/ijfs12020048 - 20 May 2024

Cited by 7 | Viewed by 8164

Abstract

The aim of this study is to analyse and assess the yields and liquidity of sovereign green bonds in selected countries and to compare the yields between sovereign green bonds and conventional bonds. Sovereign green bonds are issued by governments to finance environmental [...] Read more.

The aim of this study is to analyse and assess the yields and liquidity of sovereign green bonds in selected countries and to compare the yields between sovereign green bonds and conventional bonds. Sovereign green bonds are issued by governments to finance environmental and social projects and represent a relatively new and growing asset class. This study seeks to analyse the financial performance of sovereign green bonds by examining yields and liquidity metrics, such as bid–ask spreads. The findings of this research suggest that the yield to maturity (YTM) of sovereign green bonds is influenced by conventional bond return, while conventional sovereign bonds are affected by the financial market return. Furthermore, the results confirm that the liquidity of sovereign green bonds can be explained by bond maturity. Full article

(This article belongs to the Special Issue Green Bonds and Climate Change Mitigation)

► Show Figures

Figure 1

16 pages, 493 KB

Open AccessEditor’s ChoiceArticle

The Effects of Interest Rates on Bank Risk-Taking in South Africa: Do Cyclical and Location Asymmetries Matter?

by Clement Moyo and Andrew Phiri

Int. J. Financial Stud. 2024, 12(2), 49; https://doi.org/10.3390/ijfs12020049 - 20 May 2024

Cited by 4 | Viewed by 3124

Abstract

We examine the nonlinear relationship between interest rates on bank risk-taking behavior in South Africa between 2008:q1 and 2022:q3 using nonlinear autoregressive distributive lag (NARDL) and quantile autoregressive distributive lag (QARDL) models. Whilst the preliminary estimates from linear ARDL produce results adhering to [...] Read more.

We examine the nonlinear relationship between interest rates on bank risk-taking behavior in South Africa between 2008:q1 and 2022:q3 using nonlinear autoregressive distributive lag (NARDL) and quantile autoregressive distributive lag (QARDL) models. Whilst the preliminary estimates from linear ARDL produce results adhering to conventional theory, the NARDL and QARDL analysis shows that the relationship between the variables is more complex. On one hand, the NARDL model shows that the phase of monetary policy (cyclical asymmetries) is important in determining the pass-through effects of interest rates on bank risk behavior. We find that both contractionary and expansionary monetary policy increases long-term risk through decreased liquidity for the former and increased non-performing loans for the latter. On the other hand, the QARDL model shows that the level of bank risk behavior (location asymmetries) is also important in determining the impact of interest rates on bank risk behavior. We find that interest rates affect bank risk behavior in ‘medium-to-high risk environments’ for unsecured loans and lending and in ‘medium-to-low risk environments’ for liquidity. Overall, these results enable us to recommend ways in which the SARB can strengthen its monitoring mechanisms given the multifaceted impact of interest rates on bank risk-taking. Full article

► Show Figures

Figure 1

17 pages, 277 KB

Open AccessEditor’s ChoiceArticle

International Diversification and Stock-Price Crash Risk

by Alireza Askarzadeh, Mostafa Kanaanitorshizi, Maryam Tabarhosseini and Dana Amiri

Int. J. Financial Stud. 2024, 12(2), 47; https://doi.org/10.3390/ijfs12020047 - 15 May 2024

Cited by 14 | Viewed by 3359

Abstract

Despite the recent proliferation of research on internationalization, little attention has been paid to understanding the reasons behind the decrease in firm value accompanying international expansion. By delving into the underlying mechanisms and applying the concept of agency theory to a sample of [...] Read more.

Despite the recent proliferation of research on internationalization, little attention has been paid to understanding the reasons behind the decrease in firm value accompanying international expansion. By delving into the underlying mechanisms and applying the concept of agency theory to a sample of US firms spanning from 2000 to 2022, we posit that an increased level of information asymmetry in internationally diversified firms incentivizes managers to prioritize their own interests. To protect their careers, CEOs of internationally diversified firms often suppress bad news. This behavior can lead to the accumulation of negative news and heighten the risk of a stock-price crash. Furthermore, we propose that higher levels of international experience, enhanced monitoring effectiveness, and efficient investment practices will negatively moderate the positive relationship between internationalization and stock-price crash risk. Full article

21 pages, 392 KB

Open AccessEditor’s ChoiceArticle

Determinants of Remuneration Committee Chairman’s Pay: Evidence from the UK

by Fadi Shehab Shiyyab

Int. J. Financial Stud. 2024, 12(2), 45; https://doi.org/10.3390/ijfs12020045 - 10 May 2024

Viewed by 1930

Abstract

This study investigates the association between the compensation of Remuneration Committee Chairpersons (RCCs) and their characteristics. Utilizing data from firms listed on the UK FTSE350 index between 2010 and 2020, the research unveils that RCC remuneration is influenced by factors such as observable [...] Read more.

This study investigates the association between the compensation of Remuneration Committee Chairpersons (RCCs) and their characteristics. Utilizing data from firms listed on the UK FTSE350 index between 2010 and 2020, the research unveils that RCC remuneration is influenced by factors such as observable efforts, time commitment, and accumulated experience. Notably, the analysis reveals a substantial gender gap in RCCs’ pay. The results suggest that the contractual pricing of individual director-level attributes plays a role in explaining disparities in compensation for roles with similar responsibilities. Furthermore, the study sheds light on the intricate process of determining compensation within the directorial hierarchy. It delves into how differences in pay among individuals occupying similar positions across various companies can be elucidated by the distinct attributes and qualifications of each individual. Ultimately, the findings advocate for a nuanced examination of directorial roles, highlighting the necessity of distinguishing between different director roles rather than treating them as a homogeneous entity. Full article

(This article belongs to the Special Issue Cross-Cultural Corporate Governance, Firm Performance and Firm Value)

17 pages, 315 KB

Open AccessEditor’s ChoiceArticle

The Impact of Value Creation (Tobin’s Q), Total Shareholder Return (TSR), and Survival (Altman’s Z) on Credit Ratings

by Nazário Augusto de Oliveira and Leonardo Fernando Cruz Basso

Int. J. Financial Stud. 2024, 12(2), 44; https://doi.org/10.3390/ijfs12020044 - 8 May 2024

Cited by 4 | Viewed by 6538

Abstract

This research explores the impact of financial indicators on the credit ratings of companies listed on the S&P 500, employing a Sys-GMM model to address endogeneity concerns. Three independent variables categorized as market and survival factors alongside seven control variables sourced from leverage, [...] Read more.

This research explores the impact of financial indicators on the credit ratings of companies listed on the S&P 500, employing a Sys-GMM model to address endogeneity concerns. Three independent variables categorized as market and survival factors alongside seven control variables sourced from leverage, liquidity, interest coverage, profitability, market, survival, and macroeconomic domains were investigated. The sample consisted of 2398 observations from Capital IQ Pro, spanning nine years (2013 to 2021) and encompassing 240 public companies. The findings suggest that neither Tobin’s Q (TQ) nor Total Shareholder Return (TSR) lack significant correlations with credit ratings, implying that stock market performance and total shareholder return do not directly impact credit ratings. In contrast, the Altman Z-score (AZS) emerged as a significant predictor, indicating its importance in assessing credit risk. These insights enhance the understanding of financial indicators’ impacts on credit ratings, aiding financial institutions and companies in prudent lending and financing decisions. Full article

(This article belongs to the Special Issue Accounting and Financial/Non-financial Reporting Developments)

27 pages, 989 KB

Open AccessEditor’s ChoiceArticle

Probability Distributions for Modeling Stock Market Returns—An Empirical Inquiry

by Jayanta K. Pokharel, Gokarna Aryal, Netra Khanal and Chris P. Tsokos

Int. J. Financial Stud. 2024, 12(2), 43; https://doi.org/10.3390/ijfs12020043 - 6 May 2024

Cited by 3 | Viewed by 13285

Abstract

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal [...] Read more.

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments. Full article

► Show Figures

Figure 1

14 pages, 301 KB

Open AccessEditor’s ChoiceArticle

Social Capital and Cross-Border Venture Capital Investments in China

by Yi Tan, Xiaoli Wang, Jason Z. -H. Lee and Kun Shi

Int. J. Financial Stud. 2024, 12(2), 41; https://doi.org/10.3390/ijfs12020041 - 29 Apr 2024

Cited by 2 | Viewed by 3124

Abstract

In the context of the Chinese market, foreign cross-border venture capitalists have devised specific strategies to mitigate the challenges associated with the liabilities of foreignness, such as risks and information asymmetry. They have strategically leveraged social capital to not only decrease investment risk [...] Read more.

In the context of the Chinese market, foreign cross-border venture capitalists have devised specific strategies to mitigate the challenges associated with the liabilities of foreignness, such as risks and information asymmetry. They have strategically leveraged social capital to not only decrease investment risk but also to influence their investment preferences and behaviors. To investigate the influence of different types of social capital on the investment decisions of cross-border venture capitalists, hypotheses are proposed and tested using regression analysis. Our research reveals several key findings in this regard. Firstly, cross-border venture capitalists with a robust structural social capital network exhibit a greater propensity to invest in early-stage companies. This suggests that well-established connections and partnerships within the Chinese entrepreneurial ecosystem provide a level of comfort and confidence when investing in ventures at their infancy. Interestingly, relational and cognitive social capital, though undoubtedly valuable, do not significantly impact the decision to make early-stage investments. Furthermore, we have observed that venture capitalists with higher levels of structural and cognitive social capital are more inclined to form syndications. Collaborative partnerships and shared knowledge networks seem to be crucial factors that drive syndication decisions. Lastly, venture capitalists endowed with substantial structural and relational social capital tend to allocate larger investment amounts, signifying the influence of business or personal relationships and network connections on the scale of their investments. Full article

13 pages, 432 KB

Open AccessEditor’s ChoiceArticle

The Impact of Financial Development on Renewable Energy Consumption: The Case of Vietnam and Other ASEAN Members

by Chien Van Nguyen

Int. J. Financial Stud. 2024, 12(2), 37; https://doi.org/10.3390/ijfs12020037 - 25 Apr 2024

Cited by 5 | Viewed by 4089

Abstract

The purpose of this study was to evaluate the impact of financial development and renewable energy consumption in Vietnam and some selected countries in Southeast Asia. After researching over the period from 1970 to 2022, using quantitative analyses, including the ordinary least squares [...] Read more.

The purpose of this study was to evaluate the impact of financial development and renewable energy consumption in Vietnam and some selected countries in Southeast Asia. After researching over the period from 1970 to 2022, using quantitative analyses, including the ordinary least squares (OLS), fixed effects method (FEM), and random effects method (REM), and measuring the Driscoll–Kraay standard errors to assess cross-dependence between countries as well as a Dynamic Ordinary Least Squares (DOLS) estimation analysis to evaluate the robustness of the research, the research results confirm that financial development has a negative impact on renewable energy consumption, which reflects the important role of fossil energy sources in meeting energy consumption demand. Similarly, increased per capita income negatively affects renewable energy consumption. This study also confirms the positive impact of foreign direct investment on renewable energy use. Full article

(This article belongs to the Special Issue Sustainable Investing and Financial Services)

► Show Figures

Figure 1

23 pages, 1647 KB

Open AccessEditor’s ChoiceArticle

Data-Driven Sustainable Investment Strategies: Integrating ESG, Financial Data Science, and Time Series Analysis for Alpha Generation

by Afreen Sorathiya, Pradnya Saval and Manha Sorathiya

Int. J. Financial Stud. 2024, 12(2), 36; https://doi.org/10.3390/ijfs12020036 - 20 Apr 2024

Cited by 8 | Viewed by 8001

Abstract

In today’s investment landscape, the integration of environmental, social, and governance (ESG) factors with data-driven strategies is pivotal. This study delves into this fusion, employing sophisticated statistical techniques and Python programming to unveil insights often overlooked by traditional approaches. By analyzing extensive datasets, [...] Read more.

In today’s investment landscape, the integration of environmental, social, and governance (ESG) factors with data-driven strategies is pivotal. This study delves into this fusion, employing sophisticated statistical techniques and Python programming to unveil insights often overlooked by traditional approaches. By analyzing extensive datasets, including S&P500 financial indicators from 2012 to 2021 and 2021 ESG metrics, investors can enhance portfolio performance. Emphasizing ESG integration for sustainable investing, the study underscores the potential for alpha generation. Time series analysis further elucidates market dynamics, empowering investors to align with both financial objectives and ethical values. Notably, the research uncovers a positive correlation between ESG risk and total risk, suggesting that companies with lower ESG risk tend to outperform those with higher ESG risk. Moreover, employing a long–short ESG risk strategy yields abnormal returns of approximately 4.37%. This integration of ESG factors not only mitigates risks associated with environmental, social, and governance issues but also capitalizes on opportunities for sustainable growth, fostering responsible investing practices and ensuring long-term financial returns, resilience, and value creation. Full article

(This article belongs to the Special Issue Making Green from Green: The Truth about Sustainable Finance)

► Show Figures

Figure 1

16 pages, 494 KB

Open AccessEditor’s ChoiceArticle

Crowdfunding versus Traditional Banking: Alternative or Complementary Systems for Financing Projects in Portugal?

by Bruno Torres, Zélia Serrasqueiro and Márcio Oliveira

Int. J. Financial Stud. 2024, 12(2), 33; https://doi.org/10.3390/ijfs12020033 - 29 Mar 2024

Cited by 2 | Viewed by 4279

Abstract

In an era where crowdfunding in Portugal is garnering increased public attention, exemplified by notable campaigns like the recent funding of the nurses’ strike, we explore its potential as an alternative financial source to traditional banking. Through a comprehensive case study, we delve [...] Read more.

In an era where crowdfunding in Portugal is garnering increased public attention, exemplified by notable campaigns like the recent funding of the nurses’ strike, we explore its potential as an alternative financial source to traditional banking. Through a comprehensive case study, we delve into pertinent issues, encompassing European legislation, market dynamics, and a survey disseminated to representatives of the four prominent Portuguese crowdfunding platforms. Comprising forty-one questions across four categories, the survey extracts insights on platform details, company/project information, investor perspectives, and the financing process, along with an evaluation of platform advantages/disadvantages vis-à-vis traditional banking. Despite heightened visibility, crowdfunding remains relatively unfamiliar to the broader public, yet it diverges from banking not as a substitute but as a complementary financial mechanism. Emphasizing accessibility, process agility, and reduced bureaucracy, crowdfunding serves as a means of swiftly gaining recognition for a company or project while tapping into a broad audience. Rather than competition, it offers supplementary support, facilitating the identification and validation of investment opportunities and concepts. Moreover, it streamlines subsequent interactions with banks and investors, enhancing confidence in a project’s viability. In essence, crowdfunding emerges not as an alternative but a strategic complement, enriching the financial landscape with its unique attributes. Full article

► Show Figures

Figure 1

56 pages, 2800 KB

Open AccessEditor’s ChoiceArticle

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

by Krzysztof Drachal and Michał Pawłowski

Int. J. Financial Stud. 2024, 12(2), 34; https://doi.org/10.3390/ijfs12020034 - 29 Mar 2024

Cited by 4 | Viewed by 7860

Abstract

This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, [...] Read more.

This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed. Full article

► Show Figures

Figure 1

16 pages, 276 KB

Open AccessEditor’s ChoiceArticle

How Audit Fees Impact Earnings Management in Service Companies on the Amman Stock Exchange through Audit Committee Characteristics

by Ayman Shehadeh, Mahmoud Daoud Daoud Nassar, Husam Shrouf and Mohammad Haroun Sharairi

Int. J. Financial Stud. 2024, 12(2), 32; https://doi.org/10.3390/ijfs12020032 - 26 Mar 2024

Cited by 3 | Viewed by 3292

Abstract

The primary objective of this research was to investigate the potential moderating role of audit fees in the relationship between audit committee characteristics and earnings management. Specifically, this study aimed to establish connections between audit committee features, such as committee size, member independence, [...] Read more.

The primary objective of this research was to investigate the potential moderating role of audit fees in the relationship between audit committee characteristics and earnings management. Specifically, this study aimed to establish connections between audit committee features, such as committee size, member independence, and financial expertise, and the practice of earnings management. To address these research questions, a convenient sample of 46 service providers listed on the Amman Stock Exchange between 2016 and the subsequent year was employed. Descriptive statistical methods were applied to characterize the variables under investigation, while a multiple regression model was utilized to assess the study’s hypotheses. The findings of the study revealed that there was no significant correlation between the size of the audit committee and earnings management. However, a negative correlation was observed between the audit committee’s independence and the financial expertise of its members. Importantly, when audit fees were introduced as a moderating variable, the relationships between committee member independence and earnings management, as well as between committee member financial expertise and earnings management, were found to be weakened. These results have potential implications for policymakers and regulators in Jordan. They may offer valuable insights into corporate governance reforms that could assist Jordanian businesses in enhancing their earnings management practices. Full article

17 pages, 251 KB

Open AccessEditor’s ChoiceArticle

Venture Capital and Dividend Policy

by Yi Tan, Xiaoli Wang and Xiaoyu Fu

Int. J. Financial Stud. 2024, 12(1), 27; https://doi.org/10.3390/ijfs12010027 - 19 Mar 2024

Cited by 1 | Viewed by 3776

Abstract

In this paper, we empirically examine the impact of venture capital investment on the dividend policy of the invested companies using a sample of list companies from China’s ChiNext market during the period 2014 to 2019. Our empirical results show that different types [...] Read more.

In this paper, we empirically examine the impact of venture capital investment on the dividend policy of the invested companies using a sample of list companies from China’s ChiNext market during the period 2014 to 2019. Our empirical results show that different types of VC investments have different impacts on the dividend policies of the invested companies. To be specific, we found independent venture capital companies (IVCs) promote the company’s dividend payment and increase the level of dividend payments while corporate venture capital (CVC) inhibits the company’s dividend payment. The joint participation of multiple types of venture capital investment (syndication) also increases the company’s dividend distribution. Our main contributions are two-fold. First, we provide a comprehensive analysis in the field of VC and dividend policy; second, we differentiate VC from the perspective of investment objectives and examine its different impacts on the dividend policies of the invested companies. Full article

35 pages, 10470 KB

Open AccessEditor’s ChoiceArticle

Quantifying Impact, Uncovering Trends: A Comprehensive Bibliometric Analysis of Shadow Banking and Financial Contagion Dynamics

by Ionuț Nica, Camelia Delcea, Nora Chiriță and Ștefan Ionescu

Int. J. Financial Stud. 2024, 12(1), 25; https://doi.org/10.3390/ijfs12010025 - 5 Mar 2024

Cited by 7 | Viewed by 4185

Abstract

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced [...] Read more.

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced bibliometric methods, we explored the global network of publications, identifying key works, influential authors, and the evolution of research over time. The results of the bibliometric analysis have highlighted an annual growth rate of 22.05% in publications related to the topics of shadow banking and financial contagion, illustrating researchers’ interest and the dynamic nature of publications over time. Additionally, significant increases in scientific production have been recorded in recent years, reaching a total of 178 articles published in 2022. The most predominant keywords used in research include “systemic risks”, “risk assessment”, and “measuring systemic risk”. The thematic evolution has revealed that over time, the focus on fundamental concepts used in analyzing these two topics has shifted, considering technological advancements and disruptive events that have impacted the economic and financial system. Our findings provide a detailed insight into the progress, gaps, and future directions in understanding the complex interplay of shadow banking and financial contagion. Our study represents a valuable asset for researchers, practitioners, and policymakers with a keen interest in understanding the dynamics of these critical components within the global financial system. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Editor’s Choice Articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI