Empirical Results Research Articles

Bank Risk-Taking and Monetary Policy: Empirical Results for Taiwan

Bank Risk-Taking and Monetary Policy: Empirical Results for Taiwan

Corporate social responsibility and the likelihood of external financing

<p style="text-align:justify"><span style="font-size:16px"><span style="font-family:Times New Roman,Times,serif"><span dir="ltr" lang="EN-US"><span style="color:black">This paper empirically investigates the relation between firm performance in corporate social responsibility (CSR) and the need and likelihood of external financing to test the predictions of agency and stakeholder theories. Empirical results from Logit, Linear Probability Model, OLS and Firm fixed effects regressions indicate that CSR is negatively related to the likelihood and level of external financing. Further analysis indicates that CSR has a negative and significant effect on both net equity issued (NEI) and net debt issued (NDI), the two components of external financing. Overall, the empirical results support the predictions of agency theory.</span></span></span></span></p>

Impact of COVID-19 Crisis on Volatility Spillovers across Global Financial Markets: Evidence from Asymmetric GARCH Models

This study investigates the market volatility and asymmetric behavior in the commodity market, foreign exchange market, cryptocurrency, and stock markets by employing asymmetric GARCH models on the daily time series returns. The data covers the period from March 8, 2017, to March 17, 2023, and is divided in to three sub-periods: the entire sample period (March 8, 2017, to March 17, 2023), the pre-COVID-19 period (March 8, 2017, to March 10, 2020), and the during the COVID-19 period (March 11, 2020, to March 17, 2023). The empirical results show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Additionally, the results indicate significant positive asymmetric behavior in the crude oil and stock markets during the pandemic. The findings further document that gold exhibits a strong resilience during the pandemic period, indicating its hedging ability during crisis periods. Moreover, the results suggest that the EGARCH model is the most appropriate model to capture the volatilities of the financial markets both before and during the pandemic. The findings of this study provide useful insights for investors and policymakers, enabling them to adopt effective strategies for investing in portfolios during crisis periods in the future.

Chicken or egg? On the relation between course performance and first-year students’ well-being and self-efficacy

ABSTRACT Prior studies suggest associations between course performance and well-being and self-efficacy, although what comes first in this effect remains unclear. This study contributes to the research on noncognitive aspects of learning of first-year students, and examines the bidirectional relation between, on the one hand, students’ well-being and self-efficacy, and on the other hand, their course performance in an accounting course. The study investigates first-year undergraduate students and their performance in an accounting course, measured as the grade and ensuing pass/fail mark. Longitudinal survey data were collected at three data collection moments during two consecutive semesters. The results lead to three main findings. First, well-being a few weeks before the examination moment affects course performance (grade) in accounting, while self-efficacy does not. Second, students who passed the course had higher well-being and self-efficacy in the next semester than those who failed. Third, students who passed, reported a higher increase in well-being and self-efficacy after the examination moment, than those who failed. Analyses controlled for students’ age, gender, and high school performance. The empirical results provide valuable longitudinal insights into the important role of well-being and self-efficacy in relation to course performance, and vice versa. Implications for theory and practice are provided.

Firm ownership structure and voluntary carbon disclosure: a systematic review and meta-analysis

Purpose This study aims to systematically consolidate and quantitatively integrate the mixed empirical results on the association between ownership mechanisms and voluntary carbon disclosure using meta-analysis and further propose potential country-level moderators of this relationship. Design/methodology/approach The authors apply meta-analytic procedures on 55 empirical studies conducted during 2008–2022, covering 13 countries, 85 effect sizes and 226,473 firm-year observations. To gauge the significance of the estimated mean effect size, a random-effects Hedges and Olkin meta-analysis procedure is adopted, followed by a restricted maximum likelihood based meta-regression, to test the effect of possible moderators. Findings Aligned with agency and stakeholder theories, the results highlight institutional and state ownership (SO) as having a significant positive impact on voluntary carbon disclosure. On the other hand, ownership concentration, managerial and foreign ownership have an insignificant effect on voluntary carbon disclosure. Based on institutional theory perspectives, the authors confirm the impact of institutional ownership on voluntary carbon disclosure to be more prominent in civil law countries and those countries that have implemented an emission trading scheme (ETS). Practical implications The finding that institutional and SO in firms can translate into higher voluntary disclosures deems investors and the government as crucial stakeholders in achieving carbon neutrality. Furthermore, the finding that the effect of institutional investors on carbon disclosure is heightened in ETS-implemented countries provides evidence to the regulatory authorities in favour of this scheme. Social implications The positive impact of institutional and government ownership on voluntary carbon disclosure highlights that these ownership structures not only have the potential to transform corporate decisions but also have implications for the wider society. As firms owned by institutional investors disclose their carbon information, it provides access to critical information about their environmental practices to the public. This fosters an environment of transparency and trust between the firm and its stakeholders (the community), leading to an overall well-informed society. Originality/value While prior meta-reviews studied the impact of corporate governance on voluntary disclosures, the meta-literature, as of 2024, has yet to address its influence specifically on carbon disclosures, which are pertinent amidst the ongoing global climate change crisis. The findings inform policymakers about the pivotal institutional factors that can amplify the impact of effective ownership structures on voluntary carbon disclosure. Future scope exists for investigating the effects of ownership mechanisms on firm-level sustainable investments. Furthermore, future empirical analysis could consider the moderating influence of “culture” and “ease of doing business” on the ownership-carbon disclosure relationship.

Physical model test study on strength characteristics of gravel pile composite foundation

In gravel pile composite foundation stability design, the shear strength of composite foundation is critically important. However, due to the size limitation of test equipment, it is difficult to obtain its shear strength in field. This study designs a laboratory shear model test to obtain the shear strength of gravel pile composite foundation, where a total of four group tests were carried out, including two nature soils and two gravel pile area replacement ratios. The shear strength parameters obtained by the shear model tests are compared with the results of empirical formula methods such as the Chinese Standard Method and Priebe Method to verify the accuracy of these two methods. It is demonstrated that for the Chinese Standard Method, the empirical formula's results are smaller than the shear model test results when the pile-soil stress ratio b is 1. When the pile-soil stress ratio b is 1.5, the results are close to each other. For the Priebe Method, the empirical formula results are all larger than the shear model test results, and it is especially unsuitable for the mud soil.

Shared Capitalism and Corporate Sustainability: Broad-Based Employee Share Ownership, CEO Ownership, and Corporate Environmental Performance

This research proposes that broad-based employee share ownership (ESO) affects corporate environmental performance (CEP). Drawing upon corporate governance literature, social exchange theory, and stakeholder utility theory, we propose that employees as owners adopt more favorable attitudes toward beneficial outcomes for CEP, and that the broad-based impact of ESO overwhelms the impact of CEO ownership. Also, we propose that these relationships are contingent upon trade union presence as a form of worker voice that amplifies the ESO influence on CEP. The empirical results from U.S. publicly traded companies from 2009 to 2016 indicate that broad-based ESO is linked to positive CEP by companies, and provides some support for all proposed hypotheses. This study provides scholarly and practical implications for the effects of shared capitalist modes of ownership and employee engagement on corporate sustainability.

Oil Volatility Uncertainty: Impact on Fundamental Macroeconomics and the Stock Index

This study utilized both single-regime GARCH and double-regime GARCH models to investigate oil price volatility, Spanish macroeconomic factors, and stock prices during major crises such as geopolitical conflicts, the global financial crisis (GFC), and COVID-19, covering the period from Q2-1995 to Q4-2023. Additionally, the impact of crude oil price volatility on these factors was examined. The empirical results confirmed the presence of the leverage effect and identified multiple volatility switches associated with remarkable events like the GFC, the European debt crisis, the COVID-19 pandemic, and the Russian war. ARDL model analysis revealed a statistically significant positive relationship between oil prices and both unemployment and inflation rates in the long term, while other factors showed a negative correlation.

SOVEREIGN UNCERTAINTY

AbstractThis article investigates the impact and transmission of uncertainty regarding the future path of government finances on economic activity. Employing a data‐rich approach, I introduce a novel proxy that captures uncertainty surrounding public finances, which I refer to as sovereign uncertainty. In an application to Spain, sovereign uncertainty shocks persistently dampen the economy in the medium run, whereas macro‐financial uncertainty shocks originating in the private sector induce a negative short‐lived response in real activity. In addition, a New Keynesian model rationalizes the empirical results, emphasizing the role of financial frictions and monetary policy decisions in transmitting the effects of sovereign uncertainty shocks.

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency. By integrating Multi-Scale Dilated Convolution (MSDC) modules, our model is capable of extracting features from electrocardiogram (ECG) datasets across various scales, thus achieving an optimal balance between precision and computational savings. We have developed three MSDC modules to construct the AF-MSDC framework and assessed its performance on renowned datasets, including the MIT-BIH Atrial Fibrillation Database and Physionet Challenge 2017. Empirical results unequivocally demonstrate that our technique surpasses existing state-of-the-art (SOTA) methods in the AF detection domain. Specifically, our model, with only a quarter of the parameters of a Residual Network (ResNet), achieved an impressive sensitivity of 99.45%, specificity of 99.64% (on the MIT-BIH AFDB dataset), and an [Formula: see text] score of 85.63% (on the Physionet Challenge 2017 AFDB dataset). This high efficiency makes our model particularly suitable for integration into wearable ECG devices powered by edge computing frameworks. Moreover, this innovative approach offers new possibilities for the early diagnosis of AF in clinical applications, potentially improving patient quality of life and reducing healthcare costs.

The anglophone crisis in Cameroon and sustainable development: A causal‐process tracing approach

AbstractArmed conflict is perceived to be detrimental to sustainable development while underdevelopment is seen as the cause of armed conflict. However, little is known about the cause of the ongoing armed conflict in Southern Cameroon and its effects on the pace of sustainable development in Cameroon. This study seeks to close this gap by employing a causal‐process tracing methodology and trend analysis. The empirical results obtained suggest that there is no end to the conflict any time soon due to the divide between the Anglophone political elite, lack of international support, and repression from the government. In addition, the conflict has very minimal quantitative significant impact on the pace of development in Cameroon. By empirically analyzing the case of Cameroon, the current study lends an understanding of the causes and effects of the conflict on sustainable development and further highlights the steps that are necessary to safeguard the socio‐economic well‐being of the masses.

City civilization, employment creation and talent agglomeration: Empirical evidence from “National Civilized City” policy in China

China's rapid urbanization has intensified urban employment pressure. The National Civilized City (NCC) policy is a significant initiative in China aimed at promoting high-quality urban development. We utilize the NCC policy as a quasi-natural experiment and employ the staggered difference-in-difference method to empirically analyze the impact of urban civilization development on full employment and human capital level. The empirical results demonstrate that NCC policy significantly promotes urban entrepreneurship and employment creation, boosts labor demand of local enterprises, and enhances the human capital level. The positive effect on employment is significant in both talent-intensive and low- and medium-skilled intensive fields. In addition, heterogeneity tests show that this positive effect is more pronounced in low-tier cities and previously polluted cities. Finally, the mechanism analysis indicates that the NCC policy can enhance employment creation through investment promotion at the city level and by expanding production scale at the firm level. The NCC policy's impact on human capital level is supported by technological advancement and innovation emphasis mechanisms from the demand side, as well as talent agglomeration mechanism from the supply side. This study provides policy recommendations for local governments to leverage the achievements of civilized cities in promoting employment.

The mediating role of corporate borrowings in the nexus between financial inclusion and performance of ICT firms: new insights from Vietnam

ABSTRACT This paper examines the effect of financial inclusion on firm performance and whether corporate borrowings serve as an effective channel of the impact. In doing so, we use principal component analysis to calculate a financial inclusion index for Vietnam. We then conduct mediation analysis on this index and a large panel of Vietnamese firms in information and communication technology sector from 2008 to 2018. Empirical results indicate a negative influence of financial inclusion on firms’ return on assets. More importantly, corporate borrowings significantly mediate this relationship. In particular, financial inclusion increases corporate borrowings, which then affects return on assets. Although corporate borrowings are positively associated with firm sales, they reduce return on assets and net profit margin of firms. The findings are robust to different estimation methods and inclusion of control variables. They convey important policy implications for governments, academic researchers, and industry practitioners wishing to design programmes and policies to help firms obtain the financial resources necessary for their development.

Measuring ESG risks in multi-asset portfolios: Decomposing VaRESG into CVaRESG

This study investigates the contribution of different asset classes to investment portfolio risk by integrating environmental, social, and governance (ESG) factors into traditional financial risk measures. We propose a new methodology for decomposing VaRESG by measuring the Component VaRESG (CVaRESG) of a multi-asset financial portfolio. A pilot empirical application's results provide evidence of the reliability of CVaRESG to define the maximum contribution of the risk accepted for securities or parts of the financial portfolio. This study contributes to the debate on how ESG factors can have quantifiable long-term financial impacts and clarifies the risk contribution of each security included in a financial portfolio.

Biofuel supply chain planning and circular business model innovation at wastewater treatment plants: The case of biomethane production

Advanced biofuels, such as biomethane, could contribute to environmental sustainability in increasingly interrelated sectors, such as energy and waste management. Accordingly, innovative biomethane production technologies are argued to be the enablers of circular and waste-to-energy concepts, for example, at wastewater treatment plants (WWTPs). Nevertheless, their integration into biofuel supply chain (BSC) research is overlooked from a circular business model innovation (BMI) perspective. This study aims to address this research gap by focusing on an innovative biomethane production approach (power-to-gas, P2G) and related circular business model innovation opportunities in the context of WWTPs. We carried out lab-scale research and a case study at a mid-sized European WWTP to establish an empirical basis for large-scale techno-economic calculations. Despite the explored technological opportunity to increase advanced biofuel supply and decrease carbon dioxide emissions at WWTPs, current market risk levels challenge the economic prospects of the system concept. These empirical results demonstrate the necessity of policy interventions in different combinations (e.g., investment support, favourable taxation, feed-in tariffs). This study is one of the first to combine technological and business modelling aspects to support BSC planning, and supplement optimization-focused BSC research with explorative techno-economic analyses based on empirical data.

Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis

The number of satellites in orbit around Earth is increasing rapidly, with the risk of collision rising accordingly. Trends of the global population of satellites need to be analyzed to test the viability and impact of proposed rules and laws affecting the satellite population and collision avoidance strategies. This requires large scale simulations of satellites that are propagated on long timescales to compute the large amounts of actionable close encounters (called conjunctions), which could lead to collisions. Rigorously checking for conjunctions by computing future states of orbits is computationally expensive due to the large amount of objects involved and conjunction filters are thus used to remove non-conjuncting orbit pairs from the list of possible conjunctions. In this work, we explore the possibility of machine learning (ML) based conjunction filters using several algorithms such as eXtreme Gradient Boosting, TabNet and (physics-informed) neural networks and deep operator networks. To show the viability and the potential of ML based filters, these algorithms are trained to predict the future state of orbits. For the physics-informed approaches, multiple partial differential equations are set up using the Kepler equation as a basis. The empirical results demonstrate that physics-informed deep operator networks are capable of predicting the future state of orbits using these equations (RMSE: 0.136) and outperform eXtreme Gradient Boosting (RMSE: 0.568) and TabNet (RMSE: 0.459). We also propose a filter based on the trained deep operator network which is shown to outperforms the filter capability of the commonly used perigee-apogee test and the orbit path filter on a synthetic dataset, while being on average 3.2 times faster to compute than a rigorous conjunction check.

Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Cultural intellectual leadership and breakthrough innovation

This paper considers Chinese listed companies as samples and combines empirical analysis to explore the impact of cultural intellectual leadership (CIL) on corporate breakthrough innovation (BI). Empirical results confirm that CIL can promote BI by enterprises, affect BI differently in firms during various life cycles, and help enterprises improve the quality of their internal controls, thus promoting BI. Further, CIL can help companies increase cross-border investment, leading to BI.

A novel term-structure-based Heston model for implied volatility surface

It remains a challenge for existing financial models to accurately capture the shapes of implied volatility (IV) of options with all maturities simultaneously. Inspired by Chen et al.’s empirical finding, ‘the shorter the option expiry, the higher the IV is’, we propose a novel and simple approach to solve this challenge, by introducing a term-structure-based correction (i.e. an exponential increase function of the options expiry) to the volatility of volatility (vol–vol) term of the classical Heston stochastic volatility model. We derive an approximate formula for the IV under the corrected model with the perturbation method and further apply the formula to predict the IVs of options written on the Shanghai Stock Exchange 50 ETF. Numerical experiments and empirical results show that the introduction of a term-structure-based correction function surely overcomes the deficiency of the classical Heston model in capturing the short-term IVs, thus improving notably its performance of IV forecasting.