Search Results (106)

Many companies engage in earnings manipulation that obscures their actual financial condition and sustainability efforts, undermining the credibility of financial reports and eroding stakeholder trust. To address these concerns, the United Kingdom has strictly adhered to International Financial Reporting Standards (IFRS), enhancing financial transparency and reducing the risk of manipulation. This study applies agency theory to examine the effects of earnings management and IFRS adoption on corporate sustainability performance, while also assessing the moderating roles of institutional ownership and board independence. Data were drawn from 248 companies listed on the London Stock Exchange between 2002 and 2024, using purposive sampling and sourced from Thomson Reuters Eikon DataStream. Advanced estimation techniques, specifically the Augmented Mean Group (AMG) and fixed effects models with Driscoll-Kraay standard errors, were employed to address cross-sectional dependence and slope heterogeneity. The results indicate that earnings management, as measured by discretionary accruals, has a significant negative impact on sustainability performance. In contrast, the adoption of IFRS has a positive and significant influence on sustainability outcomes. Additionally, institutional ownership and board independence significantly moderate the adverse effects of earnings management, leading to improved sustainability performance. The findings suggest that managers should enhance the clarity and accountability of financial reporting by implementing robust internal systems aligned with IFRS, conducting regular compliance audits, and training finance staff on current disclosure standards. Full article

Environmental innovation represents a pivotal pathway toward achieving energy efficiency improvements, carbon footprint reduction, and ecological sustainability enhancement. The research investigates Chinese manufacturing enterprises listed on domestic stock exchanges throughout 2011–2023. The analytical framework utilizes count-based regression methodologies to explore how R&D investment intensity influences eco-innovation capabilities. Results demonstrate curvilinear associations linking R&D expenditure levels with both substantive and strategic environmental innovation achievements across industrial firms. This outcome successfully passed the turning-point test. Environmental oversight and financial incentives produce divergent moderating influences on innovation trajectories. Regulatory frameworks generate restrictive impacts through narrowing optimal investment ranges and dampening peak innovation outputs, whereas fiscal support mechanisms foster expansive effects via broadening resource availability and amplifying achievement levels. Cross-sectional examination uncovers substantial variations among ownership categories and geographical locations. State-owned enterprises demonstrate significantly lower optimal R&D intensity thresholds. Private firms require substantially elevated thresholds for optimal performance. Inland territories manifest unbalanced innovation dynamics. Coastal areas exhibit symmetric innovation patterns. The research enriches empirical knowledge in eco-innovation studies while offering context-specific strategic insights. The findings establish theoretical foundations and practical guidance for policy architects designing integrated environmental management systems that enhance innovation capabilities. Full article

International organizations have highlighted the importance of consistent and reliable environment, social and governance (ESG) disclosure and metrics to inform business strategy and investment decisions. Greater corporate disclosure is a positive signal to investors who prioritize sustainable investment. In this study, economic and climate sentiment are extracted from the integrated and sustainability reports of the top 40 corporates listed on the Johannesburg Stock Exchange, employing domain-specific natural language processing. The intention is to clarify the complex interactions between climate risk, corporate disclosures, financial performance and investor sentiment. The study provides valuable insights to regulators, accounting professionals and investors on the current state of disclosures and future actions required in South Africa. A time series analysis of the sentiment scores indicates a noticeable change in the corporates’ disclosures from climate-related risks in the earlier years to climate-related opportunities in recent years, specifically in the banking and mining sectors. The trends are less pronounced in sectors with good ESG ratings. An exploratory regression study reveals that climate and economic sentiments contain information that explain stock price movements over the longer term. The results have important implications for asset allocation and offer an interesting direction for future research. Monitoring the sentiment may provide early-warning signals of systemic risk, which is important to regulators given the impact on financial stability. Full article

Despite global initiatives to combat Illegal, Unreported, and Unregulated (IUU) fishing, such activities continue unabated. As a response, states are encouraged to join the Food and Agriculture Organization of the United Nations Port State Measures Agreement (PSMA) as a countermeasure. Despite these efforts, it is suspected that many IUU fishing activities involve non-party or unknown vessels that evade international sanctions. This study aims to propose technical and institutional improvement measures in light of these challenges. First, using available IUU vessel lists, we conducted independent-sample comparisons and paired-sample comparisons to analyze the characteristics of IUU vessels. As key solutions, we propose the formation of a global collaborative body to facilitate an integrated information chain, the implementation of advanced technologies for systematic operations, strategies to encourage PSMA accession by non-parties, market investigations, and enhanced national inspection and organizational capabilities. Furthermore, this study seeks to strengthen global deterrence of IUU fishing activities by proposing a phased international cooperation framework to enhance the feasibility of integrating the PSMA, Global Record (GR), Global Information Exchange System (GIES), and Regional Fisheries Management Organization (RFMO) systems. These strategies are expected to contribute positively to the transparent governance, sustainable management of fishery resources, and safety officers and vessels. Full article

This study assesses the quality of financial reporting in ten Central and Eastern European countries using a methodology based on natural language processing (NLP) techniques. 570 annual reports of companies listed on the main index on the stock exchanges of 10 Central and Eastern European (CEE) countries, over the period 2019–2023, were evaluated to determine the degree of convergence of the following four measurable qualitative characteristics: relevance, exact representation, comparability and understandability. The main objective is to identify consistency in the quality of accounting information based on the application of an international financial reporting framework. The applied methodology eliminates subjective variability by implementing a standardized scoring system, aligned with the criteria developed by NiCE, using libraries such as spaCy and NLTK for term extraction, respective sentiment analysis and word frequency evaluation. The results reveal significant heterogeneity in all characteristics examined, with statistical tests confirming substantial differences between countries. The investigation of relevance revealed partial convergence, with three dimensions achieving complete uniformity, while the exact representation showed the highest variability. The assessment of comparability showed a significant difference between countries’ extreme values, and in terms of comprehensibility a formalistic approach was evident, with technical dimensions outweighing user-oriented aspects. The overall quality index varied significantly across countries, with a notable average deterioration in 2023, indicating structural vulnerabilities in financial reporting systems. These findings support initial hypotheses on the lack of homogeneity in the quality of financial reporting in the selected region, despite the implementation of international standards. Full article

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals. Full article

In response to urban digital intelligence transformation (DIT) and the rising global emphasis on corporate carbon performance (CP), this study leverages the “National New-Generation AI Innovation Development Pilot Zones” (NAIPZs) as a quasi-natural experiment. Utilizing an unbalanced panel of A-share listed firms from China’s Shanghai and Shenzhen stock exchanges between 2010 and 2022, this study employs a multi-period Difference-in-Differences (DID) model combined with propensity score matching (PSM-DID) to examine how urban DIT affects corporate CP and its underlying mechanisms. The results indicate that the policy significantly enhances corporate CP, with robustness confirmed through parallel trend, placebo, and PSM-DID tests. Heterogeneity analysis shows stronger effects for non-state-owned enterprises, high-pollution industries, and large enterprises. Mechanism analysis reveals that green technological innovation and R&D expenditure are key drivers of improved CP. The study concludes with policy suggestions including tailored regulation, the development of innovation platforms, strengthened R&D support, and the implementation of monitoring systems to better harness AI technologies for improving corporate carbon performance. Full article

This study investigates the properties of financial markets that arise from the multi-scale structure of volatility, particularly intermittency, by employing robust theoretical tools from nonequilibrium thermodynamics. Intermittency in velocity fields along spatial and temporal axes is a well-known phenomenon in developed turbulence, with extensive research dedicated to its structures and underlying mechanisms. In turbulence, such intermittency is explained through energy cascades, where energy injected at macroscopic scales is transferred to microscopic scales. Similarly, analogous cascade processes have been proposed to explain the intermittency observed in financial time series. In this work, we model volatility cascade processes in the stock market by applying the framework of stochastic thermodynamics to a Langevin system that describes the dynamics. We introduce thermodynamic concepts such as temperature, heat, work, and entropy into the analysis of financial markets. This framework allows for a detailed investigation of individual trajectories of volatility cascades across longer to shorter time scales. Further, we conduct an empirical study primarily using the normalized average of intraday logarithmic stock prices of the constituent stocks in the FTSE 100 Index listed on the London Stock Exchange (LSE), along with two additional data sets from the Tokyo Stock Exchange (TSE). Our Langevin-based model successfully reproduces the empirical distribution of volatility—defined as the absolute value of the wavelet coefficients across time scales—and the cascade trajectories satisfy the Integral Fluctuation Theorem associated with entropy production. A detailed analysis of the cascade trajectories reveals that, for the LSE data set, volatility cascades from larger to smaller time scales occur in a causal manner along the temporal axis, consistent with known stylized facts of financial time series. In contrast, for the two data sets from the TSE, while similar behavior is observed at smaller time scales, anti-causal behavior emerges at longer time scales. Full article

The stability of financial markets is influenced by the strength and transparency of companies listed on stock exchanges. This paper explores how financial, non-financial, and macroeconomic factors influence delisting likelihood among companies listed on the Johannesburg Stock Exchange (JSE), addressing a limitation in the current body of knowledge that often overlooks the combination of these factors, especially within the context of developing economies. Using a sample of 302 companies delisted between 2010 and 2023 and 302 as a control group, we analyzed 72 variables through a multivariate panel probit regression model. Our findings reveal that delisting decisions are driven by a complex interplay of financial health, governance practices, and macroeconomic conditions. Financial health, including liquidity and market valuation, is crucial in mitigating delisting risk. Non-financial factors, such as corporate governance and shareholder composition, further reduce the likelihood of delisting. Macroeconomic conditions, including inflation and interest rates, introduce significant external pressures. This study is especially relevant in developing economies like South Africa, where economic volatility adds risks for listed companies. The results provide insights for companies, investors, regulators, and policymakers to ensure a stable and robust stock market and financial system and identify early warning signals for delisting. Full article

Promoting structural deleveraging is a key strategy for China to reduce high debt levels and mitigate systemic financial risks. In this context, the deleveraging of state-owned enterprises (SOEs) has become a national strategic priority. This study explores whether enhancing the quality of internal control as an internal governance mechanism can facilitate the deleveraging process of SOEs. Using a sample of A-share state-owned listed companies from the Shanghai and Shenzhen stock exchanges (2009–2023) and based on resource-based theory and signaling theory, we examine the impact and mechanisms through which internal control quality influences SOE leverage reduction. Our results demonstrate that higher internal control quality significantly promotes deleveraging in SOEs, and these findings remain robust after conducting endogeneity tests and employing alternative model specifications. Improved internal control mitigates resource misallocation and encourages firms to adopt two primary strategies: debt reduction (through short-term liability repayment and retained earnings) and equity expansion. However, the positive effect diminishes as Research and Development (R&D) intensity increases, reflecting the trade-off between innovation-driven growth and financial stability. Further heterogeneity analyses reveal that the deleveraging effect is more pronounced in local SOEs and over-indebted SOEs, as enhanced internal control helps eliminate non-performing liabilities. This study contributes to the literature on the economic consequences of internal control and provides empirical insights for policymakers seeking to optimize the capital structures of SOEs. Full article

Taxes represent a significant expense for many companies, prompting a strong incentive to minimize tax liabilities through strategies known as tax avoidance. This research explores the impact of tax avoidance and tax risk disclosure on the cost of debt among companies listed on the Iraqi Stock Exchange. This study analyzes data from 33 firms from 2016 to 2021, employing multivariate linear regression and the generalized least squares (GLS) model to test the hypotheses. The findings indicate that tax avoidance significantly and positively affects the cost of debt, suggesting that firms engaging in tax avoidance may experience greater borrowing costs. Additionally, tax risk disclosure is shown to directly and significantly influence the cost of debt. Importantly, this study reveals that tax risk disclosure negatively moderates the relationship between accrual tax avoidance and the cost of debt, indicating that higher tax risk disclosure can reduce uncertainties associated with tax avoidance, reducing borrowing costs. These results imply that tax avoidance and its influence on corporate debt levels can affect the overall risk profile of a country's financial system. Understanding this relationship is crucial for governance measures aimed at managing tax risks effectively. Given the limited research in this area, this study contributes to the literature by examining how tax risk and tax avoidance relate to the cost of debt in an emerging market context. Full article

This study investigates the impact of local government debt levels on the behavior of individual firms, which is crucial for understanding the systemic risks associated with local government debt and fostering economic vitality. Using data from publicly listed companies on the Shanghai and Shenzhen stock exchanges between 2013 and 2022, this study empirically examines the effect of local government debt on corporate innovation quality. The findings demonstrate that local government debt expansion has a significant negative impact on corporate innovation quality. The negative impact remains robust across endogeneity tests and multiple robustness checks. Channel analysis indicates that as local government debt increases, innovation subsidies and procurement funding led toward firms’ decline, while both tax and non-tax revenue demands indicated firm increases. This resource reallocation contributes to the observed decline in corporate innovation quality. Further heterogeneity analysis reveals that regions with lower levels of government intervention and fiscal pressure exhibit a smaller negative effect of local government debt on innovation quality. Finally, examining the economic outcomes reveals that the decline in innovation quality, resulting from current local debt expansion, significantly reduces total factor productivity and firm value in the subsequent year, posing challenges for sustainable corporate development. Full article

Stablecoins are crypto assets designed to maintain stable value by bridging fiat currencies and volatile crypto assets. Our study extends previous research by analyzing the instability and co-movement of major stablecoins (USDT, USDC, DAI, and TUSD) during significant economic events such as the COVID-19 pandemic and the collapses of Iron Finance, Terra-Luna, FTX, and Silicon Valley Bank (SVB). We investigated the temporal volatility and dynamic connections between stablecoins using wavelet techniques. Our results showed that the announcement of USDT’s listing on Coinbase in April 2021 significantly impacted the stability of stablecoins, evidenced by a decline in the power spectrum. This phenomenon has not been explored in the literature. Furthermore, the collapse of SVB was highly relevant to the stablecoin market. We observed high coherence between pairs during the pandemic, the Coinbase listing, and the collapse of SVB. After the collapse of Terra-Luna, USDT, USDC, and DAI became more connected in the medium term, with USDC and DAI extending in the long term despite a negative co-movement between USDT and the others. This study highlights the impact of exchange listings on the volatility of stablecoins, with implications for investors, regulators, and the cryptocurrency community, especially regarding the stability and safe integration of these assets into the financial system. Full article

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article

Sentiment analysis is a well-known task that has been used to analyse customer feedback reviews and media headlines to detect the sentimental personality or polarisation of a given text. With the growth of social media and other online platforms, like Twitter (now branded as X), Facebook, blogs, and others, it has been used in the investment community to monitor customer feedback, reviews, and news headlines about financial institutions’ products and services to ensure business success and prioritise aspects of customer relationship management. Supervised learning algorithms have been popularly employed for this task, but the performance of these models has been compromised due to the brevity of the content and the presence of idiomatic expressions, sound imitations, and abbreviations. Additionally, the pre-training of a larger language model (PTLM) struggles to capture bidirectional contextual knowledge learnt through word dependency because the sentence-level representation fails to take broad features into account. We develop a novel structure called language feature extraction and adaptation for reviews (LFEAR), an advanced natural language model that amalgamates retrieval-augmented generation (RAG) with a conversation format for an auto-regressive fine-tuning model (ARFT). This helps to overcome the limitations of lexicon-based tools and the reliance on pre-defined sentiment lexicons, which may not fully capture the range of sentiments in natural language and address questions on various topics and tasks. LFEAR is fine-tuned on Hellopeter reviews that incorporate industry-specific contextual information retrieval to show resilience and flexibility for various tasks, including analysing sentiments in reviews of restaurants, movies, politics, and financial products. The proposed model achieved an average precision score of 98.45%, answer correctness of 93.85%, and context precision of 97.69% based on Retrieval-Augmented Generation Assessment (RAGAS) metrics. The LFEAR model is effective in conducting sentiment analysis across various domains due to its adaptability and scalable inference mechanism. It considers unique language characteristics and patterns in specific domains to ensure accurate sentiment annotation. This is particularly beneficial for individuals in the financial sector, such as investors and institutions, including those listed on the Johannesburg Stock Exchange (JSE), which is the primary stock exchange in South Africa and plays a significant role in the country’s financial market. Future initiatives will focus on incorporating a wider range of data sources and improving the system’s ability to express nuanced sentiments effectively, enhancing its usefulness in diverse real-world scenarios. Full article