Search Results (1,039)

Amidst the green transformations around sustainable drives, organizations are striving to integrate green business strategies (GBSs) to enhance their financial viability. This research argues that green strategies promote organizational efficiency that, in turn, improves financial performance and channel investments. Checking the mediating role of organizational efficiency through process improvement, product improvement, and organizational innovation focuses on financial performance and investment decisions. The study further postulates the moderation of management control system on the links between GBS and organizational efficiency parameters. The data were gathered by using surveys of 552 firms’ managers and investors at the Shenzhen Stock Exchange, China. PLS-SEM was applied to check the psychometric properties and analyze the data. The results confirm that GBS improves organizational efficiency and financial performance, exerting significant mediation effects. The study finds that moderation helps transform the green business strategy into tangible financial goals by amplifying the positive impact of GBSs. The study enriches the understanding of GBSs, organizational efficiency and investment decisions. The study also lauds the integration of GBSs that effectively transform financial performance and investment decisions. Full article

In recent years, extensive research has focused on the relationship between corporate social responsibility (CSR) and financial performance. While past studies have explored this connection, they often faced challenges in quantitatively assessing the effectiveness of CSR initiatives. However, advancements in research methodologies and the development of Environmental, Social, and Governance (ESG) measurement dimensions have led to the creation of more robust evaluation criteria. These criteria use ESG scores as primary reference indicators for assessing the effectiveness of CSR activities. This study aims to utilize ESG indicators from the ESG InfoHub website of the Taiwan Stock Exchange Corporation (TSEC) as benchmarks, comprising 15 items from the environmental (E), social (S), and governance (G) dimensions to form the CSR effectiveness indicators and predict financial performance. The data cover the years 2021–2022 for listed companies, using return on assets (ROA) and return on equity (ROE) as measures of financial performance. With the rapid development of artificial intelligence in recent years, the applications of machine learning and deep learning (DL) have proliferated across many fields. However, the use of machine learning to analyze ESG data remains rare. Therefore, this study employs machine learning models to predict financial performance based on ESG performance, utilizing both classification and regression approaches. Numerical results indicate that two deep learning models, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), outperform other models in regression and classification tasks, respectively. Consequently, deep learning techniques prove to be feasible, effective, and efficient alternatives for predicting corporations’ financial performance based on ESG metrics. Full article

There is a lack of comprehensive evaluation on the impact of ESG rating differences on the green transformation of energy enterprises in the transition era. This study leverages data from companies listed on the Shanghai Stock Exchange in China, applying double machine-learning algorithms to precisely estimate the causal relationship between variations in ESG ratings and the green transition efficiency of energy companies. The research shows that the difference in ESG ratings of third-party rating agencies significantly promotes the efficiency of green transformation of energy enterprises. This paper also studies the influencing factors of this effect: First, ESG rating differences significantly promote the improvement of green transition efficiency of energy enterprises; Second, the positive effect is more pronounced in energy companies with more balanced board structures. Finally, energy companies with high capital market attention can also contribute to this positive impact. Through the mechanism test, this paper finds that enterprise green innovation is an important mechanism for ESG rating divergence to positively promote the efficiency of energy enterprises’ green transformation. Furthermore, this paper analyzes the impact of ESG rating on enterprises from the perspective of market cognition and short-term behavior, which provides a new perspective for analyzing the practice of enterprises pursuing long-term transformation. The study also calls for a more sober reflection on the trend toward ESG in society. Full article

Purpose: This study examines the role of board gender diversity in shaping corporate financial decisions, particularly in terms of debt structure and risk management. Focusing on the Tehran Stock Exchange, it explores how female representation on boards influences long-term and short-term leverage decisions, focusing on the moderating effect of board compensation. Design/Methodology: Utilising a quantitative ex post facto design, the study analyses data from 114 companies listed on the Tehran Stock Exchange between 2017 and 2021. Multivariate regression techniques, including year- and industry-fixed effects, are employed to investigate the relationship between board gender diversity, debt structure, and risk-taking behaviour. Findings: The results reveal a significant negative relationship between female board representation and long-term debt, suggesting that companies with more female directors tend to adopt more conservative debt structures, thereby reducing risk. Additionally, the findings demonstrate that board compensation moderates this relationship by curbing managerial risk-taking, further improving financial decision-making. Originality/Value: This research provides novel insights into the intersection of board gender diversity and risk management in financial decision-making, particularly in the context of a developing economy like Iran. It also offers practical implications for firms seeking to optimise their debt structures while maintaining sound risk management practices. 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

Driven by positive accounting, agency, and political and economic theories, this study examines the relationship between research and development (R&D) intensity and earnings management for listed pharmaceutical companies in the Amman Stock Exchange (ASE) between 2008 and 2021. Employing panel regression methods, the results reveal a positive association between R&D investment and earnings manipulation. Specifically, after two or three R&D delays, the association survived. Moreover, firm size negatively affects earnings management, showing that larger firms have less tendencies to conduct earning manipulation. Furthermore, financial leverage and earnings management are strongly connected, showing that firms may utilize earnings management to avoid credit covenants. The findings emphasize distortions in R&D reporting and profit management within Jordan’s financial reporting practices. Enhancing the accuracy of R&D investment disclosures, minimizing profit manipulation, and fostering greater transparency are crucial. Jordan’s regulators should improve capitalization standards, transparency, auditing, and shareholder activism. Full article

In the context of increasing carbon emissions and strengthening regulatory measures, an increasing number of stakeholders are paying more attention to corporate carbon information. To further explore the relationship between the quality of carbon information disclosure and enterprise value, this study uses a sample of companies listed on the Shanghai and Shenzhen stock exchanges from 2013 to 2021. The aim is to investigate the link between the quality of carbon information disclosure and enterprise value, while also analyzing the role of green innovation in this relationship. The empirical results show that the quality of carbon information disclosure can significantly enhance enterprise value, with green innovation playing a mediating role in this effect. After robustness checks, including replacing the measurement variables and addressing endogeneity issues, the conclusions remain valid. Further analysis reveals that the effect of carbon information disclosure quality on enhancing enterprise value is more pronounced in non-high-pollution industries, non-state-owned enterprises, and firms located in eastern regions. This study provides valuable insights for future policy optimization related to carbon information disclosure and the promotion of low-carbon development in enterprises. Full article

This study explores the resilience of the Indian stock market in the face of global shocks in the post-pandemic era, focusing on its volatility dynamics and interconnections with international indices. Through a combination of Vector Autoregression (VAR), DCC-GARCH, and wavelet analysis, we analysed the time-varying relationships between the National Stock Exchange (NSE) of India and major global indices, including those from the U.S., Europe, Asia-Pacific, Hong Kong and Japan. Time series data of the selected indices have been collected for the period 1 January 2021 to 30 September 2024. Results reveal that while the NSE demonstrates resilience through rapid adjustments following shocks, it remains vulnerable to substantial spillover effects from markets such as the S&P 500 and European indices. Wavelet coherence analysis identifies periods of high correlation, particularly during major economic events, indicating that regional and global factors can periodically compromise market stability. Moreover, the DCC-GARCH results show a persistent but fluctuating correlation with specific markets, reflecting a connected and adaptive nature of the Indian market that is influenced by regional dynamics. This study emphasises the importance of strategic risk management. It highlights critical periods and indices that policymakers and investors should monitor closely to understand the economic resilience of the Indian financial market better. Further research could explore sector-specific impacts and the role of macroeconomic factors in shaping market responses. Full article

In the current economic landscape, businesses are challenged by the dual imperatives of digital transformation and sustainability goals. While digital transformation is often heralded as a catalyst for innovation, its potential negative effects on green innovation remain underexplored. This study fills in this gap by analyzing 1443 listed companies on the Shanghai Stock Exchange main board between 2013 and 2022, focusing on the mechanisms by which digital transformation impacts green innovation and on the moderated role of environmental, social, and governance (ESG) performance. Our findings reveal that digital transformation hinders green innovation by increasing financing constraints. However, good ESG performance mitigates these negative impacts by alleviating financing constraints, thereby fostering green innovation. Our findings hold up against endogeneity tests by applying instrumental variable methods. Notably, the effect of digital transformation and ESG differs significantly between state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). While non-SOEs experience more pronounced challenges, ESG also demonstrates a stronger moderating role, unlike in SOEs, where institutional advantages offset some of these constraints. These findings enhance the understanding of dual transformation challenges, offering practical implications for aligning digital and green strategies in diverse organizational contexts. Full article

We investigate the common movement and information content of trades at steps away from the best bid and offer (BBO) using Tokyo Stock Exchange data. We create strings, a series of trades at the same or at an inferior price. The number of the strings is invariant for securities across trading days. The number of shares traded during a string and the time needed for the completion of a string are also significantly related across days for a given stock. The strings represent liquidity beyond the BBO. In addition, the strings characterize the price adjustment process in which we relate to the information on the underlying asset value. The strings measure order aggressiveness beyond the BBO. Finally, we show that the return for the strings is significantly related to the state of the limit order book at the start of the string. Thus, traders can infer information using strings to achieve higher returns. Full article

Legislative regulations have recognized the significance of board independence in enhancing the board’s role and strengthening its autonomy, which are among the key features that mitigate conflicts of interest between management and shareholders. External auditing serves as a pivotal element of corporate governance, acting as a monitoring mechanism to reduce information asymmetry and safeguard principal interests by ensuring the accuracy and fairness of financial statements. This, in turn, reassures data users and stakeholders. The study aimed to examine the effect of board independence on enhancing external auditor independence among 72 Jordanian service companies listed on the Amman Stock Exchange from 2017 to 2021, with a study sample of 62 companies. The findings revealed a negative impact of board member independence on external auditor independence, as measured by audit firm size. However, company size positively influenced external auditor independence, while no effect was found for financial leverage or company age. The findings highlight the need for companies to strengthen internal controls and governance practices to enhance external auditor independence. Additionally, they suggest that company size plays a crucial role, while other factors like financial leverage and company age may have limited impact, indicating areas for further exploration in future research. Full article

This study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as the Hurst exponent and R/S analysis to uncover its fractal properties and complex dynamics. The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s t errors, SV with leverage effects, and a hybrid model combining Student’s t errors with leverage. Each model offers a unique perspective on forecasting the behavior of the GSE-CI, with the SV model incorporating Student’s t errors emerging as the most effective, as evidenced by the lowest Root Mean Square Error (RMSE) in our comparative evaluation. The integration of these models highlights their robustness in capturing the intricate volatility patterns of the GSE-CI, making a compelling case for their applicability to similar financial markets in other emerging economies. This research also paves the way for future investigations into other market indices and assets within and beyond the borders of emerging markets. Full article

Candlestick charts provide a visual representation of price trends and market sentiment, enabling investors to identify key trends, support, and resistance levels, thus improving the success rate of stock trading. The research presented in this paper aims to overcome the limitations of traditional candlestick pattern analysis, which is constrained by fixed pattern definitions, quantity limitations, and subjectivity in pattern recognition, thus improving its effectiveness in dynamic market environments. To address this, a two-layer clustering method based on a candlestick sequence simlarity matching model is proposed for identifying valid candlestick patterns and constructing a pattern library. First, the candlestick sequence similarity matching model is used to address the pattern matching issue; then, a two-layer clustering method based on the K-means algorithm is designed to identify valid candlestick patterns. Finally, a valid candlestick pattern library is built, and the predictive ability and profitability of some patterns in the library are evaluated. In this study, ten stocks from different industries and of various sizes listed on the Shanghai Stock Exchange were selected, using nearly 1000 days of their data as the test set. The predictive ability of some patterns in the library was evaluated using out-of-sample data from the same period. This selection method ensures the diversity of the dataset. The experimental results show that the proposed method can effectively distinguish between bullish and bearish patterns, breaking through the limitations of traditional candlestick pattern classification methods that rely on predefined patterns. By clearly distinguishing these two patterns, it provides clear buy and sell signals for investors, significantly improving the reliability and profitability of trading strategies. Full article

The subject of this research is the performance of stocks on the Bulgarian Stock Exchange (BSE). The main issue of interest is whether the index price levels are supported by fundamentals or if there is a bubble or undervaluation on the BG stock market. The purpose of this research is to explore the true level of indexes at the BSE, as dictated by fundamentals, and compare it with actual index levels. The research method is based on the comparative analysis of price-earnings ratios (PEs) and price-to-book ratios (PBVs) of the index during the analyzed period. The 2024 PE and PBV of the index are compared with fundamental PE and PBV ratios of the BGBX 40 index, which are derived from the fundamentals, determining the value of stocks in the index. The actual PE and PBV ratios of BGBX 40 look rather low compared with the ones in the leading developed stock markets. At the same time, however, the results of this analysis show that these current PE and PBV ratios are much higher than the benchmark values of the fundamental PE and PBV ratios. In this regard, the current price levels of stocks at the Bulgarian Stock Exchange in 2024 do not seem supported by fundamentals. Full article

Brazil is a resource-rich economy that relies heavily on the exports of several important commodities. The variability of commodity prices affects both the economy and the stock market. This study investigates the relationship between commodity price shocks and stock returns in Brazil using a structural vector autoregressive (SVAR) model. This study uses monthly data on prices of five major export commodities, stock returns, and several control variables, covering the period from January 2010 to December 2022. To account for the Brazilian economic crisis between 2014 and 2016, we have analyzed the effects of commodity prices on stock prices in three different time periods, namely, before the economic crisis (January 2010–March 2014), during the economic crisis (April 2014–December 2016), and after the economic crisis (January 2017–December 2022). The empirical results of this study provide evidence to conclude that stock returns increase following a positive global commodity price shock or a positive exchange rate shock. The effects are more noticeable during the economic crisis in Brazil. The results also show that the volatility of Brazilian stock returns is mostly explained by global oil prices and exchange rate movements in the long run. Full article