Search Results (10)

Zircon is one of the most common accessory minerals in all types of granitoids. Due to its resistance to secondary processes, it preserves information about the composition of magma and conditions at the time of crystallization. Madeira albite granite, Brazil, offers optimum conditions for the study of chemistry and shape of zircon and the relation between the contents of particular trace elements in magma vs. in crystallizing zircon. Textural and chemical zircon data obtained using scanning electron microscopy (BSE) and cathodoluminescence (CL) imaging, automated mineralogy by TESCAN Integrated Mineral Analyzer (TIMA), and electron probe microanalyses (EPMA) enabled us to define four albite granite facies containing zircons of specific structures and chemistry. Zircon in the Madeira albite granite was formed during several, largely temporally and spatially independent episodes. During the crystallization of the common facies, occupying most of the intrusion volume, Zr/Hf value in zircon decreased from 40 to 20. This zircon, in some episodes, incorporated a higher amount of Th, which was later unmixed in the form of thorite inclusions. The pegmatoidal facies, representing crystallization of residual magma, contains zircon without thorite inclusions with a Zr/Hf value from 35 to 5. The Th/U and Y/Yb values during this evolution scattered but generally evolved to Th, Yb-enriched compositions (Th/U up to >10, Y/Yb down to 0.1). The Li-poor facies, located in the center of the stock near the cryolite deposit, contains zircon with comparatively high Zr/Hf = 45–70 and higher U and Y contents. Later, part of the common facies was hydrothermally altered to border facies, but zircon did not change noticeably during this process. The contents of minor elements in all zircon varieties are generally low (U + Th + Y + REE ˂ 0.05 apfu); Y and REE are incorporated exclusively in the xenotime component. Many crystals have low analytical totals, down to 95 wt%, and are enriched in Al, Fe, Mn, Ca, and F but this process does not influence the primary Zr/Hf, Th/U, and Y/Yb ratios. Zircons from other Madeira granite facies, including the neighboring Europa pluton, differ mainly in much higher Y/Yb values and in having (Y + REE) >> P, indicating a different than xenotime substitution mechanism. Zircon from the Madeira albite granite differs from zircons from many metaluminous rare-metal granites in low contents of minor elements and a common assemblage with thorite, instead of forming Zrn–Thr–Xnt solid solutions. Full article

This paper investigates the effectiveness of machine learning algorithms in enhancing the accuracy and reliability of predicting financial distress. The dataset includes Altman Z-Scores and Corporate Governance Compliance (CGC) indicators calculated for manufacturing firms listed on the Bucharest Stock Exchange (BSE) from 2016 to 2022. Leveraging Signaling Theory, the study analyzes financial and governance data for 60 non-financial firms, comprising 420 firm-year observations. Financial distress is classified into three categories: no distress, moderate distress, and severe distress. The study employs a Random Forest classification model, leveraging artificial intelligence techniques to identify critical predictive variables and evaluate their combined effectiveness in signaling financial distress. The findings reveal that machine learning algorithms significantly improve the predictive accuracy and reliability of financial distress classifications, effectively distinguishing between different distress levels by integrating financial ratios and corporate governance variables. These results emphasize the advantages of involving artificial intelligence and advanced analytics in financial distress prediction models, enhancing transparency and strengthening investor confidence. The research contributes to the literature on digital transformation in financial analysis and corporate governance, offering practical implications for investors, managers, creditors, and policymakers in emerging market environments. Full article

This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. The prediction of stock market volatility and associated risks is demonstrated by using the GARCH-M model. A sample is collected by clustering daily closing and opening prices from the official websites of the top ten pharmaceutical companies listed on the Bombay Stock Exchange for ten years, from 2012 to 2023. It is evident when using the GARCH-M model, which indicates pharma stock volatility clustering before the COVID-19 pandemic, that a significant relationship is present between risk and return and that these could cause future volatility and significant price movements. Before the COVID-19 pandemic, investors had time to adjust to market conditions, as the volatility was constant but less sensitive to transient shocks. Though it passed faster than ever, the COVID-19 pandemic produced significant market instability. The findings suggest that, especially before the COVID-19 pandemic, the high GARCH(-1) coefficients held Merton’s ICAPM, which maintains that past volatility shapes future returns. This sort of activity is compatible with the way financial markets usually operate. The findings suggest that volatility rose after the COVID-19 pandemic, but this was more because of changes in government policies and vaccines than because of regular market forces. Pricing patterns are dominated by stock interventions, liquidity constraints, and sentiments during a crisis period when volatility becomes irrelevant. Appropriate decision-making by individual investors, portfolio managers, and policymakers regarding the stock market is possible through effective prediction based on time-series analysis. The GARCH-M model is compatible with predicting future stock price changes efficiently. This study uniquely applies the GARCH-M model to the Indian pharmaceutical sector, offering valuable insights into stock volatility and risk–return dynamics, particularly during the COVID-19 pandemic. Full article

The primary objective of this study is to analyze the factors influencing the corporate sustainability performance disclosures of companies listed on the Bombay Stock Exchange (BSE) using the Global Reporting Initiative (GRI) G4 framework. This research is based on a sample of 434 firms listed on the BSE from 2017 to 2022. According to the content analysis method, the disclosure score of 434 non-financial companies is 79% (approximately), suggesting that, on an average, the sample companies have revealed 79% of the GRI-specified elements in their sustainability reports. The outcomes of the regression models indicate that profitability, firm size, innovation, board size, gender diversity, sustainability committee, and industry type are major drivers of corporate sustainability performance disclosure. Furthermore, research identified significant differences in the determinants of such practices between high-polluting and low-polluting companies. This research aims to elucidate the intricate dynamics affecting corporate sustainability performance by examining a diverse array of concerns. It employs meticulous data analysis to identify critical elements influencing sustainability disclosure. These findings may assist corporate managers, investors, policymakers, and stakeholders in comprehending the critical aspects to consider when formulating strategies that promote sustainability and enhance long-term value maximization. Full article

Although financial statements are extremely important to investors in decision-making processes, their reliability can be affected by earnings management (EM) practices, which involve manipulating financial reports in order to achieve managerial benefits. This study explores the relationship between earnings management and firm valuation, based on accounting information’s predictive value, specifically investigating how EM influences the value relevance (VR) of earnings on share price. The research focuses on a sample of audited companies listed on the Bucharest Stock Exchange (BSE) between 2019 and 2021, comprising 62 entities. Using regression analysis, we explored the importance of accounting information for investors following Ohlson’s research and examined the relationship between EM and VR based on Jones’s model. The findings indicate that earnings significantly impact stock prices, highlighting their value relevance in the Romanian stock market. However, the practice of earnings management reduces the value relevance of earnings because it decreases the reliability of the accounting information. The main contribution of this analysis is to provide a fresh perspective on earnings management (EM) within the BVB framework by highlighting its pivotal role in shaping the motivation and behavior of corporate managers. Full article

In this paper, we propose a new approach to analyze financial contagion using a causality-based complex network and value-at-risk (VaR). We innovatively combine the use of VaR and an expected shortfall (ES)-based causality network with impulse response analysis to discover features of financial contagion. We improve the current research methods by building a Granger causality network on VaR and ES and using conclusions drawn from network analysis as a foundational step before impulse response analysis. First of all, we select 30 stock indices that are very well-known globally and collect their trading data. After calculating the risk indicators of VaR and ES, we perform the Granger causality test on them and then build networks based on their respective Granger causality square matrix. Next, we examine the networks’ topological features to discover different degrees of risk transmission among all stock indices in the system. Lastly, we identify the most and the least active stock indices in the risk transmission network and conduct impulse response analysis on them. We discover that BSESN (India S&P BSE SENSEX) is the most risk-sensitive stock index as its VaR significantly increases by 0.03–0.04% and its ES jumps even more, by 0.07–0.08%, in response to an impulse from a few key stock indices. We also find that either PSI20 or XU100 is the most risk-proof stock index, depending on whether we choose VaR or ES as a risk indicator. Full article

This study utilizes a bivariate BEKK-EGARCH model with the setting of a structural break to investigate whether the interactions between stock indices in emerging and developed markets are different in terms of region, emerging stock indices, and subperiod. Then, this study investigated how the results of interactions vary with geographical location, emerging stock indices, and subperiod. Empirical results show that the interactions between emerging and developed markets indeed vary with geographical location and emerging stock indices, but are almost the same in the two subperiods. For example, for the paired stock indices in ‘Asia-America’ and ‘Asia-Europe’, or related to ‘XU100’, ‘SSE’ and ‘BSE’, the developed market mainly spills into the emerging market in terms of return and volatility. The findings from these empirical results can help investors and fund managers undertake different investment strategies in different regions and subperiods, and make effective investments. Full article

The economic and social transformations, the bankruptcies recorded, and the financial crisis affecting all economies have increased the interest for the corporate governance concept. Our intention in this paper was to study the impact of corporate governance attributes on performance given the information published by the entities listed on five stock exchanges from Europe, namely the main market from Bucharest Stock Exchange (BSE) in Romania, the Athens Stock Exchange(ATHEX) main market in Greece, Financial Times Stock Exchange 100 Index (FTSE 100) from Great Britain, Spanish Stock Exchange 35 Index (IBEX 35) from Spain, and Warsaw Stock Exchange 20 Index (WIG 20) from Poland, between 2016–2018. Through mathematical modeling and multiple linear regression, we aimed to determine the extent to which corporate governance characteristics, firm characteristics, industry and stock market fixed effects, and random effects influence the performance of 226 entities included in our sample. The empirical findings revealed that CEO duality, the number of non-executive directors and women on board, audit committee, and audit opinion influenced performance measured by the Return on Assets (ROA) and Return on Equity (ROE) indicators. The ideas highlighted and the results obtained in this research contribute to the literature that analyzes the extent to which an effective governance determines the increase in performance, needed for a sustainable development. Full article

This paper examines the association between gender diversity on corporate boards and firm performance for a European emerging market, which lags behind in terms of both corporate governance quality and social cohesion indicators. In a sample of Romanian companies listed on BSE (Bucharest Stock Exchange) during 2012–2016, this study confirms previous concerns related to the endogeneity of gender diversity variables in firm performance regression analysis and shows that, on average, diversity has no significant impact on firm-performance. However, based on a sub-sample analysis, results show a robust association in the case of profit-firms and those listed on the Standard tier. As losses can be construed as a distortion factor and Standard tier companies are the smallest and less well governed on the market, the results could be taken to suggest that Romanian listed companies do benefit from increasing gender diversity in the boardrooms, which could complement their rather poor corporate governance practices. Overall, the paper concludes that, in the context of an emerging market, policies aimed at increasing gender diversity in the boards appear to be financially viable and even beneficial for the major part of listed companies, balancing successfully the social cohesion and economic components of sustainable development. Full article

The large Xinqiao Cu–S–Fe–Au deposit in the Tongling ore district, Eastern China, is characterized by a large-scale stratiform orebody, in which garnet is widely distributed as the main gangue mineral associated with mineralization. Xinqiao garnet can be divided into early (Grt1) and late (Grt2) generations based on extensive back-scattered electron (BSE) imaging observations. Laser ablation (LA)-ICP-MS trace element and U–Pb isotope composition analyses indicate that uranium occurs homogeneously within the Xinqiao garnet, and Grt1 and Grt2 have weighted average ²⁰⁷Pb-corrected ²⁰⁶Pb/²³⁸U ages of 137.0 ± 7.8 Ma (Mean standard weighted deviation (MSWD) = 4.9) and 129.6 ± 7.1 Ma (MSWD = 1.6), respectively, similar to the zircon U–Pb age (139.6 ± 1.5 Ma) of the Jitou intrusion. These garnet U–Pb ages, combined with the low MnO content and various Y/Ho ratios, suggest that the Xinqiao garnet is likely to have a magmatic hydrothermal replacement origin associated with the Jitou stock. Based on previous studies of the Xinqiao deposit, we infer that the Xinqiao stratiform orebody may have formed from the Early Cretaceous magmatic hydrothermal fluids associated with the Jitou stock, and may have been generated by the Early Cretaceous tectono-thermal event in Eastern China. Full article