Search Results (300)

Global financial markets have experienced significant volatility during crises, particularly COVID-19 and the Russia–Ukraine conflict, prompting questions about how regional markets respond to such shocks. Previous research highlights the influence of crises on stock market volatility, focusing on individual events or global markets, but less is known about the comparative dynamics within the Gulf Cooperation Council (GCC) markets. Our study investigated volatility and asymmetric behavior within GCC stock markets during both crises. Furthermore, the econometric model E-GARCH(1,1) was applied to the daily frequency data of financial stock market returns from 11 March 2020 to 31 July 2023. This study examined volatility fluctuation patterns and provides a comparative assessment of GCC stock markets’ behavior during crises. Our findings reveal varying degrees of market volatility across the region during the COVID-19 crisis, with Qatar and the UAE exhibiting the highest levels of volatility persistence. In contrast, the Russia–Ukraine conflict has had a distinct effect on GCC markets, with Oman exhibiting the highest volatility persistence and Kuwait having the lowest volatility persistence. This study provides significant insights for policymakers and investors in managing risk and enhancing market resilience during economic and geopolitical uncertainty. Full article

This study uses national data to contribute to ongoing discussions regarding social media’s role in influencing investors in the digital economy. Grounded in social network theory, social media engagement was examined for its influence on FinTech usage, specifically cryptocurrency investments, mobile trading applications, and financial podcasts. Results showed a significant relationship between social media use for investment decisions and the embrace of FinTech. Individuals who actively engage with social media for this purpose had higher odds of investing in cryptocurrency and a higher likelihood of using both mobile trading applications and financial podcasts. However, these results were not consistent across all platforms amongst social media users. Our findings show that social media platforms enable peer influence and recommendations through networks that shape financial decisions and behaviors. FinTech firms can strategically harness social ties and the inherent information flows within social networks to broaden their reach and impact in the financial services landscape. Full article

This study examined the relationship between margin trading and cryptocurrency investment using data from the 2018 and 2021 waves of the National Financial Capability Study (NFCS) Investor Survey. Guided by behavioral finance theory, which suggests that cognitive biases may influence risk-taking, the study explored whether margin loan use and margin calls are associated with higher cryptocurrency participation. Margin loans are inherently risky, as they must be repaid regardless of investment outcomes, and margin calls are triggered when an investor’s equity falls below a required threshold. The results showed a positive and statistically significant association between margin activity and cryptocurrency investment. Specifically, individuals with a margin loan were 17 percentage points more likely to invest in cryptocurrency, while those who have experienced a margin call were 23 percentage points more likely. Given the extreme volatility of cryptocurrencies, these results highlight the increased risks investors face when using leverage in speculative markets. The analysis is based on cross-sectional data from U.S. investors; therefore, the findings should be interpreted as correlational rather than causal. Full article

Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squares (FPLS), and stacking. This hybrid model facilitates the decision-making process related to the import and export of crude oil in India. The precision and reliability of the different machine learning models utilized in this study were validated through rigorous evaluation using various error metrics, ensuring a thorough assessment of their forecasting capabilities. The conclusive results revealed that the proposed hybrid ensemble model consistently delivered effective and robust predictions compared to the individual models. Full article

The relationship between ESG and firm performance is complex and tends to yield mixed results globally. In South Africa, ESG implementation is still in its infancy stage due to economic and developmental challenges. Despite these challenges, the JSE introduced sustainability disclosure guidelines in 2022 to enhance ESG adoption in South Africa. Thus, the study seeks to understand the impact of ESG and firm size on the financial performance of JSE-listed firms in South Africa. The study utilised the JSE Top 40 firms for the period from 2002 to 2022. Furthermore, the study employed a two-step System Generalised Method of Moments, to estimate the impact of total ESG and individual dimensions of ESG on firm financial performance. Additionally, the study examined the moderating effects of firm size on the relationship between financial performance and ESG. The results revealed a positive and significant relationship between total ESG and firm financial performance. However, the findings regarding individual ESG dimensions and firm performance are mixed. Firm size has a moderating effect on the relationship between ESG and firm financial performance. The implication of these findings for South Africa is increased foreign direct investment from green investors and listed firms seriously considering ESG in their operations. Full article

Financial literacy plays a crucial role in shaping individual investment decisions by influencing susceptibility to behavioural biases such as heuristics, framing effects, cognitive illusions, and herding mentality. While most existing studies have examined financial literacy as a mediating factor, our study is among the first in the literature to analyse the role of behavioural biases as mediating factors in the relationship between financial literacy and investment decisions. Specifically, we investigate key biases, including overconfidence, herding, disposition effect, self-attribution, anchoring, availability, representativeness, and familiarity. Using purposive sampling, we collected 482 responses through a structured Likert scale questionnaire. The dataset underwent rigorous validation and reliability tests to ensure robustness. We employed Python-based statistical analysis and used Pearson’s correlation and mediation analysis to explore the relationships between financial literacy, behavioural biases, and investment decisions. With the help of these methods, we were able to uncover relationships and causal pathways which further our understanding of the role of behavioural biases in determining the impact of financial literacy on investment behaviour. The findings illustrate a notable positive correlation between investment decisions and financial literacy, implying that people with higher financial literacy levels possess greater and more rational financial decision-making capabilities. Other analyses have revealed that biases have a moderating effect on this relationship, showing another path through which financial literacy impacts behaviour at the level of the investor. By placing behavioural biases as mediating constructs, this research broadens the scope of investor psychology and the body of knowledge in behavioural finance, highlighting the need to change the approach to how financial literacy programs aimed at investors are structured and implemented. Full article

This study investigates the relationship between the CEO characteristics and investment–cash flow sensitivity (ICFS) of Indian manufacturing firms. By using the GMM technique, this study finds that CEO characteristics reduce ICFS. Further, this study examines the moderating role of business group-affiliated firms, independent firms (non-business group-affiliated firms), and firm size on the relationship between CEO characteristics and ICFS. The results reveal that group affiliation moderates the effectiveness of CEO characteristics in reducing ICFS. In addition to this, independent firms rely more heavily on the individual capabilities of CEOs to overcome financial constraints and mitigate ICFS, whereas group firms benefit from structural advantages that diminish the relative impact of CEO characteristics on ICFS. Additionally, this study finds that firm size also moderates the relationship between CEO characteristics and ICFS. The results reveal that CEO characteristics significantly reduce ICFS, with a more pronounced effect in small-sized independent firms compared to their larger counterparts. However, in group-affiliated firms, CEO characteristics have a minimal effect on ICFS, and this impact remains consistent across small and large group firms. These findings offer valuable insights for firms, lending institutions, and investors, emphasizing the role of CEO characteristics in shaping financial decision making, especially in independent and smaller firms. Full article

The emergence of online brokerage platforms, mobile banking applications, and commission-free trading has altered the investment landscape, renewing commercial and scholarly interest in retail investors. In light of these changes, the present study aims to provide a structural overview of the current state of research on the behaviour of retail investors. Based on a dataset of 386 articles sourced from the Web of Science database, this study employs a composite bibliometric approach of a co-word and co-citation analysis as well as a network analysis to determine preceding scientific discourses, current research themes, and potential avenues for future research. The co-word analysis identifies seven distinct research themes: (1) implications for financial performance; (2) information behaviour; (3) behavioural biases and investor characteristics; (4) investor attention; (5) attitudes towards financial risks; (6) socially responsible investing; and (7) complex financial retail instruments. Incorporating applicable research on individual investors, private investors, and household investors from referenced articles, the co-citation analysis reveals nine preceding scientific discourses. Additionally, the network analyses highlight the concepts and publications currently shaping and likely to influence future research in this field. The present study contributes to the academic discourse by mapping the intellectual landscape of retail investor behaviour, suggesting avenues for future research, and offering valuable insights for navigating this dynamic field. Full article

The Chinese stock market, one of the largest and most dynamic emerging markets, is characterized by individual investor dominance and strong policy influence, resulting in high volatility and complex dynamics. These distinctive features pose substantial challenges for accurate forecasting. Existing models like RNNs, LSTMs, and Transformers often struggle with non-stationary data and long-term dependencies, limiting their forecasting effectiveness. This study proposes a hybrid forecasting framework integrating the Non-stationary Autoformer (NSAutoformer), LASSO feature selection, and financial sentiment analysis. LASSO selects key features from diverse structured variables, mitigating multicollinearity and enhancing interpretability. Sentiment indices are extracted from investor comments and news articles using an expanded Chinese financial sentiment dictionary, capturing psychological drivers of market behavior. Experimental evaluations on the Shanghai Stock Exchange Composite Index show that LASSO-NSAutoformer outperforms the NSAutoformer, reducing MAE by 8.75%. Additional multi-step forecasting and time-window analyses confirm the method’s effectiveness and stability. By integrating multi-source data, feature selection, and sentiment analysis, this framework offers a reliable forecasting approach for investors and researchers in complex financial environments. Full article

This study investigates the legal and planning risks related to the implementation of individual recreational buildings in environmentally valuable areas, with particular emphasis on the municipality of Bukowina Tatrzańska in southern Poland. This research highlights the consequences of a legal loophole that allows construction in protected landscapes based solely on a notification procedure, often excluding municipal authorities from the decision-making process. This analysis combines field inventory, planning document review, and interviews with local officials to assess the scale and nature of development in areas lacking valid local development plans. The findings reveal increasing investor pressure and the misuse of individual recreational buildings for commercial purposes, leading to spatial and landscape degradation. Despite formal compliance with certain legal provisions, construction often takes place in areas of high natural and scenic value, undermining spatial order. This study recommends strengthening local planning instruments, revising construction laws, and enhancing investment control to ensure sustainable land use and landscape protection. Full article

Background/Objectives: Pediatric postoperative respiratory failure in the United States is increasingly considered a significant adverse event due to the increased risk of co-morbidities, suffering, and cost of healthcare. This study investigates associations between pediatric adverse events (PAEs) and hospital and patient characteristics within the inpatient hospital setting, focusing solely on the framework of pediatric quality indicators (PDIs) from the Agency for Healthcare Research and Quality (AHRQ). Specifically, the study focuses on PDI 09-Postoperative Respiratory Failure (PORF). Methods: This quantitative research analyzed the inpatient discharge data from the Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Databases (KID) for 2019. We performed multivariate logistic regression to analyze patient-level encounters with PORF. Results: The results indicate that smaller, rural, and non-teaching hospitals exhibit significantly lower odds of PDI 09 than large, urban, and urban teaching hospitals, reflecting a concentration of operative procedures. In comparison, the Western United States exhibits higher odds of PDI 09. Various individual factors such as gender, age, race, service lines, payment sources, and major operating room procedures demonstrate differing levels of significance concerning PDI 09, warranting further investigation into confounding factors. In contrast, hospital ownership consistently shows lower odds of PORF risk for private, investor-owned hospitals. Conclusions: This study provides contextual expansion on the findings and offers valuable insights into PAEs in the inpatient hospital setting. It highlights areas for developing evidence-based interventions and guidelines for clinicians and policymakers. Ultimately, the findings contribute to the growing understanding of factors influencing PORF and emphasize the importance of targeted strategies for improving pediatric patient safety. Full article

Predicting stock trends has garnered extensive attention from investors and researchers due to its potential to optimize stock investment returns. The fluctuation of stock prices is complex and influenced by multiple factors, presenting two major challenges: the first challenge lies in the the temporal dependence of individual stocks and the spatial correlation among multiple stocks. The second challenge emerges from having insufficient historical data availability for newly listed stocks. To address these challenges, this paper proposes a spatio-temporal hypergraph transformer (STHformer). The proposed model employs a temporal encoder with an aggregation module to capture temporal patterns, utilizes self-attention to dynamically generate hyperedges, and selects cross-attention to implement hypergraph-associated convolution. Furthermore, pretraining based on reconstruction of masked sequences is implemented. This framework enhances the model’s cold-start capability, making it more adaptable to newly listed stocks with insufficient training data. Experimental results show that the proposed model, after pretraining on data from over two thousand stocks, performed well on datasets from the stock markets of the United States and China. 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

As sustainability reporting and ESG disclosure gain global importance, understanding the factors influencing ESG outcomes becomes crucial for policymakers, investors, and corporate decision-makers. China, a major player in the global economy, has recently taken steps to align its stock exchanges with international ESG reporting standards. In this context, the study examines the individual and joint effects of digital transformation and CEO compensation on ESG performance, considering moderating factors such as firm size, state ownership, and CEO age and gender. The research employs a comprehensive dataset containing 16,205 firm-year observations from 2018 to 2022, combining financial data, ESG ratings, and a matrix of word frequencies related to digital transformation extracted from annual reports. The study adopts a firm-year two-way fixed effect model, utilizing panel data and control variables to address potential endogeneity concerns and unobserved firm heterogeneity. The findings provide evidence supporting the positive impact of digital transformation and CEO compensation on ESG performance. The level of digital transformation is positively associated with ESG performance. This relationship is stronger for larger firms and firms with older CEOs, while state-owned enterprises show mixed results compared to non-SOEs. However, the effect of CEO compensation and ESG performance is stronger for male CEOs. This study thus contributes to the growing literature on ESG performance, digital transformation, and executive compensation by providing insights into their relationships in the context of Chinese listed companies. Full article

In the conditions of modern market dynamics, corporate social responsibility (CSR) is increasingly evolving from a formal ethical principle into a powerful strategic instrument, key to achieving sustainable growth and long-term competitive advantage. Companies that integrate CSR into their business not only affirm the values of social responsibility, but also position themselves as reliable and ethically oriented actors, thereby winning the trust of investors, motivating employees and building a stable base of loyal consumers. Hence, the aim of this research is to determine, through empirical analysis, to what extent and in what way individual aspects of corporate social responsibility influence employee perception and satisfaction, as well as to develop a predictive model of their mutual connection. The specific hybrid SEM-ANN methodology in the CSR field was applied to obtain more precise results than standard data analysis methods, which fulfilled the literature gap in this research field. The detailed hypotheses designed were empirically tested using SEM methodology and indicated a positive association between the social and stakeholder aspects with employee satisfaction. These outcomes were confirmed by the results of the ANN models. The findings obtained are not only theoretical, but also have a useful application in the real business environment, which is reflected in the development of strategies that can serve as a road map for organizations in achieving employee satisfaction. This could lead to a change in organizational culture with an emphasis on ethical business and greater responsibility towards society and stakeholders. Full article