Search Results (24)

To integrate rapidly growing environmental, social, and governance (ESG) investments into agribusiness, it is essential to understand the decision-making mechanisms behind sustainable practices in small- and medium-sized farms. This study examines the role of corporate governance in promoting sustainable practices using structural equation modeling on survey data from 1111 Japanese farms. The results reveal that internal social sustainability practices, such as improving the work environment and employee well-being, are positively associated with corporate governance and, in turn, significantly enhance sales growth, cash flow, and succession prospects. In contrast, external social sustainability practices show a negative correlation with governance, reflecting the influence of socioemotional wealth and reputation-driven decision-making. Environmental sustainability practices correlate only with sustainable corporate governance, suggesting a lack of strategic integration. These findings underscore the importance of corporate governance as a factor in linking sustainable initiatives to economic outcome. Strengthening internal social sustainability through robust corporate governance is therefore critical for farmers aiming to improve performance through sustainability. Moreover, given that family management preferences shape sustainability choices, policymakers must consider both governance and socioemotional factors to effectively support agricultural sustainability. Full article

The problem of using accounting semi-identity-based (ASI) models in Econometrics can be severe in certain circumstances, and estimations from OLS regressions in such models may not accurately reflect causal relationships. This dataset was generated through Monte Carlo simulations, which allowed for the precise control of a causal relationship. The problem of an ASI cannot be directly demonstrated in real samples, as researchers lack insight into the specific factors driving each company’s investment policy. Consequently, it is impossible to distinguish whether regression results in such datasets stem from actual causality or are merely a byproduct of arithmetic distortions introduced by the ASI. The strategy of addressing this issue through simulations allows researchers to determine the true value of any estimator with certainty. The selected model for testing the influence of the ASI problem is the investment-cash flow sensitivity model (Fazzari, Hubbard and Petersen (FHP hereinafter) (1988)), which seeks to establish a relationship between a company’s investments and its cash flows and which is an ASI as well. The dataset included randomly generated independent variables (cash flows and Tobin’s Q) to analyze how they influence the dependent variable (cash flows). The Monte Carlo methodology in Stata enabled repeated sampling to assess how ASIs affect regression models, highlighting their impact on variable relationships and the unreliability of estimated coefficients. The purpose of this paper is twofold: its first goal is to provide a deeper explanation of the syntax in the related article, offering more insights into the ASI problem. The openly available dataset supports replication and further research on ASIs’ effects in economic models and can be adapted for other ASI-based analyses, as the information comprised in the reusability examples prove. Second, our aim is to encourage research supported by Monte Carlo simulations, as they enable the modeling of a comprehensive ecosystem of economic relationships between variables. This allows researchers to address a variety of issues, such as partial correlations, heteroskedasticity, multicollinearity, autocorrelation, endogeneity, and more, while testing their impact on the true value of coefficients. Full article

This study examines how sentiment analysis of environmental, social, and governance (ESG) news affects the financial performance of companies in innovative sectors such as mobility, technology, and renewable energy. Using approximately 9828 general ESG articles from Google News and approximately 140,000 company-specific ESG articles, we performed term frequency-inverse document frequency (TF-IDF) analysis to identify key ESG-related terms and visualize their materiality across industries. We then applied models such as bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa), and big bidirectional encoder representations from transformers (BigBird) for multiclass sentiment analysis, and distilled BERT (DistilBERT), a lite BERT (ALBERT), tiny BERT (TinyBERT), and efficiently learning an encoder that classifies token replacements accurately (ELECTRA) for positive and negative sentiment identification. Sentiment analysis results were correlated with profitability, cash flow, and stability indicators over a three-year period (2019–2021). ESG ratings from Morgan Stanley Capital International (MSCI), a prominent provider that evaluates companies’ sustainability practices, further enriched our analysis. The results suggest that sentiment impacts financial performance differently across industries; for example, positive sentiment correlates with financial success in mobility and renewable energy, while consumer goods often show positive sentiment even with low environmental ESG scores. The study highlights the need for industry-specific ESG strategies, especially in dynamic sectors, and suggests future research directions to improve the accuracy of ESG sentiment analysis. Full article

ESG (environmental, social, and governance) has gained widespread recognition as a fundamental investment approach on a global scale. Demonstrating strong ESG performance has evolved into a vital strategic imperative for fostering sustainable corporate growth and bolstering competitiveness. Given their critical roles within companies, it is crucial for decision-makers to investigate the impact of executive overconfidence on ESG performance. Through an examination of Chinese A-share listed companies spanning the years 2009 to 2020, this research reveals a significant correlation between executive overconfidence and improved corporate ESG performance. Mechanism tests uncover that overconfident executives exhibit robust risk-taking abilities and a heightened drive to garner attention, both of which contribute to the enhancement of ESG performance. Heterogeneity analysis demonstrates that, in companies characterized by lower-quality accounting information, lower institutional shareholding ratios, ample cash flow, and increased government subsidies, the positive influence of executive overconfidence on ESG performance is even more pronounced. Furthermore, our investigation unveils that overconfident executives exert a positive impact on corporate ESG performance through three primary pathways: assuming responsibility for environmental protection (E), embracing social responsibility (S), and fortifying corporate governance (G). It is worth noting that this boost in ESG performance, in turn, translates into an enhancement of corporate value. Ultimately, this research contributes to a deeper understanding of the economic ramifications of executive overconfidence and enriches the body of knowledge pertaining to the mechanisms for enhancing ESG performance. Full article

Financial institutions, particularly banks, have long grappled with the dilemma of structuring their capital optimally. This process, commonly referred to as capital structure decision-making, is of paramount importance, especially within the financial services sector, where strict regulations are imposed by reserve and central banks in alignment with global Basel guidelines. This study unveils the key factors that determine the capital structure choices of African banks, using panel data encompassing 45 listed banks across six nations that had embraced the Basel III Accord spanning the years 2010 to 2019. The study used the system-generalised moment methods (sys-GMM) estimator to fit the formulated panel data regression model. The study findings showed positive associations between ZSCORE, an indicator of bank financial stability, and net interest margin ratio (NIMR) with bank leverage (TCTE). In addition, the results revealed positive correlations between earnings volatility (EV), profitability (P), and risk (R) with bank leverage (TDCE). This suggests that profitable banks are inclined to favour debt financing, a phenomenon driven by their ability to comfortably service debt obligations with free cash flows. This study’s overarching conclusion underscores the dominant influence of the Liquidity Coverage Ratio (LCR) on African bank capital structures. Whether assessing traditional or Basel III-prescribed measures of bank leverage, LCR consistently emerged as the primary determinant. This finding is of significant relevance to bank executives and regulators, offering them essential insights for informed decision-making by considering striking a balance between equity and debt financing based on financial stability, profitability, and risk profiles. Full article

Evapotranspiration (ET) is an essential part of energy flow between the surface of the earth and the atmosphere, simultaneously involving the water, carbon, and energy cycles. It is mainly determined by climate, land use, and land cover changes. Additionally, there is still a need for quantitative characterization of the impacts of climate factors and human activities on ET and regional water resource efficiency in arid and semiarid regions. Based on Landsat-8 remote sensing imagery and land use data, the crop planting structures in the Liangzhou District of the middle reaches of the Shiyang River Basin were identified using a multiband and multi-temporal approach in this study. Subsequently, the ET of major cash crops was inverted using the three-temperature model. This research quantitatively describes the responses of wheat and corn to the climate and human activities over a two-year period. Furthermore, the impact of crop planting structures and climatic factors on ET was elucidated. The results indicate that a combination of multi-temporal green and shortwave infrared 1 bands is the optimal spectral combination to extract the planting structures. Compared to 2019, the wheat area decreased by 23.27% in 2020, while the corn area increased by 5.96%. Both crops exhibited significant spatial heterogeneity in ET during the growing season. The typical daily range of ET for wheat was 0.4–7.2 mm/day, and for corn, it was 1.5–4.0 mm/day. Among the climatic factors, temperature showed the highest correlation with ET (R = 0.80, p ≤ 0.05). Our research findings provide valuable insights for the fine identification of crop planting structures and a better understanding of the response of ET to climatic factors and planting structures. Full article

The winter climate of the northeastern Dominican Republic features steady rainfall, which sustains cash crop production. Using a representative season, December 2016–February 2017, the mesoscale climate is characterized by high-resolution reanalysis, satellite measurements and local observations, and statistical analyses of time series from an index area of 18.8–19.6° N, 70.4–69.6° W in the Cibao Valley, where cacao and coffee are grown. Winter rainfall depends on strong trade winds that push shallow stratiform convections over 100 km inland, where nocturnal drainage flows induce orographic uplift. Interannual climate variability is studied in the context of cacao and coffee production in the years 1976–2019. Lag correlations demonstrate that higher yields follow a wet autumn, a windy winter with cool sea temperatures, and a dry spring. Changes in high-value agricultural production in the northeastern Dominican Republic may be anticipated by the climatic determinants uncovered here. Full article

In this study, regarding listed companies of high-pollution industries in China’s A share as the research object and media coverage as the moderator variable, corporate governance data from 2015 to 2019 were selected. Studied the impact of corporate governance and media coverage on corporate environmental protection investment. Corporate governance was divided into four dimensions: shareholding structure, characteristics of the independent directors, characteristics of the board of supervisors, and management characteristics. A multiple regression model and monitoring model were constructed to study the influencing factors of the environmental protection investment behavior of enterprises, and the relationship between relevant variables was empirically tested. The results show the following: (1) The equity structure is expressed by the degree of separation of cash flow rights and shareholders’ control rights. There is a significant negative correlation between shareholding structure and enterprise environmental protection investment. The characteristics of the board of supervisors and management are measured by executive compensation. The characteristics of the board of supervisors have a significant positive impact on an enterprise’s environmental protection investment. Management characteristics have a significant positive impact on enterprise environmental protection investment. (2) Media coverage as a moderator variable is measured by the data reported by important Chinese newspapers. In the robustness test, media coverage is measured by the number of Chinese financial newspaper reports and the number of online media reports. Media coverage positively regulates the relationships among the ownership structure, the characteristics of the board of supervisors, management characteristics, and enterprise environmental protection investment. (3) Positive media reports have a more significant moderating effect than negative media reports. Full article

This paper contributes to the supply chain finance literature with an agent-based Monte Carlo simulation model focusing on the bank’s point of view. Our theoretical model assesses how a bank should screen a supply chain (SC) member and whether that requires different considerations and monitoring systems compared with traditional corporate loans. In the model, the SC members may cooperate, reducing their bankruptcy risk considerably; thus, the chance for and extent of inter-entity financial aid are critical to consider when assessing bankruptcy risk. A cooperative SC member cannot just be financed from debt taken by other members, but it may also offer protection to other SC members using its operating cash flow. Thus, based on our results, bankruptcy risk is SC-specific, rather than a characteristic of an individual firm. Therefore, to finance an SC member is a quasi-joint decision of its peers, so particular care should be paid to estimating and monitoring the correlations between the operational cash flows of cooperative SC members. One of the key results is that of edge default exposure of the bank; it might be optimal to limit the amount of the loan made available to a given collaborative SC member instead of charging higher rates or financing the most attractive SC member only. Another SC member offering an additional guarantee with its assets will provide the remaining need for financing. As this solution also reduces the total bankruptcy risk of the SC, the SC itself should prefer this financing structure. Full article

The focal aim of the project was to assess the economic anxiety (EA) and the performance of small and medium enterprises (SMEs) during partial and full lockdowns in Kuwait. The challenges facing the SMEs during COVID-19 and the potential solutions were also explored. The call for this vital investigation was due to the global economic fallout and the shocking drop within the marketplace caused by the COVID-19 pandemic. A descriptive approach was used for online survey design to collect datasets from 147 SMEs spanning all governorates of Kuwait in the period between March and June 2021. It included sociodemographic data, economic anxiety perception, potential challenges and solutions to SMEs, and SMEs’ performance. The data analysis using SPSS 25 showed that 78.2% of the SMEs were affected directly by the COVID-19 pandemic, and about 83% were affected negatively by the COVID-19 pandemic. In comparison, only 12.2% experienced a positive impact, mainly medical, technology, social media, food supplies, and delivery or logistics industries. With great concerns of SMEs for all dimensions related to economic anxiety (with an average of around 3.95), the greatest concerns were the financial and cash flow, followed by labor shortage (an average between 4.51 and 5.00). The results also showed that most of the performance indicators for the SMEs were low (with an average of less than or equal to 2.5), and more than 66% of them worked fewer hours during the pandemic; the number of operating hours was dropped dramatically. More than 74% of the SMEs used technology in more than 20% of their activities, representing an increase in using technologies of about 44%, and about 25.2% used social networks in more than 80% of their activities. The performance of SMEs is also found to be significantly and positively correlated with the economic anxiety levels, with a correlation coefficient of 0.186. The findings revealed significant and crucial outcomes for policymaking, decision-makers, and governmental agencies to build recovery plans and proper actions needed to manage the consequences caused by the disaster against the economic and other developments within the context of SMEs. Overall, there is a clear need to find ways and customize operations to adapt to the new work modes that require social distancing, online operations, and site management. In addition, new alternative modes of SMEs work follow to compensate for the lower working hours from the office and increased online working from home. Full article

The aim of this article is to analyze the economic and financial performance of Slovenian enterprises, as a European Union (EU) member state case study. A favorable economic and financial performance is crucial for long-term sustainable enterprise growth and survival. Eight economic and financial performance indicators are used to evaluate the sustainability in the growth of enterprises: seven of them are financial indicators—assets, revenues from sales, equity, net profits, operating efficiency, return on equity, and value added per employee—while the eighth variable is the economic indicator for the number of employees. A distinction is made between enterprises that did and that did not receive subsidies from national and EU funds. Three enterprise-level data sources are combined in the empirical analysis: balance sheet data from enterprise accounts, own surveys data, and government data on public subsidies to enterprises. The mean values and standard deviations of economic and financial indicators based on balance sheet data for the years in two financial periods are estimated. The summary statistics for economic and financial indicators and correlation analysis are conducted and the results of the economic and financial indicators are compared using the parametric paired sample two-tailed t-test that allows comparison between the enterprises in the two financial periods. An increase in the economic and financial indicators is investigated by comparing the enterprises that did receive subsidies with the enterprises that did not receive subsidies in the two financial periods. The empirical results confirm that the value added per employee is the only financial indicator where a positive link is found between the financial indicator and subsidies. The results suggest that subsidies can be important for cash flow into enterprises, but entrepreneurial activities are crucial for favorable economic and financial performance and long-term sustainable growth in a competitive market environment. Full article

In this paper we analyzed the link between companies’ performance, in terms of cash and income, and the labor productivity or management rates, in case of the companies from the energy sector listed on the Bucharest Stock Exchange. We focused on the energy sector because of the impact that its expansion has on the evolution of economies around the world and because of its dynamics in the sense of gradually shifting to the use of energy from renewable sources. We have used panel regression models to analyze the operating cash flow and the profitability rates and the determination of a causal or dependency relationship with labor productivity or management rates. The results of this study show a significant negative correlation between operating cash flows and the average duration of stock rotation, and no correlation between productivity and the operating cash flow. Instead, the average duration of stock turnover does not at all influence the profitability rates, and productivity is always significant for the return on assets, ie forthe return on equitywith a positive coefficient, as expected. The gap between the average duration of payment of suppliers and the average duration of receivables does not significantly influence neither the cash flow nor the rates of return. Full article

In this study, we study information processing by financial professionals benchmarked with non-professionals and how correlation among individual forecasts explains the group level forecast performance. In an experiment in which participants make price forecasts based on common financial information, we find that individual professionals are no better than individual non-professionals in forecasting, but professionals’ mean forecasts are superior. Our analysis suggests that financial professionals’ individual errors are less correlated as they process information from more diverse perspectives. This leads to superior mean forecasts because the uncorrelated individual errors cancel each other out in the aggregate. In contrast, non-professionals are similar in using salient information such as earnings or cash flow. As a result, their individual errors are highly correlated. Instead of cancelling each other out, the individual errors are enlarged in the aggregated mean forecasts. We are the first to show the difference in the comparisons of professionals and non-professionals at the group level versus at the individual level. Our paper contributes to the literature by documenting the evidence of diversity in information processing by financial professionals. Full article

The purpose of the study is to assess the efficiency of cash flow management at power supply companies of the CIS (Commonwealth of Independent States) countries. A methodological approach to cash flow forecasting with the use of linear and polynomial regression has been developed. The study is based on the data provided by 12 power supply companies operating in CIS member countries. Forecasting based on the generated polynomial models of multiple regression of cash flow for the power supply companies under study confirms the strong possibility of extrapolating the studied trends to future periods. Compared to the linear model, the polynomial one confirms higher values of the determination coefficients for the majority of power supply companies. The projected volumes of cash inflow, cash outflow, and net cash flows of power supply companies with the application of the described polynomial multiple regression models have a fairly high degree of approximation. The correlations between operating cash flows and outflows, between total cash inflow and outflow of the majority of power supply companies are high. The low level of synchronization between cash inflows and outflows of the companies under study is associated with the specifics of their financial and investment activities and the cash flow management policy. It has been proven that energy enterprises’ financial stability significantly depends on the synchronization and uniformity of cash flows. The proposed methodological approach allows identifying enterprises by the criterion of riskiness from the standpoint of the synchronization and homogeneity of their cash flows. Full article

This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies. Full article