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As a low-income country, Pakistan is particularly vulnerable to various natural and human-induced disasters, which have significant consequences for both the environment and human life. A substantial share of the provincial budget is allocated to disaster response and recovery efforts. Prioritizing investment in disaster risk reduction (DRR) is essential to protect lives and assets. Although there is a complex relationship between investments made before and after disasters and their effectiveness in DRR, this dynamic remains insufficiently understood in Pakistan's Khyber Pakhtunkhwa (KP) province. The current study was designed to analyze the developmental budgeting for DRR in the financial years 2000–2022 in KP province. The Sen's Slope Estimator and modified Mann-Kendall tests were used to determine the significance trend, while the correlation coefficient test was used to find the correlation between investment in DRR and disaster-induced damages such as deaths, injuries and houses damaged. The study findings reveal that the occurrence of disasters influences post-disaster spending in the KP province, with a significant negative correlation between expenditure and disaster-related damages, implying that increased DRR investment has significantly reduced the consequences of disasters. To minimize vulnerability to future disasters, the province should integrate risk-sensitive planning across all sectoral departments at local, district, and provincial levels, guided by a risk-informed development approach. This proactive strategy would embed disaster resilience within developmental activities, ensuring that each sector aligns with principles of risk reduction and sustainable growth. [ABSTRACT FROM AUTHOR]

Socially responsible investments represent the heart of a sustainable and inclusive economy. The goals set by the regulatory framework have encouraged the integration of responsible concerns into the corporate business strategy. In this paper, we present an operation research design under a risk management perspective that adjusts the portfolio selection problem for the ethical values of investors. We focus on the firms of the Euro Stoxx 50 during the years 2007–2019 and cluster the companies according to the Environmental Score (E). For each cluster, we build a portfolio that minimizes the Conditional Value-at-Risk (CVaR) introducing the constraint on the E into the optimization problem. We compute the Shapley Value for each portfolio using the minimized CVaR and the optimized E as the characteristic functions to yield, respectively, the contribution of the portfolios to the public welfare in terms of the exposure to the tail market risk and the required environmental commitment to mitigate it. The cluster analysis shows that the efforts of the firms to embrace environmental concerns are paid off with generally lower contribution to the tail market risk. From the two characterizations of the Shapley Value, the computation of well-known measures of financial performance (Sharpe, Sortino, and Calmar ratios) reveals that optimal portfolio choices should prioritize the environmental commitment of the companies. [ABSTRACT FROM AUTHOR]

An optimization method for distribution network investment projects on the basis of brittle link entropy and cumulative prospect theory was proposed to address the problem of single time scale and insufficient consideration of the correlation between projects in traditional distribution network investment. First, brittle link entropy was applied to analyze the correlation between projects and calculate the project implementation risks. Second, projects were divided into three levels in accordance with their implementation purposes, and the relationship between them and the distribution network indexes was established to determine the contributions of the distribution network projects to the power grid. On this basis, an evaluation index system for projects was created. Last, the projects were comprehensively evaluated using cumulative prospect theory containing the time discount function, and the scores of all the projects were given based on the project implementation risks and prospect values. With the total score of the project group being the highest, an investment optimization model was established, and the optimal investment scheme was obtained by the hybrid bat algorithm. Analyses of numerous examples show that the risks of project investment can be measured by brittle link entropy, and the necessity of project implementation can be scored using the project evaluation method based on cumulative prospect theory containing the time discount function. [ABSTRACT FROM AUTHOR]

Political economy literature documents how financial investors are more partial to right executives than left ones. Right cabinets face lower interest rates, less volatile stock prices and exchange rates, and higher credit ratings than left cabinets, even after accounting for fiscal differences. But does this advantage persist if right governments accommodate far right parties or ideas? I hypothesize that because far right populism can introduce political instability, markets' evaluation of right executives might deteriorate if they enter coalition with far right parties or adopt their positions. Employing a panel analysis of bond spread data, and a comparative case study of the Netherlands and Sweden, I find that right executives enjoy significantly lower spreads than their left-wing counter-parts, but this advantage disappears if they rule in coalition with the far right or produce overly right-wing manifestos. These findings highlight that right parties may encounter tangible borrowing costs and market rebuke if they accommodate far right populism. [ABSTRACT FROM AUTHOR]

The technological landscape in the United States is changing rapidly, and this transformation carries with it unprecedented challenges for administrators, companies, and investors alike. In particular, cybersecurity concerns are growing in light of the new opportunities for cyber malfeasance in this landscape--opportunities to pillage corporate databases and exploit the sensitive consumer information housed therein. For administrators, these new threats challenge prevailing regulatory frameworks and demand novel solutions. For companies, this landscape brings financial and reputational threats. For stakeholders, the cyber era presents the risk of investment losses and, in some cases, loss of personal data. The Securities and Exchange Commission has responded to these changes by applying increased scrutiny to corporate cybersecurity disclosures, according to its guiding maxim that sunlight--that is, transparency--is the best disinfectant. Specifically, the Securities and Exchange Commission recently adopted new reporting requirements in an effort to keep in step with technological advances. However, applying old regulatory frameworks to a novel context may trigger unintended consequences, and thereby fall short of providing the Securities and Exchange Commission's hoped-for benefits. This Note critically analyzes one of the Securities and Exchange Commission's new reporting requirements, Form 8-K's Item 1.05 disclosure obligation, and seeks to determine whether sunlight is the best disinfectant in the cybersecurity context. Ultimately, this Note answers that question in the negative, instead advocating for the adoption of a more nuanced regulatory approach to navigate this uncharted territory. [ABSTRACT FROM AUTHOR]

Credit risk constitutes a pivotal concern within the purview of commercial banking operations, commanding profound significance for regulators, the general public, and investors alike. In pursuit of a comprehensive comprehension of the intricacies of credit risk within the banking sector, this study selects a diverse cohort of 18 publicly traded commercial banks in China as its focal subjects. Employing financial and stock data from the year 2019, this research leverages the advanced KMV model to evaluate these banks' default risks and subsequently calculate their respective default distances. The innovation of this paper lies in that we will use KMV model to quantify the credit risk level of commercial banks, and empirically study the credit risk of Chinese commercial banks through the case study of 18 listed commercial banks with different nature. The KMV model, a well-established approach for the assessment of default risk, computes the default distance predicated upon the market value's volatility and the balance sheet's structure, encompassing considerations of the debtor's probability of default and default loss rate. Default distance denotes the discernible disparity between the debtor's prevailing market value and the precipice of default, serving as a reliable metric to gauge the probability of a debtor's default occurrence. This study meticulously curates financial and stock data pertaining to the 18 selected banks, subsequently subjecting them to rigorous evaluation through the KMV model. Through a meticulous analysis of the default distances thus derived, this research unveils the divergent spectra of default risks across banks of varying profiles, further elucidating potential risk factors. These analysis results are of important reference value for regulators and investors. Regulators can identify the potential high-risk institutions more accurately by analyzing the default distance of banks, so as to strengthen targeted regulatory measures and ensure the sound operation of the entire banking industry. At the same time, investors can also refer to these analysis results when making investment decisions to understand the credit status and risk levels of different banks, so as to better diversify risks and optimize the investment portfolio. In short, through the analysis of the default distance of banks, we can have a deeper understanding of the differences and potential risk factors in the default risk of banks of different properties, so as to provide strong decision support for regulators and investors. [ABSTRACT FROM AUTHOR]

When determining new equity grants, corporate boards face a tradeoff between the CEO's incentives generated from the grant's duration versus those arising from the convexity of the embedded equity risk. We hypothesize and find that boards lengthen the horizon of new compensation grants in the presence of greater pre-existing compensation sensitivity to stock return volatility (vega). In addition, consistent with our hypothesis, we find stronger results in the presence of greater left-tail risk. Further, employing two exogenous shocks to left-tail risk, we provide evidence consistent with our hypothesis that grant horizons are related to risk incentives. Our analysis of the interaction of these two incentive mechanisms provides new insights on compensation contracting. JEL Classifications: J33; M52. [ABSTRACT FROM AUTHOR]

Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms' assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer's operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm's extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408. [ABSTRACT FROM AUTHOR]

This paper explored an investment and risk control issue within a contagious financial market, specifically focusing on a mean-variance (MV) framework for an insurer. The market's risky assets were depicted via a jump-diffusion model, featuring jumps due to a multivariate dynamic contagion process with diffusion (DCPD). The process enveloped several popular processes, including the Hawkes process with exponentially decaying intensity, the Cox process with Poisson shot-noise intensity, and the Cox process with Cox-Ingersoll-Ross (CIR) intensity. The model distinguished between externally excited jumps, indicative of exogenous influences, modeled by the Cox process, and internally excited jumps, representing endogenous factors captured by the Hawkes process. Given an expected terminal wealth, the insurer seeked to minimize the variance of terminal wealth by adjusting the issuance volume of policies and investing the surplus in the financial market. In order to address this MV problem, we employed a suite of mathematical techniques, including the stochastic maximum principle (SMP), backward stochastic differential equations (BSDEs), and linear-quadratic (LQ) control techniques. These methodologies facilitated the derivation of both the efficient strategy and the efficient frontier. The presentation of the results in a semi-closed form was governed by a nonlocal partial differential equation (PDE). For empirical validation and demonstration of our methodology's efficacy, we provided a series of numerical examples. [ABSTRACT FROM AUTHOR]

Investment risk forecasting is challenging when the stock market is characterized by non-linearity and extremes. Under these conditions, VaR estimation based on the assumption of distribution normality becomes less accurate. Combining extreme value theory (EVT) with machine learning (ML) produces a model that detects and learns heavy tail patterns in data distributions containing extreme values while being effective in non-linear systems. We aimed to develop an investment risk forecasting model in the capital market with non-linear and extreme characteristics using the VaR method of the EVT approach combined with ML (VaRGPD-ML(α)). The combination of methods used is a multivariate time series forecasting model with RNN, LSTM, and GRU algorithms to obtain ML-based returns. The EVT method of the POT approach was used to model extremes. The VaR method was used for investment risk estimation. The backtesting method was used to validate the model. Our results showed that determining the threshold based on the normal distribution will identify extreme values with the ideal number, minimum bias, and distribution of extreme data following GPD. The VaRGPD-ML(α) model was valid in all samples based on backtesting at α = 0.95 and α = 0.99. Generally, this model produces a greater estimated value of investment risk than the VaRGPD(α) model at the 95% confidence level. [ABSTRACT FROM AUTHOR]

This study aims to prove the effect of technological progress, understanding, motivation, and investment risk on student investment interest in Yogyakarta. This research is focused on economics faculty students in Yogyakarta in 2019-2021. The sampling technique used in this study was convenience sampling with the number of respondents obtained as many as 110 respondents. The data analysis technique uses multiple linear regression. The data obtained in this study were collected by distributing questionnaires via google form. The results of the analysis show that technological progress and investment motivation have a positive effect on student investment interest. Investment understanding has no effect on student investment interest and investment risk has a negative effect on student investment interest. [ABSTRACT FROM AUTHOR]

Existing studies on commodity market risk spillovers recognize the pivotal role of geopolitical risk (GPR), but scarcely address how it drives tail risk spillover networks. This study adopts the Tail‐Event driven NETwork methodology to explore high‐dimensional Conditional Value at Risk (CoVaR) spillovers within energy and other strategic commodity markets. Our findings indicate that (1) In both lower and upper tail networks, metal and food commodities primarily act as net risk transmitters, whereas energy commodities are mainly net risk receivers. Additionally, these roles undergo short‐term reversals during periods of heightened market uncertainty. (2) There exists an asymmetrical pattern of CoVaR co‐movements in these commodity markets. The total connectedness (TC) in both the upper and lower tails demonstrates distinct responses to various extreme events. GPR tends to weaken the lower tail TC and strengthen the upper tail. (3) Incorporating GPR substantially improves the effectiveness of Minimum Connectedness Portfolio (MCoP) for these strategic commodities. [ABSTRACT FROM AUTHOR]

The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student‐t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student‐t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student‐t distribution‐based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student‐t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value‐at‐risk. In addition, the distribution yields higher value‐at‐risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential‐polynomial tails, we recommend the use of the GH skewed Student‐t distribution for parametric GARCH models in forecasting extreme tail risk. [ABSTRACT FROM AUTHOR]

This paper proposes analytic forms of portfolio conditional value at risk (CoVaR) and the mean of the portfolio loss conditional on it being in financial distress (CoCVaR) on the normal tempered stable market model. Since CoCVaR captures the relative risk of the portfolio with respect to a benchmark return, we apply it to relative portfolio optimization. Moreover, we derive analytic forms for the marginal contribution to CoVaR and the marginal contribution to CoCVaR. We discuss the Monte-Carlo simulation method for calculating CoCVaR and the marginal contributions of CoVaR and CoCVaR. We provide an empirical illustration to show relative portfolio optimization with 30 stocks included in the Dow Jones Industrial Average under distressed conditions. Finally, we apply the risk budgeting method to reduce the CoVaR and CoCVaR of the portfolio based on the marginal contributions to CoVaR and CoCVaR. [ABSTRACT FROM AUTHOR]

Among many fallouts of the global pandemic, firms' supply chains have been severely impacted at the global scale. Such global impact can be mainly attributed to companies' current heavy focus on lean manufacturing, resulting in a lack of preparedness and flexibility, and therefore weakening their supply chain links. At the same time, firms face severe financial distress and constraints if their investments in future risks turn out to be of no value. Facing such dilemmas, many firms are unsure of how to appropriately configure lean and risk management at their operational and supply chain levels. This study aims to understand the various configurations of lean manufacturing and supply chain risk management. Also, this study pays attention to buffer capacity associated with lean and risk management. In a dataset collected from 198 US companies, cluster analyses discover the manufacturers' configurations of the varying emphases on lean management and supply chain management. Our results indicate that firms achieve the highest levels of buffer capacity and firm performance if they implement lean practices and risk management at a higher level than others. [ABSTRACT FROM AUTHOR]

This article proposes a novel copula model to understand the dependence between extreme variations in stock and government bond returns. This model is used to introduce a tail-based extension of the safe haven concept, which we refer to as a tail risk dampener. Our findings reveal significant cross-country differences in stock–bond local tail dependence and tail risk dampening abilities. Some countries' bond market can lessen the impact on portfolio returns of extreme negative stock returns, while for others no such negative tail comovement is found. This tail comovement is also not always aligned with global comovement between stocks and bonds. The article concludes that a comprehensive understanding of the comovement and the resulting diversification potential between stock and bond returns necessitates complementing global measures of dependence with local tail measures. [ABSTRACT FROM AUTHOR]

Risk analysis is crucial in real asset investment projects. However, existing risk measures like VaR (value at risk) and CVaR (conditional value at risk) may not fully capture potential losses in such scenarios. Consequently, the current literature lacks an indicator that accurately estimates the expected value in the event of financial insufficiency. To fill this gap, this article introduces the conditional expected loss (CEL) metric, a novel approach that estimates the average loss expected in the event of financial deficits within an investment project and provides extensive numerical modeling and a detailed real-world example. This novel metric enhances risk analysis and decision making, particularly evaluating financial insufficiency within projects with limited managerial flexibility and a higher likelihood of financial deficits. By incorporating CEL alongside other risk and return metrics, practitioners gain a more comprehensive understanding of project risks, ultimately leading to better investment choices. [ABSTRACT FROM AUTHOR]

The article "Climate-Related Financial Stability Risks for the United States: Methods and Applications" examines various models used to study climate-related financial stability risks (CRFSRs) in the U.S. It discusses the strengths and weaknesses of ten types of models and their suitability for assessing CRFSRs, noting a high level of uncertainty in current assessments. The text also delves into the methodology of natural capital analysis, emphasizing the importance of measuring firms' exposure to natural resource degradation and the impacts on business models. It concludes by suggesting ways to combine existing methodologies for a more comprehensive understanding of climate risks and vulnerabilities, highlighting the need for further research in integrating climate-related risks into modeling frameworks. [Extracted from the article]

Introduction: The paper presents the results of the evaluation of the investment risk of the business proposals considered in the productive chain of a sugar company in its development in the biorefinery concept. Objective: To determine the least risky order of execution to carry out the necessary economic investments foreseen in the development of a sugar factory as a biorefinery, so as to gradually grow its business volumes. Materials and Methods: The researchers employ methods of economic risk analysis, considering the mathematical expectation and the coefficient of variation of each of the investments required by the business opportunities, for different possible scenarios. Results and Discussion: The investment order that is economically less risky, implies realizing first the business opportunity recognized with code 2, secondly the one registered with code 1, then the one referred to code 4 and finally the one identified with code 3. Conclusions: It is feasible to decide the order of lowest investment risk in a productive chain in a sugar company that is transformed into a biorefinery, using dynamic indicators and economic risk analysis. [ABSTRACT FROM AUTHOR]

This paper analyzes the volatility of the Romanian stock market as well as the impact of economic crises, the psychological factors related to market volatility in Romania, the business environment and the impact of geopolitical factors on the volatility of the Romanian stock market. Stock market volatility is an essential indicator in assessing investment risks and opportunities. In Romania, the stock market is characterized by relative maturity and continuous development, and volatility analysis provides valuable information about economic dynamics and stability. This analysis is crucial for investors, financial analysts and economic policy makers as Romania integrates ever more deeply into the global economy. [ABSTRACT FROM AUTHOR]

Extreme events such as a market crash, an economic downturn, a geopolitical war, or a situation emanating from a health crisis have proved to inflict far-reaching adverse impacts on an economy, depending on the country's market microstructure. This becomes more important due to the increasing interconnectedness across global financial markets and subsequent financial contagion. As such, it becomes imperative to quantify the relevant risk (i.e., tail risk) arising out of such events. This study examines the phenomenon of tail risk in the Indian stock market from sectoral returns, using extreme value theory (EVT). The study uses returns of 11 sectoral indices of Nifty from 2006 to 2023, and estimates the tail risk measure each quarter using a peak-over threshold approach following prior literature (Hill, 1975). It also investigates the impact of estimated tail risk on expected future returns of the respective sectoral indices. The results revealed the tail risk estimates (at 5% threshold of cross-sectional sectoral returns) to be significant. Further, tail risk positively impacts the expected Nifty sectoral returns, and the impact is robust even after controlling for macroeconomic factors like inflation, short-term interest rate, and long-term interest rate. The results of the study would help investors adopt better risk management practices and help regulators conduct stress tests to understand potential risk scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Business Law Journal / Administração de Empresas em Revista is the property of Administracao de Empresas em Revista and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning algorithms have been widely used in risk prediction management systems for financial data. Early warning and control of financial risks are important areas of corporate investment decision-making, which can effectively reduce investment risks and ensure companies' stable development. With the development of the Internet of Things, enterprises' financial information is obtained through various intelligent devices in the enterprise financial system. Big data provides high-quality services for the economy and society in the high-tech era of information. However, the amount of financial data is large, complex and variable, so the analysis of financial data has huge difficulties, and with the in-depth application of machine learning algorithms, its shortcomings are gradually exposed. To this end, this paper collects the financial data of a listed group from 2005 to 2020, and conducts data preprocessing and Feature selection, including removing missing values, Outlier and unrelated items. Next, these data are divided into a training set and a testing set, where the training set data is used for model training and the testing set data is used to evaluate the performance of the model. Three methods are used to build and compare data control models, which are based on machine learning algorithm, based on deep learning network and the model based on artificial intelligence and Big data technology proposed in this paper. In terms of risk event prediction comparison, this paper selects two indicators to measure the performance of the model: accuracy and Mean squared error (MSE). Accuracy reflects the predictive ability of the model, which is the proportion of all correctly predicted samples to the total sample size. Mean squared error is used to evaluate the accuracy and error of the model, that is, the square of the Average absolute deviation between the predicted value and the true value. In this paper, the prediction results of the three methods are compared with the actual values, and their accuracy and Mean squared error are obtained and compared. The experimental results show that the model based on artificial intelligence and Big data technology proposed in this paper has higher accuracy and smaller Mean squared error than the other two models, and can achieve 90% accuracy in risk event prediction, which proves that it has higher ability in controlling financial data risk. [ABSTRACT FROM AUTHOR]

Modelling financial tail risk such as investment or financial risk is important to avoid high financial shocks. This study adopted Bayesian techniques to complement the classical extreme value theory (EVT) models to model the exchange rate risk of Nigeria against the South African ZAR. Hence, this study proposed the Bayesian Generalized Extreme Value (BGEV) model, Bayesian Generalized Pareto distribution (BGPD), Bayesian Gumbel (BG), and classical Generalized Pareto distribution (GPD) to fit the exchange rate returns over one hundred and four observations. The model selection criteria were used to determine the best model, consequently, the model selection criteria were in favour of BGEV model. The Value-at-Risk (VaR) and the Expected Shortfall (ES) were obtained from the estimated parameters. The results show that the Nigeria Naira exchange will experience losses against the ZAR both at 95% quantile and 99% quantile. This study recommends that investors should watch closely before making financial or investment decisions. This study aligns with the sustainable development goals (SDGs), 8.1 (sustainable economic growth), SDG 8 (Promote sustained, inclusive and sustainable economic growth). [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to explore the motives (especially the agency motives) for corporate diversification from the perspective of corporate executives who make such strategic decisions and manage the diversified firms daily. Design/methodology/approach: A qualitative research approach was adopted, and 12 chief executive officers (CEOs) of diversified firms in Nigeria were interviewed for their perspectives on the motives for corporate diversification. Findings: Stewardship motives – diversification to use excess capacities in assets and resources to exploit opportunities in the market and defend against adverse environmental developments – were the most cited reasons for diversification. The relevant agency problem related to corporate diversification motive in Nigeria is the principal–principal (majority shareholder-minority shareholder) one. CEOs with substantial holdings in their firms indicated that they use diversification to reduce their investment risk and retain control of their portfolio. Practical implications: The findings suggest that in corporate environments such as Nigeria that feature blockholding prominently, the corporate strategy-related agency problem that policymakers should pay greater attention to is the principal–principal conflict rather than the traditional agent–principal problem that has influenced corporate governance over the years. There is also a need to revise the dominant view that diversification is a value-destroying strategy motivated by the self-seeking behavior of managers who have little or no shares in the companies they manage. Originality/value: The few studies on motives for corporate diversification that incorporated the perspectives of corporate executives did not address the agency motives of diversification. To the best of the authors' knowledge, this is the first study that has done so. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to explore the effects of various uncertainty measures – namely, implied volatility (VIX), tail risk (SKEW), economic policy uncertainty (EPU) and partisan conflict (PCI) indices-, on U.S. REITs returns at sector level, using the non-linear Markov regime-switching model. Our empirical results reveal that uncertainty measures have regime-dependent impacts and do not affect the return dynamics of REIT sectors in a uniform way. Office and hotel & lodging REITs exhibit the strongest sensitivity to VIX and EPU, respectively, during bearish market periods. While residential REITs are the most resilient to uncertainties, healthcare REIT returns are negatively affected from all the uncertainty factors only in the low variance regime. Hence, our findings show evidence of asymmetric, non-linear and sector-dependent linkages between REITs and uncertainties. These results provide valuable insights and important implications for REIT investors. [ABSTRACT FROM AUTHOR]

With the passage of time, the constant changes in relevant factors, and the daily maintenance of tailings ponds, the difficulty of tailings pond safety management is increasing day by day. In order to systematically improve the early warning ability for tailings pond dam break risk, the relationship between and influence of various related dam break risk factors of tailings ponds are utilised and the combination with dual attention is innovatively proposed. The risk prediction model for tailings ponds, EEMD-DA-LSTM, is improved. First, Pearson correlation coefficients are used to analyse the correlation between risk factors of tailings ponds. Then, the EEMD method is used to decompose the nonlinear displacement sequence, and the weights of input features are dynamically adjusted by double attention (DA). Finally, the LSTM network model is constructed to predict the displacement change. Taking valley-type tailings pond WKB-1 and mountainside tailings pond WKB-2 as examples, the dam break risk prediction models for tailings ponds are constructed based on three different models, the prediction results of different models are compared and analysed, and the prediction accuracy of the models is evaluated by three different evaluation criteria. The research results show that the integration of the EEMD-LSTM model with the DA model, that is, the EEMD-DA-LSTM model, has a better prediction effect for the dam break risk of tailings ponds WKB-1 and WKB-2 than other models through experimental verification. Therefore, the EEMD-DA-LSTM model is of great significance for preventing and resolving the safety risks of tailings ponds. It is valuable for practitioners in the mining industry and environmentally sustainable development. [ABSTRACT FROM AUTHOR]

Purpose: In this study, the authors argue that the private information obtained and transmitted by institutions during the corporate visits can alleviate the degree of information asymmetry between firms and investors, so institutional visits may influence investors' heterogeneous beliefs. Therefore, the authors investigated whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs. Design/methodology/approach: This study examines whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs using the data of A-share companies from the Shenzhen Stock Exchange (SZSE) during 2013–2019. Using empirical research method, this study designs and conducts an empirical research according to empirical research's basic norms. Findings: The authors find that institutional visits effectively decrease investors' heterogeneous beliefs, especially institutional investors. Meanwhile, institutional site visits and sell-side institutional visits have a more significant negative effect on investors' heterogeneous beliefs. The findings remain after robustness tests with the alternative variable, instrumental variable, propensity score matching and quantile regression methods. Originality/value: The development of China's capital market is imperfect, resulting in a strong speculative atmosphere. So, investors' irrational investment behaviors occur from time to time, leading to sizeable heterogeneous beliefs in China's capital market, which increases the risk of investment and is not conducive to the discovery of corporate value and the efficient allocation of resources. Therefore, exploring the factors influencing heterogeneous beliefs and finding ways to alleviate heterogeneous beliefs can reduce the proportion of speculative investors and promote the healthy development of China's capital market. [ABSTRACT FROM AUTHOR]

The "Author Index Volume 15" from the Journal of International Commerce, Economics & Policy lists various authors and their respective articles published in the journal. Topics covered include the impact of economic policy uncertainty on stock market indices, the effects of climate policy uncertainty on oil markets, and the influence of international food standards on firms' export behavior. The index provides a comprehensive overview of research articles on diverse economic and policy-related subjects, offering valuable insights for researchers and scholars in the field. [Extracted from the article]

This paper investigates a risk-averse firm's investment strategy in real-time event-monitoring technologies coupled with dynamic disruption response decisions. The firm does not know how long the disruption will last, and the event-monitoring solutions provide the firm with a quicker update regarding the length of the disruption. When the firm receives an update, it has the flexibility to revise its initial response action. We model a two-stage stochastic program to study this problem where risk aversion is modeled in the form of a Service-at-Risk constraint. Our paper makes four important contributions. First, it characterizes the optimal recourse strategies given an update and identifies the conditions when the firm benefits from the recourse. Second, we characterize the optimal investment and initial response strategies where we find that the investment behavior is non-monotone. Third, our paper shows an interesting impact of risk aversion on the investment decision such that the firm may benefit from investing in event monitoring at moderate degrees of risk aversion, but not necessarily at low or high degrees of risk aversion. Fourth, we find that a firm's benefit from event monitoring is more profound when the capacity of contingency response is moderately limited. [ABSTRACT FROM AUTHOR]

Copyright of Umsatzsteuer-Rundschau is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate whether international operations enhance information links between firms and foreign investors. Exploiting novel subsidiary-level data and within-location variations, we show that, after expanding into another country, a firm attracts greater investment allocation from funds from that country than from other foreign funds. This increase is economically significant, equivalent to one-fifth of the average firm weight in a country-specific portfolio. The observed effect cannot be attributed to funds' influence, persists even when funds are already familiar with the firm, and helps them generate superior risk-adjusted returns. Our results suggest that firms' cross-border economic activities contribute to global financial interconnectedness. [ABSTRACT FROM AUTHOR]

Abstract In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

We examine whether natural disaster experiences affect households' portfolio choice decisions. Using data from the National Longitudinal Survey of Youth 1979, we find that adversely affected households are less likely to participate in risky asset markets. After a disaster shock, households become more risk-averse and lower their expectations on future stock market returns. Such conservative portfolio choices persist even after households relocate to less disaster-prone areas, consistent with risk preferences being altered by disaster experiences. Overall, our evidence suggests that transient but salient experiences can be an important factor in explaining the limited participation puzzle. [ABSTRACT FROM AUTHOR]

Waste is the materials left over after the processing of ores. Significant disasters involving waste disposal structures have occurred in Brazil in recent years and caused severe damage by contaminating soil, rivers and coastal areas, destroying native fauna and flora, interrupting the water supply and compromising its potability, putting the population's health, livelihoods and economy at risk, as well as causing 289 irreparable human deaths. Regulatory laws have become stricter, and since 2019, after the tailings dam tragedies occurred in 2015 and 2019 in Mariana and Brumadinho, in Minas Gerais, the operation of upstream-raised tailings dams has been prohibited in Brazil. In 2022, a waste slide from a sterile pile at the Pau Branco Mine in Nova Lima promoted a dike overflow. There was the death of five people whose car was buried by a landslide on a hillside. New strategies and technologies, such as reprocessing and recycling, can be tested to ascertain whether they can help improve practices in tailings management. Indeed, mining companies' corporate responsibility and sustainability practices need to be evaluated to verify whether they better match expectations. On the other hand, more specific and detailed regulations and resolutions are required to ensure the safe monitoring and management of sterile waste piles. This paper presents a review of the facts, a discussion of the literature (mainly on recent tailings dam disasters), the current situation of mining-containing waste structures in Brazil, a brief sustainability analysis and perspectives aimed at preventing/minimising catastrophes in the future. [ABSTRACT FROM AUTHOR]