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Using detailed data on U.S. households' locations, employment, and financial portfolios, we document that individuals employed in locally clustered industries are more likely to invest in risky assets. This pattern is strongest among individuals with high labor income, employed in skilled occupations, and with strong cognitive skills. Our overall evidence suggests the relation between industry clusters and investment decisions is best explained by clusters enhancing human capital among local industry workers, in turn amplifying their effective risk tolerance. Our findings highlight the important role of local labor market composition in generating household portfolio patterns within and across geographies. [ABSTRACT FROM AUTHOR]

Cybersecurity presents non-negligible challenges for firm collaboration and supply chain viability, as information exchange among nodes introduces potential interdependent risks. How to make appropriate decisions on security investments and information exchange modes is a significant issue for supply chain members. Considering two information exchange modes: system interconnection and system independence, this study develops two game models to investigate the cybersecurity investments in a vertical supply chain composed of a retailer and n suppliers. Initial analysis shows that although firms learn investment decisions mutually in the face of a changing cybersecurity environment, suppliers always take a free ride on the efforts of retailer in two cases and the increased interdependent risks will damp nodes enthusiasm for security investments. Next, to compare the two cases, we introduce information exchange efficiency as the mediate parameter to link degree of system interconnection and proportion of information shared. We found that under the system independence mode, firms with high information-exchanging demand in the large-scale supply chain are more motivated to invest in cybersecurity. Furthermore, we extend our models to a centralised decision-making scenario. We find that security investment efficiency is greatly improved, and the free-riding behaviour of supplier is significantly reduced when systems are interconnected. [ABSTRACT FROM AUTHOR]

How synchronized are short sellers? We examine a unique data set on the distribution of profits across a stock's short sellers and find evidence of substantial dispersion in the initiation of their positions. Consistent with this dispersion reflecting "synchronization risk," that is, uncertainty among short sellers about when others will short sell, more dispersed short selling signals i) greater stock overpricing and ii) longer delays in overpricing correction. These effects are prevalent even among stocks facing low short-selling costs or other explicit constraints. Overall, our findings provide novel cross-sectional evidence of synchronization problems among short sellers and their pricing implications. [ABSTRACT FROM AUTHOR]

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

Taking advantage of the implementation of the 2003 European Commission (EC) directive on financial reporting, we explore the impact of mandatory financial disclosure on mergers and acquisitions (M&A). We find robust evidence that the number (and volume) of private firms becoming an M&A target increases with mandatory disclosure. Analyses of cross-industry differences, deal-level data, and post-deal performance indicate that financial disclosure increases M&A activity by reducing information frictions in the market for corporate control. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

As a crucial component of financial opening, stock market liberalization is a vital measure for China to advance market-oriented and open-oriented reforms. However, effective risk prevention and control must be in place as a prerequisite. Examining the 'Mainland-Hong Kong Stock Connect' program as a quasi-natural experiment, this study explores the mechanism through which this stock market liberalization policy affects stock tail systemic risk using a time-varying Difference-in-Differences (DID) model. The findings indicate that: (1) The 'Mainland-Hong Kong Stock Connect' program significantly reduces stock tail systemic risk in both Shanghai and Shenzhen stock markets. (2) The key to risk reduction lies in enhancing information transparency, corporate governance, and stock liquidity. (3) The risk mitigation effect of this policy is particularly pronounced in regions with weaker investor protection. [ABSTRACT FROM AUTHOR]

Volatility in the cryptocurrency market is an extremely important indicator for investors, as it allows them to manage their investment risk and define strategies that will result in profit maximization. Thus, this study focuses on determine which of the GARCH, EGARCH, and TGARCH models is the optimal model that best describes the daily returns' volatility of MATIC, SOL, BTT, and VET – four cryptocurrencies with relatively limited presence in the market compared to Bitcoin. The optimal model is selected by using the AIC statistical quality criterion. For the chosen sample period, empirical evidence suggests that EGARCH(1,1) and GARCH(1,1) are the most suitable models for describing the returns' volatility of MATIC and VET, respectively. In the cases of SOL and BTT, the lack of success in validating all the assumptions needed to apply GARCH models reveals that these models are not the most adequate for describing the cryptocurrencies under study. [ABSTRACT FROM AUTHOR]

In the process of multi-attribute group decision-making (MAGDM), the cubic q-rung orthopair fuzzy sets (Cuq-ROFSs) are utilized to express membership and non-membership degrees in the form of interval values to efficiently cope with decision makers' (DMs') complex assessment values. To more efficiently capture DM evaluation results in the MAGDM procedure, we offer a novel tool called 2-tuple linguistic cubic q-rung orthopair fuzzy set (2TLCuq-ROFS), which extends Cuq-ROFS by using 2-tuple linguistic (2TL) terms. 2TLCuq-ROFS effectively incorporates the advantages of 2TL and Cuq-ROFS, making them attractive and versatile for depicting attribute values in an uncertain and complex decision-making environment. To effectively aggregate the attribute values in the form of 2-tuple linguistic cubic q-rung orthopair fuzzy numbers (2TLCuq-ROFNs), some Maclaurin symmetric mean (MSM) operators and their weighted forms are presented in this paper. The weight information for attributes is unknown. Therefore, the criteria importance through inter-criteria correlation (CRITIC) method is employed to determine the objective weight information. The purpose of this study is to incorporate a conventional multi-attributive border approximation area comparison (MABAC) framework based on 2TLCuq-ROFNs because it addresses problematic and imprecise decision-making problems by calculating the distance among each alternative and the border approximation area by using 2TLCuq-ROFNs and MSM aggregation operators. First, some basic concepts associated with 2TLCuq-ROFNs and the CRITIC-MABAC procedure are briefly explained. Moreover, an evaluation framework based on the improved CRITIC-MABAC method is established. An explanatory case study related to the risk investment problem in Belt and Road is used to verify the validity and practicality of the designed evaluation framework. In conclusion, by utilizing the CRITIC-MABAC methodology based on proposed operators, we find that Λ 7 is the optimal alternative for risk investment. Furthermore, comparison analysis emphasizes the integrity and prominent features of the proposed methodology and provides various complementary perspectives for investors. [ABSTRACT FROM AUTHOR]

This collection of articles describes how cryopreservation enables particular imaginaries about 'suspending time' thereby creating what Lemke terms "a principle of whenever." These imaginaries come to shape both intimate personal choices relating to fertility, organizational and commercial investments, research and regulatory investments in equipment and risk assessments, as well as major societal prioritizations of ecological conservation. Taken together, these articles thus unfold how mundane freezing technologies interact with profound societal changes and impact everyday lives. The politics of suspension almost seems to circumvent the political or substitute the political with 'cooling,' and yet these articles illustrate how these dynamics do not erase politics, but call for new analytical awareness to identify the political stakes. Taken collectively, the articles also illustrate inspiring approaches to three productive tensions running through much scholarship in Science and Technology Studies (STS)—tensions between technological and social determinism; technology optimism and pessimism; and between a focus on micro- and macro-practices. In doing so, the articles can be said in various ways to do the important work of a gestalt switch: shifting our attention from the well-known politics in the foreground to the tacit politics in the background. With this commentary, I suggest that the dynamics they explore can be productively viewed as instances of the 'infrastructuring' of the futures available. [ABSTRACT FROM AUTHOR]

Renewable energy projects are subject to risk due to the uncertainty of future electricity prices and the amount of energy produced. Public support usually consists of some form of guarantee, either on the price received per MWh supplied or the return on investment per MW installed. This support reduces the investor's risk, which is then assumed by the regulator. However, most of these policies do not only grant the investor the right to receive a guaranteed payment but also limit its potential benefits (i.e., impose an obligation). We propose a model with analytical solutions in which, considering the randomness of the market price, as well as that of the energy production, we quantify the risk removed under different types of regulations and assign a value to both the rights and the obligations that each policy entails. Finally, we apply the model to the case of Spain, which has undergone several changes in its green energy support system in recent years. Our results indicate that in the context of low electricity prices, the obligations imposed by most of these policies are negligible compared to the rights received. By contrast, in the context of high electricity prices and increasingly competitive renewable energy sources, the assumed obligations become more notable to the point of the support policy becoming a liability. Nevertheless, a sufficiently risk-averse investor may be incentivized by a policy with negative expected regulatory costs. [ABSTRACT FROM AUTHOR]

This study investigates whether the inclusion of news information can help predict tail risk in the Chinese market. To quantify information from news, we develop an information volume proxy and two sentiment proxies. We employ the asymmetric GJR-GARCH model, along with different specifications for the error distributions and for the exogenous variables in the conditional volatility dynamics. It results in 24 models, including 18 models with three kinds of exogenous variables and six baseline models without exogenous variables. Subsequently, we assess the extent to which the 18 models with exogenous variables improve value-at-risk (VaR) forecasts for various Chinese industries compared to the six baseline models, utilizing both the backtesting and Model Confidence Set (MCS) procedures. The findings indicate that incorporating information volume as exogenous variables significantly enhances the VaR predicting abilities of the models, whereas incorporating sentiment variables does not. Moreover, we consider three different methods for combining VaR predictions and find that employing the MCS procedure to filter models first, and subsequently assigning weights to the selected models based on the reciprocal of their MCS ranking values, yields the best performance among the three combination approaches. [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]

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]

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]

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]

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]

While existing literature highlights the role of private real estate interests in shaping urban policy agendas, few studies have examined formal changes in planning legislation as a key site of influence. This paper investigates planning legislation reforms in Serbia and Ireland as illustrative cases of a broader trend of reducing investment risks and enhancing the financial viability of real estate development. Employing critical and context-sensitive narrative analysis of policy change, we present three key insights. First, we reveal how planning has been transformed into a tool for creating real estate assets driven primarily by financial considerations. Second, we demonstrate that public interest is often subordinated to private interests under a policy narrative dominated by financial sector logic and document how legislative and policy changes frequently introduce new barriers that restrict democratic engagement in planning policy formation. Third, we identify an increasing centralization in planning administrative structures, which undermines the principle of subsidiarity and provides opportunities for certain lobby groups to exert undue influence over policymakers. As the financial sector's influence on built environments grows globally, this study calls for more research into how real estate finance and planning systems affect each other, and how this plays out in practice. [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]

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]

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]

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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]

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]

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]

In recent years, a group of rapidly growing technology-based entrepreneurial enterprises have emerged in China, playing an increasingly important role in the national economy. However, small and mediumsized technology startups have low profitability and high risk, making it difficult to obtain sufficient funds from traditional financing channels. At present, research on venture capital evaluation mainly focuses on the evaluation of projects before venture capital, and empirical methods are mostly used to study the promoting effect of venture capital on performance after investment, ignoring the issue of continuous evaluation after investment. The performance evaluation of risk investment in small and medium-sized high-technology venture enterprises is MADM. In such study, the generalized weighted geometric Bonferroni mean (GWGBM) technique is illustrated for MADM under single-valued neutrosophic sets (SVNSs). We begin by constructing the single-valued neutrosophic number GWGBM (SVNNGWGBM) operator, followed by the introduction of the MADM based on this technique. Subsequently, we provide an illustrative example that evaluates the performance of risk investment in small and medium-sized high-technology venture enterprises, supplemented by some comparative analyses to demonstrate the SVNNGWGBM technique. The principal contributions of this research are highlighted: (1) The maximizing deviation method is employed to derive the weight; (2)The GSVNNWBM technique is developed, setting the stage for the introduction of MADM methods based on GSVNNWBM technique; (3) Numerical example is presented that focuses on the performance evaluation of risk investment in small and medium-sized high-technology venture enterprises, along with a comparative decision analysis. [ABSTRACT FROM AUTHOR]

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.

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]

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the "hot models" associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC's climate projections. We describe the state of the science on temperature variability within NYC and explain both the large‐scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure. [ABSTRACT FROM AUTHOR]

During China's industrial transformation, studying risk spillover effects and clarifying risk contagion pathways within the new energy industry chain is critical for promoting high-quality development in the new energy. The risk transmission mechanism of the new energy industry chain is explored by constructing a time–frequency index using a time-varying parameter vector autoregressive model in this paper. Additionally, we also further analyze the portfolio and hedging effectiveness of the new energy market. The findings reveal that, first, the downstream market serves as the primary risk source, propagating risk through the industry chain to the midstream and upstream sectors within the new energy industry chain contagion system. Second, the risk spillover effect of the new energy market has significant heterogeneity and time-varying due to the impact of short-term frequency domain drivers and extreme events. Third, the portfolio significantly reduces the investment risk of a single asset. With the extension of the investment horizon, investors' portfolio weights in the upstream, midstream and downstream industries gradually tend to be even. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic dependence framework to calculate various indicators of systemic sovereign default risk. Our analysis reveals a notable increase in systemic fragility among euro-area sovereigns since the onset of the Subprime Crisis, particularly during the First Greek Bailout in May 2010. Furthermore, our measures successfully capture key events within the euro area, including Mario Draghi's impactful "whatever-it-takes" speech in mid-2012 and the Cypriot Banking Crisis of 2012-2013. The incorporation of dynamic dependence into our measures provides a more comprehensive depiction of systemic risk within the euro area sovereign system, often demonstrating distinct dynamics when compared to their static counterparts. These findings carry significant policy implications and contribute to enhancing our understanding of systemic risk among euro-area sovereigns. [ABSTRACT FROM AUTHOR]

Purpose: This study analyzes the role of cooperative behavior in facing the risk of encouraging innovative agricultural production projects by small-scale farmers in the context of farmers in developing countries. Design/methodology/approach: A within-subjects field experiment was conducted with small-scale Colombian panela cane farmers. The authors used the collected data to run the regression analyses. Findings: The results suggest that when small farmers can follow cooperative behavior by joining a group and pooling resources to face risk, they are more willing to invest in a novel and profitable alternative, albeit riskier. However, the possibility of cooperating with a group to invest in a novel production project depends on its expected risk level. Research limitations/implications: These results will help develop agricultural policies for sustainable development. Establishing informal networks for small-scale farmers to deal with unpredictable risks may aid in developing innovative systems. Social implications: Agriculture is highly vulnerable to climatic impacts, which, combined with the inherent risk of innovation, may reduce small farmers' willingness to adopt innovation. Cooperation appears to be a mechanism for pooling resources and facing risk. Originality/value: Research has focused on experimentally testing the effect of cooperative behavior when facing risk. The authors contribute to the literature by demonstrating the impact of the ability of small-scale farmers in rural areas to collectively manage risk on investment in innovative projects. [ABSTRACT FROM AUTHOR]

Using daily returns of Bitcoin, Litecoin, Ripple and Stellar, we introduce a novel risk measure for quantitative-risk management in the cryptomarket that accounts for the significant co-movements between cryptocurrencies. We find that our model has a lower error margin when forecasting the extent of future losses than traditional risk measures, such as Value-at-Risk and Expected Shortfall. Most notably, we observe this in Litecoin's results, where Expected Shortfall, on average, overestimates the potential fall in the price of Litecoin by 8.61% and underestimates it by 3.92% more than our model. This research shows that traditional risk measures, while not necessarily inappropriate, are imperfect and incomplete representations of risk when it comes to the cryptomarket. Our model provides a suitable alternative for risk managers, who prioritize lower error margins over failure rates, and highlights the value in exploring how risk measures that incorporate the unique characteristics of cryptocurrencies can be used to supplement and complement traditional risk measures. [ABSTRACT FROM AUTHOR]

Investment appraisal of agricultural projects (APs) is particularly demanding due to several factors. Namely, APs may have longer time horizons, higher external and internal volatility, and uncertainty caused by less control of production and external conditions (e.g., climatic conditions and market demand). Indeed, these APs may face high and different risks that should be managed properly. Nevertheless, both the literature and practice do not address such complexity and uncertainty conveniently. Thus, this research aimed to develop an integrative and easy-to-use framework to support the investment appraisal of APs, which goes beyond the traditional approach based on simple and deterministic models. This framework is based on an approach that includes several capital budgeting techniques integrating extended multi-index methodology (EMIM), Monte Carlo simulation (MCS), and real options analysis (ROA). This framework allows dealing with different risk and uncertainty scenarios and managerial flexibilities, which allow alternative and additional investment options. A simpler and easier approach can be particularly important for family agribusinesses, which usually do not use sophisticated decision-making tools. An AP in an agrosilvopastoral system (i.e., agriculture, livestock, forestry) was used to present and discuss the proposed methodology considering the relevance of such systems for sustainable agriculture and their higher risk and complexity when compared to traditional approaches. The main contribution of the framework is structuring a set of steps based on several tools to carry out investment appraisal in APs. [ABSTRACT FROM AUTHOR]

This study conducts an empirical analysis of the impact of climate risk on Environmental, Social, and Governance (ESG) investments in Asia. Using monthly data from 2015 to 2022 for 11 Asian countries and applying wavelet coherence analysis with a panel Auto-regressive Distributed Lag Model (ARDL) cointegration approach, the study concludes that (1) ESG and stock market returns are positively correlated during economic turmoil in China and Malaysia; (2) climate risk positively influences ESG returns, suggesting that it enhances returns during uncertain times rather than having a detrimental effect; (3) returns on conventional assets are negatively impacted by climate risk; and (4) ESG investments serve as an effective hedge against climate risk and provide a sound strategy for portfolio diversification in the Asian market. [ABSTRACT FROM AUTHOR]

Developing and maintaining infrastructure (e.g., roads, airports, water supply, communication networks, power plants, or hospitals) is a priority in a rapidly changing world. However, the gap between infrastructure needs and investments will continue to increase in the coming years, mainly impacting mid- and low-income countries. This problem is aggravated by the fact that traditional long-term planning approaches often lead to under- or over-designing infrastructure with the corresponding investment risks and environmental impacts. This article introduces the "Dynamic Infrastructure Systems" (DIS) concept as a new way to understand infrastructure design and management to support sustainable continuous growth, maintenance, and adaptation. In scenarios of deep uncertainty, infrastructure can best be designed and managed by creating a strategic vision of the future, committing to short-term actions, and establishing a flexible management policy to guide future decisions. This article is motivated by the urgent need to re-think how a key sector is managed and how to make it a positive contributor to sustainability. After the factual and conceptual discussion of the main principles behind DIS, we present a framework for its implementation in practice and discuss barriers and challenges to this vision. [ABSTRACT FROM AUTHOR]

We mainly studied the dividend payout with a two-sided jumps risk model under random observation. The two-sided jumps in the model represent random claims and random returns. First, we obtained the integral differential equation of the expected dividend under the boundary conditions. Because the equations cannot be solved directly under normal circumstances, we chose the sinc numerical method here to approximate the solution of the equations. Then the error analysis of the approximate solution was carried out to illustrate the rationality of the numerical method. Finally, some concrete numerical examples were given [ABSTRACT FROM AUTHOR]

This study measures the immediate impact of Russia–Ukraine war on the global stock markets for the first four months since Russia's first invasion attempt on February 24, 2022. Daily closing stock indices have been used from selected stock markets of six different continents. By applying event study method, it observes mixed impact on different stock markets. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH 1,1) indicates the presence of significant volatility and leverage effect in all the markets. Regression estimates show significantly positive impact of VIX and negative impact of oil on the abnormal returns of the global stock markets. Diversifying energy supply and source, accelerating deployment of renewables and promoting electronic vehicles and machines might bring positive result for the financial market. It is expected that this research will provide policymakers, regulatory authorities, investors and all concerned stakeholders a precise guideline to handle the immediate impact of war on the stock prices and to formulate appropriate strategies to keep investment free from risk and uncertainties. [ABSTRACT FROM AUTHOR]

This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identification, a recently developed robust model that distinguishes itself from traditional Markov-switching models by enhancing regime persistence through a jump penalty applied at each state transition. Our JM utilizes a feature set comprising risk and return measures derived solely from the return series, with the optimal jump penalty selected through a time series cross-validation method that directly optimizes strategy performance. Our empirical analysis evaluates the realistic out-of-sample performance of various strategies on major equity indices from the US, Germany, and Japan from 1990 to 2023, in the presence of transaction costs and trading delays. The results demonstrate the consistent outperformance of the JM-guided strategy in reducing risk metrics such as volatility and maximum drawdown, and enhancing risk-adjusted returns like the Sharpe ratio, when compared to both hidden Markov model-guided strategy and the buy-and-hold strategy. These findings underline the enhanced persistence, practicality, and versatility of strategies utilizing JMs for regime-switching signals. [ABSTRACT FROM AUTHOR]

We analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside ("good") volatility, realized downside ("bad") volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model‐based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR‐RV model and the HAR‐RV‐sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment. [ABSTRACT FROM AUTHOR]

To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large. [ABSTRACT FROM AUTHOR]

The study aimed to assess the effectiveness of a tail-biting risk assessment scheme. The scheme consisted of trained private veterinary practitioners (assessors) applying a risk assessment tool on commercial pig farms to six pens per farm. The assessment tool included animal and non-animal-based observations which were used to determine the perceived risk of tail biting for each pen. For this study 27 farms were assessed, and a subsequent batch of pigs from each farm underwent post-mortem tail lesion scoring at the abattoir. The assessments revealed that a high percentage of pens had fully slatted flooring (92%) and mixed-sex populations (84%), with a significant proportion of pens containing pigs which were all tail docked (92%). Most pens (86%) did not allow all pigs simultaneous access to feeders. Enrichment was present in 88% of the pens, but most (46%) were supplied with only one item, and only 15% offering multiple enrichment types. The study found no significant associations between the risk of tail biting and visible injuries, dirty flanks, or tucked tails, as assessed by the assessors (P > 0.05). Similarly, the risk of tail biting reported per pen was not associated with aggressive, damaging, or exploratory behaviours (P > 0.05). At the abattoir, 96% of pigs' tails exhibited minor skin damage, with only 4% showing moderate to severe damage. Furthermore, no links were found between the scores obtained during slaughter and the risk of tail biting, as reported by the assessors (P < 0.05). Although the tool was useful in identifying several improvements that could be made at farm level in areas such as stocking density, enrichment provision and reducing tail docking, overall the results underscored the need for improved training of assessors, and the challenge of associating management practices and animal based measures with tail-biting risk. [ABSTRACT FROM AUTHOR]

Hydrogen plays a pivotal role in transitioning to CO 2 -free energy systems, yet challenges regarding costs and sourcing persist in supplying Europe with renewable hydrogen. Our paper proposes a simulation-based approach to determine cost-optimal combinations of electrolyser power and renewable peak power for off-grid hydrogen production, considering location and energy source dependencies. Key findings include easy estimation of Levelized Costs of Hydrogen (LCOH) and optimal plant sizing based on the regional energy yield and source. Regional investment risks influence the LCOH by 7.9 % per 1 % change of the Weighted Average Cost of Capital. In Central Europe (Austria), hydrogen production costs range from 7.4 €/kg to 8.6 €/kg, whereas regions like Chile exhibit cheaper costs at 5.1 €/kg to 6.8 €/kg. Despite the favourable energy yields in regions like Chile or the UAE, domestically produced hydrogen can be cost-competitive when location-specific risks and transport costs are taken into account. This underlines the critical role of domestic hydrogen production and cost-effective hydrogen transport for Europe's future hydrogen supply. [Display omitted] • The cost-optimal plant sizing for off-grid hydrogen production was identified. • The site-specific renewable energy yield is an indication of the expected LCOH. • Investment risk and H 2 transport are crucial to the cost-efficient H 2 supply. • Transporting H 2 by ship is more cost-intensive than transporting it by pipeline. [ABSTRACT FROM AUTHOR]

This article investigates the impact of systematic extreme correlations on the cross-section returns in the Chinese stock market. First, it extends the proposed extreme downside correlation (EDC) to the extreme upside correlation (EUC), and employs them to measure systematic tail risk. We find the asymmetry of systematic extreme correlations that EDC is always greater than EUC in the Chinese stock market. Then, we calculate the average returns of portfolios sorted by extreme measures and find that systematic extreme measures have a negative relationship with the Chinese stock returns. After controlling other potential variables such as size, lagged return, higher moments and higher co-moments, these two extreme correlation measures still have significantly negative relationship with stock returns. The results suggest that EDC and EUC help explain the cross-section returns of the Chinese stocks and manage tail risk for investors. [ABSTRACT FROM AUTHOR]

This study explores the influence between financial literacy and digital literacy, on investment sacam awareness among university students in Indonesia. Using a quantitative approach and the Structural Equation Model Partial Least Square (SEM-PLS), data was collected from 288 university students from 38 provinces in Indonesia through an online questionnaire survey. The results of the analysis show that both financial literacy and digital literacy have significant indirect effects on investment scam awareness through the mediation of cybercrime awareness. The findings support the importance of a good understanding of financial literacy and digital literacy in countering the risk of investment scams, especially in today's digital era. However, this study also found differences in the effect of financial literacy on investment scam awareness based on economic and non-economic educational backgrounds, highlighting the importance of considering demographic factors in efforts to increase investment scam awareness among university students. These findings make an important contribution to the understanding of investment fraud prevention efforts and highlight the need for a more inclusive educational approach focusing on financial literacy and digital literacy at the higher education level in Indonesia. [ABSTRACT FROM AUTHOR]

Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures. [ABSTRACT FROM AUTHOR]