This study investigates how industrial firms’ stock prices responded to Trump's tariff postponement announcement on April 9, 2025, and examines the factors driving these reactions. It emphasizes variations across market types (developed, emerging, frontier), industries, firm size, and growth, highlighting the industrial sector's exposure to trade policy uncertainty. Employing an event study methodology, the analysis measures cumulative abnormal returns for 1,732 industrial firms across 51 countries. Market responses are evaluated across market types, industries, firm size, and growth characteristics, while cross-sectional regressions assess how tariff exposure, leverage, and firm fundamentals shaped reactions to trade policy shocks. Results show that industrial markets experienced significant pre-event declines, with frontier markets most affected by limited liquidity, while developed markets led the rebound post-announcement. At the industry level, manufacturing declined sharply but recovered quickly, transportation remained stable, and construction adjusted more slowly. Firm-level analysis reveals that small-cap and low-growth firms were more adversely affected, whereas large-cap and high-growth firms recovered more strongly. Cross-sectional evidence identifies tariff exposure, leverage, and firm characteristics as significant determinants.
This paper introduces a new inhomogeneous stochastic 1/2-power Lundqvist-Korf diffusion model to forecast the gross national income (GNI) per capita of Morocco. Through a transformation and the application of It’s calculus, we identify the probabilistic properties of the process, including its analytical form. We then derive the probability transition density function (ptdf) and the mean functions associated with the model. Discrete sampling and the maximum likelihood approach are used to estimate the model parameters. However, because of the complexity of the associated system of likelihood equations, we employ the simulated annealing procedure to deal with the estimation problem. To test the methodology, we first apply the model to simulated data and evaluate the statistical results. To finalize, the model is used to fit real data corresponding to Morocco’s GNI per capita.
This paper revisits the exchange rate–stock return nexus by examining how macro-financial fundamentals—specifically interest rate and inflation differentials relative to the United States—shape the impact of currency fluctuations on equity markets in 14 developed and 16 emerging economies. Motivated by gaps in the literature that overlook threshold behaviour, cross-sectional dependence, and distributional heterogeneity, the paper develops a novel two-stage CCE-augmented Panel Smooth Transition Regression (PSTR) model combined with Quantile Treatment Effects (QTE). This methodological innovation enables the identification of nonlinear regime shifts driven by macroeconomic differentials while addressing endogeneity and global spillovers. The results reveal sharp regime transitions associated with interest rate and inflation thresholds, particularly in developed markets. In emerging markets, exchange rate depreciation significantly lowers stock returns only when inflation differentials exceed a critical threshold, underscoring the role of macroeconomic instability. For developed markets, high-interest rate differentials magnify the negative effect of exchange rate fluctuations on equity performance, consistent with carry-trade dynamics. QTE analysis shows that emerging markets underperform during bearish conditions due to higher exchange rate volatility but outperform during bullish phases, reflecting a conditional “high-risk, high-return” pattern. These findings offer new insights for monetary authorities, investors, and portfolio risk managers.
The central question of this paper is how to simply enhance a set of given supervised learning algorithms under some fairness requirements, to ensure that any sensitive variable does not “unfairly” influence the outcome. To achieve this goal, we work with several notions of fairness (Demographic Parity, Equalized Odds, Lack of Disparate Mistreatment), possibly generalised to more general concepts of conditional fairness. We linearly combine an ensemble of binary and/or continuous basis classifiers or regressors to build an “approximately optimal” solution in terms of fairness and accuracy for any given notion of fairness. The trade-off between fairness and predictive power is managed by considering penalised criteria. We rely on post-processing procedures without any transformation of the data nor of the basis training algorithms. Some empirical experiments, by simulation and on real databases, are provided to illustrate our approach.
Homebuyers in flood-prone areas must navigate increasingly complex risk information and mitigation options. Yet, little is known about how prospective buyers evaluate flood risk and mitigation features during the home purchase process, or how their preferences vary depending on how risk is communicated. This study uses a discrete choice experiment to quantify how prospective homebuyers in the U.S. Gulf Coast value flood risk, mitigation measures, and various forms of risk communication. We surveyed 1,040 respondents who intend to purchase homes within five years in coastal counties across five Gulf states. Results show that homebuyers are willing to pay substantial premiums for homes with lower flood risk and effective mitigation (e.g., elevation or flood-resistant materials). Preferences are sensitive to how flood risk is presented: monetary formats yield more consistent valuations than probabilistic ones. These findings highlight opportunities for improving risk communication and flood policy. By focusing on a region at the forefront of climate-related flood risk, this research contributes to understanding how individuals make high-stakes decisions under uncertainty and offers insights for improving climate resilience in housing markets.
In this paper, we introduce the threshold regression based on the Ornstein-Uhlenbeck (OU) process to capture the behavior of the logarithmic returns alternatively to the classical first hitting time model based on the Brownian motion. The model estimates the likelihood of the logarithmic returns reaching a threshold, thereby allowing us to interpret return risk through the first hitting time distribution. In our study, we assess the performance of the threshold regression based on the Ornstein-Uhlenbeck process comparing with threshold regression based on the Brownian motion (BM) model using different stock markets including the Stock Exchange of Thailand (SET), the Hong Kong Stock Exchange (HKSE), and the National Association of Securities Dealers Automated Quotations (NASDAQ). Empirical results indicate that the threshold regression model based on the Ornstein-Uhlenbeck process provides a better fit to the first hitting time of the logarithmic return and offers improved insight into the properties of returns upon reaching the threshold.
In the contemporary global landscape, there has been a growing uncertainty due to continuous shocks with significant implications on investment and portfolio management. This paper investigates how return spillovers and dependencies between traditional and modern financial assets evolve under varying market conditions, with a focus on the COVID-19 crisis period. Using a quantile vector autoregression (QVAR) model combined with network analysis, we analyse daily asset returns from 02 January 2018 to 30 June 2023 to capture asymmetric and state-dependent connectedness. The study reveals that asset interdependencies intensify during periods of market stress, particularly at extreme quantiles. Green bonds, gold, and AI-related assets exhibit safe-haven characteristics under these conditions. The findings underscore the dynamic nature of market connectedness and provide important insights for portfolio diversification strategies, especially for risk-averse investors navigating turbulent markets.
This study focuses on a two-stage hybrid framework for dynamic portfolio optimization. While existing two-stage approaches often rely mainly on price data, symmetric downside measures, or static allocation, they provide limited insight into fundamental efficiency and how assets behave during severe market downturns. In the first stage, Data Envelopment Analysis (DEA) is used to pre-select efficient stocks based on fundamental financial indicators. In the second stage, several risk-based allocation strategies are applied to the selected assets, including mean-standard deviation, semi-deviation, conditional value at risk, maximum Sharpe ratio, and a proposed mean-asymmetric downside risk model. This proposed model introduces an exponential penalty function to emphasize larger losses and better capture downside risk during market downturns. Portfolio performance is evaluated using a rolling-window approach with weekly rebalancing over a 65-week out-of-sample period. The DEA-based selection effectively reduced the investment universe from 36 to 9 efficient stocks. Empirical results show that the simple DEA+1/N portfolio outperformed the SET50 benchmark, and the proposed DEA+mAsymRisk delivered higher returns with lower downside risk compared to alternative strategies. These findings highlight that combining fundamental screening with downside-sensitive allocation strengthens portfolio robustness and offers practical value for dynamic risk management in financial markets.
This study explores the effect of sustainability development and Independent Board of Commissioners on operational and systematic risks in Indonesian listed companies. This study uses a five-year period of data from 2018 to 2022 and ordinary least squares regression to estimate the associations. We also applied the Generalized Method of Moments to address endogeneity issues, as well as several robustness tests, including the impact of the COVID 19 pandemic and other additional control variables. Overall, our empirical research shows that companies with higher sustainable performance and Independent Board of Commissioner members experience lower operational and systematic risks, whereas the Independent Board of Commissioners has no association with systematic risk. Furthermore, we test for endogeneity and the results remain consistent. This study has practical implications for policymakers and academics by considering sustainable development and corporate governance mechanisms in emerging economies, such as increasing ESG practices and the role of an independent board of commissioners to promote transparency and competitiveness of the organization. This study also assists investors in considering the large amount of funds issued and the investment decisions for companies in developing countries. This study examines the knowledge gap in the literature in developing countries regarding sustainability development and the independent board of commissioners in Indonesia.
Modeling the futures term structure for derivative pricing often transforms a single contract into a high-dimensional problem. Standard techniques such as Principal Component Analysis (PCA) reduce dimensionality but ignore derivative payoff structures, leading to potential mispricing. Building on empirical PCA evidence that two components explain over 99% of variance in the WTI futures curve, we propose a non-parametric two-factor Fast Factorial Model (FFM) that preserves intra-curve correlations while aligning with derivative sensitivities. The FFM is calibrated via a bootstrap on implied volatilities with cubic spline interpolation in delta-vol space and benchmarked against a full-factor Monte Carlo using identical inputs. Accuracy is assessed using spot-normalized errors (in basis points) and premium-relative RMSE, with deviations below five basis points and RMSE under 0.03% of option premiums. By combining dimensionality reduction with non-parametric calibration, the FFM achieves near-identical pricing at a fraction of the runtime and simplifies the computation of sensitivities. The contribution is empirical and numerical, providing a fast, robust, and commodity-agnostic tool for practitioners in pricing and risk management. However, the model’s accuracy depends on the quality and richness of historical data and may be less effective under extreme stress scenarios, illiquid maturities, or structural regime shifts.
This study explores how experimental flight test professionals manage catastrophic risk in complex socio-technical systems. Using an ethnographic, mixed-methods approach with survey (n = 49; validation n = 21), interviews, and observation of flight test practitioners, the research found that flight test teams maintained both statistical (Bayesian, ISO 31000-based) and non-statistical (Precautionary, heuristic) methods in parallel. Statistical tools were applied where repeatable, deterministic elements permitted probabilistic reasoning. While resource-intensive Precautionary controls and experienced-practitioner heuristics were used where novelty, emergence, and an absence of prior knowledge precluded calculation of the probability of a hazard. Practitioners commonly reported corporate mandates to probability approaches but treated those outputs with caution or disregard when clearly unsuitable. The dual approach traded efficiency for effectiveness, ensuring identified risks were managed even when statistical measures were unreliable. Aligning statistical tools with deterministic system elements, and precautionary, heuristic approaches with complex system elements offers a pragmatic, empirically grounded template for managing catastrophic risks in complex systems. This case study invites managers and researchers to reconsider reliance on probability where uncertainty is irreducible.
The COVID-19 pandemic has caused significant global economic disruptions, particularly increasing non-performing loans (NPLs) in the banking sector. This study examines the impact of bank specific factors and macroeconomics on NPLs in banks across ASEAN countries. Panel data from 39 publicly listed banks over the period 2010–2024 were examined using the Generalized Method of Moments (GMM) and system GMM approach. The results indicate that lagged NPLs, return on equity, total assets, credit growth, GDP growth, and lending interest rates significantly influence NPLs, while the Tier 1 capital ratio, non-interest income, and unemployment rate show no significant effect. This study provides important implications for bank management and policymakers in enhancing credit risk management and strengthening banking supervision within the ASEAN region.
Banks play an integral role in the economic growth of nations. However, it is necessary to take into account the modern-day economic shocks while considering the operation and profitability of banks. The United States and the United Kingdom are two such nations that are highly integrated into the global markets but also have substantially unique banking regulations. The establishment of a dynamic corporate governance framework and ERM methodology is critical for banks. In order to compare the impact of ERM and corporate governance framework on the risk of banks, while controlling for economic factors, this paper uses a GMM methodology. A Driscoll–Kraay fixed-effect estimation was also added as an additional robustness check. Data for 10 banks with a market capitalisation greater than 5 million USD has been considered from both nations between 2019 and 2023. The results of the estimation show that governance factors and risk management factors impact the NPL but not the ROA. However, in the UK, both NPL and ROA are impacted. This shows the non-binding risk in USA whereas the core-binding risk in the UK markets. Finally, macroeconomic factors have a significant impact on the risk of banks across both nations. Based on the research, the binding versus non-binding risk distinction identified here offers a conceptual framework for evaluating governance and ERM effectiveness in other banking systems.
Corporations must comply with various laws and regulations, subject to their markets and industry. To manage their compliance risks, corporations are expected to design and implement compliance programs based on risk assessments. This study investigates the impact of risk assessments on the implementation of recommended practices in Corporations’ Compliance Programs (CCPs). Through survey interviews with compliance officers from 93 Forbes 2000 companies, the research examines the relationship between risk levels and 33 recommended practices across Anti-Bribery & Corruption, Data Privacy, and Third-Party compliance risks. Contrary to the initial hypothesis, findings reveal that only nine practices significantly relate to risk levels, including rule-based policies and compliance training testing. Unexpectedly, several practices showed negative relations, particularly in the Third-Party compliance domain, suggesting that higher risk levels do not always lead to broader implementation of recommended practices. The study uncovers mixed results in the Anti-Bribery & Corruption CCP, limited risk-based alignment in the Third-Party CCP, and better alignment in the Data-Privacy CCP. These findings suggest that the relationship between risk and compliance implementation is domain-specific and may be influenced by whether the risk is perceived as core (e.g., Data Privacy) or non-core (e.g., Third-Party). They highlight the need for improved regulatory alignment with corporate practices and further exploration of CCP impacts on risk management. This study offers a novel empirical contribution by systematically examining the link between risk levels and the implementation of specific compliance practices across three compliance areas, providing a granular benchmark for future research.
This study analyzes how disaster types and research and development (R & D) characteristics play a different role in citizens’ risk perception based on 1,000 Korean citizens in 2021 by employing a partial least square (PLS) regression. This study finds that R & D play the most important role in citizens’ risk perception. For example, the development of R & D shows very high importance with values of 1.355 for interest and 1.817 for severity. Next, social disasters and safety accidents exert a meaningful impact on citizens’ risk perception, whereas natural disasters do not influence them. For instance, traffic accidents are slightly important with a value of 0.700, and leisure accidents are moderately important with a value of 0.928 for severity. Third, socio-demographic variables show a moderate effect on citizens’ risk perception. For example, age shows values of 0.719 for interest and 0.816 for severity. This study suggests that governments should increase interest and severity of citizens according to R & D, disaster types, and socio-demographic characteristics.
The central question of this paper is how to simply enhance a set of given supervised learning algorithms under some fairness requirements, to ensure that any sensitive variable does not “unfairly” influence the outcome. To achieve this goal, we work with several notions of fairness (Demographic Parity, Equalized Odds, Lack of Disparate Mistreatment), possibly generalised to more general concepts of conditional fairness. We linearly combine an ensemble of binary and/or continuous basis classifiers or regressors to build an “approximately optimal” solution in terms of fairness and accuracy for any given notion of fairness. The trade-off between fairness and predictive power is managed by considering penalised criteria. We rely on post-processing procedures without any transformation of the data nor of the basis training algorithms. Some empirical experiments, by simulation and on real databases, are provided to illustrate our approach.
This paper aims to empirically evaluate the time-varying informational efficiency of both spot and futures markets for energy commodities (Brent oil and natural gas) and precious metals (gold and silver). The study employs the Shannon entropy measure to assess the daily closing prices of these markets, providing a dynamic measure of market efficiency over time. This allows for a comparative analysis of the efficiency of different markets. The efficiency of energy and precious metals markets fluctuates over time, influenced by global events, economic conditions, and market-specific factors. The study confirms that market efficiency is not static but adapts over time, aligning with the Adaptive Market Hypothesis (AMH). This is evident in the dynamic behavior of markets such as gold, silver, natural gas, and Brent oil. Gold and Silver: Efficiency decreases during economic instability, with gold acting as a safe haven and silver being more sensitive to supply-demand changes. Natural Gas: Efficiency is highly influenced by weather conditions. Shannon entropy serves as a valuable tool for assessing market efficiency, offering insights that investors can use to adjust strategies during inefficient periods, such as opting for stable assets like gold in uncertain times. Additionally, understanding the impact of global events on market efficiency helps in building resilient portfolios. Policymakers can leverage these insights to craft regulations that enhance market stability, especially during economic uncertainty or extreme weather events.
Organizations support leadership development training programs to continuously improve the level of leadership competence and the supply of suitable applicants for leadership roles. One of the skills that entrepreneurs should cultivate to become “leaders” is leadership competency. In entrepreneurship, leadership has a significant role. Therefore, the purpose of this study is to explore the role of leadership development programs that include personal development, self-assessment, team management, strategic leadership, skilled knowledge, and relationship development, to know the way they influence entrepreneurial activities. To test the hypothesis under study, this research applies the Structural Equation Modelling (SEM) approach to the data being gathered from 365 employees and managers of entrepreneurial business firms in India. The obtained results show that personal development, skilled knowledge, and relationship development have a beneficial impact on entrepreneurial activities. In contrast, self-assessment, team management, and strategic leadership are found to have no beneficial impact on entrepreneurial activities. The combination of the ideas of leadership and entrepreneurship is suggested in this study, filling the gap in the previously provided cross-sectional data of the literature.
Tail risk analysis plays a pivotal strategic role in risk management, particularly in light of economic crises. In this context, the purpose of this paper is to examine the asymptotic properties of Joint Tail-based Cumulative Residual Entropy ([Formula: see text]) in a bivariate setup involving two variables, [Formula: see text] and [Formula: see text]. In this setup, [Formula: see text] is considered the variable of interest, while [Formula: see text] serves as the benchmark variable. We provide a generalization of the Joint Tail-based Cumulative Residual Entropy to create a more flexible version that allows for a more comprehensive analysis of extreme risk. This generalization leads to a deeper understanding of the tail relationship between [Formula: see text] and [Formula: see text] and their respective impacts on a specific system. To illustrate our results, we conducted the study under both tail-dependent and tail-independent scenarios. We supplemented our research with practical examples and applied our findings to real-world financial data, employing our proposed non-parametric estimator of [Formula: see text] as the basis for our analysis.
We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings.
