Search Results (389)

Workforce adjustments, such as mass layoffs, are significant corporate events that can influence stock returns and volatility, yet their broader asset-pricing implications remain underexplored. We examine the impact of such workforce shocks on stock performance from an asset-pricing perspective. Grounded in production-based asset-pricing theory, incorporating labor adjustment costs and search-and-matching frictions, our study posits that disruptions in the labor force significantly affect firm risk and value. This focus addresses a clear gap. Previous research has not comprehensively evaluated workforce shocks as systematic risk factors in a cross-sectional asset-pricing model. Using an extensive dataset spanning 1990–2023 and covering thousands of layoff events, we construct a novel “workforce shock” factor and conduct the first large-scale empirical tests of its pricing relevance. Our analysis reveals that workforce shocks lead to lower stock returns and heightened volatility, effects especially pronounced in labor-intensive firms. Moreover, exposure to workforce shock risk carries a significant premium, indicating that these disruptions act as a systematic risk factor priced in the cross-section of equity returns. Overall, our study provides the first comprehensive evidence linking labor force disturbances to equity risk premia, underscoring the importance of incorporating labor market considerations into asset-pricing models. Full article

A longstanding debate in finance concerns the impact of social responsibility actions on firms’ long-term profitability. This study provides a broad analysis on the relationship between ESG, its components, and stock returns. Using a dataset that spans from December 2014 to December 2023, this research analyzes an annual average of around 2260 publicly traded companies from Europe and the United States. The findings consistently show a negative link between ESG ratings, their components, and stock returns, a result that is possibly explainable by the mixed effect of a reduction of risk (lower risk premium) from social responsibility, and lower profitability from associated costs. The coefficients for ESG and its pillars in explaining stock returns are generally consistent, with a few exceptions for the environmental and governance components. The environmental pillar has a stronger influence in Europe, across firm sizes, while in the US, the effect is limited to larger companies. For governance, variations align with differing ownership structures across regions and changing investor priorities as firms grow, with stronger influence in Midcaps of both regions and in U.S. Large Caps. The effects of overall ESG scores and individual pillars on stock returns across regions, firm sizes, and their interaction, provide a more comprehensive perspective on their relationship. Full article

Wangiri fraud is a pervasive telecommunications scam that exploits missed calls to lure victims into dialing premium-rate numbers, resulting in significant financial losses for operators and consumers. This paper presents a comprehensive machine learning framework for detecting Wangiri fraud in highly imbalanced and unlabeled Call Detail Record (CDR) datasets. We introduce a novel unsupervised labeling approach using domain-driven heuristics, coupled with advanced feature engineering to capture temporal, geographic, and behavioral patterns indicative of fraud. To address severe class imbalance, we evaluate multiple sampling strategies like the Synthetic Minority Over-sampling Technique (SMOTE) and undersampling, and also compare the performance of Logistic Regression, Decision Trees, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Our results demonstrate that ensemble methods, particularly Random Forest and XGBoost, achieve near-perfect accuracy (e.g., Receiver Operating Characteristic Area Under the Curve (ROC-AUC) $>0.99$) on balanced data while maintaining interpretability. The proposed pipeline offers a scalable and practical solution for real-time fraud detection, providing telecom operators with an effective tool to mitigate Wangiri fraud risks. Full article

The Haezendonck–Goovaerts (HG) risk measure defined on Orlicz spaces via the so-called normalised Young function is a direct generalisation of the Expected Shortfall risk measure. The HG measure is known to be a coherent one, thus making it more robust than some of the alternatives, such as Value-at-Risk, for aggregating and comparing risks, and at the same time more flexible for capital allocation problems, risk premium estimation, solvency assessment, and stress testing in insurance and finance. As random risk in practical applications is often assessed in a portfolio setting—a collection of insurance policies or financial assets, like stocks or bonds—we examine the situation in which the total portfolio risk is expressed as the sum of individual random risks. For this, we consider the sum $S_n^{\left(\xi \right)}={\xi }_1+\dots +{\xi }_n$ of possibly dependent and non-identically distributed real-valued random variables ${\xi }_1,\dots ,{\xi }_n$ with consistently varying distributions. Assuming that the collection $\{{\xi }_1,\dots ,{\xi }_n\}$ follows the dependence structure, similar to the asymptotic independence, we obtain the asymptotic estimations of the HG risk measure for the sum $S_n^{\left(\xi \right)}$ when the confidence level tends to 1. The formulas presented in our work show that in the case where a portfolio of random losses contains consistently varying losses and the others are asymptotically negligible, it is sufficient for risk assessment to consider only the tails of those dominant losses. Full article

This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts’ consensus expectations, reported by Bloomberg. The third method jointly uses futures and analysts’ expectations as inputs to a multifactor stochastic pricing model, with time-varying risk premiums that smooth its data using the Kalman filter. All three alternatives are compared with the well-known no-change forecast benchmark and with each other. The main finding is that analysts’ expectations are a valuable source of data for forecasting copper prices. Also, when futures prices are relatively higher than spot prices, the model presented is the best alternative for forecasting copper prices at any horizon up to 24 months, and when prices are relatively lower than spot prices, the model is the best alternative for long-term forecasts and for LME futures prices for 1 to 12 months. Full article

The scope of this topic stems from the change in insurance companies and the subsequent transition to IFRS 17. The new code came into force on 1 January 2023. Therefore, the purpose of this article is to compare the two standards in terms of methodology and process logic. To highlight the new aspects of the new standard and to present the author’s view that IFRS 17 provides more opportunities for timely action and intervention by company management in the processes and improvement of results compared to IFRS 4. To examine how the application of the standard has affected the strategy for recognising, measuring, and reporting liabilities under insurance contracts, as well as financial results in the insurance sector in China. The study uses a mixed approach, combining a comparison of IFRS 4 and IFRS 17 with examples illustrating actual practice in the sector to examine differences in accounting treatment. It cites examples from European and Asian traders to assess how things will develop in practice. Contribution: This study adds new evidence on the impact of IFRS 17 on value and profit management. Our study found that the new standard introduces a single model for measuring insurance contracts, which significantly increases transparency and comparability in financial statements. Furthermore, one of its most important findings is that, with the equalisation of the margin on contractual services and the recognition of profits over the entire term of insurance contracts, the balance sheets for all years will show more consistent reports of profits and losses. It also calls for attention to the challenges insurers met in developing cash flow discounting methods or putting the general measurement model into effect. Overall, the report found that search engine IFRS 17 has made comparability and transparency better while making suggestions to industry stakeholders about what problems came out when they were discovered afterwards. Full article

Most low- and middle-income governments are unwilling and unable to adequately fund their health systems using tax resources. Despite this route’s popularity in public discourse, it is neither a feasible nor a desirable route for financing Universal Health Coverage (UHC), given competing public finance priorities and limited citizen demand, among other challenges. It thus becomes essential to study the underlying mechanisms behind commercial health insurance and offer citizens the best possible product, which ensures that they not only receive a high degree of protection from health and financial risk on a sustained basis but also find reasonable access and support to improve their health outcomes. In this paper, we build a system-dynamics model that simulates the aggregate behavior of the Indian health-insurance industry, with interacting feedbacks between decisions by stakeholders such as the insurer, healthy and chronically ill populations, and the regulator to outcomes like insurance penetration among segments, overall coverage, health status over the long run, a mechanism of market-discovered premium, and financial viability of the private insurer. We then investigate policy choices and scenarios to explore contrast between design choices and ideal or targeted states of this market, such as a market with 100% enrollment, risk selection by insurers, group insurance models, and managed care, and study the impact on our outcomes of interest, i.e., insurance penetration and pricing, the financial sustainability of the insurers, and the population’s health outcomes. The simulations show that even while insurers and the different population segments optimize for their respective near-term objectives, the best outcomes for all come from the managed-care policy option, which has greater insurance penetration, lower premiums, higher profitability for insurers, and better long-term health outcomes. All other choices and scenarios yield suboptimal, imbalanced systemic outcomes. We thus recommend managed care as a desirable policy alternative for low-income countries intending to improve UHC by leveraging commercial health insurance. Full article

Wheat (Triticum aestivum L.) is a major global staple crop, widely consumed in processed forms such as bread and pasta. As consumer demand for healthier food options increases, organic wheat production is gaining importance. However, organic farming excludes the use of synthetic pesticides and fungicides, potentially increasing the risk of fungal contamination and mycotoxin presence. At the same time, questions remain about whether organically grown wheat can match the grain quality needed for industrial processing, particularly in terms of protein content, gluten strength, and dough properties. This study aims to evaluate grain and flour quality parameters, as well as the occurrence of selected mycotoxins, in eight winter wheat cultivars grown under both organic and conventional farming systems in northern Serbia during the 2023/2024 season. Cultivars included modern premium varieties recommended for organic production, as well as one traditional and one conventional cultivar. Despite unfavourable weather conditions in the early stages of the vegetation in 2024, favourable conditions during grain ripening contributed to the good quality of organically produced varieties. The quality parameters of most varieties from organic production (PC > 17%, WG > 49%, WA > 62%) were significantly higher than the standard for organic wheat. NS Epoha stood out as the variety with the highest yield in organic production. As expected, varieties from the organic system had a higher incidence of mycotoxin contamination, but their concentrations were low. Full article

This study examines the portfolio optimization problem of an insurance company that issues an annuity, receives the associated premiums as a lump sum, and invests in a financial market. The insurer’s objective is to determine an investment strategy that minimizes the likelihood of defaulting on annuity payments before ceasing operations, where default occurs if the portfolio value, net of the annuity liability, becomes negative. Unlike the previous work, here the mortality intensity is stochastic and follows a Cox–Ingersoll–Ross (CIR) process. Dynamic programming is employed, and the value function is characterized by a Hamilton–Jacobi–Bellman (HJB) equation, and the former is linearized through the Legendre transform. Numerical results show that default probability declines with higher initial wealth and mortality intensity, while stochastic mortality volatility has little impact—though slightly higher volatility marginally reduces default risk. Optimal stock investment falls with increasing wealth and mortality intensity, and is nearly constant for low wealth levels. Mortality volatility has minimal influence, but a higher Sharpe ratio raises optimal investment, underscoring the role of risk-adjusted returns. Full article

This paper examines the impact of litigation risk on meeting-or-beating earnings expectations (MBE). Whether and how litigation risk affects MBE is largely inconclusive due to potential reverse causality and measurement error. To find a causal effect, this study employs differences-in-differences tests based on the unanticipated legal event that decreased litigation risk for firms headquartered in the U.S. Ninth Circuit states. The result indicates that Ninth Circuit firms are more likely to meet or beat their earnings target after the ruling, suggesting that litigation risk negatively affects the likelihood of MBE. Further analyses show that investors’ reactions are less positive to MBE premium for firms with a decrease in litigation risk. Taken together, this study contributes to the literature by documenting that litigation risk is a significant factor influencing managers’ benchmark beating behavior. Full article

In this research, we summarize the results of implementing the market risk premium into the option valuation formulas of the Black–Scholes–Merton model for out-of-the-money (OTM) options. We show that derivative prices can partly depend on systematic market risk, which the BSM model ignores by construction. Specifically, empirical studies are conducted using 50ETF options obtained from the Shanghai Stock Exchange, covering the periods from January 2018 to September 2022 and from December 2023 to October 2025. The pricing of the OTM options shows that the adjusted BSM formulas exhibit better pricing performance compared with the market prices of the OTM options tested. Furthermore, a framework for the empirical analysis of option prices based on the Capital Asset Pricing Model (CAPM) or factor models is discussed, which may lead to option formulas using non-homogeneous heat equations. The later proposal requires further statistical testing using real market data but offers an alternative to the existing risk-neutral valuation of options. Full article

Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses, whether climate-related or not, farmers widely rely on crop insurance to increase their resilience. However, farmers in Sri Lanka demonstrate a limited acceptance of crop insurance schemes. This study aimed to investigate the perceptions and Willingness-to-Pay (WTP) for crop insurance schemes among the paddy farmers in Kurunegala district. A total of 248 paddy farmers from the Kurunegala district were recruited as the study sample using the stratified random sampling approach. A pre-tested structured questionnaire and choice cards were used for primary data collection. The Conditional Logit Model (CLM) was used for data analysis. Around 77.8% of respondents were males engaged only in paddy farming, while the majority (62.5%) received an income of LKR 50,000 to 75,000. Complications experienced during the claim form-filling process (mean = 4.6), gaps in covering all crops on the crop land (mean = 4.6), and poor service quality (mean = 4.5) were perceived as the major limitations in existing crop insurance schemes. Outcomes of the CLM indicated that farmers with a positive attitude toward crop insurance significantly prefer plans with drought coverage (β = 0.823; p < 0.05), on-field assessments (β = 0.251; p < 0.05), and higher no-hazard returns (β = 0.318; p < 0.05) while showing a notable sensitivity to premium costs (β = −0.590; p < 0.05). The model also revealed an apparent willingness to switch from the status quo when presented with better-designed alternatives. The findings emphasized the need to implement responsive crop insurance schemes to enhance climate resilience and ensure the sustainability of paddy production in Sri Lanka. Full article

This paper examines the relationship between ESG ratings, as a subset of criteria and a tool for assessing sustainability, and firm performance in Southern European economies. It focuses on publicly listed large-cap companies in Portugal, Italy, Greece, and Spain. By analyzing a sample of 110 firms over a four-year period and applying Ohlson’s valuation model, we evaluate how ESG scores influence these companies’ performance. Our findings indicate that the social dimension is positive and statistically significant, suggesting that investors in Southern Europe increasingly prioritize value social responsibility initiatives as they aim to identify and manage ESG risks. In contrast, the Environmental and Governance components do not show statistical significance. The “polluting dummy” variable is positive and significant at the 1% level, indicating a valuation premium for high-emission firms, possibly reflecting investors’ preference for financial stability in economically volatile environments. The baseline model yields an R² of approximately 10%, consistent with expectations given the multifactor nature of stock prices. The study contributes to the sustainability literature by highlighting the nuanced and region-specific role that ESG factors play in market valuation. We discuss limitations related to the regional scope, rating methodologies, and model specification, and offer suggestions for future research. Full article

Professional football players face considerable occupational hazards, with injuries posing serious challenges to player safety, club performance, and regulatory oversight. This descriptive study examines the multifaceted implications of Portugal’s Laws No. 48/2023, which formally recognises professional football as a high-risk occupation and strengthens the mandatory insurance regime through a major regulatory update. Adopting a qualitative approach, the analysis focuses on Portugal, where the professional football business model heavily relies on player commercialisation, and compares regulatory frameworks in Spain, Germany, England, Italy, France, and Brazil. Findings indicate that Portugal’s legal framework enhances player safety by ensuring comprehensive coverage and improved disability protections, yet also introduces financial pressures on clubs, particularly those with lower economic capacity. These pressures are exacerbated by limited market competition and high insurance concentration, increasing premium costs. Cross-country comparisons reveal persistent disparities in legal standards, insurance scope, and institutional coordination, which complicate risk allocation in an increasingly globalised football market. Notably, Portugal’s high-risk insurance model most closely aligns with France’s hybrid approach, in contrast to fully public schemes seen in countries like Germany and Italy. While complete harmonisation remains challenging, the study identifies key principles to guide policy reform and international cooperation. Overall, the findings advance understanding of occupational risk regulation in sport and offer practical insights for designing effective, equitable, and safety-oriented protection systems for professional athletes. Full article

As an emerging production factor, data assets are gaining strategic prominence, yet their application in collateralized financing faces persistent challenges, including illiquidity and risk evaluation complexities. This study introduces an innovative $P_{max}$ model to enhance the Collateral Value of data assets through insurance mechanisms, systematically demonstrating the feasibility conditions under which risk transfer optimizes asset valuation and delineating implementation pathways to integrate data insurance with asset-backed financing. Building on the theoretical framework of Value-at-Risk (VaR), this study develops a dynamic valuation model to assess the value of the collateral before and after insurance. Our analysis shows that insurance coverage for potential losses significantly enhances financing viability when premiums satisfy $P_{max}$. Empirical analysis employing Monte Carlo simulations reveals a nonlinear positive correlation between pledgees’ risk tolerance thresholds and the maximum acceptable premium $P_{max}$. This study bridges theoretical gaps in understanding insurance-value relationships for data assets while providing conceptual foundations and operational blueprints to standardize data markets and foster financial innovation. Full article