This study employs panel data from 35 Vietnamese commercial banks over the period 2014–2024 to analyze the determinants of bank performance, with particular emphasis on the effects of risk and market power. Risk is measured using indicators such as the Z-score and loan loss provisions (LLP), while market power is proxied by the Lerner index. In addition, the study incorporates several control variables representing bank-specific characteristics, industry-specific factors, and macroeconomic conditions. Bank performance is examined from three perspectives: return on assets (ROA), return on equity (ROE), and profit before tax (PBT). The empirical analysis is conducted using the two-step system Generalized Method of Moments (GMM). The findings indicate that both market power and risk significantly affect bank performance. Among the bank-specific variables, size, liquidity, ownership structure, operating costs, diversification, and listing status are found to have a substantial impact on performance. Moreover, industry-specific factors and macroeconomic conditions also influence bank efficiency. The author also find the evidence of other factors affecting bank efficiency.

The main objective of this study was to determine the impact of financial risks on indicators measuring the development of financial markets, focusing on the size of the financial market, given its importance in attracting and mobilizing capital, increasing liquidity, diversifying funding sources, managing risks, and supporting the development process. To achieve this objective, the study adopted an analytical methodology, collecting data from various sources, such as reports and statistical bulletins issued by the Capital Market Authority and the annual statistical reports issued by Tadawul (the Saudi Stock Exchange). This data was analyzed using appropriate statistical and analytical tools. The study employed simple linear regression and multiple linear regression to determine the impact of financial risks, measured by credit risk and market risk, on the size of the Saudi Stock Exchange, as measured by the market capitalization index and the number of listed companies. The study reached several conclusions, most notably: a statistically significant positive impact of credit risk on the market capitalization index and the number of companies listed on the Saudi Stock Exchange; a statistically significant positive impact of market risk on the market capitalization index and the number of listed companies; and a statistically significant positive impact of financial risk, measured by both credit and market risk, on the size of the Saudi Stock Exchange, as measured by the market capitalization index and the number of listed companies. Among the study's key recommendations is the need for continuous development of risk management and hedging strategies in the Saudi Stock Exchange to mitigate financial risks.

This study assessed risk attitudes and mitigation strategies of arable crop farmers in Semi-Arid Makueni County, Kenya. A multi-stage sampling technique was adopted in the selection of 163 respondents. Data were collected using research questionnaire and analyzed using frequency distribution and percentages, Ordinary Least Square (OLS) regression, safety first model and a 5-point likert scale. The results revealed that the major types of risk faced by arable crop farmers are production (93.3%) and climatic (89.6%), followed by market (74.2%), financial (66.9%) and institutional (50.9%) risks; about 65.6% of farmers were risk-averse, 23.9% risk-neutral and 10.4% risk-seeking. The OLS model indicated that education, farming experience, farm size and access to credit significantly influenced farmers’ economic performance while risk attitude coefficient had a negative effect. The result also showed that the farmers adopted low-cost, experience-based strategies (crop diversification, intercropping, drought-tolerant varieties and soil and water conservation), however, uptake of formal instruments like crop insurance (11%) remained low. The study concludes that smallholder farmers in semi-arid Kenya are highly risk-averse and rely on adaptive strategies grounded in social capital and indigenous knowledge and recommended strengthening agricultural extension, access to affordable credit and insurance literacy.

Type of the article: Research ArticleThe paper aims to examine and thereby understand the development of idiosyncratic risks using financial ratios. To achieve this, the study would like to explain the company-specific risks defined in the classic equilibrium model using indicators derived from a company’s balance sheet and profit and loss statements. The objects of this empirical research are the listed companies of the Visegrád countries. Data were sourced from the Orbis database. The total number of companies included in the analysis was 35, as only these had complete data available for the years examined. Multivariate regression analysis and panel analysis (fixed and random effects) were used in the evaluation. According to the results, the market risk of equities is influenced by all the assessed solvency ratios (debt-to-capital ratio, stability ratio, debt-to-sales revenue, and net indebtedness) examined. The degree of market risk of companies is also influenced by the EBIT-to-sales ratio (profitability ratio). The results of the fixed-effect model showed that solvency ratios such as debt-to-capital ratio, stability ratio, and debt-to-sales revenue, and profitability ratios (ROS and ROE) affect the degree of idiosyncratic risks.Acknowledgment The researchers would like to express their gratitude to the J. Selye University for the support.

Risk management is at the heart of investment banking, where institutions face a spectrum of risks including market risk, credit risk, liquidity risk, and operational risk. Traditional risk management relies heavily on Value at Risk (VaR), stress testing, scenario analysis, and expert judgment to measure, monitor, and mitigate these exposures. However, these methods often assume linear relationships and static volatility, which may fail to capture realworld complexities and sudden market shocks.This study combines conventional risk management frameworks with advanced analytics. Using Machine Learning models like Random Forest and XGBoost, we aim to detect hidden, nonlinear risk factors and predict potential losses more accurately. Additionally, Deep Learning models such as LSTM networks are employed to forecast time-series risk metrics like daily VaR and liquidity ratios, effectively capturing volatility clustering and long-term dependencies in financial data.By integrating ML and DL into risk analysis, the study demonstrates significant improvements in predictive accuracy and early warning capabilities, offering investment banks a more dynamic and datadriven approach to risk management.

Monte Carlo Simulation has served as a fundamental methodology in financial risk management for decades, particularly in applications such as Interest Rate Risk in the Banking Book, market risk assessment, and regulatory stress testing frameworks. While traditional Monte Carlo approaches have proven valuable for probabilistic risk modeling, they face significant limitations, including reliance on static distributions, fixed correlation matrices, a lack of macroeconomic coherence in scenario generation, and substantial computational demands. The emergence of artificial intelligence and generative AI technologies presents a transformative opportunity to address these shortcomings while preserving the core probabilistic framework that makes Monte Carlo simulation effective. This article examines the comprehensive evolution of Monte Carlo simulation through AI integration, exploring how technologies such as Variational Autoencoders, Graph Neural Networks, Generative Adversarial Networks, and diffusion models enhance each phase of the simulation lifecycle from data preparation through governance. The transformation encompasses richer data ingestion through vector databases and natural language processing, neural density estimation for capturing complex distributions, dynamic correlation modeling that adapts to market regimes, generation of macroeconomically consistent stress scenarios, accelerated valuation through neural surrogate models, and enhanced explainability via SHAP values and large language model-generated reports. Through detailed examination of Interest Rate Risk in the Banking Book applications, particularly Economic Value of Equity analysis, this article demonstrates how AI-enhanced simulation produces results that are simultaneously more accurate, comprehensive, explainable, and aligned with regulatory expectations for model risk management and stress testing transparency, representing not a replacement but an intelligent augmentation of proven quantitative methods.

We study a supply chain distribution system and investigate experimentally operations of markets where retailers can trade digital claims (tokens) on the supplier’s capacity. Subjects play the role of retailers, have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier’s capacity. Following demand realization, retailers trade tokens with each other in markets implemented as double-sided, single-price, blind, batch auctions. We compare six behavioral treatments, featuring two wholesale prices and three market sizes. As expected, markets reduce leftovers and shortages. Interestingly, market-clearing prices are anchored to wholesale prices and do not signal the value of goods in large markets. Players deploy novel ordering and trading strategies that differ from the transshipment literature. We identify strategies by applying unsupervised machine learning algorithms. In one strategy, players buy a few claims and, after demand realization, use the market to satisfy it. Other players buy more claims than the maximum demand and, once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty but expose players to liquidity and mistakes risks. A third strategy, in which players order from the supplier initially as if expecting the market to be cleared cooperatively, is more profitable. This strategy diversifies demand and market risks. The introduction of markets causes the “pull-to-the-mean” effect and increases order variability. Thus, markets can cause the Bullwhip Effect. Retailers’ and the supply chain’s average profits are higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. This paper was accepted by Elena Katok, operations management. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03771 .

ABSTRACT This study investigates whether abuse of administrative power by local governments is a significant priced risk factor in China’s Local Government Financing Vehicle (LGFV) bond market. Using data from 7,319 publicly issued LGFV bonds (2017–2020) and administrative lawsuit documents, we find that administrative power abuse significantly raises the credit spread of LGFV bonds through increased perceived credit risk. To mitigate endogeneity concerns, we employ instrumental variables such as whether a city was a historical treaty port or a judicial reform pilot city. We conduct a series of robustness checks to confirm the reliability of our findings. Mechanism analysis further indicates that this effect is primarily driven by a deteriorating business environment and heightened ex-ante risk perception. In addition, the adverse pricing effect is more pronounced in markets with higher fiscal transparency, larger government size, or in the private placement bond market, indicating that market increasingly prices governance quality. Overall, our findings highlight the emergence of a governance-based risk premium as implicit guarantees for LGFV bonds weaken.

ABSTRACT When industries face systemic shocks, enterprise risks extend beyond the effects of isolated incidents and propagate through intricate competitive networks. Existing research predominantly addresses risk spillovers initiated by isolated events, such as financial scandals and product recalls, while neglecting the risk spillover effects associated with firms embedded in multi‐market competitive networks. This study undertakes an empirical analysis within the context of China's electronic information manufacturing industry. The findings reveal that the broader a firm's product market competition boundary, the more pronounced the peer risk spillover effects it encounters. Furthermore, the degree of product diversification and analyst coverage positively moderate the relationship between competition boundaries and risk spillovers. Firms positioned downstream in the supply chain, those with lower corporate social responsibility performance, and non‐state‐owned enterprises experience more significant impacts of competition boundary breadth on risk spillovers. This study conceptualizes the boundaries of product market competition as a crucial lens for comprehending industry risk spillovers, thereby advancing risk spillover research from static event analysis to dynamic network analysis. The findings indicate that, in the context of systemic shocks, expansive competition boundaries exacerbate risk contagion through two primary channels: direct business linkages and indirect cognitive biases. This study offers a novel decision‐making framework for firms to navigate the balance between market opportunities and risk exposure within intricate competitive environments.

Against the accelerating of global climate change and carbon neutrality transitions, energy price volatility exerts complex effects on the employment dimension of economic sustainability through both industrial and agricultural channels as intermediaries. This study employed a mixed-frequency vector autoregression model to statistically analyze the weekly prices of four major industries and 24 sub-markets in China. The main outcome was the urban unemployment rate in China, and it was verified against the urban unemployment rates in 31 cities and the unemployment rates by age group (YUR/LUR). The study investigated the employment dimension of economic sustainability. Energy and energy metal prices represent the energy transition, while food and industrial goods prices characterize the intermediary linkages. Unemployment rates serve as the employment dimension of economic sustainability. The findings reveal bidirectional interactions and heterogeneous transmission mechanisms between prices and unemployment: energy prices exhibit weaker direct links to unemployment, partly influenced by demand inelasticity and policy adjustments; agricultural products face more persistent impacts, reflecting policy interventions and demand constraints; chemical products demonstrate the highest sensitivity and fastest response to unemployment shocks; metals show significant internal variations, with sub-market-level impacts being more pronounced yet shorter-lived. These insights advance climate and energy economics by guiding low-carbon transition policies, optimizing resource allocation, and managing energy market risks for resilient economic sustainability.

Purpose We investigate how digital care platforms contribute to the marketization of care across the European Union (EU) by analyzing their business models and national variations. Design/methodology/approach Drawing on a dataset from the Centre for European Policy Studies (CEPS) and extensive hand-coding, we identify 100 care platforms and examine their characteristics using a novel three-dimensional analytical framework: scope of commodification, platform governance and distribution of market risks. Findings Our findings show that while care platforms share features with other digital labor platforms, the intimate, relational and non-standardized nature of care work limits algorithmic control and commodification. Nonetheless, care platforms contribute to marketization by intermediating a wide range of services, including emerging categories like pet care and tutoring and relying predominantly on marketplace models that shift risk onto workers. Importantly, we uncover cross-national variation: the Nordic countries, Spain, Belgium and the Netherlands host more regulated platform models with less reliance on market mechanisms, whereas Eastern Europe is dominated by marketplace models. These patterns align with broader care regime typologies and suggest that care platforms both adapt to and reshape national care markets. Originality/value Our study offers the first comparative empirical overview of care platform business models across the EU and highlights the need for more nuanced, context-sensitive research to understand their evolving role in care provision. This complements existing research on platform workers' experiences of precarity and informality by showing that these are linked to care platform business models. Our findings have implications for platform regulation, addressing issues of care provision and (informal) labor.

ABSTRACT This study develops a simulation-based structural credit risk framework to assess the financial implications of carbon pricing ambiguity. Incorporating Knightian uncertainty through a Lévy jump-diffusion process and a k-ignorance parameter, the model captures how policy ambiguity influences firms' default probabilities, equity valuations and bond spreads. The findings indicate that greater ambiguity amplifies financial risk, elevates default probabilities and reduces firm value, particularly in carbon-intensive sectors. Investor heterogeneity—between ambiguity-averse and ambiguity-tolerant behaviors—further affects the pricing of transition risk, generating interval valuations for corporate securities. The impact of carbon pricing on stock value is non-linear, exhibiting an initial increase followed by a decline. The findings emphasize that credible and transparent carbon-pricing frameworks are essential to mitigate ambiguity-induced financial instability.

Abstract As the downward pressure on the economy increases, firms are faced with increasing default risk, risk prevention is imminent. Brand equity, as a crucial intangible asset, holds significant importance in driving firm development. Using a dataset of firms in China, this paper examines the relationship between brand equity and firms’ default risk, with a focus on the mechanisms through how brand equity mitigates default risk and the moderating role of product market competition. The results reveal that brand equity significantly reduces firms’ default risk by mitigating operating risk and lowering debt financing costs. Additionally, product market competition amplifies this default risk-reducing effect, highlighting the heightened importance of brand equity in highly competitive environments. This research makes a multidisciplinary contribution to both marketing and financial risk management literature, offering valuable theoretical insights and practical implications.

Retraction NoticeTitle: Risk Pricing in Construction Tenders - How, Who, WhatAuthors: Marcus Towner and David BaccariniJournal: Construction Economics and Building, Volume 7, Issue 2,2007, Pages 12-25DOI: https://doi.org/10.5130/AJCEB.v7i2.2987 Reason for Retraction:This article has been retracted at the request of the Editor-in-Chief and the publisher. Following publication in 2007/2008, concerns were raised recently regarding the duplication of the above paper. There's a major error in the 2007 version of the above-mentioned paper in that it was cut short soon after the 'Risks Considered' subheading of the results section, and the text from an entire article from the previous issue was copied in its place. Even though the correct version was published in Vol. 8 No. 1 (2008), the error wasn’t declared in the 2008 version article or in the editorial, and there's no other indication to a reader that the 2007 version is flawed. An investigation confirmed that portions of the article published in 2008 contained substantial overlap with previously published works by the same authors in 2007 without retracting the first paper published in 2007. Unfortunately, the authors could not be contacted to acknowledged these issues and therefore the Editor-in-chief and the publisher have decided to retract the paper. Statement:This retraction serves to maintain the integrity of the scholarly record of the Construction Economics and Building journal and upholds the highest standards of publication ethics. The online version of this article has been marked as “Retracted.” Date of Retraction: 05/11/2025

[2025-11-05: This article is the corrected version of an article that appeared in vol. 7 no. 2 of this journal; see https://doi.org/10.5130/AJCEB.v7i2.10170 for the retraction notice. A minor correction has also been made in this document to the spelling of the authors' names.] Construction projects are most commonly procured in Australia by means of a traditional design–tender–build model, whereby design is largely completed then contractors submit tenders in a competitive environment. Construction contractors must consider risks within their tenders. This paper reports the research findings into pricing for risk in competitive tenders by construction contractors. The research is based on structured interviews with 10 contracting personnel; supplemented by 23 responses of construction personnel from an online survey. Two common methods to price for risk are a trade-by-trade basis or an overall percentage or lump sum addition to the base estimate. Experience and intuition plays a significant role in pricing for risk in tenders and the number and type of people involved varies with project size, with greater involvement as project size increases. The most significant risks priced in tenders were: availability of resources; design or documentation errors; incomplete design; buildability issues; and inclement weather. The most significant project factors considered by contractors when pricing for risk in tenders are: value of liquidated damages; type of contract/procurement; completeness of documentation; project complexity; and current workload. These risks and project factors are primarily those over which the contractor has limited or no control.

This article has been retracted. Retraction notice: https://doi.org/10.5130/AJCEB.v7i2.10170 Original abstract: Construction projects are most commonly procured in Australia by means of a traditional design-tender-build model, whereby design is largely completed then contractors submit tenders in a competitive environment. Construction contractors must consider risks within their tenders. This paper reports the research findings into pricing for risk in competitive tenders by construction contractors. The research is based on structured interviews with 10 contracting personnel; supplemented by 23 responses of construction personnel from an online survey. Two common methods to price for risk are a trade-by-trade basis or an overall percentage or lump sum addition to the base estimate. Experience and intuition plays a significant role in pricing for risk in tenders and the number and type of people involved varies with project size, with greater involvement as project size increases. The most significant risks priced in tenders were: availability of resources; design or documentation errors; incomplete design; buildability issues; and inclement weather. The most significant project factors considered by contractors when pricing for risk in tenders are: value of liquidated damages; type of contract/procurement; completeness of documentation; project complexity; and. current workload. These risks and project factors are primarily those over which the contractor has limited or no control.

This article examines Beijing's real estate market risk controls, focusing on challenges in megacities. Findings show Beijing's real estate finance is unique: policy guidance is strong, and there are big differences between core and non-core areas in resources and housing value. It is also closely linked to the financial system, so price changes can greatly affect financial stability. Currently, multiple risks exist together, such as price bubbles in core areas, developers' cash flow problems, and chain-reaction risks. The market is very sensitive to policy changes, making risk prevention harder. Short-term downward pressure comes from policies, unbalanced supply and demand, and weak buyer confidence. Although support policies have been introduced, their effect is delayed, and the market decline remains clear. The article suggests creating a flexible prevention system, shifting from building new homes to improving existing ones, and balancing market stability with public needs. Specific ideas include different rules for core areas (to stabilize prices and prevent speculation) and non-core areas (to improve public facilities). It also recommends a multi-department evaluation system to adjust policies each quarter based on sales data. An intelligent monitoring platform using various data sources could spot abnormal changes, improve information transparency, guide better decisions, and support healthy market development.

The inefficiency of financial markets’ regulatory frameworks has been identified as one of the threats to the stability and prosperity of emerging markets worldwide (Sheng, 2010). This study evaluates the effectiveness of the current legal enforcement framework in the Saudi financial market and its role in managing financial risks and protecting investors. As the Saudi capital market employs a mixed system of public and private enforcement, this research examined enforcement actions by the Capital Market Authority (CMA) and related civil litigation between 2020 and 2024. A multimethod approach was used, combining qualitative analysis of the literature with quantitative data on civil and enforcement actions published by the CMA. The findings indicate that, although private enforcement largely supplements the more prevalent public mechanisms, it remains essential to the strong legal safeguards provided by the Saudi Capital Market Law. The study identifies several implementation challenges that may limit the effectiveness of private enforcement in promoting market efficiency and offers Saudi lawmakers useful insights into regulatory measures that can significantly manage financial risks and combat capital market breaches. Such efforts will enhance the complementary relationship between public and private enforcement in Saudi Arabia.

Summary This study examines the market timing abilities of US mutual fund managers using an expectile‐based predictive regression model with ARMA‐GARCH covariates, effectively capturing both lower and upper tail market risks. To evaluate timing skills across different expectile levels, we introduce a nonparametric profile empirical likelihood (PEL) test, whose test statistic follows a chi‐square distribution under the null hypothesis. Simulation results confirm the accuracy of the PEL test in terms of empirical size and power across various settings. An empirical analysis utilizing CRSP data reveals significant variation in market timing abilities across expectile levels, with stronger skills observed at the tail level compared with the mean level (). Furthermore, the interplay between market timing and stock‐picking skills appears to be context‐dependent. These findings highlight the importance of assessing timing abilities across multiple expectile levels to capture diverse market dynamics.

Banks deal in public money as t hey lend and invest funds they generate in the form of deposits. This exposes them to solvency risk. After the major banking crisis in U.S. which took place between 2007-2010, the banks of US and Europe led by the Bank for International Settlement (BIS), came up with a comprehensive set of norms for banks known as Basel Accord, which was assumed to address the solvency risk of banks across the globe. Basel norms are applicable in India with minor modifications made by the Reserve Bank of India. This paper throws light on the calculation of capital Requirement to Asset Ratio (CRAR) of on balance items of the banks under pillar I of the Basel Norms. Calculation of Capital Requirement to Assets Ratio calculation typically includes the calculation of risk weighted assets for credit risk, market risk and operational risk. In India, 9% of the total risk weighted assets should be in the form of capital to be maintained by the banks to avoid solvency risk.