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Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

The purpose of this study is to investigate the influence of IFRS convergence on annual report readability in an emerging market context, with an emphasis on the contents of management discussion and analysis (MD&A), notes to the accounts (Notes) and the whole annual report.

The study performs firm-fixed effect regression on a sample of 143 Indian listed companies over a period spanning from 2012 to 2021 to examine the influence of IFRS convergence on readability. This assessment primarily focuses on broader spectrums of readability dimensions, namely annual report length and complexity, wherein complexity is measured using the Gunning Fog, Flesch Reading ease and Flesch-Kincaid grade index.

As Indian firms shift to IFRS reporting, the findings suggest that annual reports have become significantly lengthier and more complex, causing deterioration in readability. The Notes section, in particular, exhibits the most significant increase in length and complexity, followed by the entire annual report and MD&A section. Furthermore, the findings also indicate that the complexity of the Notes section is instrumental in the observed complexity growth of the whole annual report in the post-IFRS period.

The current study employs readability indices rather than directly taking into consideration the opinions of actual users of annual reports to determine readability. As a result, the study does not provide direct evidence on how information in annual reports affects users' readability.

The findings provide insightful information to managers and policymakers about the difficulties stakeholders may encounter while reading IFRS-based annual reports, which ultimately impact their investment decisions. Thus, there is an important managerial implication from this, depending upon the severity of complexity corporations participate in while complying with IFRS in the post-IFRS period.

Analyzing the influence of exogenous information shock, such as IFRS convergence, on readability is critical, particularly for emerging markets like India, where a lack of financial literacy and weaker enforcement already have detrimental effects on the capital market. In light of this, the current study provides a comprehensive examination of the impact of IFRS convergence on annual report readability and contributes to the growing IFRS literature in the less explored emerging market context.

This paper numerically investigates the effect of cavity radiation on the thermal response of hollow aluminium tubes and facade systems subjected to fire.

Finite element simulations were performed using ABAQUS 6.14. The accuracy of the numerical model was established through experimental and numerical results available in the literature. The proposed numerical model was utilised to study the effect of cavity radiation on the thermal response of aluminium hollow tubes and facade system. Different scenarios were considered to assess the applicability of the commonly used lumped capacitance heat transfer model.

The effects of cavity radiation were found to be significant for non-uniform fire exposure conditions. The maximum temperature of a hollow aluminium tube with 1-sided fire exposure was found to be 86% greater when cavity radiation was considered. Further, the time to attain critical temperature under non-uniform fire exposure, as calculated from the conventional lumped heat capacity heat transfer model, was non-conservative when compared to that predicted by the proposed simulation approach considering cavity radiation. A metal temperature of 550 °C was attained about 18 min earlier than what was calculated by the lumped heat capacitance model.

The present study will serve as a basis for the study of the effects of cavity radiation on the thermo-mechanical response of aluminium hollow tubes and facade systems. Such thermo-mechanical analyses will enable the study of the effects of cavity radiation on the failure mechanisms of facade systems.

Cavity radiation was found to significantly affect the thermal response of hollow aluminium tubes and façade systems. In design processes, it is essential to consider the potential consequences of non-uniform heating situations, as they can have a significant impact on the temperature of structures. It was also shown that the use of lumped heat capacity heat transfer model in cases of non-uniform fire exposure is unsuitable for the thermal analysis of such systems.

This is the first detailed investigation of the effects of cavity radiation on the thermal response of aluminium tubes and façade systems for different fire exposure conditions.

This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.

The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.

The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.

This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

In this study the authors examine the nature and contents of key audit matters (KAMs), and the consequences of KAMs reporting on audit quality in the context of a developing country, Bangladesh. The authors’ proxies of audit qualities are discretionary accruals, small positive earnings surprise, audit report lag, earnings management via below the line items and audit fees.

The authors use content analysis of the KAMs for the period 2018–2021 to understand the nature and extent of KAMs reported by auditors in Bangladesh. The authors then use multivariate regression analysis to examine the effect of the number and content characteristics of KAMs on audit quality by using multivariate regression analysis.

Auditors in Bangladesh disclose a higher number of KAMs compared to other countries, disclose short descriptions of KAMs and industry generic KAMs. The authors document significant cross-sectional variations in the number and content characteristics of KAMs reported by auditors in Bangladesh. The authors’ pre-post analysis suggest that audit quality has improved after the adoption of KAMs. Cross-sectional analysis suggests that KAMs number and content characteristics are related to audit quality.

The authors’ findings imply that the KAMs reporting has the potential to play significant monitoring role in reducing the opportunistic behavior of managers. Hence, KAMs reporting can play a significant role in reducing the agency problem. For regulators, shareholders and corporate managers, the authors’ findings imply that if the audit quality is to be increased, the audit effort should be supported by an appropriate amount of audit fee.

The content characteristics of KAMs significantly influence managerial reporting behavior and affect the level of audit efforts.

Unlike developed countries (Gutierrez et al., 2018; Lennox et al. 2022), this study supports that KAMs reporting improves audit quality and control opportunistic behavior of managers in developing countries. The authors show that even though the KAMs disclosure quality is poor, it has the potential to improve financial reporting quality.

Selecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.

This study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.

The proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.

This study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.