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This paper contributes to the literature on exchange rate exposure by assessing the extent to which exchange rate risk is priced in both African emerging and developed equity markets. It examines whether this risk leads to a premium or discount in market returns. The study uses the United States and South Africa as representatives for developed and emerging economies, respectively.

The paper employs two-factor and three-factor conditional CAPM approaches with a two-stage estimation process. In the first stage, time-varying risk exposures are derived using the ICAPM model estimated through rolling regression. In the second stage, the impact of these risk exposures, particularly exchange rate risk exposure, is assessed on stock market returns using Generalized Linear Model (GLM) regression.

Unlike previous studies that suggest exchange rate risk is not necessarily priced in the equity market due to hedging, this paper finds that exchange rate risk is indeed priced in both African and developed equity markets, albeit to different extents. The African equity market demands a higher premium compared to the developed equity market.

The findings of this paper have significant implications for policymakers, asset managers, and investors. They provide insights for making more informed decisions, implementing effective risk management strategies, and fostering a more stable and appealing investment environment.

To the best of our knowledge, this is the first study to evaluate the degree of exchange rate exposure in environments characterized by high currency volatility versus those with low volatility, all within the context of the conditional ICAPM model.

This study aims to gain a deep understanding of the process of international commercial terms (Incoterms) selection by small and medium enterprises (SMEs) in Bangladesh and identify the key factors that influence their choices. The study also intends to explore the discrepancy between the literature and the actual industry practice so that policymakers and traders can make more informed decisions in this respect.

The study adopts a mixed methodology. Initially, 20 factors under five principle components were identified by reviewing the literature. The semi-structured one-to-one interview method has been used to gather expert opinions on the factors and to identify the discrepancy between literature and actual industry practices. Finally, eight factors have been identified and surveyed using the best-worst method (BWM) to determine their relative significance or weights in Incoterm selection.

The study has found that government policies, the influence of banks and currency fluctuations are the top three influential factors in selecting Incoterms by SMEs in Bangladesh. As a result, the most commonly used Incoterms are free on board (FOB) for the exporters and cost and freight (CFR) for the importers. However, discrepancies have been identified between the selected and the actual application of the Incoterms.

To the best of the authors’ knowledge, this is the first study to identify and analyse the factors that influence the selection of Incoterms by the SMEs in the context of a developing nation. The study has identified the factors from both importers’ and exporters’ perspectives that have not been done by any previous studies. Moreover, this study explores the discrepancies between the agreed Incoterm and the actual industry practices which is a unique contribution to the literature.

The Afghan construction industry faces resource shortages and heavily relies on foreign aid to fund public projects on the path to recovery and reconstruction. While the resource constraints demand cost-efficient delivery of construction projects, many Afghan public projects experience delays and cost overruns. This study aims to evaluate various attributes and factors influencing cost performance in public construction projects in Afghanistan.

The literature review and Delphi method identified 30 cost performance attributes relevant to the context of Afghanistan. Next, a questionnaire survey was conducted with construction management professionals working in the public sector in the Afghan construction industry to evaluate these attributes.

This study found that the lack of resources, poor project management skills and corruption in procurement are the leading causes behind cost overruns in Afghan public projects. This study also identified five latent factors influencing cost performance in public projects in Afghanistan: competency of the project team, socioeconomic and political support, governance and public procurement, planning and risk management and project characteristics.

The exploratory factor analysis did not reveal the relative significance of different cost performance success factors. Moreover, the ranking of cost performance attributes is based on the responses from the public sector construction professionals only.

The construction industry in Afghanistan significantly contributes to the country’s social and economic growth and employment. This study’s findings will help researchers, project sponsors, government departments and industry practitioners interested in improving the cost performance in Afghan public projects.

Given the scarcity of research in war-affected and conflict-sensitive regions, this study fills a research gap on project cost performance by providing insights into the cost performance success factors in public projects in Afghanistan.

Real estate projects are capital-intensive and deeply intertwined with economic factors, making them subject to various influences besides local housing needs. This paper aims to comprehensively understand the dynamics of the Egyptian real estate market, examining real estate cycles, driving factors and their correlation and scale of impact.

The study conducts a literature review to explore real estate cycles and their driving factors, along with the relationship between real estate and macroeconomic cycles. It then delves into the dynamics of the Egyptian real estate market, followed by a time series analysis that incorporates five key indicators: economic indicator, demand indicator, supply indicator, capital flow indicator and cost indicator over a 12-year interval (2012–2023), to examine short-term cycle factors, followed by correlation and multi-linear regression analysis to elucidate interrelations among these factors.

Through measuring and comparing the prementioned indicators with different economic and social events, the study paints a comprehensive picture of the macroeconomic environment and the real estate cycle in Egypt. Where demand has been found to be more sensitive and directly affected by macroeconomic factors than the supply. With the economic factor as the factor with the highest impact, especially in times of economic fluctuations, the impact has been immediate and short-term. These findings support the idea that the demand in Egypt is speculative, laying a threat of longer recession periods in the long term and having greater and more direct impact.

This paper contributes to the understanding of the Egyptian real estate market by integrating insights from real estate cycles, macroeconomics and specific market dynamics. The application of time series, correlation and multi-linear regression analysis provides a nuanced understanding of the interplay between several factors shaping the real estate cycle. Ultimately, the findings offer valuable insights for decision-makers involved in urban development planning, facilitating more informed and precise decision-making processes.

For a sustainable and eco-friendly supply chain, the objective is to reduce the harmful effects on the environment caused by a variety of factors, such as supply chain revenue and profit concerns, water and energy use and waste production. The primary barrier to more sustainable supply chains is cost, and smaller enterprises in particular find it difficult to pay for the upfront expenses. The prices are utmost important in freight forwarding services. The chapter aims to identify the different variables that affect the spot freight rate, considering a small number of Indian logistics firms, and to determine the spot freight rate factors that affects freight rates and supply chain management's sustainability. Using a structured questionnaire, the information was gathered from 308 logistics service companies. Descriptive statistics was used and factor analysis using principal component analysis (PCA) are the tools and methodologies employed for the analysis of the data gathered. The software tool utilized for the data analysis is SPSS (Statistical Package for the Social Sciences) 17.0. SPSS helps to obtain clear and precise results. From the analysis, it was deduced that there are five factors of spot freight rate which was done by using factor analysis using PCA, which affects the volatility explaining a total of 67.75% variation in the data set. The study shows that these identified factors impact the freight rate and also the sustainability in management of supply chain practices in India.

This paper examines the impact of Central Bank Digital Currencies (CBDCs), regulated stablecoins and tokenized traditional assets on the cryptocurrency market, following the guidelines set by the Basel Committee. This study aims to analyze the implications for secure storage, cross-border transfers and necessary investments.

The paper uses a policy analysis approach to assess the potential effects of the Basel Committee’s regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It explores their impact on the cryptoasset market, strategies of central and commercial banks, payment systems and risk management.

The adoption of CBDCs, regulated stablecoins and tokenized traditional assets is expected to grow rapidly in the coming years. It raises concerns about secure storage, cross-border transfers and required investments. Central banks are likely to introduce CBDCs and authorize stablecoin issuance, aiming for efficient monetary policies and risk management. Basel III regulations may lead to asset tokenization by banks, reducing asset size and increasing fee-based income.

This paper provides insights into the potential impact of the Basel Committee's regulations on CBDCs, regulated stablecoins and tokenized traditional assets. It contributes to the understanding of the evolving cryptoasset market and the strategies of central and commercial banks in adopting these technologies. The findings offer valuable information for policymakers, regulators and market participants in navigating the changing landscape of digital assets.

Supply chain management has become critical in today’s globalized environment, with growingly intense competition on the international level. The particular characteristics of modern trade have led companies to globalize and devise increasingly sophisticated supply chains to meet customer demand worldwide. Motivated by the need to address these challenges, we have developed a new model for a global supply chain that incorporates uncertainties in exchange rates, demand fluctuations, and the quantity of produce.

The objective of the proposed model is to maximize supply chain profitability. Our model optimizes several critical decisions in the proposed global supply chain, including the location of domestic and foreign distribution centers, allocating the centers to customers, transportation mode selection, storage temperature, optimal farm purchase quantities, product flows across the network, and the shelf-life of products. Scenario-based stochastic programming approach is employed to account for the inherent uncertainties within the model. A pistachio supply chain is examined as a case study in this article, and the efficiency of the proposed model is demonstrated through computational results.

The model was solved using the CPLEX solver in GAMS and the results, the Sirjan DDC and Turkey FDC have been selected. In general, 40% of demand for customers from FDC (turkey) and 60% of demand from DDC (sirjan) is provided. Changes in the demand of foreign customers make the net profit more effective than changes in the demand for domestic customers. The decrease in exchange rate decreases the network profit with a higher slope and the increase in exchange rate will increase network profit with a relatively stable slope.

While research on GSCs for perishable products has been ongoing for several years, the importance of the subject necessitates continued investigation in this area. This paper aimed to address this gap by presenting an optimization model for designing GSCs for perishable products under uncertainty and with various transportation modes. The proposed model was designed with the aim of improving supply chain performance and real-world applicability.

This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors.

The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023.

The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value.

The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.

The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).

Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.

The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.

This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.