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While the average cost of debt capital can be calculated from historical financial statement data by dividing the interest paid each year by the total level of debt, this average cost of debt provides little information regarding the true cost of acquiring additional debt capital, and hence, its use is potentially problematic in financial decision-making. This study focuses on the linkage between observed changes in the average interest rates calculated from financial statements (balance sheet and income statement) and the marginal cost of borrowing or the cost of acquiring new debt. Motivated by the capital asset pricing model (CAPM), the marginal cost of capital is modeled as a function of a risk-free interest rate (the return on Moody’s Aaa bonds), returns on the S\&P stock index capturing overall market returns and a portfolio of agricultural stocks to represent farm sector-specific risks.

Using a unique dataset constructed from United States Department of Agriculture (USDA) state-level Financial Performance of the Farm Sector data for the years 1960 through 2003 and state-level Agricultural Resource Management Survey (ARMS) data for the years 2003–2014 and Bayesian methods, we model the observed interest rate as an autoregressive function controlling for changes in debt and key rates of return in the general economy.

The results indicate that the marginal interest rate is a function of the Aaa corporate bond rate and the stock market. We also find evidence of a negative relationship between returns to a portfolio of agricultural stocks and the marginal interest rate. Overall, the findings suggest that the imputed interest rate frequently misrepresents the marginal cost of debt capital.

Most farm financial datasets allow for the analysis of the farm firm’s average interest rate. However, farmers make decisions based on the marginal cost of credit – the interest rate on a newly issued note. This study estimates this marginal interest rate for the 15 states for which the ARMS data are representative for the years 1960 through 2014 and compares the estimated marginal interest rate with the imputed average interest rate.

The purpose of this study is to determine whether South African real estate investment trusts (REITs) with significant foreign real estate holdings produce better market performance metrics when compared to REITs with larger domestic holdings. The paper also provides a comprehensive overview of the market performance of South African REITs in the decade following the inception of the REIT regime in 2013.

The authors employ the capital asset pricing model (CAPM), using different estimation techniques to determine the stability of the estimated parameters over time. In addition to the CAPM framework, several basic and advanced portfolio performance metrics are computed to assess the performance of the various REIT portfolios.

The results show that REITs with significant offshore allocations produce superior market returns than their counterparts. Across most of the risk measures analysed, the foreign-biased REIT portfolios were found to be riskier. On the whole, foreign-biased REITs performed better on a risk-adjusted basis. The results were consistent across the different sample periods and the performance metrics analysed.

The results suggest that the decision to diversify internationally has implications for the pricing of REITs on stock markets. The differences in the performance metrics for the foreign- and home-biased REIT portfolios also imply an opportunity for investors to further diversify REIT portfolios by holding a mix of home-biased and foreign-biased REITs.

This paper is one of the few to consider the implications of international diversification on stock market performance rather than on more fundamental measures of REIT performance such as the net present value. This study also provides an emerging market (African) perspective to the literature.

The purpose of this research is to study the effect of high temperature on concrete columns which are considered the most important part of the concrete structure. Glass fibres were used to study the effect of heat on them as well as on the properties of reinforcing steel and concrete.

High tensile stress may develop at the tension zone of the column section when the column is exposed to an axial compressive load with a relatively high eccentricity. Since fibre bars have a higher tensile strength than steel bars, they can be used in the tension zone of hybrid reinforced concrete columns to resist tensile stress, while steel bars can be used in the compression zone to resist compressive stress. However, as documented in prior research studies and advised by standards and codes, the mechanical qualities of concrete, steel and fibre bars are considerably damaged when hybrid columns are exposed to high temperatures.

When the fire temperature rises, the ultimate load value of the reinforced concrete column decreases. Also, steel bar reinforcing is more efficient than glass fibre bars in resisting high temperatures. The rate decrease in the strength of reinforced concrete columns to applied load on it decreases with the rise of the temperature to which the specimen was exposed during the burning period.

The experimental and numerical work includes a study of the effect of a fire furnace on the behaviour of hybrid R.C. columns. Three types of reinforcement were used steel bars only, G.F.R.P bars only and hybrid (steel and G.F.R.P) bars. These columns specimens were cast and divided into three groups according to the details of reinforcement, the effect of fire temperature and according to the eccentricity ratio. Two types of hybrid are used in this work. Fourteen R.C. columns were casted and divided into, 4 specimens not burn and 10 specimens burn at temperatures 300, 500 and 700.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

Climate change negatively affects agriculture and food security, and jeopardizes Myanmar's agriculture, which is vital to ensure food security, rural livelihoods, and the economy. This study explores the asymmetric impacts of climate change on Myanmar's agricultural sector.

We utilize the nonlinear autoregressive distributed lag (NARDL) approach for the years 1991–2020, the Wald test to validate the asymmetric relationship between climate change and agriculture, and the FMOLS and DOLS approaches to confirm the validity of the outcomes.

Our findings reveal that temperature has a positive impact on Myanmar's agriculture, whereas rainfall and CO2 have negative effects over the long and short terms. Evidently, decreasing temperatures more favorably impact agriculture than increasing temperatures, while increasing rainfall more negatively impacts agriculture than decreasing rainfall. Increasing carbon emissions have a more detrimental effect on agriculture than decreasing them.

We gathered data over periods longer than 30 years to provide more robust findings. However, owing to data limitations, such as missing values or unavailability, the study period spans from 1991 to 2020.

This study contributes to the existing literature on the asymmetric effects of climatic and non-climatic factors on agriculture. It is the first study in Myanmar to use the NARDL approach to measuring the effects of climate change on both the agricultural gross production index and value, providing robust findings.

The study examines the impact of socio-economic governance on economic growth in Nigeria. It measures socio-economic governance from the perspective of fiscal policy, using indicators such as investment in education, research and development (R&D) and health.

This study employs the Autoregressive Distributive Lag (ARDL) Bound Testing method to achieve its objective.

The study finds that socio-economic policies aimed at increasing investment in education are crucial for Nigeria’s long-term economic growth. Additionally, investment in R&D positively impacts economic growth. However, the study reveals that investment in health negatively affects economic growth in Nigeria in the long run. This suggests that if a country overinvests in health, it may divert resources from other vital sectors such as education, infrastructure and R&D, which can hinder overall economic growth. The short-run parameter is, however, not statistically significant in this study.

The study’s originality lies in its exploration of the relationship between socio-economic governance and economic growth in Nigeria, specifically from a fiscal policy perspective. It highlights the importance of investing in education and R&D for long-term economic growth. Additionally, the finding that overinvestment in health may have a negative impact on long-term economic growth provides valuable insight for policymakers in Nigeria and other developing countries. Overall, this study’s findings can be beneficial for policymakers and researchers interested in the intersection between socio-economic governance and economic growth in developing countries.

Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm.

In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods.

The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06.

The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.

The extensive brand associations research lacks organisation when it comes to the used information cues. This paper aims to systematically map and categorise the brand knowledge associations’ components and develop a typology applicable to any brand.

Using the restaurant and hotel industries in four different European cultural clusters as contexts, this work uses well-established systematic qualitative analysis approaches to categorise, code and model pictorial content in two studies. A four-stage sampling process identified Instagram brand-posted signals (photos), 243 from 26 restaurants in Madrid, Paris and Rome for study one and 390 from 29 hotels in Moscow, Berlin and Stockholm for study two. Adhering to relevant guidelines, the manual coding procedures progressed from 246 for restaurants and 231 for hotels initially generated free information coding inductive codes to a theory-informed categorisation. Quantitative analysis complemented the qualitative analysis, revealing the information cues relative utilisation.

For both studies, the analysis produced a typology consisting of two high-level and five lower-level brand knowledge association categories, namely: (a) brand characteristics consisting of the brand as a symbol, the brand as a product and the brand as a person, and (b) brand imagery consisting of user imagery and experience imagery. The five lower-level categories comprise of sub-categories and dimensions, providing a more comprehensive understanding of the brand associations conceptual structure relevant to brands operating in any industry.

Researchers can use this typology to holistically encapsulate brand associations or design projects aiming to deepen brand knowledge association aspects/dimensions understanding.

Managers can use this typology to portray brands. Some of the identified lower-level categories and/or sub-categories and dimensions are likely to need customisation to fit specific contexts.

The suggested categorisation offers a solid, comprehensive framework for effectively categorising and coding brand knowledge associations and proposes a new theory in the form of a typology.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The purpose of this paper is to analyze the ongoing revival of industrial and innovation policies across developed and developing economies.

The paper compare the scale and scope of recent industrial and innovation policy initiatives across developed and developing economies. Also, it analyzes recent data regarding R&D investments and other innovation indicators.

There are enormous disparities across economies in their capacity to implement industrial policies, particularly those to support science, technology and innovation. Most developed economies, and a few developing economies, are implementing bold, ambitious and medium-term innovation policies towards bolstering R&D investments, supporting advanced manufacturing and green energies and strengthening technological capabilities. Amid lack of fiscal policy space and vulnerable debt sustainability positions, institutional deficiencies and weak innovation ecosystems, developing economies – particularly in Africa and Latin America – face enormous challenges to implement strategic industrial and innovation policies.

Under the current economic, financing and institutional conditions, together with subdued global trade and ongoing geopolitical fragmentation, the technological divide and innovation asymmetries across economies will likely widen even further, paving the ground for a “development divergence” in the coming decade.

The paper analyzes the implications of the current industrial and innovation policy trends across developed and developing countries. Under the current economic, financing and institutional conditions, together with subdued global trade and ongoing geopolitical fragmentation, the technological divide and innovation asymmetries across economies will likely widen even further, paving the ground for a “development divergence” in the coming decade.