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This study empirically examines the impact of some domestic as well as global factors such as trade openness (TO), money supply (MS), exchange rate, global oil prices (GOPs) and interest rate (IR) on inflation.

This study deploys a quantitative method considering 30 years of data (1991–2020) from four South Asian countries, namely, Sri Lanka, Pakistan, Bangladesh and India. To determine the potential impact of different factors on inflation, this study applies the panel analysis of the system generalized method of moments (SGMM).

This study empirically finds that TO, MS, exchange rate and GOPs have a positive impact on inflation, while IR and the structural adjustment program (SAP) have a negative impact on inflation. Out of the various determinants considered in this study, TO, exchange rate and the SAP are insignificant, while the rest of the variables are significant and consistent with previous studies.

This study informs policymakers about maintaining price stability and fostering economic growth in South Asian nations. It breaks new ground as the first empirical examination of the International Monetary Fund (IMF)’s SAP impact on inflation in the region.

This study tries to find out whether the SAP of the IMF is responsible for inflation in South Asian countries. It gives renewed attention to the causality of inflation from the perspective of countries receiving loans from donors, especially the IMF.

The purpose of this study paper is to highlight certain related barriers and issues of housing affordability and examine the factors that influence housing affordability in Malaysia.

This study used panel data including several variables, namely, household expense, population, home financing, interest rate, inflation rate (IF) and rental rate (RR). The regression models of panel data, namely, the ordinary least square model, the fixed effects model and the random effects model, were evaluated for their suitability.

The findings revealed that RR and IF have a positive and significant impact towards housing affordability. The results provide strong evidence that RR as alternative in determining the home affordability as it helped in reducing the cost and the financing duration period of houses while at the same time increasing the level of capability of homeownership. Meanwhile, the level of IF has positive and significant impact towards housing affordability because it will cause a drop or increase in the purchasing power of households, as well as a decline or increase in the capability to own a house.

The most significant aspects to consider when analysing housing affordability in Malaysia are demand and supply. However, this study focuses on only five variables and only covers Malaysia. As a result, future researchers should analyse the study’s location, such as by region or district, and include additional variables from both the demand and supply sides. Homeownership of affordability requires a broader and more realistic definition in the current context of a more disruptive environment where technology such as fintech, blockchain and the internet of things acts as enablers for not only promoting homeownership but also ensuring homeownership sustainability. As a result, democratising Islamic home financing appears to be a viable option that requires rethinking, and further research is recommended.

The study proposes an end-to-end solution to promote homeownership levels by considering the level of RR as significant variables among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies in influencing affordability in Malaysia.

This paper discusses the indicators of housing affordability index over the 21-year period of 2000–2020, covering all states in Malaysia. The comparison of affordability level can be seen through all states and by regions. Besides that, the findings revealed that RR and IF have a positive and significant impact towards housing affordability. RR is considered an essential variable in promoting homeownership in Malaysia and warrants further investigation towards policy implication. This paper also provides contribution on data on RR by states in Malaysia that can be used by policymakers to some extent.

This paper aims to unravel the impact of post-pandemic COVID-19 on foreign direct investment (FDI) and its determinants in the South Asian Association for Regional Cooperation (SAARC) Countries.

The study utilized four macroeconomic variables includes growth domestic product growth rate (GDPG), inflation rate (IR), exchange rate (ER), and unemployment rate (UR) to assess their impact on post-pandemic FDI, along with two variables control of corruption (CC) and political stability (PS) to measure the influence of good governance. Random effects, fixed effects, cluster random effects, cluster fixed effects and generalized method of moments (GMM) models were applied to a balanced panel dataset comprising eight SAARC countries over the period 2010–2021. To identify the random trend component in each variable, three renowned unit root tests (Levin, Lin and Chu LLC, Im-Pesaran-Shin IPS and Augmented Dickey-Fuller ADF) were used, and co-integration associations between variables were verified through the Pedroni and Kao approaches. Data analysis was performed using STATA 17 software.

The major findings revealed that the variables have an order of integration at the first difference I (1). Nonetheless, this situation suggests the possibility of a long-term link between the series. And the main results of the findings show that the coefficients of GDPG, CC and PS are positive and significant in the long run, showing that these variables boosted FDI inflows in the SAARC region as they are significantly positively linked to FDI inflows. Similarly, the coefficients of UR, IR, ER and COVID-19 are negative and significant.

By identifying the specific impacts of the post-pandemic FDI and its determinants, governments and policymakers can formulate targeted policies and measures to mitigate the adverse effects and enhance investment attractiveness. Additionally, investors can gain a deeper understanding of the risk factors and adapt their strategies accordingly, ensuring resilience and sustainable growth. Finally, this paper adds value to the literature on the post-pandemic impact on FDI inflows in the SAARC region.

This paper is the first attempt to trace the impact of COVID-19 on Foreign Direct Investment and its determinants in the SAARC Countries. Most of the previous studies were analytical in nature and, if empirical, excluded some countries due to the unviability of the data set. This study includes all the SAARC member countries, and all variables' data are completely available. There is still a lack of empirical studies related to the SAARC region; this study attempts to fill the gap.

Driven by the anticipated global stagflation, this straightforward yet novel study examines the cost of inflation as a macroeconomic factor by investigating its influence on stock market growth. Thus, this paper aims to examine the impact of inflation on the probability of initial public offering (IPO) withdrawal decision.

The paper employs a large dataset that covers the period January 1995–December 2019 and comprises 33,536 successful or withdrawn IPOs from 22 nations with various legal and cultural systems. This study applies a probit model utilizing version 15 of Stata statistical software.

This study finds that inflation is substantially and positively correlated with the likelihood of IPO withdrawal. Results of this study show that the IPO withdrawal decision increases up to 90% when the inflation rate climbs by 10%. Multiple robustness tests provide consistent findings.

This study's implications are important for researchers, investment banks, underwriters, issuers, regulators and stock exchanges. When processing IPO proposals, investment banks, underwriters and issuers must consider inflation projections to avoid negative effects, as demonstrated by the findings. In addition, regulators and stock exchanges must be aware of the detrimental impact of inflation on competitiveness in attracting new listings.

To the best of the authors’ knowledge, this study is the first to present convincing evidence of a major relationship between IPO withdrawal decision and inflation.

The current analysis aims to explore the role of cash holdings in the nexus of national governance and capital investment (CIN).

To achieve this aim, the authors sample the nonfinancial enterprises from 5 Brazil, Russia, India, China, South Africa (BRICS) economies and employ system generalized method of moments(GMM) models as an estimation technique.

The empirical analysis infers that national governance has a positive relationship with CIN and a negative relationship with cash holdings. The cash holdings negatively determine CIN. However, the cash holdings show a positive relationship with CIN in the presence of the national governance index (NGI).

The important policy layout of the current analysis is that corporate managers should reduce cash holdings during better governance situations. Alternatively, corporate managers can disentangle the negative impact of bad country governance conditions on CIN by holding more cash.

The study is innovative as it explores mediating impact of cash holdings in the NGI-CIN nexus.

The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.

The purpose of this study is to observe the practice of integrated reporting (IR) and investigate the impact of board characteristics on IR in three South Asian economies: Bangladesh, India and Sri Lanka.

The study uses the content analysis approach to measure the integrated reporting index (IRI) based on a structured checklist. To examine the impact of board characteristics (board size, board independence and gender diversity) on IRI, a multivariate analysis using pooled ordinary least square with panel-corrected standard error (PCSE) model has been conducted.

The content analysis findings show that the disclosure practice of IR is highest in India, followed by Sri Lanka and Bangladesh. The regression result indicates that all the proxies of board characteristics have a positive and significant impact on IRI.

The study’s outcomes may not be generalised for every region due to the differences in institutional contexts.

The findings of this study will assist the policymakers in understanding the importance of effective boards in enhancing the IR practice in their respective countries where the adoption of IR is still a voluntary requirement.

To the best of the authors’ knowledge, this is the first study in the field of existing literature to conduct a comparative analysis of IR practice among three South Asian countries. It shows how an effective board improves IR practice using a broader institutional context by underpinning the agency theory and legitimacy theory.

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.

This paper examines the main drives of encouraging Iranian investors in the Turkish real estate market, focusing on the interface between push factors and pull factors that drive them abroad.

This paper examines the main drives of encouraging Iranian investors in the Turkish real estate market, focusing on the interface between push factors and pull factors that drive them abroad. For this purpose, the trend of housing price growth in Iran and Turkey was compared. The review of the 11-year trend of rates shows that housing prices in both countries have been continuously rising, and these prices have undoubtedly experienced increasing shocks in Iran. For further analysis, 13 main variables leading to the repulsion of investment in Iran's housing market and 15 variables shaping the attractiveness of investment in Turkey were identified in this sector. Thirty experts subsequently ranked the significant variables based on a closed-end questionnaire using quantitative strategic planning matrix. Examining housing investment elasticity in Turkey also shows that “Turkey's economic stability compared to neighboring countries” and “acquiring Turkish citizenship through real estate investment” are among the most important variables. On the other hand, the pressure variables of housing investment in Iran were “decrease in the value of the Iranian currency in recent years,” “currency price fluctuations” and “severe fluctuations and instability in the Iranian housing market.”

Examining housing investment elasticity in Turkey also shows that “Turkey's economic stability compared to neighboring countries” and “acquiring Turkish citizenship through real estate investment” are among the most important variables. On the other hand, the pressure variables of housing investment in Iran were “decrease in the value of the Iranian currency in recent years,” “currency price fluctuations” and “severe fluctuations and instability in the Iranian housing market.”

From a theoretical standpoint, foreign investment is in support of Turkey and harmful to Iran because the Turkish government is bolstering investment attractiveness to bring increased capital inflows into this country. Practically speaking, Turkey has aimed to create a rational framework for investors by strengthening and changing its economic system, as well as amending existing constitutions in this domain. Nevertheless, Iran resists any changes in its economic system and legislation. Therefore, a wide range of attractiveness and repulsion variables has led to the migration of Iranian investors to Turkey. In the present study, such variables are illuminated.

This study aims at proposing and testing a model delineating how and when the quality of a special B2B professional service, investment relations (IR), would drive corporate intangible value.

This study employs a proprietary dataset on voting records of an annual investment relations (IR) awards event and the corresponding company-level archival data for analysis. Regression analysis is used to test hypotheses.

IR service quality not only directly enhances corporate intangible value, but also indirectly boosts it via information transparency. While competitive intensity does not moderate the relationship between IR service quality and corporate intangible value, its moderating effect on the relationship between information transparency and this value is negative.

The findings advance academic understanding of the mechanism and boundary conditions underlying the complex and dynamic relationships among IR service quality, information transparency, corporate intangible value and competitive intensity. Future research endeavors to verify the present findings in other service and/or geographic settings would help establish their external validity.

The findings advise companies to expand the traditional role of IR by taking it as a powerful communication and relationship marketing tool to improve their visibility and attract investors.

The findings suggest that superior IR service would strengthen the company’s social bonding with institutional investors and effectively signal to them its commitment to good corporate governance practices.

Matching a proprietary dataset on IR voting records with the corresponding company-level archival data over a five-year period to investigate the performance implications of IR service quality within the Hong Kong context rectifies methodological limitation and geographic confinement of prior IR research.