Search results

The financial system has witnessed the substantial growth of financial technology (fintech) firms. One of the strategies that banks have adopted to cope with this emergence is to cooperate with fintech firms. This study empirically investigated whether cooperation between banks and fintech companies would improve banks’ risk-adjusted returns.

We developed a novel index of bank–fintech cooperation across various fintech sectors. A system generalized method of moments (GMM) was used to examine this relationship using a sample of Vietnamese banks from 2007 to 2019.

The findings show that the diversity of bank–fintech cooperation across seven sectors tends to enhance banks’ risk-adjusted returns. The results also highlight that this relationship may depend on the types of fintech sectors and bank ownership. More specifically, the positive association between this cooperation and banks’ risk-adjusted returns only holds in the comparison sector of fintech, whereas there is a negative relationship between them in the payments and mobile wallets sector. Furthermore, state-owned commercial banks that engage in more bank–fintech cooperation tend to generate greater earnings. If we look at listed banks, the positive effect of bank–fintech partnerships on risk-adjusted returns still holds. A similar result was also found in the case of large banks.

Our empirical evidence provides motivations for incumbent banks to implement appropriate strategies toward diversity in bank–fintech partnerships when fintech firms have engaged in various financial segments.

This study adds more evidence to the existing literature on the relationship between bank–fintech cooperation and bank performance.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

This paper aims to assess the factors that may explain the International Public Sector Accounting Standards (IPSAS) convergence, considering Hofstede’s cultural dimensions as the theoretical reference for the cultural approach proposed. Additional factors include countries’ contextual and macroeconomic characteristics.

Logistic and probit regression models were used to identify the factors that may explain the IPSAS (fully or adapted) use by countries, including 166 countries in this assessment (59 for those whose cultural dimensions are available).

The findings consistently indicate collectivism and indebtedness levels as explanatory factors, providing insights into cultural dimensions along with macroeconomic characteristics as a relevant factor of countries’ convergence to IPSAS.

There are different levels of IPSAS convergence by countries that were not considered. This aspect may hide different countries’ characteristics that may explain those options, which could not be distinguished in this paper.

As a result of this paper, the International Public Sector Accounting Standards Board may gain insights that can be applied within the IPSAS due process to overcome the main challenges when collaborating with national authorities to achieve a high level of convergence. This analysis may include how to accommodate countries’ cultural differences as well as their contextual and macroeconomic characteristics.

There is a trend of moving toward accrual-based accounting standards by countries. Because the public sector embraces a new culture following the IPSAS path, it is relevant to assess if there are cultural factors, besides contextual and macroeconomic characteristics, that may explain the countries’ convergence to those standards.

To the best of the authors’ knowledge, this is the first cross-country analysis on the likely influence of cultural dimensions on IPSAS convergence as far as the authors’ knowledge.

This study aims to investigate whether the extent of corporate disclosure, proxied by COVID-19-related disclosure, affects the dividend policy of listed firms.

The study uses a multinomial logistic regression model to examine the relation between corporate disclosure and the dividend policy of the 100 largest market-cap firms in Vietnam in 2021. The COVID-19 pandemic, with its unique impact on business operations, serves as the backdrop for this analysis.

The findings indicate that firms with more extensive COVID-19-related disclosure are more inclined to distribute dividends in the form of stocks or cash instead of omitting them.

This research contributes to the understanding of how corporate disclosure practices influence a firm’s financial decisions, particularly in the context of the COVID-19 pandemic. The findings hold implications for corporate financial decision-making during times of macroeconomic shock.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

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.