Purpose
Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.
Design/methodology/approach
This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.
Findings
This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.
Research limitations/implications
The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.
Practical implications
Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).
Originality/value
This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.
2025, Journal of Asian Business and Economic Studies
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Abstract
Purpose
This study revisits the relationship between environmental, social and governance (ESG) activities and firm performance. More importantly, it tests whether this relationship is moderated by critical yet underexplored factors such as stakeholder engagement, financial constraints, and religiosity.
Design/methodology/approach
A wide range of estimation techniques, including pooled ordinary least squares (OLS), fixed effects, system generalized method of moments (GMM) and propensity score matching-difference-in-differences (PSM-DiD), are employed to investigate such issues in a large sample of firms from 31 countries.
Findings
ESG performance has a positive and significant impact on firm performance. While stakeholder engagement positively moderates this relationship, financial constraints and religiosity negatively moderate it. Interestingly, this positive linkage is driven by environmental and social performance rather than governance performance.
Practical implications
Firms should proactively engage in ESG initiatives and consider the intervening influences of stakeholder engagement, financial constraints and religiosity in making decisions to invest in ESG activities. Furthermore, our findings can help policymakers understand the financial consequences of ESG practices, which can be helpful in designing new policies to further promote corporate engagement in ESG practices.
Originality/value
First, our research findings help reconcile the long-standing debate about the value impact of ESG. Second, our paper investigates relatively new aspects of the ESG-firm performance relationship. Third, our study offers more insight into the ESG literature by showing that not all ESG dimensions equally impact firm performance.
2025, Journal of Asian Business and Economic Studies
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Abstract
Purpose
There is a need for research examining how governments and firms responded to the coronavirus disease (COVID-19) pandemic. This study investigates the interdependence between governments and innovative small and medium-sized enterprises (SMEs) during the pandemic in relation to the dynamic capabilities and resource dependence theories.
Design/methodology/approach
We use World Bank survey data collected immediately before and after the COVID-19 outbreak and a generalized structural equation model to examine the mediating role of government support in the relationship between firm innovation, resilience and survival.
Findings
Innovative SMEs exhibited higher resilience and a better chance of survival during the pandemic, partly due to attracting more government support.
Originality/value
This study offers a novel understanding of the government’s role in supporting innovative SMEs during the pandemic. The findings have implications for how government support policies can limit the deadweight effect and the substitution effect.
2025, Journal of Asian Business and Economic Studies
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Abstract
Purpose
Expected returns and risk are critical variables in financial analysis. This study demonstrates that investors’ perceptions of these factors are shaped not only by fundamental economic variables, as traditional finance suggests but also by psychological states such as distress and mood.
Design/methodology/approach
Data from Thai investors were collected through an online survey. We used regression and logistic regression to test the hypotheses.
Findings
Positive moods increase perceptions of expected returns and risk, while negative moods reduce these perceptions. Higher depression levels negatively impact investors’ perceptions of expected risk. Investors’ mood intensity, especially negative moods and higher depression levels, negatively impacts risk perception in the short term. Additionally, negative moods decrease the likelihood of optimism toward risk perception in the long term.
Practical implications
Financial advisors and investment firms can enhance their services by integrating psychological assessments into their client evaluations. Such assessments must be handled with great care, ensuring that clients give explicit consent and that their psychological data are protected in accordance with ethical standards. This approach allows for a deeper understanding of clients’ emotional and psychological states, leading to more personalized investment strategies. Additionally, investment firms can develop tailored products that address investors’ emotional and psychological needs, promoting more balanced decision-making and improving overall satisfaction.
Originality/value
We assess perceptions of expected returns and risk by collecting data directly from investors. We also evaluate investors’ psychological traits and moods with widely recognized psychological tools, including the Patient Health Questionnaire-9 and the Positive and Negative Affect Schedule.
2022, Journal of Asian Business and Economic Studies
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Abstract
Purpose
It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.
Design/methodology/approach
Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.
Findings
The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.
Originality/value
This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.
2022, Journal of Asian Business and Economic Studies
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Abstract
Purpose
The purpose of this study is to examine the financial autonomy that affects the financial accounting information quality of public organizations. This study also tests the impact of the financial autonomy on support from leadership. How this impact has affected elements of accounting information systems such as hardware, software, communications technology and chief accountant to support providing the quality of the financial accounting information.
Design/methodology/approach
The research model is in the SEM form and measurement models are reflective scales so this study applies the PLS-SEM analysis technique on the Smart PLS 3.2.7 software to test the research hypotheses. Analytical data is collected through survey questionnaires with observed variables measured using the typical 7-point Likert scales. The result obtained after cleaning the data includes 164 Vietnamese public organizations with the different levels of the financial autonomy.
Findings
This research has three primary findings: firstly, FA has a positive direct effect on FAIQ and SL. Secondly, SL influences FAIQ through four mediate variables including AM, HW, SW and CN. Finally, SL also acts as a mediate variable in the relationship of FA and FAIQ.
Originality/value
This is one of the first empirical studies to examine the role of financial autonomy in leadership support to improve the quality of the accounting information in the public sector in the context of the Vietnamese government is promoting the financial autonomy of public organizations.