Abstract

Fintech revolutionized the traditional banking business models in emerging countries. The effect of fintech on banks’ operating efficiency and risk-taking behavior is still inconclusive. The study is aimed at exploring the effect of fintech products on banks’ operating efficiency and risk-taking behavior. The study used a quantitative research approach by collecting secondary data from annual reports of 50 commercial banks from emerging countries, namely, China, India, Pakistan, and Bangladesh, for the period 2014 to 2021. The study used panel data for path analysis and structural equation modeling (SEM) to test the theoretical mediation model by using STATA. The results show that the fintech product reduces the bank’s risk-taking behavior by enhancing the bank’s operating efficiency. The path analysis results show that operating efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries. The paper offers useful recommendations for central bank and commercial bank policymakers. The study is also beneficial for commercial banks that use fintech solutions to increase operational effectiveness and reduce risk. The study is the first empirical investigation into the connection between the growth of fintech products, bank operational effectiveness, and risk-taking behavior in developing nations.

Introduction

The global financial industry has been rapidly changing with the advancement of financial technology over the last few years. New emerging technologies such as Internet finance, blockchain, and mobile banking are penetrating, forming the fintech that revolutionized the whole financial industry. It offered lower-cost services with more choices for consumers to buy financial services that shift their financial behavior to digitalization. The rise of financial technology (fintech) has brought about significant changes in the financial industry, including the way banks manage risk. Fintech has introduced new tools and technologies that enable banks to more accurately assess and manage risk, as well as new business models that challenge traditional approaches to risk-taking.

The pace of fintech development in developed and emerging economies countries is different. In developed countries such as the UK, USA, and Europe, fintech extended the financing transaction methods through algorithmic trading, helping the manager to build better compliance systems to manage risk, and it also improves the electronic payment systems for both domestic and cross-border payments [1]. However, in developing countries, fintech adoption has been slow despite having growth and expansion opportunities in the digital economy (Zhang et al., 2020). Fintech has expanded financial services digitally across countries, particularly during the COVID-19 pandemic. During the pandemic, the usage of fintech services and products has increased tremendously as identified in research [2].

Conclusion and Recommendations/Implications of the Study

The evolution of digital technology has changed the world drastically. The COVID-19 pandemic changed the whole business structure. The customers’ increasing demand for fintech products increases the competition in the financial industry. To retain their customers and remain competitive in the industry, traditional banks adopted fintech products. So that they can deliver efficient, accessible, and low-cost financial services to their customers with the help of technology. Fintech has facilitated the banking industry with better user experience and lower costs by using technological advancement.

The primary objective of the study is to explore the relationship between fintech products and bank risk-taking behavior with mediating role of a bank’s operating efficiency using a sample of commercial banks from China, India, Pakistan, and Bangladesh. The empirical evidence shows that the bank’s risk-taking behavior decreases, and bank stability is improved with the development of fintech products. Fintech products improve the bank’s operating efficiency. The effect of fintech products on bank risk-taking is more pronounced in countries such as China and India because they are extensively using fintech products in their operations. Banks are adopting fintech products such as artificial intelligence blockchain, mobile banking, and Internet banking. The results show that the banks adopting fintech products in their initial phase increase the bank risk-taking behavior, and then later on, it decreases the bank risktaking behavior by improving their operating efficiency.

Abstract

Purpose – Recent studies on the securities market’s differential pricing of earnings components indicate that cash flows from operations are valued more highly than extreme total accruals. However, no previous study has examined whether cash flows from operations have a higher valuation than moderate total accruals. Therefore, this study examines the securities market’s differential pricing of cash flows from operations and both moderate and extreme total accruals

Design/methodology/approach – The study’s sample is divided into two sub-samples: a moderate total accruals sub-sample; and an extreme total accruals sub-sample. To evaluate whether cash flows have a higher valuation when compared to total accruals, for the entire sample and for each of the two sub-samples, the study examines the statistical significance of the difference between slope coefficients of cash flows and total accruals for regression of returns on both unexpected cash flows from operations and unexpected total accruals

Findings – Consistent with prior research, results from the entire sample show a differential higher valuation of cash flows when compared to total accruals. Another finding, consistent with recent studies, is that cash flows from operations have a higher valuation when compared to extreme total accruals. However, there is no higher differential valuation of cash flows over moderate total accruals. These findings support the decomposition of earnings into the components of cash flows from operations and total accruals only when total accruals are extreme (rather than moderate).

Practical implications – A possible explanation for these results is that since accruals predict cash flows, total accruals – when moderate (i.e. not extreme) – are priced similarly to cash flows. These results reveal that when total accruals are moderate, earnings are a better proxy for the underlying cash flows (over the entire future horizon, not just the current period) than is cash flows. However, since total accruals are unlikely to persist in a permanent way over the years, these results indicate that the decomposition of earnings into the components of cash flows from operations and total accruals is consistent with the information set used to value equity securities. Therefore, separate disclosure of cash flows is value relevant. In addition, users of financial statements certainly need the cash flows information as an ex-post validation of the prior earnings.

Originality/value – This study’s contribution stems from its determination of the preferred level of disaggregation of earnings components (i.e. operating cash flows and total accruals). This is expected to help investors in their attempt to enhance the outcome of their informed investment and credit decisions.

Introduction

Reported numbers can be considered informative if they assist investors in making more informed decisions. When stock prices change in response to reported numbers, it can be presumed that investors’ valuation, buying, and selling decisions are influenced by reported numbers. It follows that reported numbers inform their decisions. Earnings can be divided into total accruals and cash flows from operations [1]. Earnings components (i.e. operating cash flows and total accruals) can be considered incrementally informative in relation to total earnings if they assist investors in making decisions that are more informed compared to those made when only the total is known. For example, if investors think that current cash flows are more or less informative when compared to total accruals with respect to their ability to predict future cash flows, then they are likely to be interested in learning about these earnings components and not simply total earnings. Identifying the preferred level of disaggregation regarding the elements of earnings can assist investors in making more effective investment and lending decisions. With these considerations in mind, the present study seeks to investigate the differential price of earnings components in the securities market.

Results

5.1 Stock returns and earnings components for the entire sample Table 2 presents the results regarding the relationship between stock returns and earnings components (i.e. cash flows from operations and total accruals) for the entire sample. The sum of the coefficients for the level and change in cash flows (α1þα3) is 1.218 (t 5 6.743), which is significant and positive at the 1% level. The sum of the coefficients for the level and change in total accruals (α2þα4) is 0.699 (t 5 11.311), which is significant and positive at the 1% level. These results suggest that both cash flows from operations and total accruals have incremental information content beyond each other.

Abstract

     This paper responds to the lack of empirical evidence on how enterprise risk management (ERM) and the financial performance of small and medium-sized enterprises (SMEs) are related. Structural equation modeling is used to explore new mediators in the relationship between ERM and SME financial performance. The results show that organizational culture (mission dimension) and strategic risk management performance are full and positive mediators between ERM and financial performance. These research results highlight the fact that the implementation of ERM in an enterprise does not by itself generate the expected effects without the existence of a mature organizational culture and the monitoring of strategic risk management performance. These findings are particularly relevant for SMEs with “pretend ERM” that lacks the strategic and operational components. ERM also helps to transform the negative effect of foreign capital in SME equity on financial performance into a positive effect.

Introduction

     Increasing levels of uncertainty call for proactive risk management in all organizations. The parallel crises triggered by the COVID-19 pandemic (Chakraborty and Maity 2020) and the military conflict in Ukraine have impacted most industries and businesses, unlike the Great Recession of the late 2000s, which primarily affected the financial sector (Gertler and Gilchrist 2018). Systematic risk has long been underestimated in advanced economies (Pagach and Wieczorek-Kosmala 2020). In such a situation, an intuitive assessment of risk outcomes, as often performed by smaller enterprises, is not enough (Grondys et al. 2021). Companies face new challenges and find it harder to maintain their profitability and competitiveness. Therefore, holistic enterprise risk management (ERM) is becoming increasingly important in small and medium-sized enterprises (SMEs).

     Nonfinancial SMEs are mostly unregulated. Thus, there is little pressure to implement a comprehensive risk management system. Nevertheless, in recent years, SMEs have started implementing formal risk management processes to increase their competitiveness (Wirahadi and Pasaribu 2022). ERM improves the quality of the information about enterprise risk profiles. The adoption of ERM reduces systematic risk. The purpose of ERM is to reduce the probability of losses and, therefore, reduce the need to borrow external resources, which positively impacts the expected cost of capital (Berry-Stölzle and Xu 2018).

Conclusion and implications

     Regarding the theoretical implications, this study reveals new mediators between ERM and the subjective financial performance of SMEs. The PLS-SEM method is suitable for analyzing complex relationships and testing causal relationships. Exploring indirect pathways can reveal consequential effects and help managers and owners understand various relationships. Complicated ERM indices (e.g., Gordon et al. 2009) are not suitable for the nonfinancial sector because the input variables for calculating such indices are difficult or impossible to obtain. The model works with the direct and indirect effects of ERM implementation. The indirect effects show the crucial role of organizational culture (mission dimension) and evaluations of strategic risk management performance in the relationship between ERM and the subjective financial performance of SMEs.

     From the management perspective, it is essential to establish functional and integrated processes for ERM implementation. ERM must be integrated with the organizational culture and the performance monitoring systems in the SME. Managers and owners should emphasize the functional implementation of ERM, not just a pretend ERM (Syrová and Špička 2022a) that lacks all the elements of an ERM approach. The ERM system must not be a “facade without the substance” that does not contribute to better planning and decision-making processes (Dvorski Lacković et al. 2022).

Abstract

In the current complex global economic background, international capital flows are becoming more frequent. Based on this, this paper takes international portfolio investment as the research object and empirically tests the causal relationship and control channel between international portfolio flows and macrofinancial risk in emerging economies. It selects the panel data of emerging economies from 2001 to 2020, constructs macrofinancial risk indicators by using contingent claim analysis and the entropy-based TOPSIS method, tests the effect of international portfolio flows on macrofinancial risk by using the panel distributed lag regression model, and explores the management effect of foreign exchange reserves and capital controls on the risk effect by using the panel threshold regression model. The results show that long-term international portfolio flows help reduce macrofinancial risk, but short-term capital flows appear to increase macrofinancial risk. In addition, both foreign exchange reserves and capital controls effectively reduce the risk effect of portfolio flows. However, when considering different types of portfolios, we find that foreign exchange reserves do not effectively control the risk effect of equity securities flows, while stricter capital controls do. This paper argues that emerging economies should be more open to international long-term portfolio flows, focus on the monitoring of short-term portfolio capital flows and equity securities flows, and coordinate the use of foreign exchange reserves and capital control instruments to manage the risk effects of portfolio flows. This paper verifies the risk effect of international portfolio investment flows through empirical analysis, tests the effectiveness of foreign exchange reserves and capital controls, and provides a decision-making reference for emerging economies to timely identify, effectively manage, and prevent the risk effect of international capital flows.

Introduction

In the context of the current COVID-19 pandemic and the Russia-Ukraine conflict, recessionary expectations of “high inflation” and “weak growth” in the United States, Europe, and other advanced economies have continued to rise, and global economic activities have been severely impacted [1]. Advanced economies, represented by the United States, implemented quantitative easing and fiscal stimulus policies to stabilize financial markets and protect economic development in response to the resulting economic recession. As high inflation continued to rise under the policy mix, the Federal Reserve began to reduce inflation by significantly increasing interest rates and tightening liquidity. Emerging economies have historically risen with the wave of industrialization through industrial transfer, aided by ample international capital liquidity. However, the Fed’s persistent policy of increasing interest rates has resulted in a stronger US dollar and depreciating currencies in emerging economies [2]. When expectations of a decline in the short-term exchange rate rise, the willingness of cross-border capital to invest weakens further, and there is a sudden halt in capital inflows or even capital flight, resulting in a cascade of asset price declines. Under the negative feedback spiral, the wealth of the household sector decreases significantly, corporate solvency declines, and the bank nonperforming loan ratio rises, resulting in heightened instability in the real economy and financial markets. The current complex and volatile international economic environment increases the frequency and unpredictability of cross-border capital flows, which will cause significant volatility on the financial markets of emerging economies [3].

International capital contributes significantly to the growth of emerging economies, but it is also extremely vulnerable to macrofinancial instability. In every financial crisis, we can see unusual global capital fluctuations. Emerging economies’ financial systems are flawed and have insufficient defenses against shocks from global capital flows. Emerging economies are additionally more susceptible to external capital shocks due to internal factors like a high external debt ratio and a mismatch in debt maturities [4]. Considering the current complex global economic context, to investigate how cross-border capital flows affect macroeconomic stability in emerging economies, this paper selects highly speculative international portfolio investment flows as the research object, discusses the effect of international portfolio flows on macrofinancial risk in emerging economies, and further explores how to effectively manage such risk. By revealing the evolution of macrofinancial risks under external shocks and the efficient control tools for identifying the risks of capital flows, it is of great theoretical significance and practical value for emerging economies to identify, manage, and prevent the impact of global capital shocks.

Conclusion

Since the 1990s, the process of economic globalisation has accelerated. Emerging economies have gradually assimilated into the global financial market, growing in popularity with foreign capital that prefers the better growth prospects of emerging economies to advanced economies that have finished industrializing. Although international portfolio capital inflows have helped fuel the market boom, they have also caused some worry about the emerging economies’ financial stability. Emerging economies’ growth is significantly influenced by foreign capital. Open capital accounts in emerging economies make them particularly susceptible to significant shifts in international capital flows, which can either precipitate financial crises there or have a more muted resolution [58]. To explore how international portfolio investment flows affect macrofinancial risk in emerging economies and how to manage this risk effect, this paper develops macrofinancial risk indicators by using contingent claims analysis and the entropy-based TOPSIS method to conduct this study, empirically investigates the short- and long-term effects of international portfolio investment and macrofinancial risk by using a panel distribution lag model, and investigates how capital flow affects the management of macrofinancial risk under capital flow from the standpoint of foreign exchange reserves and capital control by using a panel threshold model.

This paper demonstrates, through an examination of the relationship between macrofinancial risk and international portfolio investment flows, that international portfolio investment flows will increase the macrofinancial risk of emerging economies in the short term but reduce it in the long term. From the perspective of the components of international portfolio investment, high-risk and speculative international speculative capital will have a negative effect on the real economy [51]. Furthermore, this paper examines the management effects of foreign exchange reserves and capital control to mitigate the damage that the risk may cause. This paper argues that foreign exchange reserves can play a positive moderating role in international portfolio flow and macrofinancial risk, but only to a limited extent. Capital controls are tougher, but risk controls work well. Both foreign exchange reserves and capital control have effective risk management capabilities. When equity capital flows frequently, capital control works better at managing macrofinancial risk.

Abstract

Empirical studies found that earnings management (EM) explains firms’ cost of capital both in companies in emerging and developed countries, but until now, it has not been analyzed whether the effect of EM on the financial cost is different among emerging countries inside or outside an economic area (Eurozone). Our results show that the cost of debt and the idiosyncratic component of the cost of equity are related to discretionary accruals and abnormal values of operating cash-flows, that the emerging country effect is more relevant on the cost of debt, that there is a Eurozone effect that makes discretionary accruals more relevant than abnormal values of operating cash-flow and that firms in emerging countries inside the Eurozone benefit from a lower EM penalty on the cost of debt than firms in other emerging European countries.

INTRODUCTION

The literature shows that cost of capital for companies in emerging countries is higher than that for companies in developed countries (González-Sánchez, 2022; Narayan et al., 2014). This means that investments in emerging countries have to achieve higher returns to compensate for this extra financial cost. In this context, our aim is to test whether the earnings management (EM) explains this extra financial cost of capital and, if the EM effect is different for companies in emerging countries if the country is inside or outside of Eurozone.

EM is the ability of the company’s management to manipulate the company’s results, with the intention of obtaining some private gain, but reducing the quality of financial reporting. The literature has analyzed whether EM has a negative impact on the quality of financial reporting, which may reduce the value of the company, causing stakeholders to lose confidence. More specifically, EM has also been analyzed in relation to issues such as the supervisory role of the majority owner in mitigating managers’ opportunistic behavior in EM (Mellado & Saona, 2005), insider trading (Sawicki & Shrestha, 2008), mergers and acquisitions (Zhu & Lu, 2013), debt issuance (Mellado et al., 2017), or bankruptcy risk (Agustia et al., 2020).

CONCLUSIONS

Empirical evidence shows that the financial cost of companies in emerging countries is higher than that of companies in developed countries. In this context, we test whether this difference is due to the way creditors and investors take into account EM in both types of companies or whether their interpretation of earnings manipulation is independent of the extra financial cost in emerging versus developed countries. Our main objective is to study whether there is a different effect for companies in the Eurozone and whether, as a consequence, companies from emerging countries inside the Eurozone have a different effect than other companies from emerging European countries outside the Eurozone.

To study this differential effect, we built a database composed of 4514 companies from 16 developed countries and 3399 companies from 21 emerging countries. The novelty of our sample is that all countries belong to the same geographical area (Europe), so that implicitly the economic, social, and cultural relations (among others) should not show substantial differences in the way EM explains financial cost. Moreover, by including in the sample developed and emerging countries that share a common regulatory framework and a common currency (euro), this difference should be nonexistent. The sample period is from 2012 to 2020.

Abstract

This study examined whether foreign investment flow and overconfidence can influence stock price movement among the publicly listed companies in Indonesia. Subsequently, this study determined whether there was any significant difference in the influence of foreign investment flow and overconfidence on stock price movement before and after the COVID-19 lockdown in Indonesia. This study focused on the manufacturing companies listed on the Indonesian Stock Exchange for the 2020 period of which the data were taken in a period of 10 days before and 10 days after the implementation of the COVID-19 lockdown in Indonesia. Using content analysis on secondary data, this study showed that there was a significant difference between the stock prices before and after the COVID-19 lockdown. However, this study showed that foreign investment flow and overconfidence were not the main factors influencing stock price movement before and after the lockdown. The findings indicate that there are other factors that contribute to stock price movement in Indonesia. This study contributes to the existing literature on whether foreign investment flow and overconfidence influence stock price movement in a pandemic world.

Introduction

The COVID-19 pandemic has had an effect not only on the health sector but also on the economic sector all over the world (Ghani et al. 2022). Indonesia is not an exception to this situation. The Central Statistics Agency (BPS) revealed that Indonesia’s economic development in the first quarter of 2020 witnessed a growth of 2.97%, which is a significant change from the 5.02% growth that was recorded in 2019. In Indonesia, the pandemic had a negative impact on the capital market including a change in trading hours on the Indonesian Stock Exchange (BEI) (Kusnandar and Bintari 2020) which, consequently, affected stock price movement. On 8 April 2020, the Combined Stock Price Index (IHSG), which shows the aggregate change in the share price of companies listed on the BEI, showed a decrease in stock prices by around 1.34% at 4704.58. However, upon the government’s announcement of imposing the COVID-19 lockdown, trading hours resumed, causing a steady movement in the stock prices. The existence of unstable stock price movement results exposed investors to increasing risk and uncertainty (Kartika 2008).

Results

5.1. Analyses before COVID-19 Lockdown

Table 3 presents the results of the normality test before the COVID-19 lockdown. The Asymp. Sig. (two-tailed) result was 0.346, which is greater than 0.05. Therefore, it can be concluded that the data were normally distributed, or, in other words, the data passed the normality test.

Table 4 presents the results of the normality test after the COVID-19 lockdown. The table shows the value of Asymp. Sig (two-tailed) to be 0.349, which is greater than 0.05. Therefore, it can be concluded that the data were normally distributed, or, in other words, the data passed the normality test.

Abstract

A growing body of literature scrutinizes the harmful consequences of capital flight to offshore financial destinations. While financial integration is a well-known facilitator of capital flight, we shed light on an under-appreciated determinant—the availability of an IMF bailout. Expanding on previous literature analyzing moral hazard in the context of IMF programs, we introduce a socially even more destructive mechanism that we label the ‘crash for cash’ effect. We argue that by drawing on the IMF, elites can benefit from accumulating excessive debt to extract rents and hide these safely in offshore financial destinations while steering their countries into financial disaster. To test this mechanism, we show that elite wealth in offshore bank accounts has a first-order impact on a captured government’s willingness to draw on a lender of last resort. From a policy perspective, our analysis underscores the importance of closing financial loopholes to mitigate the devastating socio-economic effects of sophisticated financial engineering in a financially integrated global economy.

Introduction

Capital flight is a key obstacle to sustainable development. While policymakers often point the finger at financial investors, in many cases, well-connected local elites drive capital flight into offshore financial destinations. These elites plunder the wealth of their countries and expatriate assets into safe havens (Jayachandran and Kremer, 2006, Garcia-Bernardo et al., 2017, Andersen et al., 2020).1 Depriving a country of investible capital can lead to a loss of tax revenues and rising inequality, to the effect that elite capital flight ultimately undermines macroeconomic stability (Alesina and Tabellini, 1989, Ndikumana et al., 2014, Frantz, 2018): notwithstanding its ethical status, such behavior undermines the quality of governance, furthering endemic corruption and crony business practices that amplify financial vulnerabilities (Cerra et al., 2008, Meierrieks and Schneider, 2021).

Previous literature analyzing the determinants of elite capital flight identifies dysfunctional governance frameworks that provide a fertile breeding ground for criminal activities such as embezzlement, trade misinvoicing, and tax evasion (for a review, see Cooley et al. (2018)). Weak institutions facilitate capital flight because they allow wealthy elites to move embezzled funds abroad without punishment (Collier et al., 2001, Ndikumana and Boyce, 2003, Le and Rishi, 2006).

Conclusion

The IMF plays a vital role in supporting countries during times of economic hardship, earning it a reputation as “a global payday loan company for countries who have got into trouble and can’t meet their financial commitments — the difference being that instead of charging sky-high interest rates, it demands radical economic reforms”.33

Researchers have analyzed the circumstances under which the IMF decides to intervene and the conditions it imposes in exchange for financial relief (e.g., Reinsberg et al., 2019). We analyze the role of international capital flight and its impact on IMF involvement and program design. We argue that the ability to draw on the IMF creates perverse economic incentives so that a country’s elites can privatize economic gains by moving funds into offshore financial destinations before the arrival of the Fund. Although the Fund possesses numerous instruments to address economic crises, it does not have any means to seize elite wealth in offshore financial destinations, so the costs of elite capital flight are passed onto the population at large.

Abstract

1. Introduction

2. Literature review

3. Integrated analytical framework for sustainability analysis of supply chain management

4. Results

5. Conclusions

Disclosure statement

References

Abstract

     With the increasing importance of air pollutant emissions to the platform economy and green supply chain management, it is essential to analyse the trend and correlation between particulate matter emissions and supply chain statistics. Typical approaches do not integrate particulate matter prediction with the sustainability analysis, and suffer from common issues such as low classification accuracy and unstable prediction performance. In this study, we propose an integrated analytical framework for sustainability analysis of supply chain management through particulate matter emissions prediction. Specifically, we performance trend and correlation analysis between particulate matter emissions (PM2.5 and PM10) and supply chain statistics in Beijing of China. We combine the boosting algorithm and neural network method to predict particulate matter emissions. Experimental results show that our prediction model achieved high performance. Sustainability analysis shows that the steady growth of the supply chain operations is accompanied by decreasing air pollutant emissions in China.

Introduction

     Today, human society is facing very severe environmental and resource problems. The green and sustainable supply chain are the concrete manifestations of sustainable development strategies that comprehensively consider these two issues in platform economy management. In recent years, continued urbanisation and industrialisation, particularly in developing countries, has led to severe deterioration in air quality and a rapid increase in the degree of contamination (Shi and Yu 2020). Air pollution has substantially affected the living environment of the human population and endangered health (Stieb et al. 2019). The United Nations (UN) issued Sustainable Development Goals (SDGs) in 2015, serving as a blueprint for building a more sustainable future across the globe. There are totally 17 goals in SDGs, addressing issues in economic, environmental, and social areas. These goals are interrelated and expected to be fulfilled by 2030 (2015). PM2.5 emissions have an adverse effect on public health (Yin, Pizzol, and Xu 2017). At present, many countries attach great importance to inhalable total suspended particulates that cannot be blocked by the human upper respiratory tract, especially inhalable aerosol particles with sizes less than 2.5 μm. It is necessary to arouse the stakeholder

Conclusions

     In this paper, an integrated analytical framework targeting sustainability analysis of supply chain management through particulate matter emissions prediction has been proposed. Specifically, we firstly collect and preprocess the particulate matter emission data and supply chain statistics in Beijing of China, then we conduct correlation analysis between air pollutant concentrations and supply chain statistics to uncover the intrinsic associations. Secondly, we combine the boosting ensemble meta-algorithm and neural network method for particulate matter concentration forecasting. The boosting algorithm is used to train a set of individual subnets to improve the efficiency of prediction, and afterwards, the output results of individual subnets are weighted for acquiring the final prediction results. Finally, we perform an analysis on sustainability between air pollutant emissions and supply chain statistics after obtaining the particulate matter prediction results.

     This analytical framework is able to perform sustainability analysis with accuracy and provide reliable insights for the legislation and implementation of green supply chain management. The forecasting of particulate matter emissions conveys basis and justifications for policy-makers to enforce the environmental protection measures associated with air pollution and design efficient policies with the aim of improving the air pollutant management of the supply chain, which will further lead to an improved public health environment, especially in economically developed areas. Because this study mainly targets the prediction of PM2.5 and PM10 concentrations, it cannot comprehensively reveal the geographical distribution of air pollution. There are various factors that affect air quality, and a regression model can be established to predict PM2.5 and PM10 distributions more comprehensively and to determine the key influential factors, which would be important for formulating economic development strategies and implementing environmental protection mechanisms.

Abstract

1 Introduction

2 Related works

3 Methodology

4 Dataset information and predictors

5 Results

6 Discussions

7 Conclusions

Appendix A: The pseudocode of SVM-based classifier

Appendix B: The pseudocode of ANN-based classifier

Appendix C: The pseudocode of C4.5 DT

Appendix D: The pseudocode of RF

Appedix E: The pseudocode of bagging

Appendix F: The pseudocode of GB

Appendix G: Variables and definition

References

Abstract

     Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

Introduction

     The development of small and medium-sized enterprises (SMEs) has attracted attention from scholars and practitioners over the globe. However, because of the tightening of credit criteria for corporate loans, SMEs are facing significant challenges, mainly including capital constraints, high operational costs, and ambiguous information (Yan & He, 2020). As a major component of the economy in China, SMEs contribute almost 90% of the number of enterprises, 80% of urban employment, 70% of GDP, 60% of technological innovation, and 50% of tax revenue (see https://www.ndrc.gov.cn/ for more detail). SMEs in China also face problems mainly including high financial distress, high financing costs, high operational risks, tightening financing channels, high fraud risks, and asymmetric financing information (Weng et al., 2016; Zhu et al., 2019).

     As a popular financing channel, supply chain finance (SCF) defined by Hofmann (2005) as the inter-firms optimization of financing and the integration of financing processes with customers, suppliers, and service providers to increase the value of all participating firms, has attracted attention from both practitioners and scholars alike. The Chinese government has developed some new financial policies to ease the financing pressure on SMEs, e.g., Promoting SME Development Plan (2016–2020), which seek to “promote more supply chains to join the financing service platform of SMEs”. Similar initiatives are underway in other countries and regions, such as the United States, United Kingdom, Japan, Canada, South Korea, Europe, and Mexico. SCF is also being used to promote the development of SMEs. For example, the Office of the United States Trade Representative (USTR) is implementing a series of initiatives to address the financing problems of SMEs, including SCF.

Results and analyses

     To our best knowledge, this is the first study to consider the value of multi-source information fusion to predict the SMEs’ credit risk in SCF, and presents some managerial implications. We predict the credit risk of SMEs in SCF with an imbalance sampling strategy on machine learning techniques. Considering the value of multi-source information fusion in the big data era, we construct a broader knowledge base, including financial information, operation information, innovation information, and negative events, to predict the credit risk of SMEs in China, and develop new models to simultaneously solve for key predictor selection and imbalance classes. We then adopt six evaluation criteria to compare the prediction performances of the six machine learning techniques—SVM, NN, DT, RF, bagging, and GB—based on the data of VA and VS, respectively. We compare the results of new models via a re-sampling strategy for baseline models on VA and VS; the results indicate that the proposed CSL-RF model is optimal in terms of accuracy and robustness. The empirical results indicate that the financial-based information is the main source to predict SEMs’ credit risk in SCF, and the multi-source information fusion is meaningful. In addition, based on the preferred CSL-RF model, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. Finally, we generate strategic insights for market participants, such as regulators, investors, and managers.

Among the controversies in corporate governance, perhaps none is more heated or widely debated across society than that of CEO pay (Larcker & Tayan, 2019). How CEO pay is determined, and the performance implications of CEO pay is an issue of first-order importance. Efficient contracting (Gabaix & Landier, 2008; Murphy, 1999) and managerial power (Bebchuk & Fried, 2004) have been advanced as the two main explanations for the high level of CEO compensation and the weak links between CEO compensation and firm performance. While both are widely accepted, neither explanation is fully consistent with the available evidence (Frydman & Jenter, 2010). Much remains to be known about the determinants of CEO pay (Core, Holthausen, & Larcker, 1999), and the precise channels by which executives and boards influence CEO pay (Engelberg, Gao, & Parsons, 2013).

Recent studies have documented that geography has significant effects on corporate decisions and outcomes.1 Kedia and Rajgopal (2009) find that geographically-proximate firms adopt similar policies in broad-based option grants. They argue that this arises because geographically-proximate firms are exposed to the same local market conditions and managers at neighbouring firms engage in social interactions. Dougal, Parsons, and Titman (2015) find a high correlation in the capital investment of neighbouring firms, even those in different industries. They suggest that one reason for correlated investment is that local social networks allow managers to share ideas. Parsons, Sulaeman, and Titman (2018) find that geographic variation in social norms accounts for a large proportion of the cross-sectional variation in financial misconduct across major US cities. The evidence from these and other studies on the role of geography suggests that CEO compensation may be correlated among neighbouring firms, over and beyond what economic fundamentals warrant. However, the existence of, reasons for, and consequences of spatial correlation in CEO compensation remain under-explored. Our paper aims to fill this gap.

An empirical challenge in investigating spatial effects in corporate decisions is that the interdependence in the cross section of neighbouring firms makes ordinary least squares (OLS) an inconsistent estimator.2 Prior studies typically regress a firm-level outcome variable, such as CEO compensation (Bouwman, 2013) and corporate investment (Dougal et al., 2015), on the weighted average of that variable corresponding to the neighbouring firms. This approach is problematic, because it overlooks the “spatial lag” among neighbouring firms, and the autoregressive disturbance term (Anselin, 1988). We address such endogeneity concerns by using spatial econometric models, which are well suited for handling both types of interdependence (Kelejian & Prucha, 1998, 1999).3 More importantly, by focusing on how spatial correlation in CEO pay is affected by CEOs’ local social networks, corporate governance, as well as managerial power, and by examining the performance implications of spatial correlation in CEO pay, we shed light on the nature and consequences of spatial spillovers in CEO pay.

1- Introduction

2- Methods

3- Results

4- Discussion

5- Conclusions and ways forward

References

Abstract

Designing optimal financial products for rural households in poor countries remains a challenge, especially in a risk management perspective in disaster-prone areas. In addition to financial intermediaries’ difficulties with reaching potential customers efficiently, information asymmetries prevent fully understanding customers and developing suitable products for them. The clientele’s weak information systems, coupled with cultural aspects often unfamiliar to the supplier of financial services, make the analysis difficult and costly. This article builds on a financial statement approach to empirically explore the combination of farmers’ financial and non-financial strategies, in a portfolio risk management perspective, with a focus on shock-type risk exposure. It emerges that farmers’ decisions follow a sequential process with different combinations of savings, credit, and insurance that interact with non-financial strategies to help managing risks. The study relies on panel data collected in Ethiopia. The empirical analysis confirms the vulnerability of sample households to natural systemic shocks and entails that the conclusions of this paper can be considered part of the broad literature on the disaster risk management of low-income households.

CONCLUSIONS AND WAYS FORWARD

This article was inspired by the use of a conceptual framework based on a portfolio approach to study rural households’ risk management strategies and had a particular interest in the potential demand for insurance. The structure of the sample shows that, while several risks are affecting the households, a large percentage of the interviewees declared to suffer of disastertype events: droughts and floods. The outcome of the current analysis confirms the results of previous, simplified, attempts to prove that although insurance is often considered an alternative measure to farmers’ own riskcoping strategies, it may be a complementary solution. The information obtained by using a system analysis (in this case, the SURE methodology) is that the main dimensions of a portfolio approach to risk management can be analysed in their reciprocal relationships. Based on the findings, insurance and savings decisions are affected by the overall composition of risk management strategies (savings, insurance, loans, livestock), and borrowing decisions depend on all factors but savings. Another finding is that the direction of the influence in some cases is constant (always positive, suggesting complementarity), and in other cases, the direction changes according to the sequence of the portfolio choice. This phenomenon is evident in the case of borrowing.