Abstract

This study theoretically and empirically analyzes the impact and mechanism of corporate green transformation on accrual earnings management using a sample of China’s A-share listed companies from 2015 to 2021. The results indicate that the green transformation of enterprises helps restrain accrual-based earnings management, especially accrual-based earnings management, which increases profits, and that the inhibition effect of enterprise green transformation on earnings management is more significant in high-tech and growth-stage enterprises. The mechanism test shows that corporate green transformation can inhibit accrual-based earnings management by increasing analyst follow-up and easing financing constraints. Using China’s environmental tax law, promulgated in 2018, as an exogenous shock to construct the PSM-DID, the conclusion still holds after alleviating endogeneity. Further research has found that green transformations can also help inhibit real earnings management. This study provides new empirical evidence for a comprehensive understanding and evaluation of the governance role of corporate green transformation and provides a reference value for further comprehensively promoting the green transformation of Chinese enterprises and improving the quality of accounting information.

Introduction

With the continuous adoption of an extensive economic growth model in recent years, China has become the second largest economy in the world. However, this extensive economic growth has seriously damaged China’s environmental resources, making China’s total greenhouse gas emissions the highest in the world [1]. The green economy has always been an important tool for developing countries to achieve sustainable development [2]. Therefore, to avoid environmental degradation caused by greenhouse gas emissions and political pressure affecting sustainable economic development, the Chinese government has proposed a series of economic development strategies based on environmental protection.1 The Chinese government’s green development method of “promoting the construction of a resource-saving and environmentally friendly society” reflects the determination and attitude of the government in formulating and implementing new development policies during the transformation period of national economic development [3]. China’s listed companies have long been the main source of carbon emissions.2 Today, with increasingly serious environmental problems, the traditional “high carbon emission” development model of listed companies is unsustainable, and companies must choose a development path that takes into account both economic and environmental benefits [4], and green transformation can just become an important driving force for the sustainable development of the corporate economy [5]. Therefore, given the Chinese government’s vigorous promotion of the sustainable development of the green economy based on the “dual carbon” goal, the green transformation of listed companies has become a hot topic of concern in all walks of life.

Conclusion

Earnings management has always been a key issue in corporate governance and improving accounting information quality is an emerging market goal. As one of the most important forms of sustainable economic development in China, corporate green transformation has a governance effect on earnings management, and is worthy of study. This study selects A-share listed companies in China’s Shanghai and Shenzhen stock markets from 2015 to 2021 as the research sample and empirically tests the impact of corporate green transformation on accrual earnings management. The results were as follows:

First, there is a negative correlation between corporate green transformation and accrual-based earnings management, indicating that corporate green transformation effectively inhibits accrual-based earnings management. Simultaneously, corporate green transformation has a strong inhibitory effect on accrual-earnings management, which increases profits. After using the environmental protection “fee reform tax” policy issued by the Chinese government in 2018 to employ a PSM-DID analysis to alleviate endogeneity, the conclusion still holds after a series of robustness tests such as replacing explanatory variables and explained variables.

Abstract

     The objective of this study is to examine the role of CEO duality in audit quality of Chinese firms. To address the effect of CEO duality, this study considered four characteristics of CEO duality; corporate audit culture, audit committee independence, decision making through enormous power and single leadership. The indirect effect of audit committee decision is considered between CEO duality and audit quality. A questionnaire survey is carried out to collect primary data. A questionnaire was designed to collect data from Chinese firms. Respondents of the study were the CEO’s and directors of Chinese firms; therefore, questionnaires were distributed among them by using online survey. 135 valid questionnaires were used in data analyses which is carried out by using Partial Least Square (PLS)-Structural Equation Modeling (SEM). Results of the study highlighted that; CEO duality has both positive and negative effect on audit quality. Due to the single leadership style and supportive corporate audit culture, the audit quality may be improved. However, CEO duality influences negatively on audit committee independence which has negative effect on audit committee decisions. Similarly, CEO duality allows to enjoy enormous power which may affect negatively on the decision making. Thus, decision-making through enormous power has negative effect on audit committee decisions. Finally, while making the strategies to enhance audit quality, the practitioners can consider the important points highlighted by the current study to promote audit quality.

Introduction

     CEO duality is a phenomenon where the chief executive officer (CEO) of a company occupies the chair of the company’s board of directors, along with his/her regular management position (Oak & Iyengar, 2009; Voinea et al., 2022). This phenomenon is considered by the several previous studies. Several studies have examined the CEO duality among the business organizations through several aspects (Azhari et al., 2020; Voinea et al., 2022). Therefore, CEO duality remained the intention of the researchers as well as practitioners. The advantages and disadvantages are comprehensively discussed by the previous studies among different business organizations (Ramdani & Witteloostuijn, 2010). However, the phenomenon of CEO duality is still under discussion and its role among the organizations is not clear (Aghmiuni et al., 2020). The role of CEO duality among the companies is still unclear in the performance of organizations. Few studies show the positive effects of CEO duality, on the other hand, few studies highlighted the negative effects of CEO duality. Therefore, CEO duality is still a problematic area for the management of business organizations as well as academicians. In this way, the current study examined the role of CEO duality among Chinese firms. Particularly, this study is attempted to observe the role of CEO duality in audit quality among Chinese firms (Dhiman & Arora, 2020).

Discussion and conclusion

     The objective of this study was to observe the role of CEO duality in audit quality among the Chinese firms. Therefore, the current study carried out a survey among the Chinese firms and data analysis is carried out by using statistical tool. To achieve the study objective, the current study proposed various direct effects and indirect effect hypotheses. In this way, this study proposed 9 direct hypotheses and four indirect hypotheses based on audit committee decision. The results of the study fulfilled the objective of the present study and clearly highlights the effect of CEO duality on audit quality among Chinese firms.

     The direct hypothesis 1 is considered between corporate audit culture and audit committee decision. This hypothesis shows the positive effect of corporate audit culture on audit committee decision. It indicates that if the CEO duality exist among the Chinese companies, it can promote corporate audit culture which can enhance the audit committee decisions. Therefore, in this way the audit quality has significant positive role in audit practices. However, the effect of corporate audit culture on audit quality is not significant. There is no effect of corporate audit culture on audit quality. In this way, the audit quality is influenced by indirect way from corporate audit culture, however, the corporate audit culture has not direct effect on audit committee decision. The effect of corporate audit culture on audit quality is considered in hypothesis 2. The hypothesis 3 highlighted the connection between audit committee independence and audit committee decision. Outcomes of this study highlighted that audit committee independence has positive effect on audit committee decision. It indicates that CEO duality in form of audit committee independence has effect on audit committee decisions. However, this relationship is not positive which indicates that audit committee independence has negative effect on audit committee decisions. All the previous studies have highlighted that audit committee independence has positive effect on audit practices (Alderman & Jollineau, 2020; Cohen et al., 2008; Toumeh et al., 2020), however, results are not consistent with previous studies. In relation to the CEO duality,

Abstract

     Our investigation of 46 conventional and 22 Islamic banks from the Gulf Cooperation Council (GCC) countries during 2008–2021 reveals that sectoral diversification effects on stability are nonlinear and different for the two bank types. While Islamic banks’ stability is worsened only by moderate levels of diversification, conventional banks’ stability is enhanced by high levels and impaired by low levels of diversification. Furthermore, diversification acted as a stabilizer during the global financial crisis but exacerbated the adverse effects of the Covid-19 pandemic. Although regulators usually call for bank diversification, our results imply that it can be a double-edged sword.

Introduction

     Due to dependence on oil and gas revenue and their price plunge caused by the Global Financial Crisis (GFC) in 2008, the governments of the Gulf Cooperation Council (GCC) countries started introducing various policies aiming at developing diversified real and financial sectors.1 While oil and gas production still represents over 40% of the GDP in GCC countries, with the exception of Bahrain and UAE, (Esam, 2021) shows that there has been significant diversification away from hydrocarbon revenue since 2010 in all GCC countries, particularly in Saudi Arabia, UAE and Bahrain. Given the low development of capital markets and non-bank financial institutions in these countries, banks play a pivotal role in assisting the execution of these national diversification policies by providing financing to support investments in the existing and ‘new’ sectors. The proponents of diversification in banking would see this as an opportunity for banks to spread their loans and financing2 across different sectors and thus reduce bank default, liquidity, and bankruptcy risks (Diamond, 1984; Demsetz and Strahan, 1997; Rose and Hudgins, 2006), vulnerability to economic downturns and financial crises (Tabak et al., 2011; Doumpos et al., 2016), and become safer and more stable (Saunders and Walter, 2012). On the contrary, the opponents of diversification would argue that banks are better off by focusing on a single sector or a few sectors as that allows them to leverage their screening and monitoring knowledge and expertise in these sectors and thus reduce the unnecessary complexity, agency problems, overhead expenses (Klein and Saidenberg, 2010), earnings volatility (DeYoung and Roland, 2001), and write-downs and write-offs (Jahn et al., 2013) that usually come with diversification.

Discussion and conclusion

     Against the backdrop of the GCC countries’ ongoing economic diversification policies and banks being the primary financiers of the projects brought about by these policies, we investigate (i) if and how diversification of loans and financing across economic sectors impacts the stability of GCC banks, (ii) if the effects are the same on conventional and Islamic banks, and (iii) if diversification helps banks navigate smoothly through economic downturns such as the 2008 global financial crisis and the ongoing Covid-19 pandemic.

     anks navigate smoothly through economic downturns such as the 2008 global financial crisis and the ongoing Covid-19 pandemic. Analyzing a unique panel dataset of 46 conventional and 22 Islamic banks, we find that there is no one-strategy-fits-all even within these seemingly similar countries. Diversification effects on bank stability are nonlinear and different for Islamic and conventional banks. While Islamic banks’ stability worsens at moderate levels of diversification, conventional banks’ stability thrives at high levels and erodes at low levels of diversification. Given that the diversification measure is an inverse measure of concentration, this means that a high concentration of loans on one or a few economic sectors also benefits conventional banks. Lastly, we observe a temporal dimension of our results which suggests that diversification supported bank stability against the destabilizing effects of the global financial crisis but amplified the adverse effects of the Covid-19 pandemic.

Abstract

     This paper empirically examines the effect of inefficient corporate investment on digital transformation, using Chinese listed companies from 2007 to 2019 as a sample, and finds that inefficient investment is not conducive to improving digitalization. We find that the greater the financing pressure on a company, the worse the digital transformation, and financing constraints exacerbate this negative effect. Among state-owned, private and highly digitalized companies, the negative impact of inefficient investment on digital transformation intensifies as the pressure of financing constraints increase. To combat inefficient investment, enterprises should optimize their investment structure, reduce their financing constraints, and improve their risk prevention mechanisms.

Introduction

     The global economy and society are in a critical stage of digital transformation, and the supporting and leading role of the digital economy for industrial development is becoming increasingly important (Wen and Zhong, 2020). As we enter a “new normal” period, where growth rates gradually decrease, the development of the digital economy becomes an important tool for China on its journey to being a high quality, innovative country. With the in-depth implementation of China’s manufacturing sector, network infrastructure and big data strategy, the development of the digital economy has gained momentum, with the overall scale expanding from 18.6 trillion yuan in 2015 to 39.2 trillion yuan in 2022, and the proportion of GDP attributed to the digital economy increasing from 27% to 38.6%, thereby becoming a key driver of stable economic growth. However, these impressive achievements have also raised some concerns. Corporate digital transformation requires investment and high technical standards, leaving some who refuse to transform, as well as those who are ready (Liu et al., 2021). Therefore, understanding the potential value of digital transformation is an issue of great concern to both academia research and policy makers.

Conclusions and recommendations

     This paper takes Chinese listed companies from 2007 to 2019 as a research sample, and empirically tests the impact of investment efficiency on corporate digital development, and the mediating effect of financing constraints, and the conclusions obtained are as follows:

1 The results show that inefficient investment is detrimental to digitalization; there is a positive relationship between investment efficiency and digitalization, and the higher the financing pressure on a company, the more unfavorable the digital transformation, and financing constraints exacerbates this negative effect.

2 The financing constraint has a more significantly negative moderating effect on the relationship between inefficient investment and digitization levels for state-owned firms, private firms, and firms with higher digitization levels.

3 Companies can alleviate financing pressure to a certain extent through inefficient investment, but it is still fundamentally detrimental to their digital transformation.

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.

Abstract

Keywords

1. Introduction

2. Related work

3. Enterprise financial supply chain model based on Internet of things

4. Evolution risk anomaly analysis

5. Simulation experiment analysis

6. Conclusion

Declaration of Competing Interest

Acknowledgments

References

Abstract

The emergence of the financial supply chain provides assistance for small, medium and micro enterprises in the supply chain through a secured credit model based on real trade. Moreover, in the multi-level structure of the financial supply chain of the Internet of Things enterprise, there are information barriers and information islands. Besides, data is often not transmitted smoothly, and the intermediate offline process is complicated. What is worse, the efficiency is low, and the verification cost is high. Therefore, based on supply chain finance, an evolutionary risk model is constructed in this paper. Firstly, the income matrix of the regulatory risk model is established, and the convolutional neural network used will pool the training data to the maximum and set the local corresponding normalization layer. With the help of the evolutionary risk theory, the dynamic equation of the financial supply chain is obtained, forming the dynamic path and abnormal model of strategy selection. Then, a compact pattern tree is added to the knowledge granularity method to mine data anomalies. Finally, an experimental platform is built to verify the effectiveness of the method proposed in this paper, and experiments are performed on the accuracy of model evolution conditions, abnormal data identification, and abnormal numerical examples. The experimental results prove that the algorithm in this paper is consistent with the set parameters, and the effect is significantly higher than other comparison methods.

1. Introduction

As the development scale of the Internet of Things expands, the economic development of Internet of Things enterprises face many challenges like the great downward pressure on the economy. The development of corporate financial supply chains is becoming more and more important. However, there are many problems that need to be solved in the traditional corporate financial supply chain. For example, the high-quality credit of core enterprises has not been fully utilized, causing a lot of financial expenses. Small and medium-sized enterprises are of insufficient credit qualifications, so that the financing needs of enterprises will be large. Meanwhile, the bargaining power of the industry chain is weak, with many sales on credit. In addition, enterprises cannot maintain the continuous growth of the financing scale only with the help of their own funds. Moreover, the traditional credit model cannot meet the requirements of upstream and downstream enterprises in the supply chain [1].

Abstract

     Based on the startup investment database, we empirically examine the positive effects of a portfolio financing strategy for startups. It is known that some investment banks or venture companies have supplied risky and patient capital to U.S. startups of all financial corporations. They provide differentiated debt financing to startups who already received equity financing and invest more in equity when the startup is younger. Empirical analysis shows that the core risk management is to have a dynamic financing ratio between equity and debt as well as varied equity cost of capital, lending rate and investment horizon based on the age, technology, and financial soundness of a startup. We also find that optimal portfolio financing strategy can help startups raise funds with ease, but also it provides investors with higher expected return on investment, which can facilitate financing for startups and furthermore small and medium enterprises.

Introduction

     Startups play a significant role in economic growth. First, they create many jobs in various industries. If a startup drives technological innovation, new industries are created, resulting in various new jobs. After David Packard and William R. Hewlett set up their audio oscillator business in Silicon Valley in 1939, radio and telegraph technology grew rapidly, providing jobs to many young people. Over the years, innovative startups have emerged in the semiconductor and information technology industries, such as Apple, Facebook, Google, IBM, Microsoft, and Uber. Furthermore, as a startup grows into a large company, it also inspires workers to establish their own startups by taking inspiration from entrepreneurs who have already experienced success. Simply put, just as startups create jobs, an entrepreneur with a created job establishes a new startup, causing a positive feedback loop in job creation. As shown in Fig. 1, the job growth in Silicon Valley and San Francisco rose by 30%–35% from 2010 to 2018, which is much higher than the average job growth rates of the USA, Germany, Japan, South Korea, and China.

Conclusion and implications

     Startups drive economic growth by creating jobs, increasing TFP, and allocating resources efficiently. A portfolio financing strategy can help activate startups. Some investment banks, such as the SVB, have adopted a differentiated portfolio financing strategy for both debt and equity financing. They invest more in equity when the firm is young and invest more in debt as the firm ages. They also manage the credit risk of loan portfolios.

Abstract

1 Introduction

2 State of the art

3 Methodology

4 Result analysis and discussion

5 Conclusion

Declarations

References

Abstract

     In recent years, public–private partnership (PPP) mode has been developed greatly in China’s infrastructure and public services, which can effectively solve the financial difficulties of local governments. This article selects the residual value risk (RVR) as an important index in the risk control of PPP project and constructs and verifies the pre-optimization method of RVR. A method is presented to control and optimize RVR before the risk occurs. The vulnerability as the breakthrough point of RVR beforehand control through DRCM sensitivity analysis is carried out on the effect of the beforehand control of RVR; then, using the optimal control theory to design RVR beforehand control of optimization process, the correctness and validity of the method are verified by an example.

Introduction

     Public–private partnership (PPP) projects, namely government and social capital cooperation. The Treasury defines it as: ‘‘In the field of infrastructure and public services, government and social capital to establish a cooperative relationship based on contract, aims to use market mechanism to allocate risk, the supply of public products and services to improve the quantity, quality and efficiency’’ (Bodiako et al. 2017; Cui et al. 2018; Tariq and Zhang 2020). Specifically, the PPP model as Build–Operate– Transfer (BOT), Build–Own–Operate (BOO), Build– Lease–Operate–Transfer (BLOT), Design–Build–Operate (DBO), Transfer–Operate–Transfer (TOT) and Private– Finance–Initiative (PFI), and a series of specific financing model of the overall concept. It represents the social capital and the government different degree and different ways of cooperation and focuses on the introduction of social capital investment—construction—operation in infrastructure projects, emphasis on a ‘‘win–win’’ situation of mutual benefit. Its applications range from traditional industries such as roads, bridges, rail transit, gas, water supply, sewage treatment and garbage collection to prisons, schools, hospitals, defense, aerospace and other fields (Xu and Bureau 2016). The earliest application of PPP in China is the BOT project of Shenzhen Shajiao B power plant, and its successful experience has made it a preliminary recognition in China. Subsequently, PPP mode has been tried in various fields, and its specific content is also promoted to TOT, BOO, BOOT and other forms.

Conclusion

     Through the detailed literature, the current research status of PPP mode, PPP project residual value risk (RVR), PPP project risk management, etc., is summarized and reviewed. Then, the research idea of risk control of general engineering project is borrowed, and the risk conduction network of PPP project is constructed and verified. Then, based on the analysis of the risk conduction effect of PPP project, the causal conduction network of the risk conduction network of PPP project is deduced. And by constructing the structural equation model for causal testing, the results show that the model parameter estimation results are ideal, the model and data are fitted with higher fitting degree, and the causal hypothesis is basically correct. Based on the empirical analysis results of RVR simulation in what, risk conduction network simulation of West tunnel project and RVR comparison curve under three risk control methods. The parameter estimation results of SEM model can also provide quantitative explanation for the transmission relationship between risks: The PPP project risk can be constructed using a path coefficient and flow evaluation model (RFEM), and its function is use SEM to remove autocorrelation and purify the advantage of measuring error, easy and quickly to solve internal dynamic complex formed by the risk of cluster risk flow; the influence coefficient Y can be used to solve the transformation degree between the risk flow and provide the quantitative basis for the accurate construction of the correlation model. In the future, we will continuously optimize and improve the current model and compare it with more models.

Abstract

References

Acknowledgements

Author information

Ethics declarations

Additional information

Rights and permissions

About this article

Abstract

The purpose is to make full use of data mining and machine learning technology under big data to improve the ability of trade financial enterprises to cope with the risk of excessive financialization. In view of the above needs, based on previous studies, genetic algorithm (GA), neural network and principal component analysis (PCA) methods are used to collect and process the data, and build a risk assessment model of excessive financialization of financial enterprises. The performance of the model is analyzed through the data of specific cases. The results suggest that the data mining technology based on back propagation neural network (BPNN) can optimize the input variables and effectively extract the hidden information from the data. The specific examples show that most of the current enterprises do not have greater financial risk. However, most of the financial enterprise indexes show that the actual enterprise assets are gradually financialized. The total accuracy rate of financial risk assessment model based on deep belief network (DBN) is over 91%, and the accuracy of the model can reach 80% even if the sample size is small. Therefore, the financial risk assessment model proposed can effectively analyze the relevant financial data, and provide reference for the financial decision-making research of financial enterprises.

1 Introduction

With the continuous development of the world economy in recent years, more and more enterprises are active in the fnancial market, which promotes the sustainable development of the national economy. However, the following problems are the fracture of many enterprise capital chains due to the poor risk awareness, fnancial market turbulence, commercial fraud or mismanagement. Small enterprises are on the verge of bankruptcy because of their poor ability to resist risks, which is mainly because more enterprises do not invest in the real economy but pay more attention to virtual fnancial markets (Anginer et al., 2018). With the advent of big data era, although the risk control model of enterprises plays a certain role in credit management, due to the lack of deep understanding of data, the corresponding model accuracy is low, and it is unable to evaluate the borrower comprehensively (Florio & Leoni, 2017). Due to the more in-depth research on big data tools by many scholars, more and more methods are applied to the fnancial feld (Trelewicz, 2017). Traditional risk control model has problems such as single dimension and limited assessment ability. However, using data mining method can analyze the data of fnancial industry in depth, involving many dimensions and more comprehensive assessment (Moradi & Mokhatab Rafei, 2019). With the establishment of distributed database and the gradual maturity of various data platform architectures, more data are stored and calculated, and the dimensions of data analysis are also wider (Medvedev et  al., 2017). Especially with the construction of Hadoop cloud computing platform, the distributed computing ability of big data has been greatly improved, and the data foundation and computing ability are more efcient. Compared with the traditional risk control model, the model based on big data has great changes in the amount of information and accuracy (Ding et al., 2019; Ivanov et al., 2019). Big data can improve the credit system and help trade enterprises reduce credit risk (Saura et  al., 2019; Urbinati et  al., 2019). Data mining can use the network data to improve the scoring model and optimize the approval process through the integration of algorithms, so as to form a sustainable closed-loop management mode (Aldridge, 2019). Therefore, the use of data mining and its application in fnancial risk assessment has become one of the hot spots in this feld.

Highlights

Abstract

Keywords

JEL classification

1. Introduction

2. Background Facts

3. Data

4. Bank Funding Dynamics

5. Are the Results Driven by Informationally Less Opaque Banks?

6. How Does Capital Account Liberalization Affect Small Banks?

7. Robustness Checks

8. Concluding Remarks

Acknowledgments

Appendix A.

References

Abstract

Using a sample of almost 600 banks in Latin America, we show that capital account liberalization lowers the share of equity and raises the share of interbank funding in total liabilities of the banking system. These shifts are mostly due to large banks; smaller banks, instead, increase their resort to retail funding by offering higher average deposit interest rates than larger banks. We also find significant differences in the behavior of banks with seemingly greater information opacity. These findings have positive implications for macro-prudential regulation.

1. Introduction

Lower controls on a country’s capital account can increase the conditional probability of macro-financial crises by facilitating the accumulation of foreign liabilities (Reinhart and Rogoff, 2008, Gourinchas and Obstfeld, 2012, Claessens and Ghosh, 2013, Catão and Milesi-Ferretti, 2014, Claessens, 2017).1 In examining the channels through which abundant global liquidity and capital flows raise crisis risk, many papers have looked at the role of bank lending to private firms and the government—flows that lie squarely on the asset side of banks’ balance sheets (Popov and Udell, 2012, Jordà et al., 2013, Lane and McQuade, 2014, Taylor, 2015; Ongena et al., 2015, Correa et al., 2015; Baskaya et al., 2017, Temesvary et al., 2018; Morais et al., 2019, Dinger and te Kaat, 2020; Hoffmann and Stewen, 2020, te Kaat, 2020). On the liability side, while there has been work on how bank leverage responds to swings in global liquidity and risk aversion (Bruno and Shin, 2015a, Bruno and Shin, 2015b), and on how capital control regulations affect the cost and volume of international borrowing and lending to firms (Bonfiglioli, 2008; Beakart et al., 2011; Varela, 2018; Ahnert et al. 2021), little attention has been devoted to how the distinct components of banks’ liabilities shift in response to changes in capital controls and how those shifts are conditioned by bank-specific characteristics (large vs. small, foreign vs. domestically-owned, having a more vs. a less opaque balance sheet).

This paper aims to fill some of this gap in the literature. We examine the response of the various liability components of banks—namely, equity, retail deposits, interbank deposits, bonds, other short-term debt, and non-interest liabilities—to changes in capital control regulations and ask whether and how such responses differ across smaller and larger banks, domestic vs. foreign-owned, and those with seemingly more vs. less opaque balance sheets. We do so using bank-level data from Bureau van Dijk’s Bankscope database for 17 Latin American countries over 1995–2013. Focusing on Latin America during this period is particularly suitable for the purpose of this investigation because of the region’s pattern of liberalization in external capital accounts, which was not only far-reaching, but also displayed considerable cross-country heterogeneity through the 1990s, 2000s and early 2010s, aiding identification.2 We further aid identification by controlling our estimates for a variety of country-specific macro and global financial variables, using a more accurate metric of capital controls developed in Fernández et al. (2016), and allowing for some possible endogeneity of capital controls. The sizable dimension of our panel data—across countries, banks, and time—coupled with our use of a rich set of macro-financial covariates in turn allows our findings to speak to the intersection of the prominent literature on banks, financial globalization and macro-financial risk.

Abstract

Keywords

1. Introduction

2. Literature review

3. Data and methodology

4. Results and analysis

5. Conclusion and recommendation

Declaration of competing interest

Appendix A. Supplementary data

References

Abstract

This study examines the relationship between green finance and carbon dioxide (CO2) emissions in the top ten economies that support green finance (Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States). This study uses quantile on quantile regression (QQR), introduced by Sim and Zhou (2015), to examine the dependence structure between different quantiles of green finance and CO2 emissions. Our overall findings confirm the negative impact of green finance on CO2 emissions; however, this relationship varies across the different quantiles of the two variables. This variation might be due to green finance market conditions (e.g., bearish or bullish) and country-specific market conditions. The findings in the study confirm that green finance is the best financial strategy for reducing CO2 emissions.

1. Introduction

Since the beginning of the industrial revolution, the financial sector has been a powerful pillar of human growth. The primary role of the global financial sector is to make efficient use of the global savings. Proper use of investment enables improvement in people’s quality of life. However, because of the collapse of the financial system, people have invested their savings in real-estate bubbles and environmentally damaging projects, including those that exacerbate human-induced climate change (Sachs, 2014). Previously, the financial sector ignored the ecosystem, which enabled the emergence or worsening of environmental issues, such as habitat and natural resource depletion, climate change, and pollution.

Finance plays a crucial role in the anthropogenics (i.e., human impact on the environment), yet very little has been done to incorporate environmental issues into finance (Scholtens, 2017). Over the past few years, the financial sector has paid attention to green investments, thereby advancing sustainable growth (Falcone et al., 2018). According to Sachs (2015), green financial instruments can help achieve a green environment. In the process, financial intermediaries and markets have designed financial instruments, such as green bonds, green home mortgages, green loans for commercial buildings, environmental home equity programs, “go green” auto loans, small business administration express loans, and climate credit cards. In addition, Australia launched its first environmental deposit initiative, which consists of medium-to long-term finance tools, which not only finance environmentally friendly projects and business activities but also support sustainable development and climate-related projects directly (NATF, 2007).

Green finance is an intersection between environmentally friendly behavior and the financial and business world (Scholtens, 2017), however, few studies have link finance with ecology. Scholtens (2009) studied the link between the performance and social responsibility of financial institutions. Li and Jia (2017) concluded that environmental finance/sustainable finance is the most effective way to reduce environmental degradation. Green finance encourages investment in new technologies and innovations, including renewable energy (Böhringer et al., 2015). Thus, we are motivated to examine the dynamic impact of green finance on the carbon dioxide (CO2) emissions of the top ten countries that support green finance (Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States).

Abstract

Keywords

1. Introduction

2. Basic characteristics of new economy companies

3. Financing models of new economy company and related accounting standards

4. Solutions to disputes about the financing instruments of new economy companies

5. Conclusion

Declaration of Competing Interest

Appendix A.

References

Further reading

Abstract

New economy companies often use convertible and redeemable preferred shares with equity and debt characteristics as financing tools to reduce risk during their early stages of growth. According to relevant accounting standards, such preferred shares should be classified as financial liabilities and measured at fair value, with changes in fair value recognized in profit or loss. This can lead to confusing financial information: the better a company’s development prospects, the higher its redemption or conversion price and loss, which can result in a large negative net asset value. A successful initial public offering, however, could offset large losses and negative net asset value. Following the development of accounting standards, this article thoroughly analyzes various proposals to modify relevant accounting standards and eliminate confusing information. This article also proposes possible problems and solutions as a reference for accounting standard setters and the various stakeholders in new economy companies.

1. Introduction

New economy companies, such as information technology, biomedicine, and big data companies, have flourished since the 2008 financial crisis. In 2018, the new economy industry accounted for 16.1% of China’s GDP, becoming the new engine of China’s development. In the same year, the Shanghai Stock Exchange launched the Sci-Tech Innovation Board, and the Hong Kong Stock Exchange relaxed the conditions for the listing of biotech companies to provide a better financing environment for new economy companies. Also in 2018, the China Securities Regulatory Commission (CSRC) released a document outlining new rules for overseas-listed red-chip companies that return to the domestic market for listing. Recently, the CSRC issued another document that lowered the value threshold for returning companies but emphasized that they should have independent R&D, internationally leading technology, strong technological innovation capabilities, and other competitive advantages. The Shenzhen Stock Exchange has begun to reform the second-board market to serve growth-oriented innovative and entrepreneurial companies and support the deep integration of traditional industries with new technologies, industries, formats, and business models. In this paper, we examine whether current accounting standards meet the information-communication needs of new economy companies and provide users of financial statements with enough information to make informed investment decisions and contribute to China’s economic growth.