Abstract

We estimate the effect of global supply chain disruptions on inflation for a panel of 28 European countries. Adverse shocks have a stronger and more persistent effect than favorable shocks.

Introduction

Disruptions to global supply chains, e.g. due to natural disasters and lockdowns, are considered a major driver of the drastic increase in inflation in Europe and other advanced economies after the COVID-19 pandemic. The evidence (Carrière-Swallow et al., 2023, Burriel et al., 2023, Ascari et al., 2023, Laumer, 2023, Khalil and Weber, 2022, Finck and Tillmann, 2023, Finck et al., 2023, Liu and Nguyen, 2023, Elsayed et al., 2023, De Santis, 2023) suggests that supply chain shocks indeed have a significant effect on inflation.

More recently, supply bottlenecks eased and indices of supply chain conditions (Benigno et al., 2022) are back at pre-pandemic levels. However, inflation remains high. The Economist (2023) asks: “Supply chains are back to normal. Why is inflation still so high?” This note provides one potential answer to this question. We estimate local projections for a panel of 28 European economies between 2010 and 2023 in order to quantify the effect of a supply chain disruption on headline inflation, core inflation and producer price inflation. The supply chain shock we use is the purified change in the Global Supply Chain Pressure Index (Benigno et al., 2022). Importantly, we allow positive, i.e. restrictive, and negative, i.e. expansionary, shocks to have different absolute effect sizes.

Conclusions

This note showed that the inflationary effects of disruptions to global supply chains are asymmetric. An increase in supply chain stress strongly contributes to the increase of inflation as seen after the COVID-19 pandemic. However, an easing of supply chain pressure contributes much less to the decline of inflation. Hence, our results provide one potential answer to the question asked by The Economist (2023).

Of course, asymmetric supply chain shocks are just one potential explanation of persistent inflation rates despite an easing of supply chain conditions. A tendency to de-globalize supply chains could raises costs even as shipping conditions normalize. Inflation could also remain elevated due to second-round effects of the initial spike in energy prices and costs of intermediate goods on wages. In addition, a shortage of labor post-Covid might also prevent inflation from returning to low levels. Finally, the sectoral shift of demand during the pandemic from services to goods is still visible in the data, which causes inflationary pressure in the presence of frictions.

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 adds to the recent interest in the link between cash and firm performance, by studying how this relationship varies across boom- and bust cycles. We use data of Norwegian firms from a broad range of sectors in the period 2005–2015, and both replicate and extend previous findings on the relationship between cash and performance over the business cycle. We find that i) cash has a positive, but weakly diminishing effect on operational firm performance (ROA) throughout the entire sample period, and ii) that the curvilinear relationship between cash and firm performance is the most pronounced in the pre-recession years, while it is virtually linearly positive in recessions and post-recession periods. We conclude that cash indeed has an impact on firms’ operational performance, and especially so in recessionary times.

Introduction

     Traditionally, strategy scholars have disregarded cash as a strategic asset (e.g. Barney, 1986), while finance scholars have viewed cash holdings as a signal that a firm lacks profitable investment opportunities or have managerial issues (e.g. Jensen, 1986). Recent studies have questioned these views by documenting that cash may indeed be positively related to performance (Kim & Bettis, 2014), and that contextual factors such as state of the business cycle (Nason & Patel, 2016) and competitive dynamics (Deb et al., 2017, Jung et al., 2020, O’Brien and Folta, 2009) are important moderators for the strength of the cash-performance relationship.

     While this recent research stream has made important advancements to our understanding of the relationship between cash and firm performance, we know less about how these insights can be generalized to other contexts than large, listed (manufacturing) companies operating in relatively stable periods, and if relationships between cash and market-based measures of performance also holds for operational measures of performance. Currently, most studies on the cash-performance relationship use samples of large, listed, US, (manufacturing) firms (e.g. Deb et al., 2017; Jung et al., 2020; Kim & Bettis, 2014; O’Brien & Folta, 2009), study relatively stable periods and not more turbulent times such as economic downturns where financing constraints tends to increase (Forseth et al., 2015), and/or use market-based measures of firm performance such as Tobin’s Q as their dependent variables (e.g. Deb et al., 2017; Jung et al., 2020; Kim & Bettis, 2014; O’Brien & Folta, 2009).

Conclusion

     The purpose of this paper was to examine the relationship between cash and firm performance over the business cycle.

     Throughout our analysis we find that cash has a positive impact on firm performance across the different phases of the business cycle. The linear term of cash is highly significant in all our models and across all time periods. The quadratic term is less consistent; in some cases, we identify a curvilinear relationship, whilst sometimes the quadratic term is low and insignificant indicating a linear relationship between cash and firm performance.

     After running our full and expanded models, we conclude that the curvilinear relationship between cash and performance is weakly curvilinear for our sample of Norwegian firms, and that this curve-linearity is the most pronounced in the pre-recession period, and more or less linear during the recession and post-recession periods.

     In closing, we believe that our results add to an important stream of research at the intersection of strategy and finance. Mainstream theories, especially in strategy discard cash and other financial assets as less important drivers for heterogeneity and performance differences. Our findings, and the findings of the studies we build on, challenge these assumptions. It is, however, important to note that cash seems more likely as an indirect source of performance differences, through enabling firms to make investments and deploy strategies that in turn create performance heterogeneity. That is, cash appears to be less of a king, but more a key member of the court that pull some important strings.

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

This paper builds a network econometric model capable of analysing the impact of inflation on systemic risk. Its main contribution is the identification of a robust inverse relationship which reverses when controlling monetary policy. This reveals that the former effect is due to monetary policy reactions to inflation. It is further analysed whether this effect comes from overindebtness as in a Minsky moment. There is no evidence supporting it, which suggests that mechanisms other than excess credit underlie such a relationship. The results presented in this paper are of particular importance for understanding monetary policy reactions to current inflationary cycles.

Introduction

Analogously to many other macro-financial linkages, the effects of inflation on systemic risk are, to a large extent, still unknown. This is the result of a disconnection between the interplay of the economy and financial markets which has dominated macroeconomic research in recent decades. However, the financial crisis of 2007–2009 made clear that there is a constant interaction between the economic system and financial markets (Diebold and Yılmaz, 2015, Abbas et al., 2019, Balcilar and Bekun, 2020, Cotter et al., 2020, Silva et al., 2017, Festić et al., 2011, Aliyu, 2012), highlighting the need to consider additional contagion channels rather than the traditional interbank market (Silva et al., 2018). Thus, many of the macro-financial variables depend the other dimension, potentially in a non-linear way, as it has been theoretically argued (Bernanke et al., 1999, Gertler and Kiyotaki, 2010, Mendoza, 2010, Brunnermeier and Sannikov, 2014) and empirically estimated (Giglio et al., 2016).

In particular, the effects of inflation on systemic risk are of special importance nowadays. After more than 20 years of relatively low inflation (see red line Fig. 1, quantified on the right axis), the study of the inflation-financial stability binomial have not been of upmost importance. Thus, some studies have analysed the impact of inflation on the performance of the financial sector (Boyd et al., 2001), financial development (Festić et al., 2011), and many have analysed the relationship between inflation and public debt (Bhattarai et al., 2014, Krause and Moyen, 2016, Cherif and Hasanov, 2018). However, the recent inflationary shock has caused “seismic waves” for the stability of the financial system, as contractive monetary policy stances have revealed, among other concerns, overexposures to interest rate risks by financial institutions. Therefore, discounting monetary policy, there may exist a relationship between inflation and systemic risk which could make controlling inflation a major concern not only for the correct functioning of the economy, but also for ensuring the stability of the financial system. As shown in Fig. 1, this correlation between inflation and systemic risk connectedness may exist a priori.

Conclusions

This paper has provided some inferences about the effects of inflation on systemic risk in a financial network of twelve countries. It has been found that, after controlling the monetary policy reactions to inflation, inflation increases systemic risk. Furthermore, in the time span considered (2000–2022), inflation increases more systemic risk than interest rates. This provides partial equilibrium evidence in favour of aggressive monetary policy interventions to preserve financial stability and not only to ensure the correct functioning of the economy during inflationary shocks. Indeed, if inflation increases more systemic risk than interest rates, there exists an incentive for the central bank to increase interest rates in presence of rising inflation, since the effect of the latter outweights the negative effect of the bank rate hike for financial stability.

It has been further tested whether the channel through which inflation increases systemic risk is excessive lending during economic booms as in a Minsky moment. However, evidence has not been found which reveals that mechanisms other than excess credit are behind the relationship. All the results presented in this paper are robust to several variable selections and econometric specifications, and extend to inflation expectations as well. Further research can be directed towards increasing the understanding of the relationship between inflation and systemic risk with monetary policy, since a substantial part of it appears to be happening through interest rates.

Abstract

Inflation dynamics are investigated by estimating a generalized version of the New Keynesian Phillips curve (NKPC) of Galí and Gertler (1999) using Bayesian GMM. US macroeconomic data suggests that the generalized NKPC (GNKPC) performs best in terms of quasi-marginal likelihood among those considered both during and after the Great Inflation period. The estimated GNKPC indicates that when trend inflation fell after the Great Inflation period, the probability of price change decreased and the GNKPC flattened, which is in line with findings by previous studies.

Introduction

The dynamics of inflation have long been the subject of intense investigation in macroeconomics. To describe inflation dynamics, the New Keynesian Phillips curve (NKPC) is often derived by assuming either zero trend inflation or price indexation to trend and lagged inflation.1 However, these assumptions in the canonical NKPC are at odds with empirical observations. Recent studies thus examine the effect of nonzero trend inflation on the NKPC, particularly without the indexation.2 The studies have shown that such a generalized NKPC (GNKPC) has substantially distinct features from the canonical NKPC, thereby generating important implications for policy and welfare. This finding raises the question as to which is a more plausible description of inflation dynamics, the GNKPC or the canonical NKPC.

This paper estimates and evaluates the GNKPC using a novel model selection procedure under the framework of limited-information Bayesian estimation. In the empirical literature on NKPCs, two main approaches have been adopted: limited-information (or single-equation) methods and full-information (or system) methods. For example, the GMM estimation of the NKPC in Galí and Gertler (1999) and the minimum distance estimation of the GNKPC in Cogley and Sbordone (2008) can be classified as limited-information methods. On the other hand, NKPCs in the estimated dynamic stochastic general equilibrium (DSGE) models of Christiano et al. (2005) and Smets and Wouters (2007) can be categorized as full-information methods. In a recent paper, Hirose et al. (2020) conduct a full-information Bayesian analysis to compare a GNKPC and an NKPC in an otherwise identical DSGE model, and show that the model with the GNKPC outperforms that with the NKPC in terms of marginal likelihood.

Empirical Results

This section presents the results of the model selection and accounts for the estimation results of the selected model.

4.1 Comparison of Gal´ı-Gertler GNKPC, NKPC, and GNKPC with indexatioWe begin by comparing the empirical performance of the GNKPC (1), the NKPC (5), and the GNKPC with indexation. As noted in the preceding section, the performance is evaluated in terms of QML, which is computed with the two alternative modified harmonic mean estimators proposed by Geweke (1999) and by Sims et al. (2008). For each of the three, Table 3 reports log QML using the truncation parameter values of τ = 0.5, 0.9 for the estimator of Geweke (1999) and q = 0.5, 0.9 for that of Sims et al. (2008).

The third to fifth columns of Table 3 show that the GNKPC has higher QML than the NKPC and the GNKPC with indexation in all the three estimation periods for both estimators with both truncation parameter values. This result indicates that the GNKPC describes inflation dynamics better than the NKPC and the GNKPC with indexation both during and after the Great Inflation period as well as the full sample period. Moreover, the absence of the indexation, that is, retaining some unchanged prices in each quarter in line with micro evidence improves the GNKPC’s fit to the macroeconomic data. These findings coincide with those of Hirose et al. (2020). They conduct a full-information Bayesian analysis to compare a Gal´ı-Gertler GNKPC and NKPC in an otherwise identical DSGE model, and show that the model with the GNKPC outperforms that with the NKPC during both the Great Inflation and Great Moderation periods in terms of marginal likelihood. As noted in Introduction, recent studies have pointed out that GNKPCs possess substantially distinct features from canonical NKPCs, thereby generating important implications for policy and welfare. Therefore, our findings suggest that GNKPCs should be preferred to canonical NKPCs for the analysis of the Federal Reserve’s monetary policy.

Abstract

1. Introduction

2. Related work

3. Problem statement and summary of contributions

4. Background knowledge

5. Master Node Based Clustering (MNBC)

6. System design

7. Performance evaluation

8. Security evaluation

9. Conclusion

References

Abstract

     Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbours without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces a proximity-aware extension to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, which is based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In the MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for maintaining clusters based on physical Internet proximity. Furthermore, a reputation-based blockchain protocol is integrated with MNBC protocol in order to securely assign a master node for every cluster. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay.

Introduction

     Bitcoin is the first digital currency to attract mainstream, businesses, and people’s attention. Bitcoin is a virtual, decentralised cryptography that no one is in charge of it and it is not tracked by any hard asset or government. Instead, Bitcoin relies on a peer-to-peer (P2P) network that protects the Bitcoin’s value by means of cryptography that is performed by peers mining Bitcoins through brute-forcing double SHA-256 hash function. The concept of Bitcoin was proposed by an anonymous programmer using the pseudonymous Satoshi Nakamoto [1]. Due to the Bitcoin advantages such as the absence of intermediates and its decentralised architecture, Bitcoin is now deployed as a currency by many businesses and companies. Bitcoin performs global transactions which allow non-refundable, reasonably fast money transfers to any part of the world [2].

     Based on the publicly distributed ledger that is shared by the entire Bitcoin network nodes, a distributed trust mechanism is achieved [3]. This mechanism is considered as a monitoring technique by which the amount of available bitcoins1 in Bitcoin will be tracked. To achieve this mechanism, two main requirements need to be fulfilled: (i) transactions verification process has to be achieved in a distributed manner to ensure the validity of transactions; and (ii) successfully processed transactions have to be quickly announced to everyone in order to guarantee the consistent state of the blockchain [4]. As transactions are validated against the blockchain, reaching a consistent state over the blockchain is considered as a fundamental requirement towards achieving the distributed transactions verification process.

Conclusion

     In this paper, a brief background of the Bitcoin system as well as analysing the information propagation in the real Bitcoin network were presented. In addition, how propagation delay in the Bitcoin network could affect the security by offering an opportunity to double spend the same coins; thereby abusing the consistency of the public ledger was discussed in this paper. The MNBC, a novel clustering protocol that incorporates master node based physical proximity and reputation scheme based blockchain into the existing Bitcoin protocol, was presented in this paper. By conducting extensive simulations, MNBC evaluation results indicate an improvement in the transaction propagation delay over the Bitcoin network protocol. However, MNBC maintains a lower variance of delays than the BCBSN protocol. Furthermore, experiments with different latency thresholds have been conducted to identify the distance threshold that would give a better improvement in the transaction propagation delay. We discovered that providing a less latency distance threshold would improve the transaction propagation delay with a high proportion. Furthermore, evaluation of partitioning attacks in the Bitcoin network as well as the MNBC and BCBSN protocols were presented in this paper. The results revealed that attackers still need more resources to split the network in the proposed protocol, especially with a higher number of nodes. Furthermore, the effect of malicious nodes on the MNBC protocol was validated in this paper. The results proved that MNBC throughput had not been significantly affected by the malicious behaviour.

Abstract

1. Introduction

2. China’s launching of a central bank digital currency

3. Cryptocurrency versus digital currency

4. Is the digital yuan a threat to the US dollar?

5. Conclusion

Declaration of Competing Interest

References

Abstract

     This article provides a detailed introduction to China’s launching of a digital currency. We conduct a comparative analysis concerning whether digital currency is a more stable and reliable currency than cryptocurrency and investigate whether a digital renminbi (or yuan) could replace the US dollar as a medium of exchange in international transactions. China has gained a first-mover advantage by rolling out a central bank digital currency (CBDC). But the outcome will depend on the US response as well as the future evolution of the US and Chinese economies. Most other articles on this topic focus on domestic use of the Chinese CBDC. But this study is unique in analyzing the prospects of a digital renminbi as a replacement for the US dollar in international commerce.

Introduction

     Over the past decade, the importance of cryptocurrency, especially Bitcoin, has rapidly increased. The crypto market has a great deal of potential, but its highly decentralized nature poses threats as well. The absence of a regulatory authority for monitoring and overseeing operations associated with such digital transactions makes cryptocurrency a high-risk investment. Because it is backed by only a handful of investors or by sentiment generated in the market, crypto has a volatile nature that foreshadows the instability and frailty it can introduce into the economic system as a whole (Aysan et al., 2021). Financial experts see cryptocurrency as the biggest challenge to the financial system and believe that immediate measures are needed to retain control of that system (Aysan et al., 2020). To maintain control of this system and limit any shift in it, several central banks have introduced a national digital currency (Auer et al., 2020). However, many countries still do not support the concept of a digital currency, as they see it as farfetched and claim that introducing one will only make the financial system more vulnerable to potential cybersecurity such as ransomware and financial stability threats (Nicole, 2021; Parn and Edwards, 2019).

Conclusion

     By rolling out its CBDC, China has undoubtedly gained a first-mover advantage. Its large population and the widespread familiarity with digital and cashless payment have helped the PBOC to offer its CBDC earlier than other countries. This first-mover advantage also puts China in a privileged position in determining the future direction of financial technologies. An early launch of the digital yuan would help China to internationalize its currency and to challenge the hegemony of the US dollar in the global financial system. China’s initial focus is international payments, but in the medium and long term, it wants to promote the RMB as an international reserve and investment currency as well. The successful internationalization of the digital yuan will require China to win the confidence of global users in the strength of the Chinese economy, financial system, and the functionality and security of the digital yuan. Despite these advantages, China also faces challenges in achieving these goals. The outcome depends on how the US responds and how the US and Chinese economies evolve in the future.

Abstract

     The aim of the work underpinning this paper has been to track the evolution of tail risk in banks’ NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Düllmann and Masschelein in 2006—in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015–2017 period, as followed by the onset of a decline. Where the paper’s second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks’ lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland’s Financial Supervision Authority are formulated.

Introduction

     Concentration risk is one of the specific types of risk in banking whose inappropriate management, non-subjection to appropriate policies and regulations, or incomplete measurement may all give banks financial problems. Examples here might be the concentration of bank lending in the energy sector in Texas and Oklahoma in the 1980s; as well as over-exposure to the construction and development sector in Sweden in the early 1990s, and in Spain and Ireland in 2000. The materialisation of concentration risk during the global financial crisis of 2008–2009 was in turn a source of huge losses for European and global banks that left them weakened economically, financially, and as regards operational security. Such circumstances ensure the huge importance from a macroprudential point of view of concentration risk being measured.

     The effects of the COVID-19 pandemic will hit home just as soon as we see restriction of the emergency measures that governments, central banks and regulators across Europe have introduced. That said, it is reasonable to expect that banks struggling currently with declining interest margins and low profitability, among other things, will prove to be most affected by the crisis, where there is marked concentration in sectors more affected by the pandemic, i.e. hospitality, transport and some manufacturing sub-sectors; as well as in those other sectors already characterised by a high level of non-performing loans on account of COVID. The pandemic may emerge as concentrating bank exposures to the domestic government sector to an excessive degree. This is to say nothing of Poland’s energy transformation, with its requirement for a very considerable amount of investment (given costs at the level of 1.6 trillion PLN estimated for 2021–2040.

Conclusion and policy implications

     This article examines the extent to which sector concentration contributes to growth in economic capital, as well as assessing the effectiveness of analytical methods in measuring sector concentration risk. Unexpected losses were estimated using the multivariate model described in the articles by Düllmann and Masschelein (2006), (2007), and Düllmann and Puzanova (2011), as derived indirectly from the model after Merton (1974). Additionally, this article has focused on the development of the methodology proposed by Düllmann and Masschelein, in the direction of improved accuracy of economic-capital estimation, thanks to alternate ways of mapping the sector factor correlation matrix. Moreover, the article contains a detailed description of the methodology applied.

     It is worth noting that the study used a unique database, inter alia with data concerning bank exposures to non-financial sector enterprises, corporate PDs and model LGD estimates (based on historical data). Portfolios were characterised by reference to their degree of diversification, with aggregate, relative exposures of domestic banking represented sector by sector. Exposures from the financial sector were not included, in recognition of that sector’s specific nature. In addition, it was possible to note the high degree of heterogeneity characterising sectors of industry, hence the study’s more-precise division into industrial groups.

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

Conclusions

     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

This paper evaluates the existence of spillover effects of the “Más Familias en Acción” program on eligible children who are not beneficiaries of the subsidy while their siblings are. Using a nonexperimental design, through a propensity score matching and a difference-in-differences model, we found a negative effect on the school enrollment of nonbeneficiary siblings, as well as an increase in their absenteeism. Furthermore, we found heterogeneous spillover effects on child labor by gender and age. Based on the results, we recommend redesigning the conditional cash transfer program by expanding the coverage to a household level, instead of limiting the number of beneficiary children per household.

Introduction

During the last quarter of 2018, 1.1 million Colombian children aged 5–17 worked an hour or more per week in some economic activity and performed domestic chores for 15 h or more weekly. That is, 10.3% of children worked during their childhood or adolescence (DANE, 2019).

In Colombia and most countries in the world, child labor is considered undesirable since it can harm children’s development, training, and performance in other activities that contribute to their well-being and future achievements (ICBF, 2017). Furthermore, child labor is a social problem because it compromises the education level reached by children, limiting human capital accumulation and economic growth (Holgado et al., 2014; Psacharopoulos, 1997; Khanam, 2007; Akabayashi and Psacharopoulos, 1999).

From a public policy perspective, the first step in eliminating or reducing child labor is to understand the reason adult household members allow their children to work. According to the Great Integrated Household Survey (GEIH by its Spanish initials), 85% of young Colombians work because of economic incentives (to participate in the family’s economic activity, to have their own money, or to help with household or education expenses). Consequently, governments should design public policies that not only search to discourage child labor but alleviate poverty and reduce economic stress in households as well.

Results

We test the impact of the program on four outcomes: (i) schooling attendance, a dummy equals 1 if the child attends an educational center, 0 otherwise; (ii) absenteeism days, the number of days that the child missed classes; (iii) child labor supply, a dummy equals 1 if the child worked during the week before the application of the survey

Abstract

     An essential issue of listed firms is adjusting their capital structure as stock market volatility increases. Our study examines this concern by using panel data of the Shanghai Stock Exchange for the period 2008–2018. We find that stock market volatility has immediate positive effects on both total market leverage and short-term market leverage but a negative influence on the long-term market leverage of Chinese listed firms. In this scenario, Chinese listed firms adjust their debt structure by using high bank debts and cutting trade credit due to lower debt costs. Further analyses confirm that the proportion of bank debts to total debts visibly increases while that of trade credit to total debts distinctly decreases. Furthermore, we implement robust tests regarding potential issues, such as sample selection, model selection, endogenous factors, and quantile regression to strengthen the robustness of the main findings. This study provides the first framework for investigating a link between the stock market volatility and capital structure decisions in a typical emerging market.

Introduction

     To date, determinants of capital structure have remained an incomprehensible subject in corporate finance. When firms are allowed to issue stocks on the equity market, equity issuance plays a vital role in the surging corporate capital. In terms of volatility, Welch (2004) documents that a critical matter of corporate finance is the volatility of stock price as a driver of notable changes in the behaviors and financial decisions of corporate managers. Figlewski and Wang (2000) demonstrate that the increase of the volatilities of stock market returns and individual stock returns leads to the decline in market values at the firm level. Following market timing theory, listed companies tend to issue equity when their market values are comparatively high relative to their book and past market values, and then repurchase their issued equity when their market values are low, which exerts a persistent effect on corporate capital structure (Baker & Wurgler, 2002). Chen et al. (2014) and Ahmed and Hla (2019) show that the volatility of individual stock returns significantly influences the capital structure of listed firms in the United States (US) and Pakistan markets, respectively. Most of their findings support the prediction of Tradeoff Theory (TOT) that companies diminish debts to balance and deal with the increasing prospect of default owing to high volatility risk; still, the practical results of Ahmed and Hla (2019) from the Pakistan market are pretty complicated. Similar to Pakistan, China is known as a developing equity market that is unstable, sensitive, and enormously volatile with unexpected shocks from the global market. In addition, the unique capital structure and debt structure of Chinese enterprises are inherently an exciting topic and thus widely explored in research (Fan et al., 2021; Zhang et al., 2015). To our best knowledge, the nexus between stock market volatility and corporate capital structure has not been explored. Surviving literature and evidence have powerfully prompted us to investigate the effect of stock market volatility on the capital structure choices of listed firms in the concrete context of the China stock market rather than imitating previous studies which focus on the volatility effect of individual stock returns on the corporate leverage.

Conclusion

     Choosing the capital structure as the increased stock market volatility is a vital concern of listed firms. This matter becomes more critical for listed firms activating in emerging markets, especially in China’s equity market, due to the following problems. First, emerging equity markets are immature and volatile. Simultaneously, listed firms in developing markets are quite sensitive to the equity market’s volatility. Second, thus far, corporate financing in China market essentially proceeds from internal funding and bank debts. In addition, Chinese firms universally have a high proportion of mature debts. Third, trade credit is an unofficial debt channel popularly used by Chinese firms when facing strict procedures from banks and credit institutions.

     Our study examines how the Chinese stock market volatility immediately affects the capital structure choices of Chinese listed firms from 2008 to 2018. The unique point of this research is the approach based on the capital use routines of Chinese firms instead of using two traditional capital structure theories to explain Chinese corporate capital structure, as in excellent previous literature (Chen, 2004; Huang & Song, 2006; Ni & Yu, 2008; Tong & Green, 2005; Zou & Xiao, 2006). We find that Chinese listed firms use the total market leverage and short-term market leverage at high levels, but long-term market leverage is used at low levels when the China stock market volatility increases and the lending interest simultaneously decreases. Empirical results point out that the positive correlation between corporate market leverage and stock market volatility is derived from the decline in the listed firm’s market value and the increase in bank debts. Trade credit significantly decreases while the bank debts tend to strongly increase in the debt structure of Chinese listed firms as the China stock market increases in volatility. Furthermore, we detect that short-term debts and bank debts are preferred in the capital structure of Chinese listed firms in response to the growing uncertainty of the China stock market and the change in lending interest rate.