International Journal of Finance & Economics最新文献

Despite extensive finance-growth literature, the critical role of spatial interdependence between countries has often been overlooked. This paper addresses this gap by utilising spatial Durbin modelling on a 30-year panel dataset of 56 emerging economies, examining the spillover effects of financial development (FD) and institutions on economic growth. The findings reveal FD has a significant positive within-country impact on growth; on average, FD is expected to raise growth by approximately 5.8% holding other factors constant. Meanwhile, the FD spillover effect on growth is estimated to be around 10 times its within-country effect, which is not surprising given that the 10-nearest-neighbour is the preferred matrix for conceptualising the spatial dependence between the countries under study. The results however show no evidence of significant threshold effect of FD. Political institutions emerge as the most influential in driving growth both within and across countries, whereas improvement in economic institutions moderates the growth-effect of FD. FD's within-country effect on growth is largely driven by financial institutions, while its spillover effect stems primarily from the neighbours' financial markets. The findings' robustness is confirmed through a battery of tests. In conclusion, this study offers valuable insights into the complex finance-institutions-growth nexus in emerging economies. By considering spatial interdependencies and the role of institutions, policymakers can craft effective strategies to harness FD's positive effects and foster an environment for sustained, inclusive economic growth.

Research on how shareholder pressure influences the interests of stakeholders has recently witnessed a sharp increase, but yields mixed findings. We revisit this question by investigating the impact of managers' attempts to meet or beat earnings expectations on corporate tax avoidance. Using a sample of Chinese listed firms during 2005–2022, we find that firms with earnings pressure are more tax-aggressive. Furthermore, such a higher level of tax avoidance due to earnings pressure is associated with higher real activity management. Cross-sectional analysis shows that the effect of earnings pressure on tax avoidance is weaker among firms with better environment, society, and governance performance, but becomes more salient for state-owned enterprises, firms with higher financial constraints, and during periods when local officials are devoid of incentives for career advancement. Taken together, our study sheds light on the consequences of shareholder pressure stemming from capital markets on corporate tax policies, and provides implications for enterprises and policymakers concerning taxation in emerging markets.

Earnings value relevance, defined by the predictive and explanatory power of corporate earnings information for market value, reflects accounting earnings decision utility and the capital market's information efficiency. Economic policy uncertainty (EPU) reduces information quality, impairs the information environment, and disrupts interpretation of information, diminishing earnings value relevance. Using a dataset comprising Chinese A-share listed companies from 2006 to 2019, we empirically examine the impact of EPU on earnings value relevance. Our finding indicates that EPU reduces earnings value relevance, and the effect is more pronounced in firms with lower governance effectiveness and higher firm complexity. Channel analysis demonstrates that decreasing information quality, undermining the information environment, and impeding investor decision-making are the main three mechanisms. In light of these findings, the government should strive to enhance stability in economic policies and improve information efficiency, fostering a positive interaction between firms and the capital market.

Since 2012, many central banks have implemented negative interest rate policies (NIRPs). While two opposing hypotheses about the effectiveness of NIRPs have emerged in the academic: the “de-leverage effect” and the “search-for-yield effect.” The long-term use of NIRPs provides a rare and important setting to re-examine the relationship between interest rates and bank risk-taking. We conduct an empirical analysis by using commercial banks' data from 2007 to 2020 for 23 countries (19 eurozone countries plus Japan, Denmark, Sweden, and Switzerland), which had adopted NIRPs. It indicates that 1% reduction in the policy rate would reduce bank risk-taking by 4.9%. This result is stronger after the NIRPs implemented. Our results support the “de-leverage effect” under NIRPs. We next show that the “de-leverage effect” is greater for banks with more diversified income, smaller size or under more competitive environment. The findings help to make the debates around NIRPs effectiveness clearer as well as support for the central banks to make more effective monetary policy decisions in different economic situations.

This study attempts to identify and compare the critical determinants and the speed of adjustment to optimal capital structure across various stages of the firm life cycle (FLC). Signifying the attitude of family firms (FFs) owing to risk aversion and the need to preserve firm control, the study differentiates the debt policies of family and non-family firms (NFFs) in an emerging economy. We use a target adjustment model and two-step system generalised method of moments to analyse panel data on a sample of 1435 listed non-financial firms spanning from 2013 to 2022. We find that compared to NFFs, FFs are inherently more indebted and adjust faster towards achieving optimal capital structure. Next, we find that firm's profitability, liquidity and tangibility are the major factors that significantly impact the quantity of debt across different stages of the FLC in both FFs and NFFs. Our results are robust to a battery of sensitivity tests. Our study suggests the significance of appropriate capital structure at different stages of the FLC.

The motivation of this article is to help financial soundness companies understand their specific financial status so that they can take timely measures to avoid financial distress. Existing multi-class financial distress prediction (FDP) studies have mainly segmented financial crisis status, with less attention paid to financial soundness companies. To fill this gap, we propose a new multi-class definition of FDP from the perspective of financial soundness enterprises. The financial states are defined as financial soundness, moderate financial soundness, mild financial soundness and financial distress. We propose a stacking ensemble model for multi-class FDP. First, deep neural network, multinomial logit regression (MNLogit) and multivariate discriminant analysis models are used as basic classifiers to obtain preliminary prediction results. Second, MNLogit is used to integrate the results from the previous step. To increase the effective information, stock information is then added into the model. The proposed model was trained using data from 2007 to 2019 for Chinese listed companies and tested using data from 2020. The results show that the MacroR-Pre, MacroR-Rec, MacroR-F1 and MacroR-AUC of the proposed model are better compared with the benchmark model, including individuals and ensembles, with 87.05%, 90.68%, 88.70% and 88.20%.The addition of stock information and non-financial indicators can improve the accuracy of the multi-class FDP model by about 8%. The innovativeness of this paper is twofold. First, it proposes a new multi-class definition of enterprise financial status. Second, a multi-class FDP based on stacking is constructed, which provides a new method for solving the multi-class FDP problem. The study shows that the proposed multi-class definition and stacking model are suitable for analysing financial soundness enterprises, which can help managers effectively grasp the specific financial status and have strong practical significance.

This paper explores the influence of inflation targeting (IT) policy on macroeconomic performance in 24 Asian economies by taking a wide range of macroeconomic variables from 2001 to 2022. Specifically, the study seeks to evaluate the influence of IT on macroeconomic performance during the global financial crisis (GFC) and the COVID-19 pandemic periods separately. The empirical analysis comprises three approaches: propensity score matching, difference-in-differences, and panel-corrected standard error methods. The empirical investigation reveals that IT significantly impacts inflation and its volatility during the GFC. In contrast, the effect on the unemployment rate is not statistically significant. Further, IT statistically influences the inflation and unemployment rates throughout the COVID-19 pandemic. In contrast, its impact on inflation volatility during the COVID-19 pandemic is not evident. Our results have significant policy implications for the Asian economies. It may be suggested that low-income countries could benefit from implementing IT policy as a tool to ensure stable inflation. However, there is a need for continuous policy adaptation and targeted interventions to address evolving economic challenges, especially in situations like crises.

Based on the data of listed firms in China from 2009 to 2020, this study investigates whether environmental, society and governance (ESG) peer effects reduce the risk of corporate financial distress from an executive social network perspective. Using two-stage least squares method, our empirical results suggest that the ESG peer effects exist in executive social networks, and the ESG peer effects can alleviate corporate financial distress. ESG subcategory analysis shows that the governance peer effect has the most obvious alleviating effect on financial distress. The negative impact of ESG peer effects on corporate financial distress is stronger when firms have high network power, network cohesion and network control in executive social networks. Our conclusions still hold after a series of robustness tests. Our research expands the literature on peer effects from the perspective of social relations, and sheds additional light on the critical role of ESG peer effects in financial risk management.

This study investigates the total, quantile, and frequency risk transmission among five widely traded metals namely copper, gold, lead, silver, and zinc using forecast error variance decomposition. The analysis spans from 1 January 2002, to 30 June 2023. Our findings reveal that the total connectedness index (TCI) changed over time, indicating sensitivity to time-specific developments and major events during different periods. The TCI is influenced more by extreme positive or negative shocks, as the lower and upper quantile TCIs are higher compared to the medium quantile TCI. Furthermore, the short-term TCIs exhibit higher values than the medium- and long-term TCIs. These variations imply that the TCI is influenced by different types of shocks or mechanisms across different quantiles. Specifically, the short-term TCIs are driven by global economic policy uncertainty, real global economic activity, and the geopolitical risk index (GPR). However, the medium- and long-term TCIs are solely influenced by the GPR.

In non-life insurance, insurance companies aim to accurately assess their reserves in order to fulfil their future obligations. They are based on methods provided by the literature review to evaluate their reserve risk. However, these methods do not take all claim characteristics and ignore the temporal dependence structure of claims, which can affect reserve amounts and lead to delayed payments for policyholders. Therefore, the aim is to investigate the temporal dependence structure among claim amounts (losses) in order to evaluate the accurate amounts of reserves. To achieve this goal, a model called the Generalized Autoregressive Conditional Sinistrality Model is proposed, which considers the temporal dependence characteristics of claims. This model is used to estimate model parameters, so the consistency of such an estimate is proven. Additionally, a bootstrap method adjusted to the Generalized Autoregressive Conditional Sinistrality model is proposed for predicting reserves and errors. The results reveal that considering temporal dependence between losses improves reserve distribution estimation and enhances solvency capital requirement. This means that insurance companies will be able to ensure they have sufficient funds available to meet their obligations to policyholders, thereby enhancing customer satisfaction and trust. Additionally, this can assist insurance companies in maintaining better regulatory compliance.