COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?

The purpose of this study is to determine whether Indian banks were able to weather the COVID-19 storm. We estimate banks’ deposits-generating and operating efficiencies using a two-stage directional distance function-based network data envelopment analysis (DDF-NDEA) approach and seek to capture the immediate impact of COVID-19 on these efficiency measures by comparing their magnitudes in the pre-pandemic (2014/15–2019/20), just 1-year prior to the pandemic (2019/20), and during the pandemic year (2020/21) periods. The study looks at whether the impact of the COVID-19 pandemic was uniform across ownership types and size classes. The empirical findings suggest that the Indian banking system was resilient and withstood the immediate impact of the COVID-19 pandemic. During the study period, however, the large and medium-sized banks experienced some efficiency losses. By and large, regardless of bank group, banks have shown resilience to the shock of the global health pandemic and improvements in efficiency.     

Impacts of COVID-19 on global stock sectors: Evidence from time-varying connectedness and asymmetric nexus analysis

In this paper, we analyze the impact of the COVID-19 crisis on global stock sectors from two perspectives. First, to measure the effect of the COVID-19 on the volatility connectedness among global stock sectors in the time–frequency domain, we combine the time-varying connectedness and frequency connectedness method and focus on the total, directional, and net connectedness. The empirical results indicate a dramatic rise in the total connectedness among the global stock sectors following the outbreak of COVID-19. However, the high level of the total connectedness lasted only about two months, representing that the impact of COVID-19 is significant but not durable. Furthermore, we observe that the directional and net connectedness changes of different stock sectors during the COVID-19 pandemic are heterogeneous, and the diverse possible driving factors. In addition, the transmission of spillovers among sectors is driven mainly by the high-frequency component (short-term spillovers) during the full sample time. However, the effects of the COVID-19 outbreak also persisted in the long term. Second, we explore how the changing COVID-19 pandemic intensity (represented by the daily new COVID-19 confirmed cases and the daily new COVID-19 death cases worldwide) affect the daily returns of the global stock sectors by using the Quantile-on-Quantile Regression (QQR) methodology of Sim and Zhou (2015). The results indicate the different characteristics in responses of the stock sectors to the pandemic intensity. Specifically, most sectors are severely impacted by the COVID-19. In contrast, some sectors (Necessary Consume and Medical & Health) that are least affected by the COVID-19 pandemic (especially in the milder stage of the COVID-19 pandemic) are those that are related to the provision of goods and services which can be considered as necessities and substitutes. These results also hold after several robustness checks. Our findings may help understand the sectoral dynamics in the global stock market and provide significant implications for portfolio managers, investors, and government agencies in times of highly stressful events like the COVID-19 crisis.     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

Powerful CEOs,debt financing,and leasing in Chinese SMEs: Evidence from threshold model

This study investigates the impacts of CEO power on firm financing policies (i.e. debt financing and operating leasing) using the Caner and Hansen (2004) instrumental variable threshold regressions approach. The sample consists of a panel of 297 Chinese listed small and medium sized enterprises (SMEs) over the period 2009–2012. The empirical results indicate that there are threshold effects in the CEO power-debt relationship and CEO power-operating lease relationship. In particular, we find that firms tend to use more debt financing (and operating leasing) when CEO power index below a certain threshold level; beyond the threshold level, CEO tends to manipulate firm capital structure to pursue their own interests, thus using less debt financing and operating leasing. In addition, our estimation results suggest a positive relationship between debt and operating leases when CEO power is smaller than certain threshold, while it becomes negative if the power index exceeds the threshold level.     

COVID-19, ESG investing,and the resilience of more sustainable stocks: Evidence from European firms

Following the COVID-19 outbreak, orientation toward sustainability is a critical factor in ensuring firm survival and growth. Using a large sample of 1,204 firms in Europe during the year 2020, this study investigates how more sustainable firms fare during the pandemic compared with other firms in terms of risk–return trade-off and stock market liquidity. We also highlight the drivers of the resilience of more sustainable firms to the pandemic. Particularly, we document that higher levels of cash holdings and liquid assets in the pre-COVID period help these firms to perform and absorb the COVID-19 externalities better than other firms. Our results are robust to a host of econometric models, including GMM estimations and several measures of stock market performance. These findings contribute to the theoretical and empirical debate on the role of the sustainability as a source of corporate resilience to unexpected shocks.     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

The pandemic and SME supply chains: Learning from early experiences of SME suppliers in the U.S. defense industry

While the COVID-19 pandemic has been disrupting supply chains in an unprecedented fashion, one type of firms that has been particularly affected are small- and medium-sized enterprises (SMEs). We focus on these SMEs, specifically on SME suppliers to the U.S. Department of Defense (DoD), and investigate the impact that the pandemic has had on these suppliers, as well as the effectiveness of various government procurement efforts to alleviate the challenges. In doing so, we rely on survey data collected by the National Defense Industry Association (NDIA) during the early stages of the pandemic in March and April 2020 to assess initial government responses and SME supplier receptions. To derive more granular insight, we scrutinize the results across firm size, dependence on the DoD, whether the SME is a first-tier supplier or not, and industry. Through this investigation, we for instance find that the weakest suppliers are the very small SMEs (1–49 employees), and that most government measures were judged to not be that effective—at least in these early stages of the pandemic. Overall, our study leverages insight from one of the few large-scale surveys conducted on the impact of the pandemic on SME suppliers and their relationship with government agencies in the very early phases of the pandemic.     

The stochastic volatility in mean model: empirical evidence from international stock markets

In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is developed elsewhere for Autoregressive Conditional Heteroscedastic (ARCH) models, known as the ARCH in Mean (ARCH‐M) model. The estimation of ARCH models is relatively easy compared with that of the Stochastic Volatility (SV) model. However, efficient Monte Carlo simulation methods for SV models have been developed to overcome some of these problems. The details of modifications required for estimating the volatility‐in‐mean effect are presented in this paper together with a Monte Carlo study to investigate the finite sample properties of the SVM estimators. Taking these developments of estimation methods into account, we regard SV and SVM models as practical alternatives to their ARCH counterparts and therefore it is of interest to study and compare the two classes of volatility models. We present an empirical study of the intertemporal relationship between stock index returns and their volatility for the United Kingdom, the United States and Japan. This phenomenon has been discussed in the financial economic literature but has proved hard to find empirically. We provide evidence of a negative but weak relationship between returns and contemporaneous volatility which is indirect evidence of a positive relation between the expected components of the return and the volatility process. Copyright © 2002 John Wiley & Sons, Ltd.     

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.     

投资期权对经理薪酬的影响分析

文章对2004年我国853家上市公司的数据进行回归检验,分析投资期权对经理薪酬的影响效果。结果显示,相对于低IOS的企业,高IOS的企业经理能够获得更多的薪酬。     

Nonparametric methods and local‐time‐based estimation for dynamic power law distributions

This paper introduces nonparametric econometric methods that characterize general power law distributions under basic stability conditions. These methods extend the literature on power laws in the social sciences in several directions. First, we show that any stationary distribution in a random growth setting is shaped entirely by two factors: the idiosyncratic volatilities and reversion rates (a measure of cross‐sectional mean reversion) for different ranks in the distribution. This result is valid regardless of how growth rates and volatilities vary across different economic agents, and hence applies to Gibrat's law and its extensions. Second, we present techniques to estimate these two factors using panel data. Third, we describe how our results imply predictability as higher‐ranked processes must on average grow more slowly than lower‐ranked processes. We employ our empirical methods using data on commodity prices and show that our techniques accurately describe the empirical distribution of relative commodity prices. We also show that rank‐based out‐of‐sample forecasts of future commodity prices outperform random‐walk forecasts at a 1‐month horizon.     

Oil dependence and entrepreneurship: Non-linear evidence

This study examines the empirical nature of the oil dependence-entrepreneurship nexus, building on recent research that extends the “resource curse” beyond its effects on economic growth. For the period from 2006 to 2018, the sample includes 115 countries at various stages of economic development. Using panel threshold methods, we discover an inverted U-shaped relationship, indicating that even small increases in oil rents increase entrepreneurship below the threshold. Globally, oil rents above a threshold of 77 % stifle entrepreneurship. Regional differences and institutional vulnerability act as moderators.     

Advancing the field of employee assistance programs research and practice: A systematic review of quantitative studies and future research agenda

The COVID-19 pandemic has accentuated the critical role of organizational support for the workforce. An employee assistance program (EAP) represents an inclusive strategy which organizations adopt to provide supportive and empathic care to help employees overcome undesirable situations. To date, we have limited knowledge of what EAP issues have been researched from the human resource management (HRM) perspective and what theoretical underpinning these studies have used. This article systematically reviews quantitative empirical studies on EAPs. Drawing upon 115 articles from 72 journals across 40 years (1981–2020), we trace the evolutionary trend of the construct of EAP and shed light on the internal link of EAP with HRM. After summarizing research themes, methods, theories, and approaches to the evaluation of EAPs, we identify pitfalls in the current research and contribute to extending the field by proposing several research agendas for future investigation.     

On the cointegration of international stock indices

This paper investigates the cointegration relationship among a group of international stock indices in light of new developments of econometric methods. Kasa (1992) first documented strong evidence for cointegration relations among five national stock indices, which suggests that there exists a common trend among those stock indices. Using Johansen multivariate cointegration test, we find that his findings are persistent in a sample of longer periods and more countries. In order to investigate whether these results are driven by statistical biases related to the sample size, we apply to our tests the Johansen’s small sample correction factor. The results still point toward the existence of a cointegration relationship but the evidence becomes much weaker. We next examine the empirical patterns emerged from different lag specifications and argue that Kasa’s findings are more likely due to the size distortion in extreme long lag VAR models. Indeed, when we employ a newly developed non-parametric test that does not require estimation VAR models, the null hypothesis of no cointegration cannot be rejected for the original sample of Kasa’s five-country stock indices from 1974 to 1990, nor for the extended period from 1970 to 2003.     

The Relationship Between Mission Statements and Firm Performance: An Exploratory Study

This paper examines the relationship between mission statements and firm performance using a sample of 136 large Canadian organizations. Previous writings suggest that mission statements are essential for superior organizational performance results. However, there is little empirical evidence to support this claim. The data from the present study demonstrate that mission statements and some of their specific characteristics are selectively associated with higher levels of organizational performance. The paper concludes with several propositions to guide future research.     

对股权结构与公司IRM的实证分析

投资者关系管理是上市公司战略管理的重要职责。本文以2005年内地首届投资者关系(IR)年会评选出的50家获奖公司为样本,对股权结构与公司1RM关系进行实证分析,结果显示:优秀的IRM公司市价总值大,流通股比例较高,股权集中度接近市场平均水平,基金持股比例高。通过对获奖公司上述股权结构特点进行理论分析,得出政策启示。     

Money stock targeting and money supply: A closer examination of the data

In a recent paper Mercenier and Sekkat (1988) use a linear-quadratic model to examine the willingness of a monetary authority in a small open economy to target its exchange rate. Based on their empirical results, the authors conclude that the Bank of Canada has displayed a willingness to use the money supply to target the Canada—US exchange rate. We re-examine their empirical results using a different estimation approach and with different assumptions about the forcing process of the exogeneous variables. We also extend the sample period to include more recent observations. While we find some weak evidence to support their conclusion, the results, in general, suggest that a linear-quadratic model may not be a particularly useful representation of the assumed exchange rate targeting by a monetary authority.     

MNC's competitive strategies,experiences, and staffing policies for foreign affiliates

This study investigates the relationship between the strategic role of a multinational corporation's (MNC) foreign affiliates and its international staffing policy. Specifically, this study examines how an MNC's decision on expatriation is affected by strategic roles assigned to foreign affiliates: global integration of activities versus local market seeking. An empirical study is conducted using a sample of 808 foreign affiliates of Japanese firms. The research findings suggest that strategic roles of foreign affiliates alone may not adequately explain the international staffing policies of Japanese MNCs. Rather, we found a significant moderating effect of international as well as host country experience on staffing practices for foreign affiliates. While the staffing policies of MNCs striving for improving global efficiency of their operation are moderated by both international and host country experience, those seeking a specific local market position are influenced only by host country experience.     

Semi‐parametric Regression under Model Uncertainty: Economic Applications

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross‐country growth regression.     

Inference on stochastic time-varying coefficient models

Recently, there has been considerable work on stochastic time-varying coefficient models as vehicles for modelling structural change in the macroeconomy with a focus on the estimation of the unobserved paths of random coefficient processes. The dominant estimation methods, in this context, are based on various filters, such as the Kalman filter, that are applicable when the models are cast in state space representations. This paper introduces a new class of autoregressive bounded processes that decompose a time series into a persistent random attractor, a time varying autoregressive component, and martingale difference errors. The paper examines, rigorously, alternative kernel based, nonparametric estimation approaches for such models and derives their basic properties. These estimators have long been studied in the context of deterministic structural change, but their use in the presence of stochastic time variation is novel. The proposed inference methods have desirable properties such as consistency and asymptotic normality and allow a tractable studentization. In extensive Monte Carlo and empirical studies, we find that the methods exhibit very good small sample properties and can shed light on important empirical issues such as the evolution of inflation persistence and the purchasing power parity (PPP) hypothesis.