Macroeconomic fundamentals, institutional quality and shadow economy in OIC and non-OIC countries

PurposeThe purpose of this paper is to analyze the relationship between macroeconomic fundamentals, intuitional quality and shadow economy.Design/methodology/approachBy utilizing data setspanning from 2004 to 2015 of 141 countries, the study has employed advanced panel technique, i.e. Generalized Method of Moments (GMM) method. In order to check consistency of the results, the study also used fixed effect and random effect for robustness.FindingsThe study finds that for the full sample, institutional quality has negative effect on shadow economy while macroeconomic fundaments effect shadow economy differently. After splitting the sample into Organization of Islamic Cooperation (OIC) and non-OIC countries subsamples, it observes same influence of macroeconomic fundaments and institutional quality on shadow economy, but the effect of macroeconomic fundaments and institutional quality on shadow economy is less observed for OIC countries. The results are found consistence by using different estimation methods.Originality/valueThe current literature has focused on estimating the size of shadow economy and literature linking the macroeconomic fundaments, institutional quality and shadow economy is scarce. Additionally, this study provides the evidence for cross comparison between OIC economies and non-OIC economies.

Layoffs and stock market performance during the COVID-19 pandemic: evidence from the US

PurposeThe authors examine the short-term stock market reaction surrounding US layoffs during the coronavirus disease 2019 (COVID-19) period. The authors’ specific interest is on any changes that may be observed in US stock markets during the COVID-19 outbreak. This information will help us assess the extent to which policymakers adopted at time revenue and expenditures measures to minimize its negative impact.Design/methodology/approachThe authors study the linkage between layoffs announced by firms and stock markets in US for the COVID-19 period between March 2020 and October 2020. This period shows important economic figures; a huge number of job cuts announced by blue-chip companies listed in the New York Stock Exchange (NYSE) due to widespread economic shutdowns. The authors examine whether and to what extent stock markets in US have reacted to layoff announcements during the COVID-19 pandemic using an event-study methodology.FindingsThe study’s results show that US layoffs during the pandemic did not cause any abnormalities on the stock returns, either positive or negative. Based on the mean-adjusted volume, the authors find that layoffs increase the stocks' trading volume, especially on the event date and the day following the event. US stocks become more volatile on the days following the event. Interestingly, on the event date, the authors find that stocks get the highest abnormal volatility; however, the result is statistically insignificant.Practical implicationsThe authors suggest that layoffs announcements follow the business cycle quite closely in most industries. The study’s results have implications for investors, regulators and policymakers as they permit to examine the effectiveness of the measures adopted.Social implicationsThe study’s results show that policymakers reduced uncertainty implementing intensive measures quickly and should follow similar policy in the future pandemic and/or unexpected events.Originality/valueThis paper contributes to the literature in two directions: First, to the best of the authors’ knowledge this is the first study that provides empirical evidence and assesses the extent to which a major global shock such as the COVID-19 pandemic may have altered the reaction of US stock markets to layoff announcements. Second, this is the first study on this topic that examines volume and volatility abnormalities, while the authors check the robustness of the findings with different methods to calculate abnormal returns.

A broad analysis of short-term overreactions in the market for cryptocurrencies

PurposeThe purpose of this study is to analyze market inefficiencies in the market for cryptocurrencies by providing a comprehensive analysis of short-term (over)reactions that follow significant price changes of such currencies.Design/methodology/approachThis study identifies and analyzes overreactions and mispricing in markets for cryptocurrencies by applying a broad set of thresholds that depend on market-specific dynamics and volatilities. This study also analyzes the returns on days following abnormal returns and identifies significant differences from normal returns using the t-test and the Mann–Whitney U-test. The researchers further complement the literature by using end-of-the-day returns in addition to high-low returns. Additionally, this study considers a broad sample of 50 cryptocurrencies for an expanded time span (2015–2020) that includes the big currencies as well as smaller currencies.FindingsFindings detect the existence of overreactions and, thus, market inefficiencies in crypto markets. The findings for different methodological approaches are similar, which underpins the robustness of the findings. By considering a broad sample that includes small and big currencies, we can show the existence of a market size effect. By considering a broad set of thresholds, the authors further found evidence for a magnitude effect, which means that higher initial abnormal returns are related to higher inefficiencies.Practical implicationsThis paper has practical implications. Market inefficiencies were detected, which can be used in practical trading to obtain excess returns. In fact, methodological approach of this study and its results can be used to derive a strategy for trading in cryptocurrencies that can be easily implemented. Based on the study’s findings, the authors can expect positive access returns by applying this trading strategy.Originality/valueThe authors complement the literature on market inefficiencies and mispricing in crypto markets by analyzing price patterns after initial abnormal returns. Researchers contribute by applying different methodological approaches in addition to the approaches used so far, by considering a set of different thresholds and by applying a much broader data set that enables the study to analyze additional aspects.

Market overreaction, firm-specific information and macroeconomic variables in US and Chinese markets during COVID-19

PurposeThis study examines the impact of firm-specific information and macroeconomic variables on market overreaction of US and Chinese winner and loser portfolio before and during COVID-19.Design/methodology/approachThe firm-specific information includes firm size, volume, volatility, return of asset (ROA), return of equity (ROE), earning per share (EPS) and quick ratio while the macroeconomic variables are export rate, import rate, real GDP, nominal GDP, FDI, IPI and unemployment rate. Besides, one-third of the top performance stocks are categorized as winner portfolio while one-third of lowest performance stocks are categorized as loser portfolio. This study uses AECR to indicate stock return and measure market overreaction. GAECR is used to determine contrarian profit. The data range of pre-COVID-19 is from 1-Jan-2015 to 31-Dec-2019 while the period of COVID-19 is from 1-Jan-2020 to 31-Dec-2020.FindingsIn pre-COVID-19, firm-specific information (volatility, ROA, ROE and EPS) and macroeconomic variables are found to be correlated to stock return in US and Chinese portfolios except Chinese winner portfolio. Nonetheless, the impact of firm-specific information has vanished and macroeconomic variables are significant to stock return in COVID-19. It shows that investors rely on the economic indicators to trade in turbulent period due to emergence of COVID-19 as a disruption in market. Furthermore, US and Chinese portfolios are overreacted during COVID-19. Chinese loser portfolio has higher tendency of overreaction than US loser portfolio while US winner portfolio has higher tendency of overreaction than Chinese winner portfolio.Originality/valueThe results of this study assists academician, practitioners and investors on understanding and create awareness to the existence of market overreaction and the determinants that can cause the phenomenon.

Does the short squeeze lead to market abnormality and antileverage effect? Evidence from the Gamestop case

PurposeThis study examines the Gamestop (GME) short squeeze in early 2021. Using intraday data for the period 4/1/2021–5/2/2021, the author provides empirical evidence that the GME stock price exhibited abnormal behavior.Design/methodology/approachThe author uses the popular Runs test to show that the GME returns were not randomly distributed, which is an indication of a violation of the Efficient Market Hypothesis (EMH). The main objective of the paper is to provide new quantitative evidence that stock returns are abnormal when short squeeze conditions emerge. The author employs the asymmetry Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models (the Exponential GARCH (EGARCH) and the Threshold GARCH (TGARCH)) and provides evidence that an exceptional time series feature emerged during the examined period: the antileverage effect.FindingsThe results show that the GME returns were not randomly distributed during the examined period and the asymmetry GARCH models indicate that, in contrast to what the time series normally show, volatility increased when the GME prices increased.Research limitations/implicationsThis paper presents a new/alternative approach for the study of EMH and abnormal returns in financial markets. Further studies on market performance during similar short squeeze conditions should be carried out in order to obtain empirical evidence for the antileverage effect abnormality.Practical implicationsThis paper could be useful for scholars who examine the EMH in financial markets because it suggests an additional method for testing abnormalities. It also presents a useful tool that allows practitioners to monitor for indications of abnormality in the stock market during a short squeeze, since the emergence of the antileverage abnormality could function as such an indication. Additionally, the outcome of this analysis could be useful for regulators because coordination among investors is easier than ever in the Internet era and such events may happen again in the future; even under normal (not short squeeze) conditions and lead to market instability.Originality/valueThis research differs from other studies that examine the GME case because it presents a new way to quantitatively present the abnormal performance of the stock markets for reasons that could be linked with the emergence of short squeeze conditions.

Impact of COVID-19 on listed European electricity companies: a comparative analysis of investment in renewable and traditional electricity

PurposeThe authors analyze the impact of COVID-19 on listed European electricity companies and differentiate between renewable and traditional electricity, to show the heterogenous characteristics of electricity subsectors and the differences between renewable and traditional electricity.Design/methodology/approachUsing the event study method, the authors calculate the cumulative average abnormal returns (ARs) before and after the World Health Organization pandemic announcement and the declaration of national lockdowns in Europe.FindingsThe results show that while the European electricity sector was overall negatively impacted by the COVID-19 announcement, this impact was larger for renewable companies due to their riskier investment profile. Moreover, after the national lockdowns came into effect, the recovery in the financial markets return was smaller for the latter.Research limitations/implicationsThere may be variables to be included in the model to analyze possible differences between companies and countries, as well as alternative econometric models. Limited to the data, the authors did not investigate the different impact of the economic policy uncertainty from various countries inside or outside the EU.Practical implicationsThe results have important implications for both investors and policymakers since the heterogenous characteristics of electricity subsectors. This heterogeneity prompts different investor reactions, which are necessary to know and to understand.Originality/valueAs far as the authors know, this is the first study that analyses the effect of COVID-19 in heterogeneity profile of both types of electricity, renewable and traditional.

Public debt management and the interaction between fiscal and monetary policies

PurposeThis paper aims to investigate the existing relations between the management of public bonds on the dynamics of debt, term structure of interest rates and economic cycle, through a dynamic stochastic general equilibrium model (DSGE), which was estimated through Bayesian inference techniques using data from Brazil.Design/methodology/approachThe model developed was used to investigate the effects of the public debt average maturity management when the economy faces a monetary policy shock. For this, three management scenarios are evaluated, including Brazilian securities average term.FindingsContrary to what might be inferred from DSGE models that limited the analysis of the debt term by imposing only one-period bonds, a contractionary monetary policy shock does not necessarily cause public debt to increase significantly. Debt term structure plays a crucial role in this result since the government does not need to roll the debt over at higher costs when the debt term profile is longer, reducing the debt service costs and then the impact on the overall debt.Originality/valueDespite the relevance of this theme and its implications for the dynamics of the economy, there is still a gap to be filled in the literature when using DSGE models, since most part of the work that used this methodology limited the analysis of the debt term by imposing that government issues only one-period bonds. This paper differs from the others insofar as it promotes an investigation focused on the role played by debt maturity management on the performance of the contractionary monetary policy. This approach can generate a better understanding of debt management policy and its interaction with fiscal and monetary policies.

Financial globalization and income inequality nexus: panel quantile regression approach

PurposeThe general purpose of the paper is to examine the effect of financial globalization on income inequality. The specific purposes are: 1) To examine the effect of overall financial globalization on income inequality. 2) To analyze whether de facto and de jure financial globalization have differential effects on income inequality. 3) To scrutinize whether the effect of financial globalization on income inequality varies across countries of different income groups and quantiles of income inequality.Design/methodology/approachThe authors employed panel quantile regression using 73 countries over 2000–2016 to examine the effect of financial globalization on income inequality. The authors employed fixed effect and panel quantile regressions and classified the countries into income groups to compare differential effects of financial globalization across different income groups. Further, the authors unbundled financial globalization into de facto and de jure financial globalizations to investigate whether their effects on income inequality vary.FindingsOverall financial globalization raises income inequality more at lower quantiles of inequality. De jure financial globalization reduces income inequality in high-income countries. In high-income countries, de jure financial globalization has more favorable income distribution at lower quantiles of inequality. In contrast, de facto financial globalization raises inequality regardless of income classification of the countries.Originality/valueTo the best of the authors’ knowledge, the authors for the first time employed panel quantile regression to analyze whether financial globalization affects income inequality across different quantiles. In addition to de facto globalization, the authors used the newly developed de jure financial globalization index to examine its impact on income inequality. The de jure dimension is largely neglected in the literature. The authors provide empirical evidence on how the different dimensions of financial globalization, de facto and de jure, impact inequality in high-income, middle-income and low-income countries.

Tornadoes, poverty and race in the USA: A five-decade analysis

PurposeThe study aims to analyze the roles of poverty and African American status in terms of vulnerability to tornado damages and barriers to recovery afterward.Design/methodology/approachUsing five decades of county-level data on tornadoes, the authors test whether economic damages from tornadoes are correlated with vulnerability (proxied by poverty and African American status) and wealth (proxied by median income and educational attainment), controlling for tornado risk. A multinomial logistic difference-in-difference (DID) estimator is used to analyze long-run effects of tornadoes in terms of displacement (reduced proportions of the poor and African Americans), abandonment (increased proportions of those groups) and neither or both.FindingsControlling for tornado risk, poverty and African American status are linked to greater tornado damages, as is wealth. Absent tornadoes, displacement and abandonment are both more likely to occur in urban settings and communities with high levels of vulnerability, while abandonment is more likely to occur in wealthy communities, consistent with on-going forces of segregation. Tornado damages significantly increase abandonment in vulnerable communities, thereby increasing the prevalence of poor African Americans in those communities. Therefore, the authors conclude that tornadoes contribute to on-going processes generating inequality by poverty/race.Originality/valueThe current paper is the first study connecting tornado damages to race and poverty. It is also the first study finding that tornadoes contribute to long-term processes of segregation and inequality.

Which professional skills value more under digital transformation?

PurposeThis study seeks to explore the demand side of the labour market influenced by the digital revolution. It aims at identifying the new composition of skills and their value as implicitly manifested by employers when they look for the new labour force. The authors analyse the returns to computing skills based on text mining techniques applied to the job advertisements.Design/methodology/approachThe methodology is based on the hedonic pricing model with the Heckman correction to overcome the sample selection bias. The empirical part is based on a large data set that includes more than 9m online vacancies on one of the biggest job boards in Russia from 2006 to 2018.FindingsEmpirical evidence for both negative and positive returns to computing skills and their monetary values is found. Importantly, the authors also have found both complementary and substitutional effects within and between non-domain (basic) and domain (advanced) subgroups of computing skills.Originality/valueApart from the empirical evidence on the value of professional computing skills and their interrelations, this study provides the important methodological contribution on applying the hedonic procedure and text mining to the field of human resource management and labour market research.