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This study aims to inspect the impact of Chief Executive Officers’ (CEOs) education in a “specific field,” such as CEOs with science and engineering backgrounds on firms’ innovation. Based on agency theory, this study also reports how an endogenous factor, i.e. CEOs’ compensation, and an exogenous factor such as intellectual property rights (IPR), moderate the CEOs with a scientific background (CEOSB)-innovation relationship.

This study uses a sample of Chinese nonfinancial firms listed on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2018 by applying the ordinary least squares regression method. To deal with the endogeneity issues, this study also performs a series of additional tests.

The results indicate that the effects of CEOSB on the firm innovation activities are positive and significant. Further, this study finds that CEOs’ compensation and IPR protection positively and significantly moderate the CEOSB-innovation relationship. These outcomes are robust to a series of additional tests.

The results of this study have valuable implications for various stakeholders interested in stimulating innovation. To sum up, the results of this study inculcate these stakeholders that the enhancement of firm innovation is contingent on the appropriate selection of CEOs, effective compensation packages and IPR regulations.

Distinct from the existent studies, the focus of the study is on the perspectives of CEOs’ scientific backgrounds. Further, based on agency theory, this study also reports how CEOs’ compensation and IPR protection moderate the CEOSB-innovation relationship, which has not been tested earlier to our knowledge, especially in the context of an emerging economy like China.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

This study analyzes the impact of the information environment (IE) and credit default swap (CDS) transaction costs on information transmission between the stock and CDS markets. Using the daily regression analysis on the Korean firm’s stock and CDS data from 2004 to 2023, the results show that companies with superior IE in the stock market exhibit a larger and more sensitive total information flow from the stock market to the CDS market. Companies with lower transaction costs in the CDS market demonstrate faster information flow. In the case of companies with superior IE, fundamental information is reflected in stock prices with high weight and thus the CDS spreads change reflecting information about stock prices. According to this study’s findings, the primary factor influencing the information flow from the stock market to the CDS market is the information environment of the company in the stock market, rather than transaction costs in the CDS market.

This study aims to investigate the effect of national culture (power distance, individualism, masculinity, uncertainty avoidance and long-term orientation) on audit report lag.

The authors use two econometric approaches (ordinary least squares (OLS) and quantile regression) using STATA software for a sample of 1,208 firm-year observations over the period of 2017–2018.

Using Hofstede’s (2001) cultural dimensions (power distance, individualism, masculinity, uncertainty avoidance and long-term orientation), the authors find that masculinity and long-term orientation are positively associated with audit report lag, while uncertainty avoidance is negatively associated with the same variable. Quantile regressions suggest that the adverse effect of masculinity on audit report lag is more prevailing for companies communicating companies' annual reports in a timely manner. Furthermore, the positive association between power distance and audit report lag exists only under tardy disclosure regime. Quantile regressions also confirm that the negative (positive) effect of uncertainty avoidance (long-term orientation) on audit report lag is maintained under different timely disclosure regime. Additional analysis conducted with respect to legal system shows that individualism becomes a significant predictor of audit delays with a significant negative effect for common law countries, while uncertainty avoidance has a positive effect on the same variable in civil law countries characterized by high level of discretion and secrecy.

The results of this study suggest that national culture as an informal institution may complement formal institutions (e.g. financial markets) in promoting timely disclosure. For instance, foreign investors may view high uncertainty avoidance scores, in common law emerging economies, as an indicator of transparency and timely disclosure.

This study adds to the extant literature a further understanding of the impact of cultural dimensions on timely disclosure, as proxied by, audit report lag. The use of quantile regression approach shows how different timely disclosure regime may affect the association between masculinity, power distance and audit report lag.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

This paper aims to investigate the impact of the implementation of the critical audit matters (CAMs) disclosure requirement and the subsequent relationship between CAM disclosures and audit report lag, as well as audit fees in the USA.

This study used difference-in-differences analyses to investigate the impact that the implementation of the requirement for auditors to report CAMs on their audit report has on the audit process. It also used levels regression models to examine the relationship that CAM disclosures have with audit report lag and audit fees.

This study found that the implementation of the CAM disclosure requirement in the USA reduced audit report lag while not significantly affecting audit fees. This suggests that the CAM disclosure requirement may increase the cooperation between auditors and managers and improve the efficiency of the audit process.

This study’s results are informative for assessing the economic impact of requiring CAM disclosures, which should be of importance to regulators, auditors and accounting researchers.

This study used different approaches to investigate two aspects of the CAM disclosure requirement – the effect of the implementation of the disclosure requirement and the subsequent effects related to CAM reporting outcomes. Unlike many previous studies investigating CAM disclosures, which relied on experiments and questionnaires, this study used actual CAM disclosure data in the USA to investigate the impact on audit report lag and audit fees.

This paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.

Panel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.

ADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.

The sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.

Based on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.

This paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.