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This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

This study aims to validate the link between production manipulation and a firm’s performance variability (fundamentals and stock returns). It explores whether executives' risk-taking incentives encourage production deviations around the normal level during uncertainty.

Utilizing panel data of manufacturing firms from Compustat over three decades, the study investigates production management practices during economic uncertainty. The Economic Policy Uncertainty Index (EPU) is employed as a key metric. The empirical strategy involves documenting the effect of economic uncertainty on overproduction and underproduction, examining the role of executive compensation and assessing the impact on risk.

The research finds that risk-taking incentives increase over/underproduction, particularly amplifying the extent of underproduction during uncertainty. Production deviation rises, indicating that firms take greater risk by engaging in abnormal business operations. The study’s results are robust against various econometric methods, emphasizing the influence of risk-taking incentives on corporate production decisions.

While providing valuable insights, the study acknowledges inherent limitations, including factors influencing production decisions beyond risk-taking incentives. Further research could explore additional determinants for a comprehensive understanding.

The findings highlight the potential dark side of executive compensation that motivates suboptimal risk-taking decisions, impacting risk, cost of capital and firm performance. Policymakers and compensation committees can use these insights to design efficient systems that mitigate moral hazard problems associated with productivity changes.

The study emphasizes the broader social implications of production manipulation under uncertainty. It prompts discussions on the ethical considerations of managerial opportunism, its potential consequences for stakeholders and market dynamics.

This study contributes to the literature by examining the role of economic uncertainty on production manipulation and the influence of risk-taking incentives. It extends the earnings management literature by considering real activity manipulation and emphasizing the importance of decomposing production deviation into positive and negative values.

This study aims at examining the value relevance of tax-related information in Canada. Tax-related information in this study includes taxable income, tax aggressiveness, and tax risk (i.e., unsustainable tax planning).

This study analyzes the Canadian listed firms covering the period of 2012–2021 using the Feltham–Ohlson valuation model.

The findings are: (1) taxable income provides incremental value relevance information; (2) tax risk reduces the value relevance of both taxable income and accounting income and (3) tax aggressiveness reduces the value relevance of accounting income but not of taxable income. Further tests show that the COVID-19 pandemic increases the value relevance of taxable income but decreases the value relevance of accounting income. An analysis of the association between stock price volatility and tax-related information documents that taxable income and accounting income are both informative. Tax risk reduces the informativeness of taxable income, but tax aggressiveness and the pandemic do not.

The sample in this study covers the period up to 2021. Future research could use more recent data. Additionally, this study examines the Canadian setting. The results may not be generalized to other countries that have different accounting and tax rules.

This study sheds light on whether tax aggressiveness and tax risk affect the value relevance of taxable income and accounting income separately. In addition, to our knowledge, this is the first study that examines whether tax-related information is informative about stock price volatility.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This study aims to examine whether auditors who specialize in research and development (R&D) activities help reduce managers’ opportunistic adjustment of R&D expenditure for real earnings management (REM).

Using a sample of US firms during the 2001–2017 period, the authors identify auditors’ R&D specialization as their prior experience of auditing R&D expenses spent by each client’s peers. The authors measure R&D-based REM as the negative deviation from the predicted level of R&D expenditure.

The authors find that clients of R&D specialist auditors are less likely to engage in REM through a discretionary reduction of R&D expenditure. This effect is more pronounced when clients face higher competition, have larger investment opportunities and entail higher audit risks.

This study shows that auditors’ specialized knowledge can facilitate stronger monitoring of clients’ real decisions, providing implications for auditors’ knowledge acquisition and transfer in specific types of transactions.

This study contributes to the literature by documenting the governance role played by R&D specialist auditors in clients’ real economic decisions. Moreover, the study identifies R&D as a distinct area of auditor specialization.

This study aims to empirically investigates the effect of the COVID-19 pandemic on ethical and nonethical stocks in Indonesia. Ethical stocks which are characterized by moral-based companies’ activities and lower debt are expected to have better resilience during the COVID-19 crisis compared to nonethical stock.

This study observes 589 firms of ethical and nonethical stock during sample periods ranging from March 2, 2020 (first case announced) to June 30, 2021. Panel regression, with some control variables, was applied.

Testing firms in Indonesia revealed a significant difference in stock resilience, in which ethical stock has a better resilience compared to nonethical, with Islamic socially responsible investment (SRI) stock having the highest resilience, followed by Islamic stock and then SRI stock. This study documents a significant effect of some financial criteria on the stock resilience, namely, return market (RM), market capitalization (MCAP) and share turnover (TURN). Overall, after splitting the sample into different time horizons, this study consistently reveals that ethical firms have better resilience compared to nonethical stocks.

This study makes several contributions to the literature on Islamic finance, especially concerning Islamic screening with SRI factors. In practical terms, this study supports the argument that focusing on integrating environmental, social and governance criteria in sharia screening will improve the quality of Islamic firms. The “Islamic” label is not only a marketing label but also a quality certification.

This study can be used as a reference for developing Islamic finance more focused on sustainability issues including socioeconomic and human development by improving the quality of screening of Islamic firms. Therefore, this study suggests that the establishment of Islamic SRI index is very crucial and significant to promote ethical-based investment.

The study evaluates the role of institutional framework and macroeconomic instability on financial market development among emerging economies.

The study uses panel data compiled from 32 countries from the sub-region of Sub-Sahara Africa (SSA), covering the period starting from 1996 to 2019. Empirical analyses were carried out using the two-step system generalized method of moments (TS-GMM) statistical framework.

Reviewed results suggest that institutional quality, effective governance and corruption control have a significant positive impact on financial market development among economies in the sub-region. Further empirical estimates show that macroeconomic risk and macroeconomic uncertainty have significant adverse effects on financial market development. Additionally, reported empirical estimates suggest that an improved institutional framework has the potential to lessen the adverse effect of macroeconomic instability on financial market development among economies in the sub-region.

The uniqueness of this empirical inquiry compared to related studies in the present literature stems from the fact that studies employing similar empirical approaches on the subject matter for economies in the sub-region are rare. Additionally, the analysis pursued in this study employs critical variables whose impact on financial market performance in the sub-region has not been examined per our review. These variables include indexes such as macroeconomic risk and institutional quality, which are unique to this study based on their construction; these indexes are generated using a principal component analysis procedure with different underlying variables compared to what may be found in the literature.

We investigate connections between the development of Fintech and the blue economy from September 14th, 2020, to August 11th, 2023.

In this research, we use a cutting-edge model-free connectedness approach to investigate the relationships between FinTech and blue bond volatility. Our work is the first to investigate the effects of unknown events, such as the COVID-19 pandemic and Ukraine–Russia conflicts, on the interconnection of volatility derived from FinTech development and blue bond volatility.

Our results highlight the two-way relationship between the development of Fintech and the blue economy during our sample period. The net total connectedness shows that the blue economy index is a net shock receiver, especially in late 2021 and the second half of 2022, while most of the fintech indexes in our sample are mainly net shock transmitters. The Ukraine–Russia tension threatens the development of a sustainable blue economy. The development of Fintech plays an important role in promoting the blue economy.

Our results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies.

Our paper contributes to the literature in at least four ways. First, as previously stated, our article is the first to investigate the relationship between FinTech and blue bond volatility. Second, this study presented a framework for studying volatility interconnections between distinct variables that is more suited to analyzing these interconnections. In this research, we use a cutting-edge model-free connectedness approach to investigate the relationships between FinTech and blue bond volatility. Third, our work is the first to investigate the effects of unknown events such as the COVID-19 pandemic and Ukraine–Russia conflicts on the interconnection of volatility deriving from FinTech development and blue bond volatility. Lastly, our research provides a daily dataset for the BNP Paribas Easy ECPI Global ESG Blue Economy UCITS ETF to analyze 50 businesses from various markets that are at the forefront of the responsible application of ocean resources and other ESG standards. The Global X FinTech ETF (FINX) and the ARK FinTech Innovation ETF (ARKF) seek exposure to companies developing financial technology innovations. The development sectors include insurance, investment, fundraising and third-party lending by utilizing cutting-edge mobile and digital technologies. Our time series runs from September 14th, 2020, to August 11th, 2023. By using this database, we provide a comprehensive analysis of the link between the volatilities arising from various markets.

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

This study examines whether a firm’s management earnings forecasts affect its technical innovation activities. Our study also examines whether the cost of debt plays a mediating role between the management earnings forecasts and the innovation nexus.

We obtained data from 1,032 Chinese non-financial firms listed on the Shanghai and Shenzhen stock markets from 2005 to 2022 (i.e. 18,576 firm-year observations). We used various econometrics techniques, such as Heckman’s (1979) two-stage selection method and two-stage least square, to examine the relationship between management earnings forecasts and the firm’s technical innovation activities.

We find a positive relationship between management earnings forecasts and the firms' technical innovation. We also find that the cost of debt mediates the relationship between management earnings forecast and technical innovation. Further analysis indicates that frequent earnings forecasts provide incremental information regarding a firm’s future value and cash flows, thus reducing the volatility and uncertainty in cash flow calculations. Our findings are robust to several tests.

Our study has implications for policymakers, practitioners and high-level management of Chinese firms, enabling them to understand the relationship between management earnings forecasts and firms' innovation activities.