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It is largely acknowledged that arbitrating the flow of knowledge can help firms strategically leverage tacit and explicit internal knowledge. However, despite the apparent scholarly and managerial acceptance of the criticality of the flow of knowledge between various stakeholders, the academic understanding of knowledge arbitrage remains coarse-grained. There are practically no empirical insights available to unravel the consequences of firms’ knowledge arbitrage choices regarding rewards and risks. This study aims to identify the risks that emerge as firms channel the flow of knowledge from surplus to deficit areas within organizational boundaries. To this end, the authors investigate several subsumed subprocesses in knowledge arbitrage to map the associated risks.

This study used an exploratory qualitative approach to examine the risks that emerge as firms attempt to support knowledge flows within their organizational boundaries. The data were collected through open-ended essays via an online research platform from 45 full-time employees of firms operating in different sectors. The collected data were analyzed inductively through open, axial and selective coding.

The research findings identified three key subprocesses of knowledge arbitrage: knowledge diffusion, knowledge brokering and knowledge absorption. These subprocesses are susceptible to various risks arising the form of channels, champions, sharers and receivers of knowledge flows. In general, the study showed that a firm’s decision regarding knowledge flows, such as structured or random flows, or the presence or absence of designated coordinators to broker the flow carries specific risks for both sharers and receivers. In particular, while the risks of knowledge hiding, misinformation and disinformation manifest in all three subprocesses, low employee engagement, loss of knowledge and information overload also emerged as key risks in any two of the three subprocesses.

This study offers valuable insights by uncovering the hitherto unexplored risks in intrafirm knowledge arbitrage. Given that knowledge is a crucial organizational tool for driving performance, innovation and competitive advantage, understanding the risks associated with intrafirm arbitrated knowledge flows can help firms anticipate and mitigate the associated adverse consequences. The findings make a novel contribution by offering (a) a comprehensive categorization of the risks associated with knowledge arbitrage rooted in processes, people and structures and (b) a macro overview of knowledge arbitrage risks associated with the processes of knowledge diffusion, knowledge brokering and knowledge absorption.

The empirical studies of the options market efficiency have reported contradictory results, which sometimes confuse practitioners and academicians. The aim of this study was to clarify several aspects of options market efficiency by exploring the answers to two main questions: Under what conditions is the options market more efficient? Are the discrepancies in the estimated efficiency due to the reality of efficiency or mismeasurement?

Using a meta-analysis approach, 54 studies have been analyzed, which included 1,315 tests. The sum of the observations for all of the tests is 3.7 m observation sets. The effect size (type r) has been used to compare the different statistics in different studies. The cumulative effect size and its diversification have been calculated by the random effects model and Q statistic, respectively.

The most interesting finding of the study was that the options market, in all circumstances, is significantly inefficient. Another important finding was that the heterogeneity of options market efficiency is due to the complexity of pricing relations, test time, violation index and price type. To overcome this heterogeneity and accuracy, future studies should test the no-arbitrage options pricing relations at different times and by different price types, using complex and simple pricing relations and either mean violation or violation ratio efficiency measures.

Public disagreement about the options market efficiency in past studies means that this variable is heterogeneous in different conditions. As a significant contribution, this study develops the literature by proposing the causes of options market efficiency heterogeneity.

In this research, arbitrage opportunity is tested between the yield rates computed by the NSS model, and the computed forward rates between conventional and Islamic finance to see any arbitrage opportunity. The research questions are the conventional and Islamic finance yields at the same level and equal to each other to avoid arbitrage? Whether conventional and Islamic forward rates differ significantly and thus create any arbitrage opportunity. This study aims to find the presence or absence of arbitrage between conventional and Islamic finance yield rates.

The NSS model is the latest model in calculating yield and forward rates. In the method the error level is minimized so expected yield rate and given yield rate both converged (Vahidin and Anastasios, 2020). When they converged it gives the researchers all six months’ yield rates. For the Nelson Siegal method, all the six months’ yield rates are available and these yield rates can be used to compute the forward rates.

The authors concluded there is a significant difference between the conventional yield rate and the Islamic yield rate. It suggests that because there are significant differences, its suggest arbitrage is possible. So anyone interested in making a guaranteed profit. The conventional yield rates are lower; hence, anyone can borrow from the conventional finance system and invest the money in the Islamic financial system because investments are getting higher rates of income in the form of yield rate in Islamic Finance. So, one can make money because of this difference. Statistically, it is possible to make money, but practically, the authors observed the difference, however it is very meager. The arbitrage opportunity between Islamic finance and conventional finance will not affect the economy because the significant difference is too small. The disturbance in the arbitrage opportunity due to the values is very meager and insignificant.

This research does not address the derivative contracts’ role in risk management; future researchers could take up this as another research.

This research will be beneficial for financial institutions, especially institutional investors. Besides, this research will help the regulators and investment bankers in assisting where and future losses especially bond portfolios in conventional finance and Islamic finance. This study will also contribute and help the asset manager of mutual funds in the mutual fund industries.

In effect, this research will strengthen the financial system, capital market and bond market, derivative contracts such as options contracts, futures contracts, swap contracts and forward contracts will use computed forward rates for assessing future losses (value at risk [VaR]) and to hedge them (Balakrishnan, 2020).

As this topic is rarely studied it will increase the literature present in this domain.

This study aims to integrate entrepreneurship theories and acculturation perspectives into a unified lens to understand opportunity development by transnational entrepreneurs (TNEs).

This study uses a conceptual method, considering how acculturation strategies of TNEs influence cross-cultural arbitrage.

We develop six propositions that define how acculturation strategies relate to different levels of cultural embeddedness of transnational entrepreneurs and ultimately influence the process by which the entrepreneur engages in cross-cultural arbitrage.

We are one of the first to integrate the sociology of immigrants with entrepreneurship to better understand how TNEs engage in cross-cultural arbitrage.

This paper aims to highlight firms' profitability as an alternative channel by which changes in leverage could affect stock returns in an imperfect market setting. The author also analytically argues that the benefits of debt, if any, may accrue beyond the usual tax benefit channel.

The author used multivariate regression models based on firms' characteristics and the models' changes along with a two-stage least-square (2SLS) type procedure to estimate the impact of leverage changes on stock returns. The author controls for the varying arbitrage risk that is measured by forecasted idiosyncratic volatility of stock prices and overcome simultaneous or endogenous determination by using inter-temporal non-synchronous variation in leverage and control variables.

The author finds that increase in leverage increase (decrease) stock returns for firms with the gross operating profitability higher (lower) than the cost of debt. The author also finds that the variation in arbitrage risk does not substitute for the primary effect of leverage changes on stock returns.

The author's findings provide tacit support to the recent literature attempting to resolve the empirically puzzling pattern of the negative relationship between profitability and leverage. The findings suggest inclusion of profitability as a crucial asset-pricing factor in the contemporary empirical models.

The non-trivial role of profitability in determining the effect of leverage on firms' stock returns that may be useful to managers, credit analysts and policy makers to assess the impact of net profitability on any change in leverage and its ensuing consequences on firms' value.

The paper develops analytical insights into the marginal role of profitability in influencing the relationship between firms' financing decisions and firms' stock returns beyond the conventional mechanisms of tax benefits, bankruptcy costs and information asymmetry.

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Investigation of the anomalies associated with crashes and jackpots in the Chinese stock market.

We propose a logit model to predict the events of crashes and jackpots in the Chinese stock market. The model introduces a new variable of the price-to-sales ratio and takes into account the market states, Up and Down.

The anomalies associated with crashes and jackpots are not related to variations in economic conditions, but are associated with limits to arbitrage. High-liquidity stocks have strong mispricing effects. The institutions’ speculative trading will push liquid stock prices further away from their fundamentals but avoid buying illiquid stocks with a higher probability of price crashes and jackpots.

We propose a logit model to predict the extreme events of both crash and jackpot in the Chinese stock market. Our model effectively disentangles from CRASHP and JACKP. Compared with the traditional model, it substantially enhances in-sample and out-sample predictions. Based on the predictions of the extreme events, we find two strong and robust pricing effects associated with ex ante CRASHP and JACKP in the Chinese stock market.

This paper analyzes the ex-ante determinants of asset securitization in Moroccan banks, providing a detailed exploration of factors influencing securitization in the Moroccan banking sector.

The study focuses on funding, performance, risk transfer and regulatory capital arbitrage hypotheses. By employing a probit model, we examined all Moroccan banks that securitized their assets from 2002 to 2022. Additional analyses were conducted with alternative variables and by splitting the sample into two periods, 2002–2013 and 2014–2022, to assess the impact of the regulation law 119-12 implemented in 2013 on the Moroccan securitization market.

The results indicate that the search for alternative funding sources and bank size emerge as significant factors driving securitization in Morocco. Additionally, there is limited evidence that loan portfolio quality is a decisive factor to securitize. Meanwhile, there is no evidence that securitization is driven by performance and regulatory capital arbitrage. Robustness tests further support these findings, while also suggesting that banks may engage in securitization to enhance their performance and, to a lesser extent, reduce regulatory capital.

This paper contributes to the empirical literature by identifying the determinants that drive Moroccan banks to securitize, addressing a research gap in the relatively understudied Moroccan securitization market. The findings provide valuable insights for bankers, investors and policymakers, highlighting the potential benefits of securitization and suggesting policy changes to foster a robust securitization market while ensuring financial stability.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020–16 August 2021.

At the empirical stage, the Fourier-augmented vector autoregression approach has been used.

According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns; and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers.

Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.