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The abnormality of the month-of-the-year and Ramadan effects has extensively existed in the stock and other markets. The commercial strategy pattern and the computation of such predictable patterns in the market allow investors to make money. By using anomalies such as the month-of-the-year and the Ramadan effects on earnings management (EM), it is possible to achieve such a goal. This study aims to investigate the month-of-the-year effect and the Ramadan effect on the relationship between accrual earnings management and real earnings management (AEM and REM, respectively) and liquidity in the Iranian capital market.

This empirical analysis comprises a panel data set of 80 listed firms (400 observations) on the Tehran Stock Exchange from 2016 to 2020.

The findings exhibit that when AEM and REM increase, information asymmetry also increases. The simultaneous increase of these variables leads to a decrease in stock liquidity. Furthermore, the results indicate that the month-of-the-year and Ramadan effects intensify the negative relationship between AEM and REM with stock liquidity. Therefore, EM is affected by the investor’s behavior in specific months.

Anomalies caused by the Ramadan effect and the month-of-the-year effect on reducing liquidity in the Iranian stock market were confirmed. Investors can use these anomalies to identify predictable patterns, exchange securities according to those patterns and earn abnormal returns.

To the best of the authors’ knowledge, this is the first study that empirically examined the simultaneous effect of Gregorian and Islamic calendar anomalies on the relationship between EM and liquidity, and while helping managers and other readers, it can be the basis for future research.

In the research of stock market efficiency, it is argued that the stock market moves randomly and absorbs all the available information. As a result, it is quite impossible to make predictions about the possible future movement by the investors. But literatures have detected certain calendar anomalies where a day(s) in a week or month(s) in a year or a particular event in a year becomes conducive for investors to earn more than the normal. Hence, the purpose of this study is to find out the month of the year effect in the Indian stock market.

In this study, daily time series data of Sensex and Nifty from 1996 to 2021 is used. The study uses month dummies to capture the effect. Different variants of generalised autoregressive conditional heteroskedasticity (GARCH) models, both symmetric and asymmetric, are used in the study to model the conditional volatility in the presence month effect.

This study found the September effect in the return series of both the stock market. Apart from that, asymmetric GARCH models are found to be the best fit model to estimate conditional volatility.

This study is an endeavour to study month of the year effect in the Indian context. This research will provide valuable insight for studying the different calendar anomalies.

This study aims to investigate the day-of-the-week (DoW) effect in globally listed private equity (LPE) markets using daily data covering the period 2004–2021.

To investigate the existence of the DoW effect in globally LPE markets, ordinary least squares regression, generalised autoregressive conditional heteroscedasticity (GARCH) regression and robust regressions are used. In addition, robustness audits are conducted by subdividing the sampling period into two sub-periods: pre-financial and post-financial crisis.

Limited statistically significant evidence is found for the DoW effect. By taking time-varying volatility into account, a statistically significant DoW effect can be observed, indicating that the DoW effect is driven by time-varying volatility. Economic significance is captured through visual inspection of average daily returns, which illustrate that Monday returns are lower than the other weekdays.

The results have important implications on whether to adopt a DoW strategy for investors in LPE. The findings show that higher returns on selected days of the week for certain indices are possible.

To the best of the author’s knowledge, this paper provides the first study to examine the DoW effect for globally LPE markets by using LPX indices and contributes valuable insights on this growing asset class.

This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

Business creation is an important measure of real economic activity as it shows the dynamics with which new firms are born, create jobs, move their capital, innovate and compete with old firms. In this sense, this paper aims to analyze the short-term impact of the lockdown policies implemented to stop the spread of the COVID-19 on the creation of new formal firms in Ecuador.

This paper uses a regression discontinuity in time (RDiT) design jointly with official administrative real-time data. This data is collected by the supervisory and regulatory institution of formal companies in Ecuador. The authors use real-time data from January 13, 2020, to May 15, 2020. This period allows to use the President’s order of effective lockdown on March 16, 2020, as the exogenous event. This gives 43 working days on each side of the cutoff date on the baseline model.

The authors find: an overall large drop in the creation of new formal firms (−73%) and a decrease in the total amount of initial capital coming from the new formal firms (−40%). Additionally, the results suggest that the negative impact of the COVID-19 lockdown on the creation of new formal firms seems not to decrease in the short term. The main conclusion is that lockdown policies have a negative impact on firm creation, a result that is of high policy relevance and can be a tool to design business attraction policies.

The analysis is carried out in a short period because on May18, 2020, a new policy was applied in Ecuador that allowed firms to be created more quickly, with 1 USD of capital, and 1 shareholder, among other benefits, and this may affect the outcomes analyzed in this document, so extending the analysis of the impact of the lockdown to a longer period could result in biased results due to this policy. Additionally, studying daily sales would be of the utmost importance; however, these data are not found in the database of the supervising institution.

This study contributes to the empirical literature and the policy debate in various aspects. First, it is important to generate facilities for the creation of new formal firms, from the reduction of days it takes to create one (using technology as a support in this matter) to the decrease of the minimum capital to formalize a company. Second, improve the business conditions of the new formal firms that were born during the pandemic, but also that these conditions create stimulus for the creation of new companies. Third, the authors show that induced-lockdown policies have a negative impact on the creation of new formal firms and the total amount of initial capital from new formal firms; this effect could be a full-blown recession if governments do not apply mechanisms to revert this situation that could be a drag on the economy.

This paper opens the debate on the effects of the COVID-19 lockdown on the creation of new formal firms; therefore, future research could study the impact in a broader time window to analyze medium and long-run effects, but also in different economic sectors and in the effects on firm bankruptcy, which added to an analysis of job loss, will show a total effect of damage in the economy.

This study aimed to empirically examine the effectiveness of disclosing user comment history without disclosing personal identity as a nudge policy to refrain users from posting malicious content online.

The authors collected the number of comments and posters from the leading portal website in South Korea, Naver.com. To causally investigate the impacts of the new nudge policy on the number of comments and posters, the authors used the regression discontinuity design (RDD) approach.

The authors found that the new policy reduced all types of comments, including the number of malicious comments, self-deleted comments and current comments. This resulted in an overall decrease in the total number of posted comments, which is considered a side effect. In addition, the authors found that the effect of the nudge policy, which disclosed user comment history, has a stronger effect on older female users than their counterparts.

The study findings extend the current knowledge on a nudge policy being implemented by a website as a means to reduce malicious online content and how it impacts user content posting behaviors.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

This study examines the effects of scent arousal on consumers' time perception in retail service environments and further explores how the effect is moderated by consumer-perceived stress.

A laboratory experiment (Study 1) and a field experiment (Study 2) were conducted to examine the relationship between scent arousal and time perception and the mediating effect between scent arousal and consumers' store evaluations. Another laboratory experiment (Study 3) was conducted to explore how consumers' stress modifies the scent arousal effect.

Consumers in a low-arousal scent condition perceived a shorter duration of time than those in a high-arousal scent condition. This finding was verified in a field experiment, whereas scent arousal affects consumers' store evaluations through the mediating effects of time perception. However, the impact of scent arousal on time perception was attenuated in high-stress conditions.

Time duration perception is an important indicator in the retail service marketing process. Evidence shows that underestimating time duration in the shopping process represents positive responses. This study extends prior research by examining how scent arousal influences time perception and how consumers' stress moderates scent arousal’s effect.

This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency?

Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications.

First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value.

Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.