Annals of Financial Economics

Cross-sectional characteristics of stocks such as market value, market-to-book ratio and accumulated past return can be applied to formulate equity portfolios in the stock-picking process to generate good profits in some markets, which relate to the well-known size, value and momentum or contrarian strategies in the literature. Alternatively, income measures in financial statements drive investors in stock markets to buy or sell in an intuitive way that can be also used in the stock-picking process to generate good profits in some markets. This study applies these types of information in the formation period to formulate long-short strategies and investigates both the returns and risk profiles in the holding period afterward and checks whether the measures can be used to generate good profits in the Taiwan markets for the period from January 1980 to June 2020. Given different lengths of the holding period and different equity segments, our empirical analysis shows that strategies filtered by the income measure of gross profitability outperform the counterparts filtered by the operating profitability. Moreover, while the momentum or contrarian effect is not, the size and value effects are helpful to improve the performance of long-short strategies filtered by gross profitability.

This paper contributes to the literature by employing a multifractal cross-correlation analysis (MFCCA) to study the effect of the global COVID-19 pandemic on cross-correlations between oil and US equity markets. First, we examine the detrended moving average cross-correlation coefficient between oil and S&P 500 returns before and during COVID-19 and find that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long-range cross-correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equities in the long range and a deeper dynamic connection between oil and US equity markets, as indicated by the multifractality tests. We also investigate the connectedness between oil and the S&P 500 using a dynamic procedure based on time-varying parameter vector autoregression. We find that oil is a net transmitter of shocks to the forecast error variance of the S&P 500 during March, April and May 2020, whereas the S&P 500 is a net transmitter of shocks to oil variance early in the pandemic (January and February 2020).

Purpose: This paper aims to analyze the impact of COVID-19 on Pakistan’s traditional (KSE-100) and Islamic (KMI-30) stock market returns.

Methodology: This study uses daily data of total cases and deaths of COVID-19 from February 25, 2020 to May 26, 2021. We utilize continuous wavelet transform (CWT), partial wavelet transforms and wavelet coherence transform (WCT) approaches to inspect the impact of COVID-19 on the stock return of KSE-100 and KMI-30 from March 13, 2020 to May 26, 2021.

Findings: Contrary to European and several Asian stock markets, these both indexes behave the opposite during COVID-19. This study indicates that COVID-19 influences both these indexes and has a significant impact on both KSE-100 and KMI-30 index in the longer time frame. This study also discloses that with the increasing number of total cases, total death stock market daily return.

Practical implications: Investors diversify their portfolio in the desire to achieve maximum return on minimum risk so they diversify across different countries and certain emerging market indexes might provide them a big edge to maximize their return. This diversified strategy can financially support different well-performing emerging markets and save emerging economies. This study enhances the investors trust and confidence to invest in both KSE-100 and KMI-30 due to favorable return of stocks.

Originality/value: This examines the co-movement between COVID-19 and the traditional and Islamic stock index of Pakistan whereas, the previous paper only examined the volatility of these indexes during COVID-19. This study also extends the literature that examines how COVID-19 affected the traditional and Islamic stock market indexes.

Blockchain technology has attracted a lot of attention due to its revolutionary potential to upend the established economic structures, leading to a deluge of literature on the topic. Academics and business leaders alike are eager to see this technology used in the financial industry, although its full potential and limitations are currently unknown. To bridge this knowledge gap, 3312 records from the Dimensions database were obtained between 2014 and 2022 and used for bibliometrics and network analysis on the subject of blockchain technology in the financial sector. This analysis articulates the various potentials of blockchain literature as well as its adoption by various countries and authors, collaboration patterns, and applications of blockchain in finance using the visual mapping technique provided by VOSviewer. The top two innovators in the field, China and the USA, have used blockchain more extensively in financial research. Furthermore, the results show that the coverage of blockchain in finance has exploded in the last three years. Although India ranks third in documentation, behind China and the USA, it is lacking in citations and networking opportunities. The results, which identify important journals and authors in this field, will aid future researchers in better understanding the literature and conducting a PRISMA-based systematic review. This research is unique because it is the first bibliometric study to use the Dimensions AI database to look into how blockchain technology can be used in the financial sector.

Various empirical studies have been conducted. However, these studies fail to examine the asymmetric effect of income and price across different quantiles of consumption in the emerging 7 countries. This study extends the existing literature using a novel approach called the quantile ARDL model along with the standard nonlinear ARDL model. Findings based on the nonlinear ARDL model indicate that positive shocks in income positively and significantly affect consumption in the short- and long-run. On the other hand, negative shocks in income do not significantly affect consumption which, therefore, suggests an asymmetric effect of income on consumption. In addition, the quantile ARDL estimates indicate that income positively affects consumption across all quantiles of the consumption except the 95th quantile. Moreover, the quantile ARDL estimates indicate that price variations negatively affect consumption across all emerging 7 countries. These estimates suggest that devising policies without considering the asymmetric effect may lead to unfavorable consequences.

This paper investigates the Indian equity market for the presence of a beta anomaly. A beta anomaly occurs when the additional market risk taken by an investor is not rewarded. Academic literature shows mixed evidence on whether the market rewards risk-takers or not for the additional risk taken. Using a sample of monthly returns of 265 companies during a period of 240 months from January 2000 to December 2019, the authors test the Indian equity market for the presence of an anomaly. A decile descriptive analysis shows a positive relationship between market risk and returns, and a negative relationship between company-specific risk and returns. A two-stage Fama–MacBeth (FMB) regression procedure is employed to empirically test for the relationship between beta and expected returns. The findings refute the presence of a beta anomaly in the Indian capital market. Also, the study concludes that a linear model of slope-intercept form is enough to explain the beta and expected returns’ relationship. The findings benefit investment managers and wealth advisors by explaining the market risk and expected returns relationship.