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Purpose The purpose of this paper is to study the market reactions of the banking industry to the Russian–Ukraine war. Design/methodology/approach This paper uses an event study methodology, regression analyses and interaction effects to study the effect of the war on banks stock prices and analyze factors that explain the cumulative abnormal return. Findings First, this study finds a significant decline of almost 1.5% in return on the war date. Similar patterns were observed for all continents, but Europe had the most severe drop of about 4%. Second, after excluding the contemporaneous influence of the whole market using the market model, global bank equities returns fell by about 1% on the war date, indicating that bank stocks were more severely impacted by the war than the average stock market. Net-of-market return approach further reveals that bank stock prices decreased 1.4% more on the event day compared to the prewar market average. Third, the impacts of the war and sanctions were persistent when the war continued. Banks stocks were most hit in Europe, Asia and North America. Originality/value This paper pioneers the study of the effect of the Russia–Ukraine war on the banking industry. This paper also analyzes the reaction pattern of bank stocks before, during and after the war to explain the behavior and expectations of investors toward the war.

Purpose This paper aims to investigate a fundamental research question of whether the Islamic banking business model makes corporate earnings more uncertain. This question arises because prior research shows that Islamic banks do well in loan performance but incur more operational costs than conventional banks, indicating the systemic limitation of Islamic banks in business risk management. Design/methodology/approach The study used a sample of banks to conduct the panel regression analysis with 15 years of data for 532 banks (129 Islamic and 403 conventional) from 23 Muslim countries across the world. The authors estimate earnings uncertainty in two ways: the spread and standard deviation of the country-adjusted return over the sample period and applied the difference-in-difference approach interacting cost to income ratio with the Islamic bank dummy, checking if Islamic bank’s high operational costs contribute to more earning uncertainty. Findings Islamic banks’ returns on assets are significantly more uncertain than conventional banks due to higher operational costs. Consistent with earlier evidence, the study also finds that Islamic banks generally have fewer nonperforming loans than conventional banks. The authors conclude that Islamic banks trade-off between reducing credit risk and escalating business risk. Originality/value This study documents that the Islamic banking model helps build a safer asset portfolio but gives rise to the uncertainty of corporate earnings. Therefore, the choice between Islamic and conventional banking models involves a trade-off between credit and business risks. It is a new finding that we add to the literature body on Islamic finance.

Micro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.

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This study provides new evidence on how risk spillovers occur from the United States to developing economies in Africa during the COVID-19 pandemic. The results show that downside risk exposures of African markets, financial firms and banks particularly increased during Phase I (30 January to 30 April 2020). The nature and magnitude of downside risk exposures of African financial markets were similar to those of the United States. Our results also reveal that the United States is a net transmitter of risk spillovers while Nigeria, South Africa, Egypt and Morocco are net recipients. Our conclusions offer guidance to risk managers, policymakers and investors.

This study examines the link between product market competition and labor investment efficiency. We find that competitive pressure distorts the efficiency of corporate employment decisions by creating an underinvestment problem. This finding withstands a battery of robustness checks and remains unchanged after accounting for endogeneity concerns. Additional analysis shows that the relation between product market competition and labor investment efficiency is stronger for firms facing higher competitive threats, greater financial constraints, higher information asymmetry, and higher labor adjustment costs. Our results suggest that since competition increases bankruptcy risk, it leads managers to underinvest in labor to avoid incurring labor-related costs.

The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives, this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low, the impact of semantic novelty and content richness on the IPO market is respectively enhanced.

This paper investigates the potential hedging and safe-haven properties of several alternative investment assets, including gold, Bitcoin, oil, and the oil price volatility index (OVX), against the risks of the Saudi stock market and its constituent sectors in different phases of the COVID-19 pandemic. Using daily data, we employ the bivariate dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) technique to model volatilities and conditional correlations. Our findings show that all investigated alternative investment assets had a time-varying hedging role in the Saudi stock market, which became expensive during the early stages of the COVID-19 pandemic. Our results also show that the optimal weights for gold were substantially higher than those of other assets, reaching a peak during the pandemic, implying that investors consider gold a flight-to-safety asset. Additionally, we find that gold and OVX were strong hedges and could have served as weak safe havens for investors during the early stages of the COVID-19 pandemic, while the remaining assets generally lacked these properties and could be merely used as diversifiers. Our empirical findings offer several key implications for policymakers and portfolio managers in Saudi Arabia that may be applicable to similar markets. In particular, we show that OVX-based products can serve as a promising hedging asset for stock markets in oil-exporting countries.

This study examines the effect of voluntary disclosure in annual reports on tax avoidance activities. The agency theory of tax avoidance suggests that tax sheltering is associated with important agency costs, underlining the importance of corporate governance mechanisms such as voluntary disclosure in shaping tax planning. Using a sample of 3448 firm-year observations of French listed firms over 2007–2013, the results show that voluntary disclosure is associated with lower tax avoidance activities, providing evidence that this disclosure can be seen as an effective monitoring tool that reduces the insiders’ likelihood to engage in rent extraction through tax avoidance activities. The results also indicate that the negative effect of voluntary disclosure on tax avoidance is significant only when family control is below 40%, suggesting that the disciplinary role of voluntary disclosure is limited to firms with relatively low family control levels. Overall, our findings are consistent with the agency theory of tax avoidance and highlight the important role of corporate disclosure in improving corporate governance.

This paper investigates the effect of corporate social responsibility on the information content of stock prices. Using a sample of 877 U.S.-listed firms, we provide evidence that a firm’s CSR performance has a negative effect on stock price synchronicity, suggesting that socially responsible firms commit themselves to high transparency standards, leading to more informative stock prices. Moreover, the negative relationship between sustainable corporate policies and stock price synchronicity is more pronounced in better-governed, highly competitive, and closely monitored firms. Our findings are robust to potential endogeneity problems, additional control variables, and alternative sample composition. This study contributes to the literature by providing original insights on the CSR performance link with firm-specific information impounded in stock prices. Further, we uniquely contribute to the CSR dimensions literature by separately analyzing which CSR dimension contributes to improving the firm’s information environment.

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.