Is there a CSI-leverage nexus?

We examine how various measures of consumer sentiment index (CSI) affect firms' debt policy decisions. Using U.S. firm-level quarterly data from 1993 to 2017, we provide a strong positive relationship between CSI measures and corporate debt policy, implying that firms use external borrowing during a positive economic outlook and reap the tax-shield benefit. We also find that improved household optimism over financial and business sentiments leads to future household consumption. The CSI-leverage nexus is moderated by the state of firms' financial condition, reputation, and profitability. Importantly, our results are robust to sub-sample analysis, firm-level and macroeconomic controls, econometric specifications, alternative measures of sentiment including Shiller's cyclically adjusted price-earnings ratio (i.e., CAPE_SH), Baker and Wurgler (2006)’s stock market sentiment index (i.e., SENT_BW) and search-based uncertainty measure such as FEARS (i.e., Financial and Economic Attitudes Revealed by Search) index of Da, Engelberg, and Gao (2015).     

Bibliometric and Scientometric analysis on CSR practices in the banking sector

This paper provides a bibliometric and Scientometric analysis of the corporate social responsibility (CSR) in banking sector. Our study analyzes 551 articles from the Scopus database to find out the relationship between CSR and banking. A bibliometric method was used to visualize the results using R-studio and VOS viewer software. The Scientometric analysis was conducted to determine the findings and mappings of the research themes, directions of current and future research, impact, co-occurrence, co-citations and impact and collaboration trends. We explore how CSR literature has evolved over the years in the banking sector between 1993 and 2021. We find that publication in the CSR and banking domain has increased significantly during 2017 and 2021. Social aspects, board of directors, CSR, environment, competitions, Islamic banking, sustainability, disclosure, ethics, legitimacy theory, sustainable banking, loyalty, and brand equity are the popular research trends and collaboration trends identified. We also provide further scope of the study based on the extensive review of the past literature. Our findings may provide help to future researchers, bankers, and regulators in understanding the current trends and future research progression in the CSR and banking sector.     

Modern Slavery Disclosure Regulation and Global Supply Chains: Insights from Stakeholder Narratives on the UK Modern Slavery Act

Journal of Business Ethics - The purpose of this article is to problematise a particular social transparency and disclosure regulation in the UK, that transcend national boundaries in order to...     

Economic policy uncertainty and imitation behaviors of corporate social responsibility practices: Evidence from China

In this paper, we examine whether firms facing higher economic policy uncertainty (EPU) are more likely to show similar corporate social responsibility (CSR) practices compared with their peer firms. Drawing upon institutional theory, in response to uncertainty under complex circumstances, managers tend to imitate peer firms' strategic actions to acquire legitimacy. Consistent with our theoretical expectations, we find that EPU increases the likelihood that a focal firm will show CSR practices similar to its peer firms. Such a likelihood is amplified for firms that (1) bear more negative media coverage, (2) have higher industry competition intensity, (3) belong to heavy-polluting industries, and (4) for the first-time disclosures. Our results hold when we employ a series of endogeneity tests and robustness checks.     

Predicting inflation expectations: A habit-based explanation under hedging

One of the main challenges monetary policymakers face is predicting the trajectory of short-term inflation, especially considering the consistently flat Phillips curve observed in recent years. A promising approach to tackling this challenge involves modifying the hybrid New Keynesian Phillips curve (HNKPC) by incorporating a hedging factor. This factor accounts for the efforts risk-averse economic agents make to safeguard their spending decisions against uncertainties arising from inflation. Our study provides evidence that the hedging factor plays a crucial role and is a statistically significant predictor of the upcoming year's inflation rate. Specifically, a one-standard-deviation increase in the hedging factor predicts a positive rise in short-term inflation. Furthermore, we demonstrate that the hedging factor significantly determines the common component found in both well-known survey-based and model-based inflation expectation indicators.     

Conditional Mandates on Management Earnings Forecasts: The Impact on the Cost of Debt

Exploiting a unique conditional disclosure mandate on management earnings forecasts (MEFs) in China, we examine the differential effects of voluntary and mandatory MEFs on the cost of debt. We find that firms providing voluntary MEFs have lower cost of debt than do mandatory forecasters and nonforecasters. The results of the channel analyses reveal that voluntary forecasters have greater commitment to voluntary MEFs in future periods than do mandatory forecasters and nonforecasters, and the precision, accuracy, and timeliness of MEFs are higher for voluntary forecasters than for mandatory forecasters. Additional analyses show that the differential effects of voluntary and mandatory MEFs on cost of debt are stronger for voluntary forecasters operating in opaque information environments, issuing high-quality and confirming forecasts, controlled by private shareholders, and operating in highly competitive product markets. Overall, our results indicate that, compared with mandatory MEFs, voluntary MEFs are more informative for credit investors, particularly for firms facing greater information risk and operating uncertainty.     

Shifting Strategies: the Pursuit of Closure and the ‘Association of German Auditors’

Drawing on Weber’s [(1972). Wirtschaft und Gesellschaft: Grundriss der verstehenden Soziologie [Economy and society. An outline of interpretive sociology]. (5th ed.). Tübingen: Mohr Siebeck] theories of social stratification and closure, and on Parkin’s [(1979). Marxism and class theory. London: Tavistock] development thereof, this paper contributes a case study of Germany to the literature on closure processes (rather than outcomes), and outcomes falling short of professionalization. It explores the early history of the first German association of auditors, the Verband Deutscher Bücherrevisoren (VDB), founded in 1896. The paper traces how closure was pursued, at various times, by different means and to different degrees, depending on the changing social, economic and political contexts and on changing perception of what would best serve the association’s interest. By thus focusing on the shifting aims and strategies of the VDB, the paper contributes new insights into the dynamics within professions and between professions and their environments, and the dynamics and conditions underlying failed professionalization projects. The paper shows that, while the VDB had a degree of success in creating many of the structure and processes of a modern professional body, its ambition to achieve market and professional closure was not fulfilled.     

The European IFRS Endorsement Process – in Search of a Single Voice

We analyse the creation and development of the European Financial Reporting Advisory Group (EFRAG), a key part of the EU endorsement mechanism for International Financial Reporting Standards (IFRS), which was probably the first example of a dedicated IFRS endorsement system. We discuss the historical background to the EU approach and we analyse how EFRAG evolved over its early years up to the Maystadt reform. Our analysis addresses its remit, its operational structure and financing and the key decisions made in the endorsement process over this period. We find that while national standard-setters had a limited role in the early stages, and EFRAG had limited resources, over time the pressure to find more resources and to try to achieve a unified voice in the creating of international standards have resulted in national standard-setters playing an ever-greater role. Nonetheless, a single voice for Europe is not likely to be attained     

Does social network sentiment influence the relationship between the S&P 500 and gold returns?

This study explored the relationship between investor sentiment (extracted from the StockTwits social network), the S&P 500 Index and gold returns. We investigated bilateral causality between gold prices and S&P 500 prices, the power of investor sentiment and gold returns to predict S&P 500 returns, and the influence of gold returns on S&P 500 volatility. We also considered whether the influence of sentiment varies according to the user's degree of experience. We considered the sentiment of messages that mentioned the S&P 500 Index and that users posted between 2012 and 2016. Granger causality analysis, ARIMA models and GARCH models were used for predicting S&P 500 Index returns and S&P 500 volatility. We observed a causal relationship between gold price and the S&P 500 Index. Our results also suggest that sentiment and gold returns predict S&P 500 Index returns. Finally, we observed that gold returns influence S&P 500 volatility and that the sentiment of experienced users affects S&P 500 returns.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.