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Applying universal ownership theory and drawing on a multiplecase study design, this study aims to analyze what drives institutional investors to engage with government entities and what challenges they find in the process.

The authors relied on document analysis and conducted 12 semi-structured interviews with representatives from asset owners, asset managers, investor associations and academia.

The authors identify a trend where investors conduct policy engagement to fulfill their fiduciary duty, improve investment risk management and create an enabling environment for sustainable investments. As for engagement challenges, investors report the longer-term horizon, a perceived limited influence toward governments, the need for capacity building for investors and governments, as well as the difficulty in accessing government representatives.

This research contributes to filling a gap in the literature on this new form of investor activism, as a growing number of investors engage with sovereign entities on environmental, social and governance issues.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

The objective of this paper is to study the impact of political connection and governance on credit rating and whether there is a substitution or complementary relationship between them.

In order to achieve the objective, a succession of eight ordered probit regressions has been carried out. Moderating variables between the political connection and governance characteristics were introduced. The whole population is taken as a sample, i.e., 27 Tunisian companies that are evaluated by FITSH NORTH AFRICA agencies over a period of 10 years (2009–2018).

The outcomes are mixed. They show that the political connection does not always influence credit rating; the size and board independence always improves credit rating; the duality between the functions affects credit rating; whereas the majorities’ proportion does not influence credit rating; and a substitution between the political connection and the governance characteristics is validated.

Like any other research, our results are factors of our measures and variable choice and depends heavily on the how these variables were conceived. Also, although our number of observations responds to the statistical result generalization requirements, our sample remains relatively narrow with 27 companies only.

In practice, the research will allow investors to have a better vision upon the future of their investments based on whether to develop their governance system or promote political networking. It will also prompt lenders to look beyond ratings and consider factors such as political connections to make a rational judgment on their future placements.

This study leads us to find various solutions: the establishment of credit agencies that take into consideration all the data of all the operators taken as a whole (bank, leasing company, and factoring). It encourages the reorganization of the Tunisian banking sector through mergers for example.

This study is a pioneer in the credit rating field in Tunisia, where the source of debt financing is the most used by all enterprises across all sectors. This study extends the literature of political connection effectiveness, independent directors, board size, in improving corporate performance and credit rating.

Bank lending is the major source of monetary expansion. Bank-led money creation is a key issue in both conventional and Islamic financial systems. The purpose of this paper is to examine the issues related to Islamic banking money creation. In this conceptual paper, the authors investigate the involvement of profit and loss sharing (PLS) in money creation and especially how can PLS limit money creation “out of nothing.” In this regard, the authors examine the potential of the PLS principle in tackling the excessive money creation phenomenon.

This study uses a normative approach regarding Islamic bank money creation that fits Sharia directives. In fact, this study discusses “what ought to be,” that is, the values and norms of PLS money creation that impede excessive money creation.

Overall, Islamic banks create money differently compared to conventional ones. Especially, by avoiding a purely financial intermediary, money creation under the PLS principle sustains a strong relationship with the real economy and leads to a lower money multiplier. Therefore, PLS mechanisms allow financing through real assets and not credit assets “out of nothing.” This could prevent excessive money creation from causing harmful effects on indebtedness and financial instability.

PLS offers a valuable resolution for banking system money creation through the optimization of Islamic bank financing by facilitating the separation of the monetary function from the credit one. This reform thought reinforces the stability value of money allowing it to fully perform its functions with reference to the directives of Sharia. This especially allows the integrity and purchasing power of money, the reduction of the gap between the evolution of both real and financial economies and, consequently, the indebtedness and crisis. It is recommended to promote PLS financing by reforming institutional and regulatory constraints.

This study addresses the contemporary issue of money creation by Islamic banks through the PLS approach. The conceptual framework of this paper highlights the reformist role of PLS in limiting money creation through Mudarabah approach within fractional reserve banking.

This chapter examines the impact of sectoral indebtedness on GDP in Czechia, initially a low-indebted small open economy in which debt dynamics are becoming a major concern. The impact of household debt, non-financial corporation debt and public debt is analysed with the use of local projections based on instrumental variable estimations. The results show a more pronounced influence of household debt compared to non-financial corporation and government debt. Initially, increasing household debt stimulates short-run economic activity, but in the medium run, it limits household consumption and negatively affects output. This negative impact gradually turns into a positive effect in the long run. Non-financial corporation debt has a negative short- to medium-run impact but can have a small positive effect in the long run due to the prevalence of tradable industries. Public debt initially has a short-run negative impact, but then gradually becomes positive. Overall, the findings have implications for macroeconomic policies and the importance of monitoring financial stability.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.