Contributions in Merlin

This paper provides evidence that corporate boards violate the informativeness principle in their forced CEO turnover decisions by failing to ignore uninformative performance outcome signals. I show that CEOs of firms with barely positive shareholder returns in the previous year are less likely to be dismissed than CEOs of firms with barely negative returns, even though this return outcome is conditionally uninformative. I observe a similar pattern for stock returns relative to the S&P 500 index return: a firm's board is less likely to dismiss its CEO if the firm barely outperformed the S&P 500 index than if the firm barely underperformed the S&P 500 index. Moreover, I demonstrate that the tendency of boards to consider uninformative absolute return outcomes has decreased over time, while their tendency to consider uninformative relative return outcomes has increased over time. This suggests that boards have shifted their focus toward relative returns while continuing to violate the informativeness principle.

Serious games have emerged as a promising new form of education and training. Even though the benefits of serious games for education are undisputed, there is still a further need for research on the efficacy of such games. The main goal of our research is to examine the effectiveness of a serious moral game—uFin: The Challenge—that was designed to promote moral sensitivity in business, a precondition of ethical decision-making and behavior and a core moral competence of moral intelligence. A second goal is to examine the role of metacognitive prompting and prosocial nudging in influencing learning effectiveness. Participants (N = 345) took part in an experimental game-based intervention study and completed a pre- and post-test questionnaire assessing moral sensitivity. The analyses of both questionnaire and game data suggest that the game is effective in promoting moral sensitivity. Neither self-reflection nor exposure to prosocial nudges, however, were determined to be factors that improve learning effectiveness. In contrast, those interventions even decreased the learning outcome in some cases. Theoretical and practical implications, limitations and avenues of further research are discussed.

Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a novel method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment.

There is an ongoing debate about how proxy advisory firms affect corporate decisions. A major concern is that shareholders seeking to save costs use a proxyadvisor’s voter ecommendation as substitute for own research, thereby reducing efficiency of shareholder decision-making. We show that the opposite effect - complementarity between a proxyadvisor’s recommendation and shareholders’ research effort - occurs if two conditions are met: (i) the board o fdirectors is sufficiently well informed; and (ii) shareholders can condition their investment in research on the proxyadvisor’s recommendation. Insum, a profit-maximizing proxyadvisor can improve corporate decisionmaking by stimulating shareholders’ research through its own.

Stock factor returns exhibit greater predictability from the weighted least squares (WLS) estimator of autoregressions with time-varying volatility. The predictability transmits into superior mean-variance optimal portfolio performance that is hardly achieved by other strategies that utilize volatility timing and return predictability. This outperformance can be interpreted through an empirically weak factor risk-return relation. A WLS-based systematic factor long-short strategy subsumes the stock momentum factor, industry momentum and factor momentum, and produces sizable excess returns. The outperformance is not limited to particular ways of portfolio formation, which testifies that the WLS-based strategy fully exploits predictability.

Do financial crises affect long-term public health? To answer this question, we study the connection between the 2007-2009 Global Financial Crisis (GFC) and the 2020-2022 pandemic. Specifically, we examine the relation between macroeconomic and financial losses derived from the GFC, and the health outcomes associated with the first wave of the pandemic. At the European level, countries more affected by the financial crisis had more deaths relative to coronavirus cases. An analogous relation emerges across Spanish provinces and US states. Part of the transmission from finance to health outcomes appears to have occurred through cross-sectional differences in health care facilities.

Although sustainability is one of human mankind’s grand challenges, the contribution from the financial sector has still been limited so far. But the financial system is currently being reshaped by sustainability and digitization in parallel as the core drivers of a long-term transformation process covering novel solutions such as blockchain based for peer-to-peer payment platforms in the energy sector or crowdfunding platforms for agricultural supply chain. This trend is reflected in a growing number of start-ups in this field. However, existing research on the topic of Sustainable Digital Finance, which bridges both areas, is still rare and either focuses on single case studies, on certain industries or on single 16 Sustainable Development Goals (SDGs). A more comprehensive understanding of the business models, products and services, processes and the technologies of these approaches and their impact on the SDGs is a missing component in existing research. Therefore, this paper first develops a taxonomy based on a comprehensive literature analysis to structure this emerging field more precisely. In the second step, based on the taxonomy developed from 32 research papers, 126 startups in this field are analyzed and mapped against the taxonomy. The taxonomy includes a strategic, an organizational, a systems and a sustainable benefits perspective which are structured along the dimensions business model, product/service type (strategic perspective), network type, stakeholder interaction, business processes (organizational perspective), application type, application design (systems perspective) and the SDGs 1-17 (sustainable benefits perspective). The application of the taxonomy to 126 startups, which were identified in a global search exercise, reveals that their solutions primarily focus on applications in the energy and financial services sector while other sectors are still hesitant in applying FinTech models to their businesses to increase their sustainability efforts. In addition, the major focus of these models is only on five of the seventeen SDGs (7, 9, 11, 13, 17), which shows a clear preference for climate, energy, infrastructure, cities, and partnerships as the primary goals. Surprisingly, the startups focus merely on operational efficiency optimization like improved processes rather than disruptive innovations in product or customer-related areas.

Using all loans granted to firms recorded in the Italian credit register, we estimate correlations between risk-transfer and default probabilities to gauge the severity of informational asymmetries in the loan securitization market. First, the analysis confirms the presence of information frictions in the SME loan securitisation market. Second, the unconditional quality of securitized loans remains significantly better than that of non-securitized ones, in line with the notion that markets anticipate the presence of information frictions and lead to a selection of loans which offsets the detrimental effects of asymmetric information. Third, using data for firms that maintain multiple bank relationships, we obtain indications of the relative importance played by two forms of information friction, adverse selection and moral hazard. While the former is widespread, the latter is present in weak relationships only, in line with the notion that such loans are characterised by a limited commitment to exert costly monitoring by the bank.

Analytical queries often require a mixture of relational and linear algebra operations applied to the same data. This poses a challenge to analytic systems that must bridge the gap between relations and matrices. Previous work has mainly strived to fix the problem at the implementation level. This paper proposes a principled solution at the logical level. We introduce the relational matrix algebra (RMA), which seamlessly integrates linear algebra operations into the relational model and eliminates the dichotomy between matrices and relations. RMA is closed: All our relational matrix operations are performed on relations and result in relations; no additional data structure is required. Our implementation in MonetDB shows the feasibility of our approach, and empirical evaluations suggest that in-database analytics performs well for mixed workloads.

Around the world, policymakers and news reports have warned that domestic violence (DV) could increase as a result of the COVID-19 pandemic and the attendant restrictions on individual mobility and commercial activity. However, both anecdotal accounts and academic research have found inconsistent effects of the pandemic on DV across measures and cities. We use high-frequency, real-time data from Los Angeles on 911 calls, crime incidents, arrests, and calls to a DV hotline to study the effects ofCOVID-19 shutdowns on DV. We find conflicting effects within that single city and even across measures from the same source. We also find varying effects between the initial shutdown period and the onefollowing the initial re-opening. DV calls to police and to the hotline increased during the initial shutdown, but DV crimes decreased, as did arrests for those crimes. The period following re-opening showeda continued decrease in DV crimes and arrests, as well as decreases in calls to the police and to the hotline. Our results highlight the heterogeneous effects of the pandemic across DV measures and caution against relying on a single data type or source.