Contributions in Merlin

How financial investors may react to policy events related to sustainability and climate change mitigation in particular, is a key question with implications for sustainable finance and financial stability. We address this question by carrying out a multi-period difference-in-difference approach on a confidential database of securities holdings of the European Central Bank, and we provide evidence of several effects related to the Paris Agreement. In aggregate, investors reduced their participation in the equities of high-carbon firms in response to the agreement, and the trend reverted after the US’s announcement of withdrawal from the agreement. However, the reaction varies across categories and geographies of the securities holders, their ownership size, and the emissions of owned firms. In particular, transition risk has been taken up by less regulated financial institutions and the BRIC countries. Our results highlight that the redirection of global financial flows towards climate action requires clear and unanimous signals from the global community of policy makers.

In this cover paper, we introduce a Special Issue (SI) published after the fourth edition of a series of financial stability conferences organized by Bank of Mexico, CEMLA, Bank of Canada, Zurich University and the Journal of Financial Stability in November 2021. Before providing our perspective on why the research papers included into the SI are of great relevance, we give a brief and personal overview of recent directions in financial stability research in general, esp., related to topics accentuated by the COVID-19 pandemic or post-pandemic economic and financial conditions and their complexity. Papers published in the SI cover four topics of research in the financial stability field, featuring some outstanding and innovative projects presented during the conference. The first topic is on interconnectedness and shock transmission in the financial system, diving deep into asset fire sales, interconnectedness of various segments of the financial system, in addition to banks, on the optimality of systemic risk capital buffers, and on how risks are priced in the interbank market network. The second one touches upon climate change risks looking at investors’ reactions to international climate policy developments, in particular on the Paris Agreement front and how to jointly model physical and transition risk in the banking system, including the important concept of double materiality. The third topic is represented by projects focused on policy analysis for systemic risk mitigation, specifically dealing with macroprudential policy instruments and crisis mitigation policies. Finally, research papers in the last topic on big data and market data focus on the innovative ways to explore the growing body of data sources, such as data collected by regulators, including credit register data, supervisory data and market data on financial transactions, to better understand sources and implications of systemic risk.

In many workplaces, managers provide some employees with unique privileges that support their professional development and stimulate productivity and creativity. Yet with some employees more deserving of a privileged status than others, co-workers feeling left out of the inner circle may begin to exhibit feelings of envy. With workplace envy and intergroup conflicts going hand in hand, the question arises whether co-worker acceptance of employee privileges—where conflict can be constrained through an affirmative re-evaluation of co-workers’ privileged status—may lower the envy experienced by employees. Using virtual reality technology, 112 employees participated in a virtual employee meeting at a virtual organization where they were exposed to a new workforce differentiation practice. We show through our experiment that co-worker acceptance of employee privileges negatively influences workplace envy, which was partially mediated by the anticipated ostracism of employees. Moreover, we show that this effect is only found for employees with privileges, who worry more about being ostracized than their non-privileged co-workers. We anticipate that our findings will enable managers to conscientiously differentiate between their employees, using virtual reality simulations to steer employees’ thoughts and feelings in a direction that benefits both employees and organizations.

We study the provision of financial services to small firms, consumers, and workers in developing countries as part of value chain relationships: value chain microfinance (VCMF). We first explore how VCMF can both overcome barriers to financial access—including asymmetric information, enforcement, and behavioural biases—and strengthen value chains, but also how it can introduce new challenges. We then review a recent empirical literature at the intersection of value chains and microfinance, studying the demand for and effects of VCMF in credit, insurance, and savings markets. We conclude by highlighting promising directions for future work.

We investigate how interdependence among investors' beliefs affects the reliance on market prices as a performance measure and how this in turn affects the firm's preference for financial reporting quality. When investors want to align their values more with other investors' beliefs, optimal contracts become more reliant on the accounting report and less on the market price, emphasizing the stewardship role of accounting in a herding market. If the baseline accounting quality required by a reporting standard is high enough, the firm prefers to increase its accounting quality for the sake of contracting efficiency. However, if the baseline quality is low, the firm further lowers accounting quality for the same reason. The benchmark level that determines whether the firm prefers to increase accounting quality increases with the interdependence of investors' beliefs, implying that it is difficult to align the information and stewardship roles of accounting in a herding market.

Conference calls provide opportunities for CEOs to inform market participants (i.e., financial analysts and investors) about their companies’ prospects. Much research has focused on how CEOs speak about business-related topics in these calls, yet surprisingly the literature has not considered how statements that go beyond financial information affect market participants. When we explored archival data of how CEOs of publicly traded U.S.-based companies from the Russell 3000 Index spoke about COVID-19 in conference calls as the pandemic began in 2020, we noticed that about half of CEOs made human care statements that expressed a concern for people, with seemingly little direct financial relevance. However, although these statements were largely generic, vague expressions rather than clear plans, we discovered that the more such statements CEOs made, the better their companies fared on the stock market when stock prices tumbled globally. Follow-up explorations unveiled a negative association between CEO human care statements and stock volatility, meaning that market participants discounted these companies’ future earnings less. Our explorations suggest that it pays off for CEOs to go beyond mere financial information and show some humanity, with implications for downstream theorizing about CEO impression management.

Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting spectrum sensing data falsification (SSDF) attacks. However, the amount of data needed to train models and the scenario privacy concerns limit the applicability of centralized ML/DL. Federated learning (FL) addresses these drawbacks but is vulnerable to adversarial participants and attacks. The literature has proposed countermeasures, but more effort is required to evaluate the performance of FL detecting SSDF attacks and their robustness against adversaries. Thus, the first contribution of this work is to create an FL-oriented dataset modeling the behavior of resource-constrained spectrum sensors affected by SSDF attacks. The second contribution is a pool of experiments analyzing the robustness of FL models according to i) three families of sensors, ii) eight SSDF attacks, iii) four FL scenarios dealing with anomaly detection and binary classification, iv) up to 33% of participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms. In conclusion, FL achieves promising performance when detecting SSDF attacks. Without anti-adversarial mechanisms, FL models are particularly vulnerable with > 16% of adversaries. Coordinate-wise-median is the best mitigation for anomaly detection, but binary classifiers are still affected with > 33% of adversaries.

Digitalization allows retailers to target customers with personalized promotions when they enter the store. Although traditional promotional retailer objectives, such as store visit, become obsolete once the shopper is already in the store, retailers still tend to target customers based on indicators that drive store visit, such as recency, frequency, and monetary value (RFM). In order to improve promotional efficiency, the authors propose targeting shoppers based on information derived from regularity patterns in individual interpurchase times at the point of sale. When compared to RFM-based targeting, the proposed live targeting approach translates into higher redemption rates (+ 10.5 percentage points), revenues (+ 42.3 percentage points), and purchase frequencies (+ 44.2 percentage points). The findings emphasize the importance of promotional timing and of considering customers’ outside potential for dynamic in-store targeting.

Decentralized Finance (‘DeFi’) has gained tremendous momentum over the past three years by using novel approaches to disintermediating financial institutions in the provision of financial services. However, empirical research in this field is still rare, and a more comprehensive understanding of the domain is a missing component in academic research. This paper develops a taxonomy based on a comprehensive literature analysis to structure this emerging field systematically. The taxonomy includes three perspectives (strategy, organization, technology) and seven dimensions (blockchain, value proposition, token type, business process, price mechanism, protocol type, integration type) as well as thirty-six characteristics. The application of the taxonomy to 278 DeFi start-ups reveals that most of the DeFi start-ups focus on Ethereum (36.3%) and have a focus on analytics and automation (52%), while, surprisingly only a few incorporate decentralized governance approaches (3.3%), provide decentralized exchanges (14%) or integrate off-chain data.