Contributions in Merlin

Diese Arbeit untersucht den Einfluss des Faktors Volatilität auf die Aktienpreisbildung. In einem ersten Teil wird auf etablierte Finanzmarkttheorien und ihre Modelle eingegangen. Der zweite Teil behandelt die dazugehörige Empirie und die aktuellen Untersuchungsergebnisse zum Volatilitätsfaktor. Die Analyse des Volatilitätsfaktors stellt sechs Regressionsmodelle mit unterschiedlicher Parameterzusammenstellung im Kontext des FamaFrench-Dreifaktormodells (Fama und French (1993)) einander gegenüber, wobei die durchschnittlichen adjustierten R2 als Vergleichsmass genutzt werden. Die Ergebnisse zeigen, dass der Einfluss des Volatilitätsfaktors signifikant ist, da das Bestimmtheitsmass des FamaFrench-Dreifaktormodells kleiner ausfällt als dasjenige des Modells mit dem zusätzlichen Volatilitäts-Faktor. Die Untersuchung zeigte zudem eine grössere Relevanz des Faktors Volatilität gegenüber dem Value-Faktor auf. Nichtsdestotrotz ist der Faktor Volatilität kein geeigneter Ersatz weder für das Marktrisikopremium noch den Size-Faktor.

Morality scandals emphasise the necessity to establish ethical catalysts to diminish immoral behaviour. This thesis uncovers possible impacts of mirror and publicity contemplation ques-tions on multiple awareness types to enhance self-regulation and mitigate cognitive motiva-tional biases. Leveraging self-regulation and self-theories, this thesis argues that contemplative questions enhance accessibility to discrepancies, raise emotional discomfort and motivate self-alignment. Mirror questions are suggested to foster objective and private self-awareness, acting as amplifying tools for individuals to examine their private self-image, while publicity questions stimulate impression management and positive self-presentation by increasing public self-awareness. Future research approaches involve empirical validation of these theoretical in-sights.

Diese Arbeit untersucht zwei der meistverbreiteten Aktienmarkt-Anomalien, Value und Momen-tum, im Schweizer Aktienmarkt über den Zeitraum von 1990 bis 2022. Weiter wird untersucht, wie die beiden Strategien miteinander korrelieren. Sowohl Value- wie auch Momentum-Effekt sind in der Schweiz vorhanden. Der Momentum-Effekt ist dabei mit Renditen von jährlich 13.76% deutlich stärker ausgeprägt als der Value-Effekt mit jährlich 2.06%. Value-Strategien scheinen ausserdem von 1990 bis 2010 hervorragend funktioniert zu haben, danach aber nicht mehr. Werden jedoch Faktorrenditen berechnet, die anderen Variablen gegenüber neutral sind, ist nur noch Momentum signifikant. Die Korrelation der beiden Effekte ist mit -0.1 zwar negativ, aber nicht stark.

We adapt the design of four experimental studies on retirement decision-making and conduct replications with a larger online sample from the broader population. We replicate most of the main effects of the original studies. In particular, we confirm that consumption decisions are less efficient when subjects need to borrow from the future than when they need to save from the present. When subjects collect retirement benefits as lump sum instead of annuities, they choose to retire later, as suggested by the original study. We also confirm that savings are higher when they are incentivized with matching contributions than when incentivized with tax rebates. However, when faced with varying survival risks, subjects in our replication make only partial adjustments to spending paths when ambiguity is reduced. We also propose a further experimental research agenda in related topics and discuss practical issues on subject recruitment, attrition, and redesign of complex tasks.

Social entrepreneurship has emerged as an important means of addressing grand challenges. Although research on the topic has accelerated, scholars have yet to articulate an overarching framework that links the different pathways taken by social entrepreneurs with the positive effects of these efforts. To address this shortcoming, we conducted a systematic literature review which enabled us to conceptually differentiate between social value and social change as distinct outcomes of social entrepreneurship and identify seven pathways for achieving these outcomes. Building on our analysis, we outline a research agenda for questions pertaining to: the dynamics between social value and social change; how contextual factors and social entrepreneurs influence various pathways; design principles of business models and innovations that facilitate social value and social change; and defining, measuring, and ensuring accountability for social value and social change.

This paper examines the role of lifelong learning in counteracting skill depreciation and obsolescence. We differentiate between occupations with more hard skills versus more soft skills and draw on representative job advertisement data that contain machine-learning categorized skill requirements and cover the Swiss job market in great detail across occupations (from 1950 to 2019). We examine lifelong learning effects for “harder” versus “softer” occupations, thereby analyzing the role of training in counteracting skill depreciation in occupations that are differently affected by skill depreciation. Our results reveal novel empirical patterns regarding the benefits of lifelong learning, which are consistent with theoretical explanations based on structurally different skill depreciation rates: In harder occupations, with large shares of fast-depreciating hard skills, the role of lifelong learning is primarily as a hedge against unemployment risks rather than a boost to wages. By contrast, in softer occupations, in which workers build on more value-stable soft-skill foundations, the role of lifelong learning instead lies mostly in acting as a boost for upward career mobility and leads to larger wage gains.

Sensory information encoded by humans and other organisms is generally presumed to be as accurate as their biological limitations allow. However, perhaps counterintuitively, accurate sensory representations may not necessarily maximize the organism’s chances of survival. To test this hypothesis, we developed a unified normative framework for fitness-maximizing encoding by combining theoretical insights from neuroscience, computer science, and economics. Behavioural experiments in humans revealed that sensory encoding strategies are flexibly adapted to promote fitness maximization, a result confirmed by deep neural networks with information capacity constraints trained to solve the same task as humans. Moreover, human functional MRI data revealed that novel behavioural goals that rely on object perception induce efficient stimulus representations in early sensory structures. These results suggest that fitness-maximizing rules imposed by the environment are applied at early stages of sensory processing in humans and machines.

This chapter presents an overview of methodological developments that attempt to deal with the fundamental problem as to what common 'true' brain state may underlie the method-specific observations. All of these developments focus on combinations of existing methods from the present armory of cognitive neuroscientists, an approach often referred to as multimodal imaging. The chapter discusses three such combinations: functional magnetic resonance imaging (fMRI)–electroencephalography (EEG)/magnetoencephalography (MEG), noninvasive brain stimulation (NIBS)-EEG, and NIBS-fMRI. The chapter begins with a brief description of the basic mechanisms of action for EEG/MEG, fMRI, and NIBS. It outlines only the strengths and shortcomings of each technique to motivate their multimodal combination. The chapter also describes the three multimodal combinations in detail. It presents the general rationale of each approach, gives a brief outline of technical considerations, and discusses the unique insights that can be gained with the particular methodical combination by means of illustrative studies.

We study extensive‐form games and mechanisms allowing agents that plan for only a subset of future decisions they may be called to make (the planning horizon). Agents may update their so‐called strategic plan as the game progresses and new decision points enter their planning horizon. We introduce a family of simplicity standards which require that the prescribed action leads to unambiguously better outcomes, no matter what happens outside the planning horizon. We employ these standards to explore the trade‐off between simplicity and other objectives, to characterize simple mechanisms in a wide range of economic environments, and to delineate the simplicity of common mechanisms such as posted prices and ascending auctions, with the former being simpler than the latter.

When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system is suffering a cyberattack. It is also fundamental to explain why the system is under a cyberattack and which are the assets affected. In this context, the Anomaly Detection based on Machine Learning (ML) and Deep Learning (DL) techniques showed great performance when detecting cyberattacks in industrial scenarios. However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. Secondly, the use of black‐box ML and DL techniques makes it impossible to interpret the decision made by the model. This article proposes an interpretable and semi‐supervised system to detect cyberattacks in Industrial settings. Besides, our proposal was validated using data collected from the Tennessee Eastman Process. To the best of our knowledge, this system is the only one that offers interpretability together with a semi‐supervised approach in an industrial setting. Our system discriminates between causes and effects of anomalies and also achieved the best performance for 11 types of anomalies out of 20 with an overall recall of 0.9577, a precision of 0.9977, and a F1‐score of 0.9711.