Burak Öz, Benjamin Kraner, Nicolo Vallarano, Bingle Stegmann Kruger, Florian Matthes, Claudio Tessone, Time Moves Faster When There is Nothing You Anticipate: The Role of Time in MEV Rewards, In: CCS '23: ACM SIGSAC Conference on Computer and Communications Security, ACM Digital library, 2023-11-30. (Conference or Workshop Paper published in Proceedings)
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We present a novel analysis of a competitive dynamic present on Ethereum known as "waiting games", where validators can use their distinct monopoly position in their assigned slots to delay block proposals in order to optimize returns through Maximal Extractable Value (MEV) payments, a type of incentive outside the Proof-of-Stake incentive scheme. However, this strategy risks block exclusion due to missed slots or potential orphaning. Our analysis reveals evidence that, although there are substantial incentives to undertaking the risks, validators are not capitalizing on waiting games, leaving potential profits unrealized. Moreover, we present an agent-based model to test the eventual consensus disruption caused by waiting games under different settings, arguing that such disruption only occurs with significant delay strategies. Ultimately, this research provides in-depth insights into Ethereum's waiting games, illuminating the trade-offs and potential profit opportunities for validators in this evolving blockchain landscape. |
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Kevin Chow, Thomas Fritz, Liisa Holsti, Skye Barbic, Joanna McGrenere, Feeling Stressed and Unproductive? A Field Evaluation of a Therapy-Inspired Digital Intervention for Knowledge Workers, ACM Transactions on Computer-Human Interaction, Vol. 31 (1), 2023. (Journal Article)
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Today’s knowledge workers face cognitively demanding tasks and blurred work-life boundaries amidst rising stress and burnout in the workplace. Holistic approaches to supporting workers, which consider both productivity and well-being, are increasingly important. Taking this holistic approach, we designed an intervention inspired by cognitive behavioral therapy that consists of: (1) using the term “Time Well Spent” (TWS) in place of “productivity”, (2) a mobile self-logging tool for logging activities, feelings, and thoughts at work, and (3) a visualization that guides users to reflect on their data. We ran a 4-week exploratory qualitative comparison in the field with 24 graduate students to examine our Therapy-inspired intervention alongside a classic Baseline intervention. Participants who used our intervention often shifted toward a holistic perspective of their primary working hours, which included an increased consideration of breaks and emotions. No such change was seen by those who used the Baseline intervention. |
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Emmanuel Mamatzakis, Steven Ongena, Mike G Tsionas, Why do households repay their debt in UK during the COVID-19 crisis?, Journal of Economic Studies, Vol. 50 (8), 2023. (Journal Article)
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Purpose: In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK.
Design/methodology/approach: This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances.
Findings: The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags.
Originality/value: To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing. |
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Fabio Braggion, Felix Von Meyerinck, Nic Schaub, Inflation and Individual Investors' Behavior: Evidence from the German Hyperinflation, Review of Financial Studies, Vol. 36 (12), 2023. (Journal Article)
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We analyze how individual investors respond to inflation. We introduce a unique dataset containing information on local inflation and security portfolios of more than 2,000 clients of a German bank between 1920 and 1924, covering the German hyperinflation. We find that individual investors buy less (sell more) stocks when facing higher local inflation. This effect is more pronounced for less sophisticated investors. Moreover, we document a positive relation between local inflation and forgone returns following stock sales. Our findings are consistent with individual investors suffering from money illusion. Alternative explanations such as consumption needs are unlikely to drive our results. |
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Albert Steck, David Dorn, Wegen der 10-Millionen-Schweiz die Wirtschaft zu schwächen, wäre absurd, In: NZZ am Sonntag, p. 33, 19 November 2023. (Newspaper Article)
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Maya Tamir, Atsuki Ito, Yuri Miyamoto, Yulia Chentsova-Dutton, Jeong Ha Choi, Jan Cieciuch, Michaela Riediger, Antje Rauers, Maria Padun, Min Young Kim, Nevin Solak, Jiang Qiu, Xiaoqin Wang, Aldo Alvarez-Risco, Yaniv Hanoch, Yukiko Uchida, Claudio Torres, Thiago Gomes Nascimento, Asghar Afshar Jahanshahi, Rakesh Singh, Shanmukh V Kamble, Sieun An, Vivian Dzokoto, Adote Anum, Babita Singh, Gianluca Castelnuovo, Giada Pietrabissa, María Isabel Huerta-Carvajal, Erika Galindo-Bello, Verónica Janneth García Ibarra, Emotion regulation strategies and psychological health across cultures, American Psychologist, 2023. (Journal Article)
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Emotion regulation is important for psychological health and can be achieved by implementing various strategies. How one regulates emotions is critical for maximizing psychological health. Few studies, however, tested the psychological correlates of different emotion regulation strategies across multiple cultures. In a preregistered cross-cultural study (N = 3,960, 19 countries), conducted during the COVID-19 pandemic, we assessed associations between the use of seven emotion regulation strategies (situation selection, distraction, rumination, cognitive reappraisal, acceptance, expressive suppression, and emotional support seeking) and four indices of psychological health (life satisfaction, depressive symptoms, perceived stress, and loneliness). Model comparisons based on Bayesian information criteria provided support for cultural differences in 36% of associations, with very strong support for differences in 18% of associations. Strategies that were linked to worse psychological health in individualist countries (e.g., rumination, expressive suppression) were unrelated or linked to better psychological health in collectivist countries. Cultural differences in associations with psychological health were most prominent for expressive suppression and rumination and also found for distraction and acceptance. In addition, we found evidence for cultural similarities in 46% of associations between strategies and psychological health, but none of this evidence was very strong. Cultural similarities were most prominent in associations of psychological health with emotional support seeking. These findings highlight the importance of considering the cultural context to understand how individuals from diverse backgrounds manage unpleasant emotions. (PsycInfo Database Record (c) 2023 APA, all rights reserved) |
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Claudia Custodio, Miguel Ferreira, Emilia Garcia, Indirect Costs of Financial Distress, Review of Finance, Vol. 27 (6), 2023. (Journal Article)
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We estimate the indirect costs of financial distress due to lost sales by exploiting real estate shocks and cross-supplier variation in real estate assets and leverage. We show that for the same client buying from different suppliers, the client’s purchases from distressed suppliers decline by an additional 13% following a drop in local real estate prices. The effect is more pronounced in more competitive industries, manufacturing, durable goods, less-specific goods, and when the costs of switching suppliers are low. Our results suggest that clients reduce their exposure to suppliers in financial distress. |
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Micah Goldsmith Edelson, Todd Anthony Hare, Goal-dependent hippocampal representations facilitate self-control, Journal of Neuroscience, Vol. 43 (46), 2023. (Journal Article)
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Hippocampal activity linking past experiences and simulations of the future with current goals can play an important role in decision-making. The representation of information within the hippocampus may be especially critical in situations where one needs to overcome past rewarding experiences and exert self-control. Self-control success or failure may depend on how information is represented in the hippocampus and how effectively the representation process can be modified to achieve a specific goal. We test this hypothesis using representational similarity analyses of human (female/male) neuroimaging data during a dietary self-control task in which individuals must overcome taste temptations to choose healthy foods. We find that self-control is indeed associated with the way individuals represent taste information (valance) in the hippocampus and how taste representations there adapt to align with different goals/contexts. Importantly, individuals who were able to shift their hippocampal representations to a larger degree to align with the current motivation were better able to exert self-control when facing a dietary challenge. These results suggest an alternative or complementary neurobiological pathway leading to self-control success and indicate the need to update the classical view of self-control to continue to advance our understanding of its behavioral and neural underpinnings. |
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Diego M Hager, Thomas Nitschka, Responses of Swiss interest rates and stock prices to ECB policy surprises, Swiss Journal of Economics and Statistics = Schweizerische Zeitschrift für Volkswirtschaft und Statistik, Vol. 159, 2023. (Journal Article)
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We employ local projections to analyse the responses of Swiss asset prices to scheduled policy decisions of the European Central Bank (ECB) as a case study of ECB policy spillovers to European countries outside the euro area. Focusing on ECB policy shocks that are related to different policy instruments of the ECB, our empirical results leave the impression that surprises related to the ECB target policy rate and to the ECB’s longer-term forward guidance or its asset purchases tend to move Swiss interest rates and stock prices in the same direction. Shocks explicitly designed to capture pure ECB monetary policy and information effect shocks are weakly associated with movements in Swiss asset prices on average. |
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David P Newton, Steven Ongena, Ru Xie, Binru Zhao, Firm ESG reputation risk and debt choice, European financial management, 2023. (Journal Article)
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Using a novel sample covering 3783 US public firms from 2007 to 2020, we examine how negative media coverage of firm‐level environmental, social, and governance (ESG) practices affects a firm's debt choice. We find that firms with higher ESG reputation risk rely more on public bond than bank loan. The social and governance components, in particular, matter. Moreover, firms that receive more negative news coverage display a higher propensity to issue new bonds as opposed to securing new bank debt. Overall, our study presents empirical evidence on the relation between firm ESG reputation risk and debt financing. |
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Wissem Soussi, Maria Christopoulou, Gürkan Gür, Burkhard Stiller, MERLINS – Moving Target Defense Enhanced with Deep-RL for NFV In-Depth Security, In: 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Institute of Electrical and Electronics Engineers, 2023-11-07. (Conference or Workshop Paper published in Proceedings)
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Moving to a multi-cloud environment and service-based architecture, 5G and future 6G networks require additional defensive mechanisms to protect virtualized network resources. This paper presents MERLINS, a novel architecture generating optimal Moving Target Defense (MTD) policies for proactive and reactive security of network slices. By formally modeling telecommunication networks compliant with Network Function Virtualization (NFV) into a multi-objective Markov Decision Process (MOMDP), MERLINS uses deep Reinforcement Learning (deep-RL) to optimize the MTD strategy that considers security, network performance, and service level requirements. Practical experiments on a 5G testbed showcase the feasibility as well as restrictions of MTD operations and the effectiveness in mitigating malware infections. It is observed that multi-objective RL (MORL) algorithms outperform state-of-the-art deep-RL algorithms that scalarize the reward vector of the MOMDP. This improvement by a factor of two leads to a better MTD policy than the baseline static counterpart used for the evaluation. |
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Oana Inel, Tim Draws, Lora Aroyo, Collect, measure, repeat: Reliability factors for responsible AI data collection, In: Eleventh AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2023), Association for the Advancement of Artificial Intelligence, Delft, the Netherlands, 2023-11-06. (Conference or Workshop Paper published in Proceedings)
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The rapid entry of machine learning approaches in our dailyactivities and high-stakes domains demands transparency andscrutiny of their fairness and reliability. To help gauge ma-chine learning models’ robustness, research typically focuseson the massive datasets used for their deployment,e.g., cre-ating and maintaining documentation to understand theirorigin, process of development, and ethical considerations.However, data collection for AI is still typically a one-offpractice, and oftentimes datasets collected for a certain pur-pose or application are reused for a different problem. Addi-tionally, dataset annotations may not be representative overtime, contain ambiguous or erroneous annotations, or be un-able to generalize across domains. Recent research has shownthese practices might lead to unfair, biased, or inaccurate out-comes. We argue that data collection for AI should be per-formed in a responsible manner where the quality of the datais thoroughly scrutinized and measured through a systematicset of appropriate metrics. In this paper, we propose a Re-sponsible AI (RAI) methodology designed to guide the datacollection with a set of metrics for an iterative in-depth analy-sis of thefactors influencing the quality and reliabilityof thegenerated data. We propose a granular set of measurements toinform on theinternal reliabilityof a dataset and itsexternalstabilityover time. We validate our approach across nine ex-isting datasets and annotation tasks and four input modalities.This approach impacts theassessment of data robustnessusedin real world AI applications, where diversity of users andcontent is eminent. Furthermore, it deals with fairness andaccountability aspects in data collection by providing system-atic and transparent quality analysis for data collections. |
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Dieter Pfaff, Patricia Dorothee Ruffing-Straube, David Staubli, Aufdeckung von Steuerhinterziehung in der Schweiz durch den automatischen Informationsaustausch, Perspektiven der Wirtschaftspolitik, Vol. 24 (3), 2023. (Journal Article)
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Die Hinterziehung von Steuern soll durch den international eingeführten automatischen Informationsaustausch (AIA) aufgedeckt und eingedämmt werden. Zudem besteht in der Schweiz seit 2010 die straflose Selbstanzeige. Im Zusammenhang mit der internationalen Einführung des AIA ist ein deutlicher Anstieg der straflosen Selbstanzeigen zu beobachten. Dabei werden erhebliche kantonale Unterschiede sichtbar. So ist die Anzahl strafloser Selbstanzeigen pro zehntausend Steuerpflichtige in Kantonen mit hohen Vermögensteuern, getrieben durch die bevölkerungsstarken Genferseekantone, nach der Einführung des AIA tendenziell höher. Da das Selbstanzeigeverhalten im Zuge des AIA vorwiegend ausländisches Vermögen und Einkommen betrifft, könnte auch der Anteil der zugewanderten Wohnbevölkerung eine Rolle spielen. |
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Rafael Henrique Vareto, Manuel Günther, William Robson Schwartz, Open-Set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation, In: 2023 36th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Institute of Electrical and Electronics Engineers, 2023-11-06. (Conference or Workshop Paper published in Proceedings)
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Open-set face recognition is a scenario in which biometric systems have incomplete knowledge of all existing subjects. This arduous requirement must dismiss irrelevant faces and focus on subjects of interest only. For this reason, this work introduces a novel method that associates an ensemble of compact neural networks with data augmentation at the feature level and an entropy-based cost function. Deep neural networks pre-trained on large face datasets serve as the preliminary feature extraction module. The neural adapter ensemble consists of binary models trained on original feature representations along with negative synthetic mix-up embeddings, which are adequately handled by the designed open-set loss since they do not belong to any known identity. We carry out experiments on well-known LFW and IJB-C datasets where results show that the approach is capable of boosting closed and open-set identification accuracy. |
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Ruijie Wang, Zhiruo Zhang, Luca Rossetto, Florian Ruosch, Abraham Bernstein, NLQxform: A Language Model-based Question to SPARQL Transformer, In: Scholarly QALD Challenge @ ISWC 2023, CEUR-WS, 2023. (Conference or Workshop Paper published in Proceedings)
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In recent years, scholarly data has grown dramatically in terms of both scale and complexity. It becomes increasingly challenging to retrieve information from scholarly knowledge graphs that include large-scale heterogeneous relationships, such as authorship, affiliation, and citation, between various types of entities, e.g., scholars, papers, and organizations. As part of the Scholarly QALD Challenge, this paper presents a question-answering (QA) system called NLQxform, which provides an easy-to-use natural language interface to facilitate accessing scholarly knowledge graphs. NLQxform allows users to express their complex query intentions in natural language questions. A transformer-based language model, i.e., BART, is employed to translate questions into standard SPARQL queries, which can be evaluated to retrieve the required information. According to the public leaderboard of the Scholarly QALD Challenge at ISWC 2023 (Task 1: DBLP-QUAD — Knowledge Graph Question Answering over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA task, demonstrating the competitiveness of the system. |
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Loris Sauter, Tim Bachmann, Luca Rossetto, Heiko Schuldt, Spatially Localised Immersive Contemporary and Historic Photo Presentation on Mobile Devices in Augmented Reality, In: MM '23: The 31st ACM International Conference on Multimedia, ACM Digital Library, New York, NY, USA, 2023-11-02. (Conference or Workshop Paper published in Proceedings)
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These days, taking a photo is the most common way of capturing a moment. Some of these photos captured in the moment are never to be seen again. Others are almost immediately shared with the world. Yet, the context of the captured moment can only be shared to a limited extent. The continuous improvement of mobile devices has not only led to higher resolution cameras and, thus, visually more appealing pictures but also to a broader and more precise range of accompanying sensor metadata. Positional and bearing information can provide context for photos and is thus an integral aspect of the captured moment. However, it is commonly only used to sort photos by time and possibly group by place. Such more precise sensor metadata, combined with the increased computing power of mobile devices, can enable more and more powerful Augmented Reality (AR) capabilities, especially for communicating the context of a captured photo. Users can thereby witness the captured moment in its real location and also experience its spatial contextualization. With the help of a suitable data augmentation, such context-preserving presentation can be extended even to non-digitally born content, including historical images. This offers new immersive ways to experience the cultural history of one's current location. In this paper, we present an approach for location-based image presentation in AR on mobile devices. With this approach, users can experience captured moments in their physical context. We demonstrate the power of this approach based on a prototype implementation and evaluate it in a user study. |
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Andreas Barth, Valerie Laturnus, Sasan Mansouri, Alexander Wagner, Conflicted Analysts and Initial Coin Offerings, Management Science, Vol. 69 (11), 2023. (Journal Article)
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This paper studies the contribution of analysts to the functioning and failure of the market for Initial Coin Offerings (ICOs). The assessments of freelancing analysts exhibit biases due to reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. 90 days after listing on an exchange the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs. |
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Stijn Decoster, Leander de Schutter, Jochen Menges, David De Cremer, Jeroen Stouten, Does change incite abusive supervision? The role of transformational change and hindrance stress, Human Resource Management Journal, Vol. 33 (4), 2023. (Journal Article)
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To remain competitive, organizations tend to change their established ways of working, their strategy, the core values, and the organizational structure. Such thorough changes are referred to as transformational change. Unfortunately, transformational change is often unsuccessful because organizational members do not always welcome the change. Although organizations often expect their supervisors to be successful role-models and change-agents during the transformational change process, we argue that initiating transformational change could increase supervisors' hindrance stress levels, which may result in abusive behaviors towards employees. More specifically, in a multi-source survey and an experimental study, we find evidence that transformational change is associated with supervisors' experienced hindrance stress, which subsequently led to more abusive behaviors towards employees. |
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Violetta Splitter, Paula Jarzabkowski, David Seidl, Middle Managers’ Struggle Over Their Subject Position in Open Strategy Processes, Journal of Management Studies, Vol. 60 (7), 2023. (Journal Article)
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In this paper we examine middle managers’ struggle over their subject position as strategists in the context of participative strategy processes. Based on a longitudinal case study of a company undertaking an Open Strategy process, we show how the wider inclusion of front-line employees in developing new strategy undermines the traditional subject position of middle managers. Based on these findings, we develop a process model depicting the recursive dynamics of middle managers’ struggles to maintain their subject positions in the face of employee participation. With these findings we contribute to the literature on middle managers by advancing our understanding of the implications of employee participation for middle managers’ subject position as strategists and their different ways of reclaiming their subject position. We also contribute to the literature on Open Strategy by revealing the implications for traditional strategy actors as well as by explaining the processual dynamics of participation over time. |
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Erdinc Akyildirim, Oguzhan Cepni, Shaen Corbet, Gazi Salah Uddin, Forecasting mid-price movement of Bitcoin futures using machine learning, Annals of Operations Research, Vol. 330 (1-2), 2023. (Journal Article)
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In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil. |
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