José M Jorquera Valero, Pedro M Sánchez Sánchez, Manuel Gil Pérez, Alberto Huertas Celdran, Gregorio Martínez Pérez, Cutting-Edge Assets for Trust in 5G and Beyond: Requirements, State-of-the-Art, Trends & Challenges, ACM Computing Surveys, Vol. 55 (11), 2023. (Journal Article)
 
In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, being reputation-based trust models one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Afterward, it analyzes pivotal 5G assets on which trust can enhance their performance. Besides, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area. |
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Simon Laumer, Christiane Barz, Reductions of non-separable approximate linear programs for network revenue management, European Journal of Operational Research, Vol. 309 (1), 2023. (Journal Article)
 
We suggest a novel choice of non-separable basis functions for an approximate linear programming approach to the well-known network revenue management problem. Considering non-separability is particularly important when interdependencies between resources are large. Such a situation can be illustrated for example by a bus line, where different origin-destination pairs have many overlapping segments. Traditional separable approximation approaches tend to ignore the resulting interactions.
We suggest to group resources into non-separable subnetworks. For each chosen subnetwork, basis functions either span the whole function space or consist of linear functions. Given this more general choice of basis functions, we extend existing reductions of approximate linear programs. If there is only one subnetwork, for which the basis functions span the whole function space, we prove the equivalence to a compact linear program of polynomial size. For the general case, we suggest an approximate reduction. Numerical examples illustrate our novel upper bounds for the maximum expected revenue and the corresponding competitive policies. In particular, we find that the added benefit of non-separability heavily depends on the network structure and the capacity.
Our work helps to better understand the impact of assuming separability in network revenue management. The polynomial sized reductions make it possible to estimate the added average revenue resulting from incorporating interactions between resources. The theory we develop demonstrates how the interpretation of dual variables as state-action probabilities can be applied to reduce exponentially large approximate linear programs via variable aggregation. |
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Markus Leippold, Jingwei Ni , Zhijing Jin, Qian Wang, Mrinmaya Sachan, When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP, In: 61st Annual Meeting of the Association for Computational Linguistics (ACL’23), Toronto, Canada, 2023. (Conference or Workshop Paper published in Proceedings)

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Markus Leippold, Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, A Dataset for Detecting Real-World Environmental Claims, In: 61st Annual Meeting of the Association for Computational Linguistics (ACL’23), Toronto, Canada, 2023. (Conference or Workshop Paper published in Proceedings)

In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies. We train and release baseline models for detecting environmental claims using this new dataset. We further preview potential applications of our dataset: We use our fine-tuned model to detect environmental claims made in answer sections of quarterly earning calls between 2012 and 2020 - and we find that the amount of environmental claims steadily increased since the Paris Agreement in 2015. |
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Kremena Bachmann, Thorsten Hens, Andre Lot, Xiaogeng Xu, Experimental Research on Retirement Decision-Making: Evidence from Replications, Journal of Banking and Finance, Vol. 152, 2023. (Journal Article)

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. |
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Elliot Beck, Gianluca De Nard, Michael Wolf, Improved inference in financial factor models, International Review of Economics and Finance, Vol. 86, 2023. (Journal Article)

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama–French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil. |
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Benjamin Grossmann-Hensel, Between Meta-Organization and Sub-Organization: Bureaucracies As Internal Reflectors of Complex Environments, In: ISA World Congress of Sociology. 2023. (Conference Presentation)

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Linda Weigl, Tom Barbereau, Johannes Sedlmeir, Liudmila Zavolokina, Mediating the Tension between Data Sharing and Privacy: The Case of DMA and GDPR, In: 31st European Conference on Information Systems (ECIS 2023), Norway, 2023-06-11. (Conference or Workshop Paper published in Proceedings)
 
The Digital Markets Act (DMA) constitutes a crucial part of the European legislative framework addressing the dominance of 'Big Tech'. It intends to foster fairness and competition in Europe's digital platform economy by imposing obligations on 'gatekeepers' to share end-user-related information with business users. Yet, this may involve the processing of personal data subject to the General Data Protection Regulation (GDPR). The obligation to provide access to personal data in a GDPR-compliant manner poses a regulatory and technical challenge and can serve as a justification for gatekeepers to refrain from data sharing. In this research-in-progress paper, we analyze key tensions between the DMA and the GDPR through the paradox perspective. We argue through a task-technology fit approach how privacy-enhancing technologies-particularly anonymization techniques-and portability could help mediate tensions between data sharing and privacy. Our contribution provides theoretical and practical insights to facilitate legal compliance. |
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Airi Lampinen, Chiara Rossitto, Roel Roscam Abbing, Ann Light, Anton Fedosov, Luigina Ciolfi, Spatial tensions in CSCW: The political and ethical challenges of scale., In: the 21st European Conference on Computer-Supported Cooperative Work, Trondheim, Norway, 2023. (Conference or Workshop Paper)

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Steven Ongena, Manthos D. Delis, Evangelos V. Dioikitopoulos, Population diversity and financial risk-taking, Journal of Banking and Finance, Vol. 151, 2023. (Journal Article)

We hypothesize that financial risk-taking originates in preindustrial interpersonal population diversity. We use data on immigrants residing in the United States and show that controlling for all known determinants of portfolio decisions and more than 100 control variables, diversity in the country of immigrants’ origin positively affects stock market participation and the level of risky asset holdings. Our results remain robust when instrumenting diversity with plant variety. We also identify the channels through which the effect of diversity operates (mostly individualism and human capital), but also conclude that diversity exerts an independent effect. |
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Tobias Schultheiss, Curdin Pfister, Ann-Sophie Gnehm, Uschi Backes-Gellner, Education expansion and high-skill job opportunities for workers: Does a rising tide lift all boats?, Labour Economics, Vol. 82, 2023. (Journal Article)
 
We examine how education expansions affect the job opportunities for workers with and without the new education. To identify causal effects, we exploit a quasi-random establishment of Universities of Applied Sciences (UASs), bachelor-granting three-year colleges that teach and conduct applied research. By applying machine-learning methods to job advertisement data, we analyze job content before and after the education expansion. We find that, in regions with the newly established UASs, not only job descriptions of the new UAS graduates but also job descriptions of workers without this degree (i.e., middle-skilled workers with vocational training) contain more high-skill job content. This upskilling in job content is driven by an increase in high-skill R&D-related tasks and linked to employment and wage gains. The task spillovers likely occur because UAS graduates with applied research skills build a bridge between middle-skilled workers and traditional university graduates, facilitating the integration of the former into R&D-related tasks. |
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Marco Ceccarelli, Stefano Ramelli, Alexander Wagner, Low carbon mutual funds, Review of Finance, Vol. forthcoming, 2023. (Journal Article)

Climate change poses new challenges for portfolio management. In our not-yet-low carbon world, investors face a trade-off between minimizing their exposure to climate risks and maximizing the benefits of portfolio diversification. This paper investigates how investors and financial intermediaries navigate this trade-off. After the release of Morningstar's novel carbon risk metrics in April 2018, mutual funds labeled as "low carbon" experienced a significant increase in investor demand, especially those with high risk-adjusted returns. Fund managers actively reduced their exposure to firms with high carbon risk scores, especially stocks with returns that correlated more with the funds' portfolios and were thus less useful for diversification. These findings shed light on whether and how climate-related information can re-orient capital flows in a low carbon direction. |
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Alberto Huertas Celdran, Pedro Miguel Sánchez Sánchez, Miguel Azorín, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller, Intelligent and behavioral-based detection of malware in IoT spectrum sensors, International Journal of Information Security, Vol. 22 (3), 2023. (Journal Article)
 
The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of operational platforms, IoT spectrum sensors are vulnerable to heterogeneous malware. The usage of behavioral fingerprinting and machine learning has shown merit in detecting cyberattacks. Still, there exist challenges in terms of (i) designing, deploying, and evaluating ML-based fingerprinting solutions able to detect malware attacks affecting real IoT spectrum sensors, (ii) analyzing the suitability of kernel events to create stable and precise fingerprints of spectrum sensors, and (iii) detecting recent malware samples affecting real IoT spectrum sensors of crowdsensing platforms. Thus, this work presents a detection framework that applies device behavioral fingerprinting and machine learning to detect anomalies and classify different botnets, rootkits, backdoors, ransomware and cryptojackers affecting real IoT spectrum sensors. Kernel events from CPU, memory, network,file system, scheduler, drivers, and random number generation have been analyzed, selected, and monitored to create device behavioral fingerprints. During testing, an IoT spectrum sensor of the ElectroSense platform has been infected with ten recent malware samples (two botnets, three rootkits, three backdoors, one ransomware, and one cryptojacker) to measure the detection performance of the framework in two different network configurations. Both supervised and semi-supervised approaches provided promising results when detecting and classifying malicious behaviors from the eight previous malware and seven normal behaviors. In particular, the framework obtained 0.88–0.90 true positive rate when detecting the previous malicious behaviors as unseen or zero-day attacks and 0.94–0.96 F1-score when classifying them |
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Redaktion, Marc Chesney, Credit Suisse - Bonus en suspens, In: RTS Un, 24 May 2023. (Media Coverage)

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Andrea Giuffredi-Kähr, Lucia Malär, Influencer Sharenting – How Can Children’s Privacy Rights Be (Better) Protected?, In: Conference of the European Marketing Academy, EMAC. 2023. (Conference Presentation)

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Giulia Crestini, Andrea Giuffredi-Kähr, Radu Tanase, Martin Natter, DOES PRICE TRANSPARENCY BENEFIT OR HARM ONLINE RETAILERS? A RETAILER AND CUSTOMER PERSPECTIVE , In: Conference of the European Marketing Academy, EMAC. 2023. (Conference Presentation)

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Redaktion, Markus Leippold, Tokenisierung von nachhaltigen Infrastrukturprojekten, In: Absolut Research, 22 May 2023. (Media Coverage)

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Jon King, Marc Chesney, Credit Suisse's 'rushed' UBS rescue blasted by economist as taxpayers foot the bill, In: Express, 16 May 2023. (Media Coverage)

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Steven Ongena, Banks and Climate, In: Workshop on Climate Finance . 2023. (Conference Presentation)

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Katy Romy, Marc Chesney, "Dovremo sopportare per anni il costo del salvataggio di Credit Suisse", In: SWI swissinfo.ch, 15 May 2023. (Media Coverage)

Anche se la Svizzera si vanta volentieri di essere un modello di democrazia, né il Parlamento né il popolo hanno avuto voce in capitolo nel salvataggio di Credit Suisse. "Si tratta di un diniego della democrazia", affermano la giornalista economica Myret Zaki e l'economista Marc Chesney nel nostro dibattito. |
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