Contributions in Merlin

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.

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.

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.

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.

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.

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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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.