Mergers and acquisitions are often motivated by the intention of creating value from intangible assets. We develop a novel word list of intangibles and apply it to takeover announcements. Deals presented with more “intangibles talk” complete more quickly. However, the value of these deals to the acquirer is questionable: One standard deviation more in intangibles talk results in 0.45 percentage points lower abnormal announcement returns of bidders. Agency problems explain little of these results. Instead, payment mode choices and insider trades suggest that intangibles talk reflects managerial overoptimism. Overall,takeover announcements can provide important information regarding the quality of deals.
This article analyses how distinct argumentation spaces-media, law, and science-interact and collide, using Liebeck v. McDonald's as a legal case study and the hydroxychloroquine COVID-19 debate as a case of scientific dispute. Building on these two case studies, this analysis shows that arguments that intervene in many public debates originate from and move across different spaces, which employ different criteria, rules of evidence, and modes of reasoning for evaluating arguments. Furthermore, our study shows media-driven simplification, reframing, and truncation of nuanced expert exchanges, producing polarized public perceptions and policy consequences. The key features we identified include sliding arguments, repetition-driven strength (clones), shifting proof standards, hedgehog arguments, and zombie information, with implications for formalizing real-world argumentative dynamics and improving cross-space dialogue.
The Maximum Independent Set (MIS) problem is a fundamental combinatorial optimization task that can be naturally mapped onto the Ising Hamiltonian of neutral atom quantum processors. Given its connection to NP-hard problems and real-world applications, there has been significant experimental interest in exploring quantum advantage for MIS. Pioneering experiments on King's Lattice graphs suggested a quadratic speed-up over simulated annealing, but recent benchmarks using state-of-the-art methods found no clear advantage, likely due to the structured nature of the tested instances. In this work, we generate hard instances of unit-disk graphs by leveraging complexity theory results and varying key hardness parameters such as density and treewidth. For a fixed graph size, we show that increasing these parameters can lead to prohibitive classical runtime increases of several orders of magnitude. We then compare classical and quantum approaches on small instances and find that, at this scale, quantum solutions are slower than classical ones for finding exact solutions. Based on extended classical benchmarks at larger problem sizes, we estimate that scaling up to a thousand atoms with a 1 kHz repetition rate is a necessary step toward demonstrating a computational advantage with quantum methods.
The debate on the capacity of the European Union Emissions Trading System (EU ETS) to effectively induce CO2 emissions reduction is still ongoing. This is particularly noteworthy in the case of the power sector, where numerous decarbonization policies overlap. This paper contributes to this discussion by leveraging a methodological approach that circumvents the challenges of constructing credible counterfactuals for causal inference and allows for disentangling the impact of the EU ETS from other measures on the power sector's abatement efforts, alongside influencing factors such as weather. Specifically, we employ a Bayesian structural time series (BSTS) model, conceptually related to synthetic control techniques, to assess the effectiveness of the three completed phases of the EU ETS (2005-2020) in reducing CO2 emissions in the power sector across 24 Member States. We analyze the policy implementation effect over the course of each phase by comparing actual power sector emissions with counterfactual estimates derived from contemporaneous predictors related to such emissions. The results indicate a statistically significant emissions reduction in the second and third phases, with no significant reduction in the first phase. The power sector's centrality to the EU ETS, and its critical role in our economies emphasize the importance of our findings in evaluating emissions reduction objectives.
In this paper, we review recent results on stability and instability in logarithmic Sobolev inequalities, with a particular emphasis on strong norms. We consider several versions of these inequalities on the Euclidean space, for the Lebesgue and the Gaussian measures, and discuss their differences in terms of moments and stability. We give new and direct proofs, as well as examples and discuss the stability of a logarithmic uncertainty principle. Although we do not cover all aspects of the topic, we hope to contribute to establishing the state of the art.
