Stock Market Reactions to Corporate Blockchain Announcements

The dissertation's central focus lies in investigating the influence of temporal, industry-specific, firm-specific, and project-specific factors on the stock market risk and return associated with corporate blockchain announcements. Structured into three chapters, the research employs theoretical frameworks and empirical analyses to uncover nuanced insights. Chapter 2, anchored in signaling theory, examines the general market impact of corporate blockchain announcements, considering temporal factors, cryptocurrency hype phases, and differences between US and EU-based companies. It reveals significant positive stock market returns associated with blockchain news, amplified by project success, business-relatedness, and cryptocurrency hype periods. Chapter 3 extends the analysis to industry-level factors, revealing that firms in the Information Technology industry benefit more from blockchain announcements. It explores additional project-level effects, such as blockchain partnerships and consortium joinings, and assesses their impact on market risk. The findings suggest that blockchain announcements do not substantially alter a firm's risk profile. Chapter 4 focuses on specific blockchain use cases, emphasizing environmental, social, and governance (ESG) issues. It uncovers significant positive market reactions to ESG-related blockchain announcements and explores shareholder returns in supply chain management and finance-related use cases. The study suggests that shareholders react more favorably to project-specific announcements and less favorably to initiatives involving external IT service providers. By thoroughly analyzing diverse factors, this dissertation contributes to the ongoing academic discourse on the valuation of blockchain technology, offering a comprehensive understanding of the dynamics shaping corporate market value and risk in the era of blockchain adoption

Pay-per-Stress — Belastungsorientierte Leasingmodelle im Maschinenbau

Die hohen Kosten komplexer Werkzeugmaschinen stellen insbesondere kleine und mittlere Unternehmen (KMU) der produzierenden Industrie vor große finanzielle Herausforderungen. Leasingmodelle bieten daher eine wichtige Möglichkeit zur Beschaffung solcher Maschinen. Der Leasingnehmer bezahlt klassisch nach Zeit und hat damit den Anreiz, die eigene Produktivität zu maximieren. Eine kontinuierlich hohe Auslastung oder Überlastung hat eine starke Abnutzung oder sogar nicht unmittelbar sichtbare Schäden zur Folge, die den Restwert der Maschine zum Nachteil des Leasinggebers reduzieren. Der Leasinggeber muss bisher durch diese Informationsasymmetrie eine Risikoprämie aufschlagen, da er die Belastung der Maschine im Leasingzeitraum nicht kontrollieren und den Zustand bei Rückgabe schwierig bemessen kann. Dies führt sowohl zu höheren Kosten und unflexiblen Zahlungsströmen beim Maschinennutzer (Leasingnehmer) als auch zu einer schwierigen Planbarkeit der Zahlungen durch Intransparenz für Leasinggeber und Leasingnehmer. Die Abhängigkeit der Leasingrate von der Belastung der Maschine und dadurch auch indirekt von der Maschinenauslastung hat das Potenzial, das Leasing von komplexen Maschinen effizienter und fairer für alle Partner zu gestalten

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

International audienceObjective: To estimate the prevalence of amyloid positivity, defined by positron emission tomography (PET)/cerebrospinal fluid (CSF) biomarkers and/or neuropathological examination, in primary progressive aphasia (PPA) variants.Methods: We conducted a meta-analysis with individual participant data from 1,251 patients diagnosed with PPA (including logopenic [lvPPA, n = 443], nonfluent [nfvPPA, n = 333], semantic [svPPA, n = 401], and mixed/unclassifiable [n = 74] variants of PPA) from 36 centers, with a measure of amyloid-β pathology (CSF [n = 600], PET [n = 366], and/or autopsy [n = 378]) available. The estimated prevalence of amyloid positivity according to PPA variant, age, and apolipoprotein E (ApoE) ε4 status was determined using generalized estimating equation models.Results: Amyloid-β positivity was more prevalent in lvPPA (86%) than in nfvPPA (20%) or svPPA (16%; p < 0.001). Prevalence of amyloid-β positivity increased with age in nfvPPA (from 10% at age 50 years to 27% at age 80 years, p < 0.01) and svPPA (from 6% at age 50 years to 32% at age 80 years, p < 0.001), but not in lvPPA (p = 0.94). Across PPA variants, ApoE ε4 carriers were more often amyloid-β positive (58.0%) than noncarriers (35.0%, p < 0.001). Autopsy data revealed Alzheimer disease pathology as the most common pathologic diagnosis in lvPPA (76%), frontotemporal lobar degeneration-TDP-43 in svPPA (80%), and frontotemporal lobar degeneration-TDP-43/tau in nfvPPA (64%).Interpretation: This study shows that the current PPA classification system helps to predict underlying pathology across different cohorts and clinical settings, and suggests that age and ApoE genotype should be considered when interpreting amyloid-β biomarkers in PPA patients. Ann Neurol 2018;84:737-748