Managerial Hedging and Portfolio Monitoring

Incentive compensation induces correlation between the portfolio of managers and the cash flow of the firms they manage. This correlation exposes managers to risk and hence gives them an incentive to hedge against the poor performance of their firms. We study the agency problem between shareholders and a manager when the manager can hedge his incentive compensation using financial markets and shareholders cannot perfectly monitor the manager’s portfolio in order to keep him from hedging the risk in his compensation. In particular, shareholders can monitor the manager’s portfolio stochastically, and since monitoring is costly governance is imperfect. If managerial hedging is detected, shareholders can seize the payoffs of the manager’s trades. We show that at the optimal contract: (i) the manager’s portfolio is monitored only when the firm performs poorly, (ii) the more costly monitoring is, the more sensitive is the manager’s compensation to firm performance, and (iii)conditional on the firm’s performance, the manager’s compensation is lower when his portfolio is monitored, even if no hedging is revealed by monitoring.executive compensation, incentives, monitoring, corporate governance.

Exclusive Contracts and the Institution of Bankruptcy

This paper studies the institution of bankruptcy when exclusive contracts cannot be enforced ex ante, e.g., a bank cannot monitor whether the borrower enters into contracts with other creditors. The institution of bankruptcy enables the bank to enforce its claim to any funds that the borrower has above a fixed “bankruptcy protection” level. Bankruptcy improves on non-exclusive contractual relationships but is not a perfect substitute for exclusivity ex ante. We characterize the effect of bankruptcy provisions on the equilibrium contracts which borrowers use to raise financing

Prediction of Displacements in Unstable Areas Using a Neural Model

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers

Instant recovery of shape from spectrum via latent space connections

We introduce the first learning-based method for recovering shapes from Laplacian spectra. Our model consists of a cycle-consistent module that maps between learned latent vectors of an auto-encoder and sequences of eigenvalues. This module provides an efficient and effective linkage between Laplacian spectrum and geometry. Our data-driven approach replaces the need for ad-hoc regularizers required by prior methods, while providing more accurate results at a fraction of the computational cost. Our learning model applies without modifications across different dimensions (2D and 3D shapes alike), representations (meshes, contours and point clouds), as well as across different shape classes, and admits arbitrary resolution of the input spectrum without affecting complexity. The increased flexibility allows us to address notoriously difficult tasks in 3D vision and geometry processing within a unified framework, including shape generation from spectrum, mesh superresolution, shape exploration, style transfer, spectrum estimation from point clouds, segmentation transfer and pointtopoint matching

Data-drive decision support system for selecting building retrofit strategies

The building sector in EU countries is primarily comprised of outdated and inefficient structures, which are of high energy consumption and seismic vulnerability. As a result, building retrofit is being stressed as a viable option for addressing existing energy and seismic issues in the construction industry, particularly in residential properties. For this purpose, strategic decisions should be made about the retrofit strategies, which require great time, effort, resources, and expertise. While traditional case-based retrofit scenarios fail to provide rapid and objective solutions, data-driven methods such as Artificial Intelligence (AI) technologies can serve as an effective and efficient decision support system for selecting retrofit strategies. This research offers a clustering of residential properties in the CENED database (Lombardia 2007)(comprising over 1 million energy labels of residential properties), based on the construction year and U-value. These clusters are associated with the type of material and building technique using the National scientific report on the TABULA activities (Corrado, Ballarini, and Corgnati 2012), and the probability distribution of EHP values. Therefore considering a given U-value and an energy class, the most optimum retrofit strategy is suggested to obtain a particular energy label. This research benefits from AI technologies to enhance strategic decision-making for building retrofit by connecting the current dispersed databases. It also helps increase energy-saving on an urban level

Integrated waveguides and deterministically positioned nitrogen vacancy centers in diamond created by femtosecond laser writing

Diamond's nitrogen vacancy (NV) center is an optically active defect with long spin coherence times, showing great potential for both efficient nanoscale magnetometry and quantum information processing schemes. Recently, both the formation of buried 3D optical waveguides and high quality single NVs in diamond were demonstrated using the versatile femtosecond laser-writing technique. However, until now, combining these technologies has been an outstanding challenge. In this work, we fabricate laser written photonic waveguides in quantum grade diamond which are aligned to within micron resolution to single laser-written NVs, enabling an integrated platform providing deterministically positioned waveguide-coupled NVs. This fabrication technology opens the way towards on-chip optical routing of single photons between NVs and optically integrated spin-based sensing

Intracranial pressure after subarachnoid hemorrhage

Objectives: To describe mean intracranial pressure after aneurysmal subarachnoid hemorrhage, to identify clinical factors associated with increased mean intracranial pressure, and to explore the relationship between mean intracranial pressure and outcome. Design: Analysis of a prospectively collected observational database. Setting: Neuroscience ICU of an academic hospital. Patients: One hundred sixteen patients with subarachnoid hemorrhage and intracranial pressure monitoring. Interventions: None. Measurements and Main Results: Episodes of intracranial pressure greater than 20 mm Hg lasting at least 5 minutes and the mean intracranial pressure for every 12-hour interval were analyzed. The highest mean intracranial pressure was analyzed in relation to demographic characteristics, acute neurologic status, initial radiological findings, aneurysm treatment, clinical vasospasm, and ischemic lesion. Mortality and 6-month outcome (evaluated using a dichotomized Glasgow Outcome Scale) were also introduced in multivariable logistic models. Eighty-one percent of patients had at least one episode of high intracranial pressure and 36% had a highest mean intracranial pressure more than 20 mm Hg. The number of patients with high intracranial pressure peaked 3 days after subarachnoid hemorrhage and declined after day 7. Highest mean intracranial pressure greater than 20 mm Hg was significantly associated with initial neurologic status, aneurysmal rebleeding, amount of blood on CT scan, and ischemic lesion within 72 hours from subarachnoid hemorrhage. Patients with highest mean intracranial pressure greater than 20 mm Hg had significantly higher mortality. When death, vegetative state, and severe disability at 6 months were pooled, however, intracranial pressure was not an independent predictor of unfavorable outcome. Conclusions: High intracranial pressure is a common complication in the first week after subarachnoid hemorrhage in severe cases admitted to ICU. Mean intracranial pressure is associated with the severity of early brain injury and with mortality

Cardiovascular Reasons for Access to a Tertiary Oncological Emergency Service: The CARILLON Study

Background: The prevalence of acute cardiovascular diseases (CVDs) in cancer patients is steadily increasing and represents a significant reason for admission to the emergency department (ED). Methods: We conducted a prospective observational study, enrolling consecutive patients with cancer presenting to a tertiary oncological ED and consequently admitted to the oncology ward. Two groups of patients were identified based on main symptoms that lead to ED presentation: symptoms potentially related to CVD vs. symptoms potentially not related to CVD. The aims of the study were to describe the prevalence of symptoms potentially related to CVD in this specific setting and to evaluate the prevalence of definite CV diagnoses at discharge. Secondary endpoints were new intercurrent in-hospital CV events occurrence, length of stay in the oncology ward, and mid-term mortality for all-cause. Results: A total of 469 patients (51.8% female, median age 68.0 [59.1–76.3]) were enrolled. One hundred and eighty-six out of 469 (39.7%) presented to the ED with symptoms potentially related to CVD. Baseline characteristics were substantially similar between the two study groups. A discharge diagnosis of CVD was confirmed in 24/186 (12.9%) patients presenting with symptoms potentially related to CVD and in no patients presenting without symptoms potentially related to CVD (p < 0.01). During a median follow-up of 3.4 (1.2–6.5) months, 204 (43.5%) patients died (incidence rate of 10.1 per 100 person/months). No differences were found between study groups in terms of all-cause mortality (hazard ratio [HR]: 0.85, 95% confidence interval [CI] 0.64–1.12), new in-hospital CV events (HR: 1.03, 95% CI 0.77–1.37), and length of stay (p = 0.57). Conclusions: In a contemporary cohort of cancer patients presenting to a tertiary oncological ED and admitted to an oncology ward, symptoms potentially related to CVD were present in around 40% of patients, but only a minority were actually diagnosed with an acute CVD