Empirical studies on share issuance and repurchase decisions

Financial constraints influence corporate policies of firms, including both investment decisions and external financing policies. The relevance of this phenomenon has become more pronounced during and after the recent financial crisis in 2007/2008. In addition to raising costs of external financing, the effects of financial crisis limited the availability of external financing which had implications for employment, investment, sale of assets, and tech spending. This thesis provides a comprehensive analysis of the effects of financial constraints on share issuance and repurchases decisions. Financial constraints comprise both internal constraints reflecting the demand for external financing and external financial constraints that relate to the supply of external financing. The study also examines both operating performance and stock market reactions associated with equity issuance methods. The first empirical chapter explores the simultaneous effects of financial constraints and market timing on share issuance decisions. Internal financing constraints limit firms’ ability to issue overvalued equity. On the other hand, financial crisis and low market liquidity (external financial constraints) restrict availability of equity financing and consequently increase the costs of external financing. Therefore, the study explores the extent to which internal and external financing constraints limit market timing of equity issues. This study finds that financial constraints play a significant role in whether firms time their equity issues when the shares are overvalued. The conclusion is that financially constrained firms issue overvalued equity when the external equity market or the general economic conditions are favourable. During recessionary periods, costs of external finance increase such that financially constrained firms are less likely to issue overvalued equity. Only unconstrained firms are more likely to issue overvalued equity even during crisis. Similarly, small firms that need cash flows to finance growth projects are less likely to access external equity financing during period of significant economic recessions. Moreover, constrained firms have low average stock returns compared to unconstrained firms, especially when they issue overvalued equity. The second chapter examines the operating performance and stock returns associated with equity issuance methods. Firms in the UK can issue equity through rights issues, open offers, and private placement. This study argues that alternative equity issuance methods are associated with a different level of operating performance and long-term stock returns. Firms using private placement are associated with poor operating performance. However, rights issues are found empirically to be associated with higher operating performance and less negative long-term stock returns after issuance in comparison to counterpart firms that issue private placements and open offers. Thus, rights issuing firms perform better than open offers and private placement because the favourable operating performance at the time of issuance generates subsequent positive long-run stock price response. Right issuing firms are of better quality and outperform firms that adopt open offers and private placement. In the third empirical chapter, the study explores the levered share repurchase of internally financially unconstrained firms. Unconstrained firms are expected to repurchase their shares using internal funds rather than through external borrowings. However, evidence shows that levered share repurchases are common among unconstrained firms. These firms display this repurchase behaviour when they have bond ratings or investment grade ratings that allow them to obtain cheap external debt financing. It is found that internally financially unconstrained firms borrow to finance their share repurchase when they invest more. Levered repurchase firms are associated with less positive abnormal returns than unlevered repurchase firms. For the levered repurchase sample, high investing firms are associated with more positive long-run abnormal stock returns than low investing firms. It appears the market underreact to the levered repurchase in the short-run regardless of the level of investments. These findings indicate that market reactions reflect both undervaluation and signaling hypotheses of positive information associated with share repurchase. As the firms undertake capital investments, they generate future cash flows, limit the effects of leverage on financial distress and ultimately reduce the risk of the equity capital.

Regional differences of outpatient physician supply as a theoretical economic and empirical generalized linear model

BACKGROUND: Regional differences in physician supply can be found in many health care systems, regardless of their organizational and financial structure. A theoretical model is developed for the physicians' decision on office allocation, covering demand-side factors and a consumption time function. METHODS: To test the propositions following the theoretical model, generalized linear models were estimated to explain differences in 412 German districts. Various factors found in the literature were included to control for physicians' regional preferences. RESULTS: Evidence in favor of the first three propositions of the theoretical model could be found. Specialists show a stronger association to higher populated districts than GPs. Although indicators for regional preferences are significantly correlated with physician density, their coefficients are not as high as population density. CONCLUSIONS: If regional disparities should be addressed by political actions, the focus should be to counteract those parameters representing physicians' preferences in over- and undersupplied regions.

Numerical methods for solar tomography in the STEREO era

In this thesis, we propose several advances in the numerical and computational algorithms that are used to determine tomographic estimates of physical parameters in the solar corona. We focus on methods for both global dynamic estimation of the coronal electron density and estimation of local transient phenomena, such as coronal mass ejections, from empirical observations acquired by instruments onboard the STEREO spacecraft. We present a first look at tomographic reconstructions of the solar corona from multiple points-of-view, which motivates the developments in this thesis. In particular, we propose a method for linear equality constrained state estimation that leads toward more physical global dynamic solar tomography estimates. We also present a formulation of the local static estimation problem, i.e., the tomographic estimation of local events and structures like coronal mass ejections, that couples the tomographic imaging problem to a phase field based level set method. This formulation will render feasible the 3D tomography of coronal mass ejections from limited observations. Finally, we develop a scalable algorithm for ray tracing dense meshes, which allows efficient computation of many of the tomographic projection matrices needed for the applications in this thesis.

Empirical evidence of bias in the design of experimental stroke studies: a metaepidemiologic approach

Background and Purpose: At least part of the failure in the transition from experimental to clinical studies in stroke has been attributed to the imprecision introduced by problems in the design of experimental stroke studies. Using a metaepidemiologic approach, we addressed the effect of randomization, blinding, and use of comorbid animals on the estimate of how effectively therapeutic interventions reduce infarct size. Methods: Electronic and manual searches were performed to identify meta-analyses that described interventions in experimental stroke. For each meta-analysis thus identified, a reanalysis was conducted to estimate the impact of various quality items on the estimate of efficacy, and these estimates were combined in a meta meta-analysis to obtain a summary measure of the impact of the various design characteristics. Results: Thirteen meta-analyses that described outcomes in 15 635 animals were included. Studies that included unblinded induction of ischemia reported effect sizes 13.1% (95% CI, 26.4% to 0.2%) greater than studies that included blinding, and studies that included healthy animals instead of animals with comorbidities overstated the effect size by 11.5% (95% CI, 21.2% to 1.8%). No significant effect was found for randomization, blinded outcome assessment, or high aggregate CAMARADES quality score. Conclusions: We provide empirical evidence of bias in the design of studies, with studies that included unblinded induction of ischemia or healthy animals overestimating the effectiveness of the intervention. This bias could account for the failure in the transition from bench to bedside of stroke therapies.

Identifying stationary series in panels: A Monte Carlo evaluation of sequential panel selection methods

Sequential panel selection methods (spsms — procedures that sequentially use conventional panel unit root tests to identify I(0)I(0) time series in panels) are increasingly used in the empirical literature. We check the reliability of spsms by using Monte Carlo simulations based on generating directly the individual asymptotic pp values to be combined into the panel unit root tests, in this way isolating the classification abilities of the procedures from the small sample properties of the underlying univariate unit root tests. The simulations consider both independent and cross-dependent individual test statistics. Results suggest that spsms may offer advantages over time series tests only under special conditions.

AN EMPIRICAL ASSESSMENT OF USER ONLINE SECURITY BEHAVIOR: EVIDENCE FROM A UNIVERSITY

The ever-increasing number and severity of cybersecurity breaches makes it vital to understand the factors that make organizations vulnerable. Since humans are considered the weakest link in the cybersecurity chain of an organization, this study evaluates users’ individual differences (demographic factors, risk-taking preferences, decision-making styles and personality traits) to understand online security behavior. This thesis studies four different yet tightly related online security behaviors that influence organizational cybersecurity: device securement, password generation, proactive awareness and updating. A survey (N=369) of students, faculty and staff in a large mid-Atlantic U.S. public university identifies individual characteristics that relate to online security behavior and characterizes the higher-risk individuals that pose threats to the university’s cybersecurity. Based on these findings and insights from interviews with phishing victims, the study concludes with recommendations to help similat organizations increase end-user cybersecurity compliance and mitigate the risks caused by humans in the organizational cybersecurity chain.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Evaluating tests for convergence of economic series using Monte Carlo methods with an application to real GDP's per head.

Doutoramento em Economia.

Estimation of leaf area in grapevine cv. Syrah using empirical models

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment.

Chinese Outward Foreign Direct Investment: A Review of Empirical Research

In the past decade, Chinese outward foreign direct investment (OFDI) has become a major element of global capital flows. As a consequence, recent years have witnessed an increasing growth in the number of papers focusing on Chinese companies “going global.” This paper reviews 112 empirical papers focusing on Chinese OFDI that were published in major scholarly journals between 2002 and 2014. We report on individual and institutional contributions, citations, the theories and methods used and the research topics. We also identify the research gaps and discuss the implications of our literature review for future theory building.

The Relationship between Job Structure, Burnout, and Coping Methods among Public School county Bus Drivers, Bus Aides, Mechanics, and Clerical Workers

The purpose of this study was to examine the relationship between the structure of jobs and burnout, and to assess to what extent, if any this relationship was moderated by individual coping methods. This study was supported by the Karasek's (1998) Job Demand-Control-Support theory of work stress as well as Maslach and Leiter's (1993) theory of burnout. Coping was examined as a moderator based on the conceptualization of Lazarus and Folkman (1984). Two overall overarching questions framed this study: (a) what is the relationship between job structure, as operationalized by job title, and burnout across different occupations in support services in a large municipal school district? and (b) To what extent do individual differences in coping methods moderate this relationship? This study was a cross-sectional study of county public school bus drivers, bus aides, mechanics, and clerical workers (N = 253) at three bus depot locations within the same district using validated survey instruments for data collection. Hypotheses were tested using simultaneous regression analyses. Findings indicated that there were statistically significant and relevant relationships among the variables of interest; job demands, job control, burnout, and ways of coping. There was a relationship between job title and physical job demands. There was no evidence to support a relationship between job title and psychological demands. Furthermore, there was a relationship between physical demands, emotional exhaustion and personal accomplishment; key indicators of burnout. Results showed significant correlations between individual ways of coping as a moderator between job structure, operationalized by job title, and individual employee burnout adding empirical evidence to the occupational stress literature. Based on the findings, there are implications for theory, research, and practice. For theory and research, the findings suggest the importance of incorporating transactional models in the study of occupational stress. In the area of practice, the findings highlight the importance of enriching jobs, increasing job control, and providing individual-level training related to stress reduction.

La convivialità urbana nei quartieri di Milano, Bologna e Roma. Un'analisi mixed-methods sulle Social Streets

Le Social Street sono gruppi di vicini di casa che vogliono ricreare legami di convivialità avendo notato un indebolimento delle relazioni sociali nei loro quartieri. Nascono come gruppi online, tramite la piattaforma Facebook, per materializzarsi in incontri offline andando a costruire legami conviviali grazie pratiche di socialità, inclusività e gratuità. Questa Tesi ha come obiettivo l’analisi dei profili socio-demografici degli Streeter e dei quartieri coinvolti per comprendere come sia possibile creare convivialità e come la variabile urbana intervenga in questi processi. Inoltre, si vuole comprendere le dinamiche di attaccamento al quartiere, gli interessi portati avanti dagli Streeter, il loro profilo civico e il posizionamento di quest’esperienza rispetto all’associazionismo tradizionale. Per perseguire l’obiettivo della ricerca, sono state studiate le tre città che vedono la maggiore presenza di Social Street: Milano, Bologna, Roma. La ricerca ha previsto sia un’analisi degli Streeter grazie a un questionario online replicato in tutti i contesti. Inoltre, sono state realizzate 131 interviste ad amministratori e fondatori di Social Street e condotte osservazioni etnografiche e netnografiche. I risultati mostrano come gli Streeter siano appartenenti alle classi medio-alte, tra trenta e cinquanta anni, che hanno sperimentato la mobilità tra un quartiere e l’altro o tra diversi contesi nazionali ed internazionali e trovano nelle Social Street un modo per creare legami di vicinato che hanno perso nei loro trasferimenti. Gli stessi quartieri dove si diffondono le Social Street sono agiati e vi è una buona corrispondenza tra Streeter e modello della centralità sociale elaborato da Milbrath (1965) per cui anche la partecipazione civica è molto sentita tra gli aderenti alle Social Street. Il contributo di questa Tesi al dibattito sociologico risiede nell’aver offerto un’analisi empirica di un’azione collettiva a livello urbano, quella delle Social Street, mostrando come vi sia circolarità tra azione e contesto grazie all’azione mutualistica conviviale.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Empirical ontology design patterns

Knowledge graphs (KGs) and ontologies have been widely adopted for modelling numerous domains. However, understanding the content of an ontology/KG is far from straightforward: existing methods partially address this issue. This thesis is based on the assumption that identifying the Ontology Design Patterns (ODPs) in an ontology or a KG contributes to address this problem. Most times, the reused ODPs are not explicitly annotated, or their reuse is unintentional. Therefore, there is a challenge to automatically identify ODPs in existing ontologies and KGs, which is the main focus of this research work. This thesis analyses the role of ODPs in ontology engineering, through experiences in actual ontology projects, placing this analysis in the context of existing ontology reuse approaches. Moreover, this thesis introduces a novel method for extracting empirical ODPs (EODPs) from ontologies, and a novel method for extracting EODPs from knowledge graphs, whose schemas are implicit. The first method groups the extracted EODPs in clusters: conceptual components. Each conceptual component represents a modelling problem, e.g. representing collections. As EODPs are fragments possibly extracted from different ontologies, some of them will fall in the same cluster, meaning that they are implemented solutions to the same modelling problem. EODPs and conceptual components enable the empirical observation and comparison of modelling solutions to common modelling problems in different ontologies. The second method extracts EODPs from a KG as sets of probabilistic axioms/constraints involving the ontological entities instantiated. These EODPs may support KG inspection and comparison, providing insights on how certain entities are described in a KG. An additional contribution of this thesis is an ontology for annotating ODPs in ontologies and KGs.

Evaluation Of Different Treatment Methods Against Denture Stomatitis: A Randomized Clinical Study.

The aim of this clinical study was to determine the efficacy of Uncaria tomentosa (cat's claw) against denture stomatitis (DS). Fifty patients with DS were randomly assigned into 3 groups to receive 2% miconazole, placebo, or 2% U tomentosa gel. DS level was recorded immediately, after 1 week of treatment, and 1 week after treatment. The clinical effectiveness of each treatment was measured using Newton's criteria. Mycologic samples from palatal mucosa and prosthesis were obtained to determinate colony forming units per milliliter (CFU/mL) and fungal identification at each evaluation period. Candida species were identified with HiCrome Candida and API 20C AUX biochemical test. DS severity decreased in all groups (P < .05). A significant reduction in number of CFU/mL after 1 week (P < .05) was observed for all groups and remained after 14 days (P > .05). C albicans was the most prevalent microorganism before treatment, followed by C tropicalis, C glabrata, and C krusei, regardless of the group and time evaluated. U tomentosa gel had the same effect as 2% miconazole gel. U tomentosa gel is an effective topical adjuvant treatment for denture stomatitis.