The purpose of this study was to investigate possible relationships between school district characteristics and bond underwriter costs for Texas independent school districts. Bond data for all school districts issuing bonds in the five-year period 2004 – 2008 was collected from the Texas Bond Review Board. School district information, including financial, socio- economic/ demographic, debt, and managerial information, was collected from those same districts. The data was analyzed using both descriptive and inferential statistical methods. Descriptive statistics were developed on both bond issue and bond issuer data. Relationships between issue costs and school district characteristics were then examined using multiple regression and factor analysis. Results indicate that, in general, larger districts have an advantage over smaller districts, with underwriter costs generally lower in larger districts. Results also offer modest support for the hypothesis that underwriter fees are related to financial, socio-economic/demographic, debt, and managerial characteristics of school districts.

The initial matching relationships between underwriters and bonds/issuing firms and the certification quality of underwriters, as determined by changes in the issuing firm’s financial strength post issue, are the two primary research topics in this dissertation. Based on total underwriter syndicate market share, two distinct categories, low market power (LMP) syndicates and high market power (HMP) syndicates were defined. Firm financial strength is examined based on a new factor developed in this research. A comparison of the two underwriting categories, or pools, indicates that the HMP underwriters take on firms of lower initial financial strength and additionally, the issuing firms decline more in financial strength two years following bond issuance than do firms using LMP underwriters. Notwithstanding these results, the more interesting findings are the relationships within each of these pools. In the LMP pool of underwriters, financially stronger firms used the larger LMPs to underwrite their bonds, while the weaker firms used smaller LMPs. In contrast, among HMP underwriters, the largest HMPs aligned with the firms of relatively lower financial strength. The relationships in both pools reverse when changes in financial strength are examined. Larger LMPs are associated with greater issuing firm financial decline while larger HMPs correlate with lower levels of decline in firm financial strength. Divergent patterns in initial underwriter-issuer matching and underwriter certification found in this research indicate that there are true differences in the “small” underwriting syndicates as compared to the larger syndicates. These patterns should be considered by both issuing firms and investors as both constituencies contemplate the corporate bond market.

In the study, we choose the companys that had conducted IPOs or SEOs between 2005 and February 2006 as studying samples. The purpose of the study is to investigate how the issuing firms choose among different underwriting mechanisms and the factors influencing the choice of underwriting mechanisms. Empirical result shows that issuing factors are the important factors that influence the choice of underwriting mechanisms. The firms that conduct IPOs usually trend to choose bookbuilding method, but the firms that conduct SEOs trend to choose fixed price method instead. On the other hand, we also find that issuing scale is another significant factor influencing the choice of underwriting mechanisms and the effect is more apparent on the firms that conduct SEOs. The firms with bigger issuing scale trend to choose bookbuilding method but the firms with smaller issuing scale trend to choose fixed price rather than bookbuilding method.

Taiwan carries out the new underwriting rules since 2005, it is very important to the reformation of financial system, and its purpose is mainly for responding to the reformation of underwriting rules so as to promote the quality of listed companies. At present, the underwriting method of Taiwan¡¦s major Initial Public Offerings (IPO) stock uses the mix and collocation of two systems, public subscription by lot drawing and book building, as the underwriting method, the previous studies emphasized that the underwriter had higher right to decide the amount of placing, which can increase the responsibility and sensibility for the underwriting price, and then the result of promoting placing efficiency can be reached. This study compares the markets in Mainland China, Hong Kong, and Taiwan that have carried out book building system. This study mainly finds out, because the underwriters in Taiwan can provide higher stock weight of the placing of book building and the underwriters have higher right to decide the amount of placing, thus, it is more possible that when placing IPO stock, the company would regard its customers as the core, and this act is entirely different with the conception of the initial reformation, and unfavorable influence would be produced to the issue company or individual investor at the same time. This study sums up the previous literatures and questionnaire statistical analyses, and then proposes the influence that degree of rigor of laws and regulations, anticipated result of placing, institutional function of book building and decisive factors of placing have towards the actual results of placing of underwriters. The questionnaires are distributed to the personnel who actually are responsible for the IPO placing strategy of underwriters that have hosted actual placing results in Taiwan, Hong Kong, and Mainland China. After the comparison using single factor variance, one can find that the underwriters from Mainland China significantly think that the laws and regulations are stricter, and IPO stock has more good prospects of gain. However, the underwriters from Taiwan significantly think that the decisive factors of placing are stronger. When placing IPO stock, the underwriters from Mainland China prefer placing according to the system, while the underwriters from Taiwan prefer placing according to relationship. This study uses regression analysis to discuss the factors that influence the underwriters to carry out placing according to system or relationship. This study finds out if the degree of rigor for the two factors in laws and regulations, objective & fair and book building standard, is higher, then the underwriters will tend to carry out placing according to system more. And also, the system-oriented and price-oriented factors in decisive factors of placing also promote the underwriters carry out placing according to system. On the other hand, if the degree of rigor for the objective & fair factor in laws and regulations is higher, then the underwriters tend not to carry out placing according to relationship. When the factors such as considering underwriter¡¦s benefits, anticipated profit, and relationship-oriented are higher, then the underwriters tend to carry out placing according to relationship. In view of the above, this study has proposed many suggestions to improve the problems that might happen when underwriters in Taiwan placing IPO stock at present, including report beforehand, exposing the principles, ratio of book building placing, classifying the investors and placing in proportion, regulations of holding term, strengthening the supervision on notary public during the placing process, and canceling the use of person as the basic unit when purchasing but the use of purchasing capital¡¦s scale as the drawing unit. This study also hopes that the relevant departments in governments and underwriters could consult this study thesis and take this study as the reference for the follow-up reformation direction of IPO system.

由於近年國內承銷價之訂定大多採議定的方式，由發行公司與承銷商根據發行公司與採樣公司的每股稅後純益，本益比，每股股利加以設算參考價共同議定。上述設定價格可能無法反應市場供需，將造成股價的穩定。因而有些專家學者建議以競價拍賣取代現行議價，因此本研究乃探討承銷商在競價拍賣下之策略行為與可能面臨的狀況。

One of the most important fields regarding growth and development for mostorganizations today is the digitalization, or digital transformation. The offering oftechnological solutions to enhance existing, or create new, processes or products isemerging. That is, it’s of great importance that organizations continuously affirm thepotential of applying new technical solutions into their existing processes. For example, a well implemented AI solution for automation of an existing process is likely tocontribute with considerable business value.Medical underwriting for individual insurances, which is the process consideredin this project, is all about risk assessment based on the individuals medical record.Such task appears well suited for automation by a machine learning based applicationand would thereby contribute with substantial business value. However, to make aproper replacement of a manual decision making process, no important informationmight be excluded, which becomes rather challenging due to the fact that a considerable fraction of the information the medical records consists of unstructured textdata. In addition, the underwriting process is extremely sensible to mistakes regarding unnecessarily approve insurances where an enhanced risk of future claims can beassessed.Three algorithms, Logistic Regression, XGBoost and a Deep Learning model, wereevaluated on training data consisting of the medical records structured data from categorical and numerical answers, the text data as TF-IDF observation vectors, and acombination of both subsets of features. The XGBoost were the classifier performingbest according to the key metric, a pAUC over an FPR from 0 to 0.03.There is no question about the substantial importance of not to disregard anytype of information from the medical records when developing machine learning classifiers to predict the medical underwriting outcomes. At a very risk conservative andperformance pessimistic approach the best performing classifier did manage, if consider only the group of youngest kids (50% of sample), to recall close to 50% of allstandard risk applications at a false positive rate of 2%, when both structured andtext data were considered. Even though the structured data accounts for most of theexplanatory ability it becomes clear that the inclusive of the text data as TF-IDF observation vectors make for the differences needed to potentially generate a positivenet present value to an implementation of the model

During the past two decades, it was witness a dramatic global shift in economic policy away from state-owned enterprises (SOEs) towards privatization. Since privatization improves incentives, a rapid transfer of ownership and control right should be desirable. In Taiwan, the Executive Yuan of the government organized a group to promote privatization in 1989 and the officers then took the initiative to carry out the privatization program enthusiastically. By classifying the privatization process into two stages, we analyzed the effect of different stock floatation schedules, different underwriting mechanisms, and different levels of government intervention on ownership structure and corporate governance. Based on the model inference, we found that when maximizing stock floating revenue, wider share ownership, and promoting the SOEs¡¦ efficiency, a sequential transfer of ownership and control right should be better than privatized instantaneously. Under the same goals, the mechanism with partial public offering and partial auction was better than the mechanism with partial public offering and partial book building. Finally, it was not optimal for government to intervene the operating of SOEs after privatization.

Tabular data is the most common form of data, and is abundant throughout crucial industries, such as banks, hospitals and insurance companies. Albeit, deep learning research has largely been dominated by applications to homogeneous data, e.g. images or natural language. Inspired by the great success of deep learning in these domains, recent efforts have been made to tailor deep learning architectures for tabular data. In this thesis, two such models are selected and tested in the context of transactional underwriting. Specifically, the two models are evaluated in terms of predictive performance, interpretability and complexity, to ultimately see if they can compete with gradient boosted tree models and live up to industry requirements. Moreover, the pre-training capabilities of the deep learning models are tested through transfer learning experiments across different markets. It is concluded that the two models are able to outperform the benchmark gradient boosted tree model in terms of RMSE, and moreover, pre-training across markets gives a statistically significant improvement in RMSE, on a level of 0.05. Furthermore, using SHAP, together with model specific explainability methods, it is concluded that the two deep learning models’ explainability is on-par with gradient boosted tree models. / Tabelldata är den vanligaste formen av data och finns i överflöd i viktiga branscher, såsom banker, sjukhus och försäkringsbolag. Även om forskningen inom djupinlärning till stor del dominerats av tillämpningar på homogen data, t.ex. bilder eller naturligt språk. Inspirerad av den stora framgången för djupinlärning inom dessa domäner, har nyligen ansträngningar gjorts för att skräddarsy djupinlärnings-arkitekturer för tabelldata. I denna avhandling väljs och testas två sådana modeller på problemet att estimera vinst marginalen på en transaktion. Specifikt utvärderas de två modellerna i termer av prediktiv prestanda, tolkningsbarhet och komplexitet, för att i slutändan se om de kan konkurrera med gradient boosted tree-modeller och leva upp till branschkrav. Dessutom testas för-träningsförmågan hos djupinlärningmodellerna genom överföringsexperiment mellan olika marknader. Man drar slutsatsen att de två modellerna kan överträffa benchmark gradient boosted tree-modellen när det gäller RMSE, och dessutom ger för-träning mellan marknader en statistiskt signifikant förbättring av RMSE, på en nivå av 0,05. Vidare, med hjälp av SHAP, tillsammans med modellspecifika förklaringsmetoder, dras slutsatsen att de två djupinlärning-modellernas förklaringsbarhet är i nivå med gradient boosted tree-modellerna.

E-commerce has grown significantly the last decade, and made a considerable leap during Covid19. The final step in e-commerce is payments, and as a result of this, credit risk management in real-time has become increasingly important. An imperative function in credit risk management is underwriting, in which it is decided which purchases to accept and which not to. However, events can occur that cause increases or decreases in for instance acceptance rates, and these must be detected in order to for instance maintain good stakeholder relationships. Thus, KPI:s are monitored with the aim of detecting outliers as soon as possible.  The purpose of this study is to explore the business value and obstacles of automating outlier detection for credit risk KPI time series in e-commerce. In addition, aspects to think about on implementation are investigated. The research is a case study and is founded in thematic analysis of qualitative data collected at an e-commerce company.  The results of the study show that automation can contribute to significant business value due to for instance a decrease in monetary and alternative costs of manual monitoring, as well as a potential for better quality in the monitoring, and thus also enhanced stakeholder relationships. However, results also imply that there are several obstacles to actually implementing full automation such as a lack of trust in the automation, along with opinions that automation will impair knowledge and communication, and that the implementation is complex. / Under det senaste årtiondet har e-handel signifikant växt, och under Covid19 eskalerade utvecklingen ännu mer. Det sista steget i e-handel är betalningar, och till följd av detta har kreditriskhantering blivit allt viktigare. En signifikant funktion i kreditriskhantering är underwriting, där det bestäms vilka köp som skall accepteras och inte. Dock kan händelser ske som ökar eller minskar till exempel andelen köp som accepteras, och dessa händelser måste identifieras bland annat för att kunna upprätthålla goda relationer med företagets intressenter. Således monitoreras KPI:er med syftet att upptäcka anomalier så tidigt som möjligt. Syftet med denna studie är att undersöka affärsvärdet, samt barriärer, av implementation av automatiserad outlier detection för kreditrisk KPI tidsserier i e-handel. Denna forskning är en fallstudie som grundas i tematisk analys av kvalitativ data som samlas in på ett e-handelsföretag.  Vidare visar resultaten av studien att automatisering kan bidra till betydande affärsvärde bland annat till följd av minskade monetära såväl som alternativa kostnader från manuell monitorering, samt potential till bättre kvalitet i monitoreringen och således förbättrade intressentrelationer. Dock tyder resultaten även på att det finns ett flertal hinder för att faktiskt implementera full automatisering såsom brist på tillit till automatisering, tillsammans med åsikter såsom att automatisering kommer bidra till minskad kunskap och kommunikation, och att en implementation skulle vara både tekniskt och logiskt utmanande.

Nos últimos anos, o mercado segurador brasileiro tem apresentado forte expansão, a qual foi impulsionada pela estabilização econômica e o conseqüente aumento do consumo. No entanto, mediante um crescimento acelerado dos prêmios, eventuais desvios nas premissas adotadas na precificação podem expor as seguradoras a riscos pouco suportáveis no longo prazo. Este é um dos componentes do risco de subscrição, sendo o objeto do presente estudo. No âmbito regulatório, frente ao aumento das complexidades dos serviços financeiros e aos escândalos envolvendo grandes corporações, fez-se necessário o Novo Acordo da Basiléia, o qual introduziu metodologias de apuração da necessidade mínima de capital mais sensível a risco, beneficiando instituições melhor administradas na medida em que requer menor alocação de capital. Nessa mesma linha, no mercado segurador dos países membros da União Européia, segue o projeto Solvência II. Acompanhando a tendência mundial, no Brasil, foram promulgadas novas regras de solvência para o mercado segurador, sendo estabelecidas, num primeiro momento, regras de alocação de capital para cobertura do risco de subscrição, sendo os demais tipos de risco a serem tratados na seqüência. É importante esclarecer que, diferentemente do setor bancário, no mercado segurador brasileiro não é permitida a utilização do próprio modelo interno ou dos parâmetros deste para determinação do capital mínimo requerido regulatório, mas apenas a aplicação de fatores mais suavizados para tal fim. Assim, como este não observa o risco mensurado internamente, o capital regulatório passa a representar um potencial custo imposto às seguradoras, o qual pode impactar diretamente na rentabilidade das linhas de negócio. Nesse sentido, o presente estudo buscou investigar, sob a ótica e limitação de usuário externo das demonstrações contábeis, a existência de indícios que levam a supor que a nova regulamentação sobre o capital mínimo para cobertura do risco de subscrição penalizou as seguradoras de menor porte, tendo-se em vista o seu valor em risco para o nível de confiança de 99,5%. Para tanto, foi necessário: (a) apurar o capital mínimo regulatório, seja com ou sem modelo interno, (b) estimar o valor em risco de cada seguradora para o nível de confiança de 99,5% e (c) distinguir as seguradoras por porte, o qual foi determinado pela técnica de Análise de Conglomerados. O maior desafio foi determinar, para cada seguradora, o item (b), o qual consistiu na estimação das distribuições marginais das perdas por categoria de negócio e a agregação dessas pela aplicação da teoria de cópulas. Depois, calculou-se a razão entre (i) a somatória do grau de provisionamento com a alocação do capital regulatório (abordagens com e sem modelo interno) e (ii) o valor em risco ao nível de confiança de 99,5%. Em seguida, aplicou-se o teste de Mann-Whitney para comparar médias em função do porte. A partir da análise desenvolvida, observou-se que modelo regulatório se mostrou mais coerente quando aplicado às seguradoras médias e grandes, tendo-se em vista que apresentou menor dispersão no parâmetro calculado, cuja mediana estava em torno de 1. Ou seja, para essas, tal resultado sugere que o grau de provisionamento juntamente com o capital regulatório retrata, aproximadamente, o nível de confiança de 99,5%, em consonância com o Projeto Solvência II. A dispersão para as seguradoras pequenas é bem maior e a mediana está próximo a 1,5, o que indica que a abordagem regulatória requer em torno de 50% a mais de recursos que o nível de confiança de 99,5% exige. Esse resultado indica uma desvantagem competitiva se comparada às seguradoras de médio e grande porte. Portanto, os resultados dos testes sugerem que as novas regras de alocação de capital para o mercado segurador brasileiro penalizou as seguradoras de menor porte, impactando na rentabilidade, na precificação e na competitividade se comparada às médias e grandes, o que, por sua vez, tende a favorecer a concentração do setor. / In recent years, the Brazilian insurance market has shown strong growth, which was driven by economic stabilization and the consequent increase in consumption. However, on an environment accelerated growth of premiums, any deviations in the pricing assumptions may expose insurers to unbearable risks in the long term. This is one of the components of the underwriting risk which is the object of this study. In a regulatory side, increased complexities of the financial services and scandals involving large corporations resulted in the creation of the New Basel Accord, which introduced new methodologies to analyze the minimum capital required, considering the risk based capital approach, benefiting the better managed institutions as they require less capital allocation. In a similar vein, countries of the European Union follow the Solvency II project for their insurance market. Following the global trend, new solvency rules for the insurance market were approved in Brazil, being established in the first instance, rules of capital allocation to cover the underwriting risk. Other risk types will be addressed later by the government. It is important to clarify that, unlike the banking sector, the Brazilian insurance market is not allowed to use its own internal model or the parameters of this model to determine the minimum regulatory capital required, but only the application softened factors for this purpose. Thus, as it does not observe the risk internally measured, the regulatory capital becomes a potential cost imposed on the insurers, which can impact directly the profitability of the business lines. Therefore, from the point of view and limitation of external user of financial statements, the present study investigated the existence of signs that could lead to suppose that the new regulations on minimum capital to cover the underwriting risk have penalized the smaller insurance companies, when keeping in view their value at risk for the confidence level of 99,5%. To this end, it was necessary: (a) to determine the minimum regulatory capital, either approaches with or without internal model; (b) to estimate the value at risk of each insurer for the confidence level of 99,5%; and (c) to distinguish insurers by size, according to the cluster analysis technique. The biggest challenge was to determine, for each insurer, the item (b), which consisted in the estimation of marginal distributions of losses and aggregation of these by applying the theory of copulas. Then we calculated the ratio of (i) the sum of the degree of provisioning with the allocation of regulatory capital (approaches with and without internal model) and (ii) the value at risk at the level of confidence 99,5%. Next, we applied the Mann-Whitney Test to compare means of the insurers by size. From the developed analysis, it was observed that the regulatory model was more consistent on medium and large insures as they have shown a lower dispersion in the parameter of interest, presenting a median around 1. That is, for them, the result suggests that the level of provisioning along with the regulatory capital has approximately reflected the confidence level of 99,5%, which is in line with the Solvency II project. Small insurers have shown much higher dispersion and their median is close to 1,5. This indicates that the regulatory approach requires around 50% more resources than the confidence level of 99,5% requires. This represents a disadvantaged competition, if compared with large and medium sized companies. Therefore, the test results suggest that the new rules of capital allocation for the Brazilian insurance market has penalized the smaller insurers, impacting their profitability and competitive pricing when compared with the medium and large ones, which, in turn, tend to favor an industry concentration.