The Saudi Government’s 2030 Vision directs local banks to increase and improve credit for the Small and Medium Enterprises (SMEs) of the economy (Jadwa, 2017). Banks are, however, still finding it difficult to provide credit for small businesses that meet Basel’s capital requirements. Most of the current credit-risk models only apply to large corporations with little constructed for SMEs applications (Altman and Sabato, 2007). This study fills this gap by focusing on the Saudi SMEs perspective. My empirical work constructs a bankruptcy prediction model based on logistic regressions that cover 14,727 firm-year observations for an 11-year period between 2001 and 2011. I use the first eight years data (2001-2008) to build the model and use it to predict the last three years (2009-2011) of the sample, i.e. conducting an out-of-sample test. This approach yields a highly accurate model with great prediction power, though the results are partially influenced by the external economic and geopolitical volatilities that took place during the period of 2009-2010 (the world financial crisis). To avoid making predictions in such a volatile period, I rebuild the model based on 2003-2010 data, and use it to predict the default events for 2011. The new model is highly consistent and accurate. My model suggests that, from an academic perspective, some key quantitative variables, such as gross profit margin, days inventory, revenues, days payable and age of the entity, have a significant power in predicting the default probability of an entity. I further price the risks of the SMEs by using a credit-risk pricing model similar to Bauer and Agarwal (2014), which enables us to determine the risk-return tradeoffs on Saudi’s SMEs.

This thesis investigates the anomalous underperformance of distressed stocks in the US computer and electronics industry. It shows that such anomaly can be explained by a parallel analysis of risk based rational pricing and profitability (earnings) levels to returns relationship propositions. For the 1990 to 2006 period, distressed stocks have on average underperformed their non-distressed counterparts. However, once the conditional relationship with profitability is taken into account, the distress risk is rewarded by a continuous positive return hence priced appropriately. In the computer and electronics industry growth stocks (low B/M) outperform on average value stocks (high B/M). The size factor has not been confirmed to be significant in explaining stock returns for this specific industry over the 1990 to 2006 period. The study also reveals that B/M and size factors do not proxy for distress risk. The B/M factor follows an inverted u-shape along the distress risk deciles axis. As result, stocks in low and high distress portfolios share similarly low B/M values. Cash flow based bankruptcy predictors estimated on a quarterly basis from a Cox proportional hazard model, that are used as proxy for a continuous distress risk factor in asset pricing tests, are able to predict bankruptcies at higher accuracy rates than the Z-Score as alternative measure.

This Master's Thesis project set out with the objective to propose a machine learning model for predicting insurance risk at the level of an individual coverage, and compare it towards the existing models used by the project provider Gjensidige Försäkring. Due to interpretability constraints, it was found that this problem can be translated into a standard tabular regression task, with well defined target distributions. However, it was early identified that the set of feasible models do not contain pure black box models such as XGBoost, LightGBM and CatBoost which are typical choices for tabular data regression. In the report, we explicitly formulate the interpretability constraints in sharp mathematical language. It is concluded that interpretability can be ensured by enforcing a particular structure on the Hilbert space across which we are looking for the model.  Using this formalism, we consider two different approaches for fitting high performing models that maintain interpretability, where we conclude that gradient boosted regression tree based Generalized Additive Models in general, and the Explainable Boosting Machine in particular, is a promising model candidate consisting of functions within the Hilbert space of interest. The other approach considered is the basis expansion approach, which is currently used at the project provider. We make the argument that the gradient boosted regression tree approach used by the Explainable Boosting Machine is a more suitable model type for an automated, data driven modelling approach which is likely to generalize well outside of the training set. Finally, we perform an empirical study on three different internal datasets, where the Explainable Boosting Machine is compared towards the current production models. We find that the Explainable Boosting Machine systematically outperforms the current models on unseen test data. There are many potential ways to explain this, but the main hypothesis brought forward in the report is that the sequential model fitting procedure allowed by the regression tree approach allows us to effectively explore a larger portion of the Hilbert space which contains all permitted models in comparison to the basis expansion approach. / Detta mastersexamensarbete utgår från målsättningen att föreslå en maskinlärningsmodell för att förutspå försäkringsrisk, på nivån av enskilda försäkringar. Denna modell ska sedan jämföras mot nuvarande modeller som används hos Gjensidige Försäkring, som tillhandahåller projektet. Detta problem kan formuleras som ett traditionellt regressionsproblem på tabulär data, med väldefinerade målfördelningar. På grund av begränsningar kring krav på modellens förståbarhet identifierades det tidigt i projektet att mängden av tillåtna modeller inte innehåller ren black box modeller som XGBoost, LightGBM eller CatBoost, vilket är typiska förstahandsval för den här problemklassen. I rapporten formulerar vi förståbarhetskraven i skarpt, matematiskt språk, och drar slutsatsen att önskad förståbarhet kan uppnås genom en specifik struktur på det Hilbertrum där vi letar efter den optimala modellen. Utifrån denna formalism evaluerar vi två olika metoder för att anpassa modeller med god prestanda som uppnår önskade förståbarhetskrav. Vi drar slutsatsen att Generalized Additive Models anpassade till datat genom gradientboostade regressionsträd i allmänhet, och Explainable Boosting Machine i synnerhet är en lovande modellkandidat bestående av funktioner i vårt Hilbertrum av intresse. Vi utvärderar dessutom ett tillvägagångssätt för att anpassa Generalized Additive Models till datat genom basexpansioner, vilket är den metod som primärt används idag hos Gjensidige Försäkring. Vi argumenterar för att metoder som bygger på gradientboostade regressionsträd, såsom Explainable Boosting Machine, är mer lämplig för ett automatiserbart, datadrivet arbetssätt till att bygga modeller som generaliserar väl utanför träningsdatat.  Slutligen genomför vi en empirisk studie på tre olika interna dataset, där Explainable Boosting Machine jämförs mot nuvarande produktionsmodeller, vilka bygger på den tidigare nämnda basexpansionsmetodiken. Vi finner att Explainable Boosting Machine systematiskt överpresterar kontra nuvarande modeller på osedd testdata. Det finns många potentiella förklaringar till detta, men den huvudsakliga hypotsen som diskuteras i denna rapport är att den gradientboostade regressionsträdsmetodiken gör det möjligt att effektivt utforska en större delmängd av det Hilbertrum som innehåller alla tillåtna modeller i jämförelse med basexpansionsmetodiken.

[pt] A reestruturação do setor de energia elétrica, iniciada nos anos 90, teve como uma de suas principais implicações a introdução da competição na atividade de geração. A expansão do parque gerador, necessária para garantir o equilíbrio estrutural entre oferta e demanda, é estimulada por contratos de longo prazo negociados em leilões, na modalidade de menor tarifa. Destarte, o investidor deve oferecer um limite de preço para que o seu projeto seja competitivo (de forma a ganhar a licitação), mas que ao mesmo tempo seja suficiente para remunerar seu investimento, custos de operação e, sobretudo, protegê-lo contra todos os riscos intrínsecos ao projeto. Nesse contexto, as duas principais contribuições do presente trabalho são: (i) a proposição de uma metodologia de precificação de riscos, utilizando o critério do Value at Risk (VaR), que indica a máxima perda admitida pelo invetidor avesso a risco, com um determinado nível de confiança, e (ii) a aplicação de diferentes modelos de seleção de carteiras, que incorporam o critério do VaR para otimizar um portfolio com diferentes tecnologias de geração de energia. Os resultados da precificação de riscos são úteis para determinar os componentes críticos do projeto e calcular a competitividade (preço) de cada tecnologia. A aplicação de diferentes métodos de seleção de carteiras busca determinar o modelo mais indicado para o perfil das distribuições de retorno dos projetos de geração, que apresentam assimetria e curtose elevada (caldas pesadas). / [en] The new structure of the brazilian electric sector, consolidated by the end of the 90s main implication the introduction of competition in the power generation activity. The expansion of generation capacity, responsible to ensure structural equilibrium between supply and demand, is stimulated by long-term contracts negotiated through energy auctions. Therefore, the investor must give a competitive price (in order to win the auction), but also sufficient to pay his investment, operational costs and, especially, protect him against all project risks. In this role, the two main contributions of this work are: (i) to suggest a methodology of risk pricing, using the Value at Risk (VaR) criterium, which gives the maximum loss admitted by the risk averse investor, with a specified confidence level, and (ii) to apply different portfolio selection models, which incorporates the VaR criterium to optimize a portfolio with different power generation technologies. The risk pricing results are usefull to determine the project critical components and to calculate the competitiviness (price) of each technology. The study of different portfolio selection methods aims to investigate the most suitable model for the return distribution shape, characterized by having assimetry and curtosis (heavy tails).

Made available in DSpace on 2010-04-20T20:20:34Z (GMT). No. of bitstreams: 1 68070200638.pdf: 1746289 bytes, checksum: f0346ef0059ceefcafb161ef506a8d08 (MD5) Previous issue date: 2009-12-14T00:00:00Z / Este estudo tem como objetivo descrever e detalhar importante ferramenta de transferência de risco de crédito já disponível no mercado securitário, e propor um processo para que a mesma possa ser avaliada por empresas no Brasil. Apesar de pouco explorado no meio acadêmico e pouco difundido no Brasil, o Seguro de Crédito é muito utilizado em países da Europa e Ásia e pode ter importância fundamental em épocas de crise e no novo cenário econômico mundial. Descreve-se como o seguro de crédito está inserido no contexto de gerenciamento ativo de risco, os tipos de seguro disponível no mercado e os complexos parâmetros de uma apólice dessa natureza, os quais são derivados da necessidade de mitigação de riscos de moral hazard e de assimetria de informações por parte da seguradora. Apresenta-se também um resumo das principais metodologias existentes para precificação de risco de crédito, apontando suas vantagens, desvantagens e adaptações necessárias para a aplicação em empresas não financeiras. Por fim, é proposto um processo para avaliação e contratação do seguro de crédito por empresas no Brasil, o qual considera diversos aspectos que envolvem a realidade das operações e os impactos previstos pelo uso do seguro. Com base na teoria e nas características das apólices de seguro de crédito interno, buscou-se também desenvolver um modelo para avaliação da precificação do prêmio e dos parâmetros da apólice, sendo que sua aplicação é exposta nos exemplos ao final do trabalho.