Sökning: "kreditrisk"

Visar resultat 16 - 20 av 148 uppsatser innehållade ordet kreditrisk.

  1. 16. Deep Learning Approach for Time- to-Event Modeling of Credit Risk

    Master-uppsats, KTH/Matematisk statistik

    Författare :Mehnaz Kazi; Natalija Stanojlovic; [2022]
    Nyckelord :Survival Analysis; Credit Risk; Credit Scoring; Time-To-Event; Default Probability; Överlevnadsanalys; Kreditrisk; Kreditprövning; Tid-till-utfall; Sannolikhet för fallissemang;

    Sammanfattning : This thesis explores how survival analysis models performs for default risk prediction of small-to-medium sized enterprises (SME) and investigates when survival analysis models are preferable to use. This is examined by comparing the performance of three deep learning models in a survival analysis setting, a traditional survival analysis model Cox Proportional Hazards, and a traditional credit risk model logistic regression. LÄS MER

  3. 17. Kreditriskens påverkan på aktieavkastning

    Kandidat-uppsats, Uppsala universitet/Företagsekonomiska institutionen

    Författare :David Rosdahl; Hugo Dalmalm; [2022]
    Nyckelord :;

    Sammanfattning : Det är väl etablerat i tidigare empirisk forskning att kreditrisk har viktiga konsekvenser för prissättningen av skuldebrev. Det är dock inte lika tydligt hur kreditrisk påverkar prissättningen av aktier. LÄS MER

  4. 18. Anticipating bankruptcies among companies with abnormal credit risk behaviour : Acase study adopting a GBDT model for small Swedish companies

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Simon Heinke; [2022]
    Nyckelord :Bankruptcy prediction; Credit risk analysis; Abnormal credit risk behaviour; Gradient boosted decision trees; SHAP-values.; Konkurs förutsägelse; Kredit riskanalys; Abnomralt kreditbeteende; Gradient baserat beslutsträd; SHAP-värden.;

    Sammanfattning : The field of bankruptcy prediction has experienced a notable increase of interest in recent years. Machine Learning (ML) models have been an essential component of developing more sophisticated models. Previous studies within bankruptcy prediction have not evaluated how well ML techniques adopt for data sets of companies with higher credit risks. LÄS MER

  5. 19. Optimization of Collateral Allocation for Corporate Loans : A nonlinear network problem minimizing the expected loss in case of default

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Sofia Grägg; Paula Isacson; [2022]
    Nyckelord :Nonlinear optimization; network problem; transportation problem; Markowitz; credit risk; Loss Given Default; Loan to Value; collateral management; many-to-many relations; modern portfolio theory; expected loss; risk management; optimization; allocation; portfolio; modeling; Icke-linjär optimering; nätverksproblem; transportproblem; Markowitz; kreditrisk; förlust givet fallisemang; belåningsgrad; säkerhetshantering; många-till-många relationer; modern portföljteori; förväntad förlust; riskhantering; optimering; allokering; portfölj; modellering.;

    Sammanfattning : Collateral management has become an increasingly valuable aspect of credit risk. Managing collaterals and constructing accurate models for decision making can give any lender a competitive advantage and decrease overall risks. LÄS MER

  7. 20. Using Gradient Boosting to Identify Pricing Errors in GLM-Based Tariffs for Non-life Insurance

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Felix Greberg; Andreas Rylander; [2022]
    Nyckelord :GLM; Gradient Boosting; XGBoost; Non-life insurance; Property Casualty; Rate making; Insurance Tariff; MTPL insurance; Machine learning; Regression trees; Tweedie regression; Credit risk; GLM; Gradient Boosting; XGBoost; Skadeförsäkring; Prissättning; Försäkringstariff; Trafikförsäkring; Regressionsträd; Maskininlärning; Tweedie-regression; Kreditrisk;

    Sammanfattning : Most non-life insurers and many creditors use regressions, more specifically Generalized Linear Models (GLM), to price their liabilities. One limitation with GLMs is that interactions between predictors are handled manually, which makes finding interactions a tedious and time-consuming task. LÄS MER