Essays about: "Random Portfolio"

Showing result 1 - 5 of 41 essays containing the words Random Portfolio.

  1. 1. Predicting the Movement of the S&P 500 Index using Machine Learning

    University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

    Author : Bakary Bah; [2023]
    Keywords : Machine Learning; S P 500 Index; Random Forest; Logistic Regression; Business and Economics;

    Abstract : Predicting the stock market has been a longstanding topic of interest in financial research. It is regarded as a highly challenging but important task given the vital role the financial markets play in shaping the global economies. In this thesis, the goal is to predict the movement of the S&P 500 Index using machine learning methods. READ MORE

  3. 2. Portfolio Risk Modelling in Venture Debt

    University essay from KTH/Matematisk statistik

    Author : John Eriksson; Jacob Holmberg; [2023]
    Keywords : Startup Default Probability; Venture Debt; Gaussian Copula; Value-at-Risk; Expected Shortfall; Exposure at Default; Loss Given Default; Forecast; Linear Dynamic System; ARIMA Time Series; Monte Carlo Simulation; Linear Regression; Central Limit Theorem;

    Abstract : This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. READ MORE

  4. 3. Statistical Modelling of Price Difference Durations Between Limit Order Books: Applications in Smart Order Routing

    University essay from KTH/Matematisk statistik

    Author : Hannes Backe; David Rydberg; [2023]
    Keywords : Smart Order Routing; Market Microstructure; Statistical Modelling; Survival Analysis; Kaplan-Meier; Cox Proportional Hazards; Random Survival Forest; Smart Order Routing; Marknadsmikrostruktur; Statistisk Modellering; Överlevnadsanalys; Kaplan-Meier; Cox Proportional Hazards; Random Survival Forest;

    Abstract : The modern electronic financial market is composed of a large amount of actors. With the surge in algorithmic trading some of these actors collectively behave in increasingly complex ways. Historically, academic research related to financial markets has been focused on areas such as asset pricing, portfolio management and financial econometrics. READ MORE

  5. 4. Applying the Shadow Rating Approach: A Practical Review

    University essay from KTH/Matematik (Avd.)

    Author : Viktor Barry; Carl Stenfelt; [2023]
    Keywords : Shadow Rating; probability of default; low default portfolio; credit risk; statistical learning; financial regulation; Basel; Pluto and Tasche; Skuggrating; sannolikhet av fallissemang; lågfallissemangsportfölj; kreditrisk; statistisk inlärning; finansiella regelverk; Basel; Pluto och Tasche;

    Abstract : The combination of regulatory pressure and rare but impactful defaults together comprise the domain of low default portfolios, which is a central and complex topic that lacks clear industry standards. A novel approach that utilizes external data to create a Shadow Rating model has been proposed by Ulrich Erlenmaier. READ MORE

  6. 5. Peeking Through the Leaves : Improving Default Estimation with Machine Learning : A transparent approach using tree-based models

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Elias Hadad; Angus Wigton; [2023]
    Keywords : Machine learning; Expected credit loss; Probability of default; ECL; PD; Risk Management; Credit Risk Management; Default Estimation; AI; Artificial intelligence; Fintech; Supervised learning; Decision tree; Random forest; XG boost; Transparency; Machine learning transparency;

    Abstract : In recent years the development and implementation of AI and machine learning models has increased dramatically. The availability of quality data paving the way for sophisticated AI models. Financial institutions uses many models in their daily operations. READ MORE