Essays about: "Risk classification model"

Showing result 16 - 20 of 90 essays containing the words Risk classification model.

  1. 16. 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

  3. 17. Machine Learning to predict student performance based on well-being data : a technical and ethical discussion

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Lucy McCarren; [2023]
    Keywords : Machine Learning; Data Science; Learning Analytics; Maskininlärning; Data Science; Inlärningsanalys;

    Abstract : The data provided by educational platforms and digital tools offers new ways of analysing students’ learning strategies. One such digital tool is the wellbeing platform created by EdAider, which consists of an interface where students can answer questions about their well-being, and a dashboard where teachers and schools can see insights into the well-being of individual students and groups of students. READ MORE

  4. 18. Investigating the Performance of Random Forest Classification for Stock Trading

    University essay from KTH/Skolan för teknikvetenskap (SCI)

    Author : Oscar Nordfjell; Gustav Ring; [2023]
    Keywords : Random Forest; Random Forest Classification; Stock Trading; Trading Strategy;

    Abstract : We show that with the implementation presented in this paper, the Random Forest Classification model was able to predict whether or not a stock was going to increase in value during the coming day with an accuracy higher than 50\% for all stocks included in this study. Furthermore, we show that the active trading strategy presented in this paper generated higher returns and higher risk-adjusted returns than the passive investment in the stocks underlying the strategy. READ MORE

  5. 19. An Investigation and Comparison of Machine Learning Methods for Selecting Stressed Value-at-Risk Scenarios

    University essay from Uppsala universitet/Avdelningen för systemteknik

    Author : Moa Tennberg; [2023]
    Keywords : Value-at-Risk; Total margin; Procyclicality; Machine learning; Binary classification; Supervised learning; Unsupervised learning; Random forest; Multilayer perceptron;

    Abstract : Stressed Value-at-Risk (VaR) is a statistic used to measure an entity's exposure to market risk by evaluating possible extreme portfolio losses. Stressed VaR scenarios can be used as a metric to describe the state of the financial market and can be used to detect and counter procyclicality by allowing central clearing counterparities (CCP) to increase margin requirements. READ MORE

  6. 20. Exploring the Feasibility of Exercise Detection on the Exxentric kBox Platform

    University essay from KTH/Medicinteknik och hälsosystem

    Author : Mahyar Mehr; [2023]
    Keywords : Machine learning; Flywheel training; Exercise detection; Signal processing; Exxentric kBox platform; Automatic exercise classification; Feature engineering; Maskininlärning; Träning med svänghjul; Upptäckt av träning; Signalbehandling; Exxentric kBox-plattformen; Automatisk träningklassificering; Funktionsutveckling;

    Abstract : Flywheel training is an increasingly popular training method that aids in the recovery process and promotes strength development while reducing the risk of re-injury. Additionally, automatic exercise classification offers athletes the convenience of effortlessly monitoring and tracking their training progress, enabling them to maintain consistency and achieve their fitness goals effectively. READ MORE