Sökning: "Financial Time Series"

Visar resultat 16 - 20 av 251 uppsatser innehållade orden Financial Time Series.

  1. 16. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

    Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Oskar Nilsson; Benjamin Lilje; [2023]
    Nyckelord :Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

    Sammanfattning : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. LÄS MER

  3. 17. The Development of Debt Policies : A Case Study of Investor’s and Industrivärden’s Portfolio Companies

    Master-uppsats, Uppsala universitet/Företagsekonomiska institutionen

    Författare :Alma Karlsson; Jenny Olsson; [2023]
    Nyckelord :debt; debt policies; ownership; investment companies; time series; content analysis;

    Sammanfattning : Debt financing can be seen as both an opportunity to increase profits as well as a financial risk and is thus an important issue for company owners to consider. This study examines the portfolio companies of the investment firms Investor and Indsutrivärden, and how their debt policies have developed from 2004 to 2022. LÄS MER

  4. 18. Financing the Nordic Energy Transition: An Empirical Analysis of Leverage, Pricing and Return Expectations in Renewable Energy Transactions

    D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

    Författare :Franziska Grünthaner; [2023]
    Nyckelord :Buyouts; Leverage; Valuation; Renewable Energy; Infrastructure Investment;

    Sammanfattning : This study examines whether leverage and pricing in transactions of renewable energy infrastructure assets are impacted by the same factors that have been found to determine financial structures in buyout transactions. It primarily draws on a proprietary data set of 261 wind and solar photovoltaic (PV) transactions in the Nordics between 2011 and 2023 and explores the effect of acquirer-, asset-, and industry-specific characteristics as well as time-varying variables on leverage, pricing and return expectations. LÄS MER

  5. 19. Machine learning for detecting financial crime from transactional behaviour

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och system

    Författare :Markus Englund; [2023]
    Nyckelord :machine learning; deep learning; financial crime; time series; time series classification; XGBoost; maskininlärning; djupinlärning; finansiell brottslighet; tidsserier; klassificering av tidsserier; XGBoost;

    Sammanfattning : Banks and other financial institutions are to a certain extent obligated to ensure that their services are not utilized for any type of financial crime. This thesis investigates the possibility of analyzing bank customers' transactional behaviour with machine learning to detect if they are involved in financial crime. LÄS MER

  7. 20. Undersökning av möjligt nyttjande av outnyttjad kapacitet genom energieffektiviserande våningspåbyggnad på befintliga murade stommar

    Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Avdelningen för Konstruktionsteknik

    Författare :Rasmus Ekeroth; Isak Ekman; [2023]
    Nyckelord :Technology and Engineering;

    Sammanfattning : As a part of the Green Deal within the EU, as of the start of 2023, new demands have been put in place in an energy taxonomy on companies providing financial services, a category in which real estate companies are included. The investments of these companies are now to be classified as sustainable or not sustainable. LÄS MER