Sökning: "Factor scores"
Visar resultat 6 - 10 av 121 uppsatser innehållade orden Factor scores.
6. Unveiling the Impact of ESG Ratings on Risk-Adjusted Returns : Evidence from European Companies
Master-uppsats, Uppsala universitet/Företagsekonomiska institutionenSammanfattning : This study uses a sample of 600 companies from Europe to investigate the risk-adjusted returns of four portfolios with high and low ESG ratings between 2011 and 2021. Four asset pricing models and additional measures for risk and return are tested on different portfolio weights. LÄS MER
7. How Does ESG Impact Firms’ Financial Performance: Empirical evidence from European companies
Magister-uppsats, Lunds universitet/Företagsekonomiska institutionenSammanfattning : This study investigates the relationship between ESG factors and corporate financial performance (CFP) by using data from listed companies in the European market and Thomson Reuters’ ESG scores. The analysis reveals a complex picture, with mixed results for different ESG components. LÄS MER
8. The Relationship of Training Frequency and Wilks Score in Competitive Swedish Classic Powerlifters : A Quantitative Questionnaire Study
Kandidat-uppsats, Gymnastik- och idrottshögskolan, GIH/Institutionen för fysiologi, nutrition och biomekanikSammanfattning : Background: In strength sports, athletes must take several training variables into consideration whencreating a training program. One of the ground pillars is training frequency, but there is a lackof research done on competitive powerlifters. LÄS MER
9. Fibroblasttillväxtfaktor-23 (FGF-23) – en möjlig biomarkör för osteoartrit hos katt?
Master-uppsats, SLU/Dept. of Clinical SciencesSammanfattning : Osteoartrit är en kronisk obotlig ledsjukdom som är vanlig hos katt. Trots att osteoartrit är associerad med smärta kan sjukdomen hos katt vara svår att diagnosticera. LÄS MER
10. Using Social Media and Personality Predictions to Anticipate Startup Success
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : This thesis explores the potential of integrating predicted founder personalities, based on the Big 5 Personality Framework, into Machine Learning (ML) models to enhance the accuracy of early-stage startup success predictions. Leveraging Natural Language Processing (NLP) techniques, we extracted personality insights from founders' tweets, focusing on US startups funded between 2013 and 2015. LÄS MER