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Nabil Nakbi

Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS

Doctoral Thesis / Dissertation from the year 2021 in the subject Economics - Finance, grade: Distinction (4.81), Wilberforce Island (Post Graduate School), course: Banking and Finance, language: English, abstract: The study examines the determinants of access to and usage of financial services in sub-Saharan Africa. The study employed panel data spanning 2006 to 2018 from 12 sub-Sahara African countries. The ex-post facto research design and econometric techniques were employed to explain the determinants of financial inclusion. The descriptive statistics was employed to describe the nature of the data. The LLC and IPS tests of stationarity indicated that all the variables were stationary at first difference. The Pearson pairwise correlation test revealed that the strength of association among the regressors were weak. The panel data was estimated using Pooled OLS, Fixed Effects, and Random Effects panel estimation techniques. The findings show that aside GDP per capita and political stability that had insignificant effect on access; money supply and regulatory quality had a significant effect on access to financial services. Furthermore, the findings suggest that aside from regulatory quality and deposit rate, GDP per capita and automated teller machine exerts a significant effect on financial usage. The study further reveals that money supply and lending rate significantly stimulates usage of loan accounts, while regulatory quality was insignificant. The study concludes that a combination of macroeconomic, institutional, and bank-level factors determine the level of financial inclusion in sub-Saharan Africa. The study recommends that regulators should encourage a conducive environment that spurs competition and innovation in the private sector. This may encourage financial access and usage due to economic growth and rising GDP per capita levels. Regulators should adopt policies like Regulatory Impact Assessment (RIA) in order to enhance the regulatory quality and boost public confidence. Regulators and banks should encourage the expansion of the number of functional point of service outlets in the region. Finally, government agencies should formulate policies that would encourage socio-political stability for the financial service industry to strive in the region.

Dumani Markjackson

Determinants of Financial Inclusion in Sub-Saharan Africa

Tamilla Javadi

Tesla, Inc. Strategic Expansion and Value Creation through the Gigafactory Berlin-Brandenburg

 Anonymous

Warum wurde die Vermögensteuer in Deutschland de facto abgeschafft und welche Gründe sprechen für und gegen die Wiedereinführung der Vermögensteuer?

Vermögensteuer in Deutschland

Studienarbeit aus dem Jahr 2012 im Fachbereich VWL - Finanzwissenschaft, Note: 2,0, Bergische Universität Wuppertal (Wirtschaftswissenschaften), Veranstaltung: Seminar Finanzwissenschaft, Sprache: Deutsch, Abstract: Diese Seminararbeit befasst sich mit Ursachen, Zusammensetzung und Entwicklung der kommunalen Verschuldung in Deutschland. In der Einleitung wird ebenfalls Bezug auf die Staats- und Länderverschuldung und die Zusammenhänge mit der Kommunalverschuldung genommen. Im weiteren Verlauf werden der Einfluss der Schuldenbremse sowie Verbesserungsansätze vorgestellt. Ebenfalls wird eine Gegenüberstellung der beiden Städte Wuppertal und Düsseldorf hinsichtlich ihrer unterschiedlichen Verschuldung vorgenommen.

Anna Rottke

Staatsverschuldung in Deutschland. Die kommunale Verschuldung

M. Arul Jothi

Assessing the Impact of Capital Structure on Profitability. A Case Study of Indian Railways Finance Corporation (IRFC)

Political Science and International Studies (R0)

Natalie Rauscher

Welf Werner

Making Sense of Risks and Vulnerability

Natural Disasters in the United States

Business and Management (R0)

Fausto Pedro García Márquez

Benjamin Lev

Huchang Liao

Cases and Studies in Using Operations Research and Management Science Methods

Outsourcing

Erdem Dogan

Eine empirische Analyse mittels SPSS

Der Zusammenhang zwischen Operationsfrequenzen und der Behandlungsqualität in öffentlichen und privaten Kliniken

Contributions to Finance and Accounting

This book discusses the evolution of technical features in decentralized finance and focuses on machine-learning finance in emerging economies. As technological advancement evolves at an unpredictable pace, the financial industry, like every other sector, must adapt accordingly. Furthermore, the rapid expansion of diverse financial products and services is creating new applications and markets. Alongside technological progress, the exploration of complex patterns in vast amounts of data, known as big data, is facilitated by its commonly acknowledged characteristics: volume, variety, veracity, value, and velocity.

Overall, machine learning has become crucial in the financial industry, allowing businesses to automate operations, gain insights from data, and make more informed decisions in real time. This edited book covers algorithmic trading, risk management, fraud detection, customer service and personalization, portfolio management, credit scoring, sentiment analysis, and algorithmic pricing. The book connects theoretical concepts with practical real-world applications, benefiting professionals looking to enhance their proficiency in using these methods efficiently. It offers insightful guidance for theorists, market participants, and policymakers by exploring financial theories and practices in light of contemporary machine-learning approaches, with a special emphasis on emerging economies.

This book discusses the evolution of technical features in decentralized finance and focuses on machine-learning finance in emerging economies. As technological advancement evolves at an unpredictable pace, the financial industry, like every other sector, must adapt accordingly. Furthermore, the rapid expansion of diverse financial products and services is creating new applications and markets. Alongside technological progress, the exploration of complex patterns in vast amounts of data, known as big data, is facilitated by its commonly acknowledged characteristics: volume, variety, veracity, value, and velocity.Overall, machine learning has become crucial in the financial industry, allowing businesses to automate operations, gain insights from data, and make more informed decisions in real time. This edited book&nbsp;covers algorithmic trading, risk management, fraud detection, customer service and personalization, portfolio management, credit scoring, sentiment analysis, and algorithmic pricing. The book connects theoretical concepts with practical real-world applications, benefiting professionals looking to enhance their proficiency in using these methods efficiently. It offers insightful guidance for theorists, market participants, and policymakers by exploring financial theories and practices in light of contemporary machine-learning approaches, with a special emphasis on emerging economies.

Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS

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