Refine
Has Fulltext
- yes (47)
Document Type
- Master's Thesis (25)
- Bachelor Thesis (22)
Is part of the Bibliography
- yes (47) (remove)
Keywords
- BRICS (1)
- Behavioral Finance (1)
- Business Model Canvas (1)
- Case Studies (1)
- Commerzbank (1)
- Common Risk Factors (1)
- Covid-19 (1)
- Digitalisierung (1)
- ESG Rating (1)
- Emerging Markets (1)
Studies on the empirical validation of beta-return relationship postulated in the CAPM has a relatively long history. The first study was conducted by John Lintner in 1965, shortly after the introduction of the model. In one of the recent studies, Malcolm Baker (forthcoming) examines two portfolios, one consisting of the 30% of U.S. stocks with the lowest beta, another of the 30% with the highest beta. By the end of the period, the low-beta portfolio significantly outperforms the high-beta portfolio (Economist, 2016).
Following two recognized testing methods developed by Lintner (1965) and Fama/MacBeth (1973), this bachelor thesis examines the nature of the relationship between beta and return for the German stock market. The sample periods observed in this study range from January 1973 to May 2016 depending on the testing method. The obtained results are then compared with the outcome of the previous studies for the European stock market. The analysis reveals that the relationship between beta and return is insignificant in most of the models. These results are consistent with the European stock market, if the unconditional cross-sectional analysis is considered.
The digital currency Bitcoin offers investors high returns
and a low correlation with other asset classes. However, Bit-
coin's unusually high volatility raises doubt about its eligibility
for investors.
The objective of this thesis is to ascertain, if the market risk
of Bitcoin can be adequately forecasted with the prevalent risk
measures Value-at-Risk and Expected Shortfall. To this end, an
empirical analysis is performed, which applies commonly used
techniques of risk modeling on seven years of Bitcoin return
data. Forecasts for VaR and ES are backtested and the results
compared with those of other asset classes.
The empirical results show, that although Bitcoin's fore-
casts perform significantly worse than those of other asset classes,
risk models with conditional volatility are able to estimate re-
liable VaR and ES for Bitcoin. Other findings incluce the inef-
fectiveness of historical simulation models and the importance
of the assumed distribution of returns.
This paper prices risk factors in the Capital Asset Pricing Model (CAPM) to
explain portfolio returns of the German stock market. Using a two-part
regression procedure, we show that beta exhibits slight significance in
capturing the variations of asset returns. When higher co-moments and Fama
French factors are added to the model, we find a moderate improvement in the
significance levels of all risk factors and in the overall explanatory power of
the model. Moreover, following the conditional beta method employed in
Pettengill et al. (1995), we show that risk factors perform fairly well in crosssection
settings, especially in the down-market condition. Our chosen long
time horizon shows that the composite model with all factors included
performs better in post-recession periods.
This thesis undertakes an investigation on the potential impact of FDI on Balkan countries economic growth after the 90‘. The main purpose is to analyze whether inward FDI had been a determinant of growth for this region leading to the FDI-led growth hypothesis. The empirical procedure relies on a multivariate VAR approach and Granger causality test to check for a possible causal relationship. The main finding suggests evidence of a weak causality running from economic growth to FDI. Furthermore, possible reasons are analyzed why the FDI-led growth hypothesis was not possible to be supported. Lastly, potential policy recommendations are proposed in order to assist the countries in tackling various challenges and benefiting more from FDI inflows in the future.
The area of scientific research around the relationship between stocks and macroeconomic activity has been of great interest for scholars, especially after the introduction of the Arbitrage Pricing Theory. Such macro variables as industrial production and long-term interest rates, are expected to influence the stock price through the firms’ expected cash flows and the discount rate (Rapach, et al., 2005: 137).
Following this body of research, this master thesis examines the relationship between stock returns and six macro variables in the German stock market. The applied methodology ranges from standard OLS regressions with different leads of macro variables to more advanced time-series techniques.
Overall, the explanatory power of OLS regressions is quite low meaning that only a small fraction of stock returns is explained by the selected macro factors, even if some of them are statistically significant. The findings from Granger causality and Johansen cointegration tests are more conclusive but should be taken with caution
In the last recent years, Bitcoin along with many other cryptocurrencies has grown and developed into multibillion dollar industry. Cryptocurrencies are perceived or even misunderstood by many people and experts as purely speculative assets, which have unpredictable values and extreme volatility. The public opinion is separated into two sides: the first side considers cryptocurrency, respectively, Bitcoin as one of the biggest financial bubble and the second side thinks of cryptocurrency as the future which has great potential to revolutionize the financial industry. In fact, the revolution initiated by digital currency does not lie in the transaction value or short-term earning opportunities for cryptocurrency investors, but rather in the underlying technology behind it called „Blockchain”. Since the very beginning of currency and private infor-mation, the existence of thieves and fraudster has always been problematic. Due to the complexity of fraud and the serious risks that fraud presents to business, fraud detection and fraud prevention is often conceived as cat-and-mouse game between companies and fraudsters. Admittedly, there is no completely fool-proofed system or technology, on the other hand fraudsters nowadays constantly change and develop their technique in order to bypass even the most complex security system. Block-chain is a shared distributed ledger that is immutable and resistant to tampering. Only verified contributors by blockchain are allowed to store, view and share digital infor-mation in a security-enhanced environment. The three following features of block-chain: distribution, immutability, permission which help to maintain trust, accountabil-ity and transparency in business relationships. This cutting-edged technology called blockchain has the capabilities and potentials to provide companies and organiza-tions the ultimate fraud prevention solution which they have been waiting for in a long time.
“The technology likely to have the greatest impact on the next few decades has ar-rived. And it is not social media, it’s not big data, it’s not robotic, it’s not even AI. You will be surprised to learn that it is the underlying technology of digital currencies like Bitcoin. It is called the blockchain – Blockchain.”.
Don Tapscott (Tapscott, 2016)
In countries such as the UK and Australia, Public-private partnerships (PPPs) have become the preferred tool for the public sector to procure infrastructure. With this development it has become necessary to assess PPPs regarding their success, and it seems that research has been insufficient to this point. This thesis will give an in-depth insight into the field of success measurement for PPPs. First, it explains the theory behind PPPs by explaining the theory. Then, in the next step, a framework for success measurement in PPPs will be derived based on this theory. It will serve as the basis for the Success Measurement System (SMS) that will be developed. In this regard, the thesis adds value to existing research by using a fully encompassing approach. The SMS will allow for a sound measurement of success for PPPs, which includes a measurement of success for different stakeholders, different phases and categories.
In the main part of this thesis, the system is applied on eight transport PPPs through case studies. The insights, from these case studies, will be used to answer the following questions: First, are PPPs in general successful and should governments continue their implementation? Second, in which areas do PPPs fail? This is especially relevant to improve outcomes of PPPs in the future and to enhance PPP policies. Third, how can PPPs be compared with each other? In this regard, the results of the case studies will serve as a benchmark for future PPPs.
In regard of the first and the second question, the research has shown that PPPs are in general successful but often fail to meet cost and time targets. In addition, sometimes problems were encountered during the procurement phase, contract management and risk allocation. These are the areas where the public and the private sector should focus to improve outcome of future PPPs. The results of the SMS assessment led to an average success rate which can be used as a benchmark to compare outcomes of other PPPs.
Despite its simplicity, the yield curve is one of the best predictors of future economic activity. Empirical studies suggest that the yield curve is capable of forecasting recessions in major economies. In this paper, the relationship between the yield curve and stock bear markets will be studied with the focus on predicting bear markets in the U.S. and Germany. Also this paper seeks to answer the question if a market-timing strategy, based on yield-curve information, can outperform the market.
The results of this study suggest that for the U.S. the spread between 10-year and 1-year interest rates outperforms other spreads in predicting bear markets. Furthermore, the yield spread can be used to profitably time the market and outperform a buy-and-hold strategy.
For the Germany yield curve, the study has found a statistical significant relationship between the yield curve and bear markets. However, depending on the observation period, the forecasting ability differs tremendously. For the entire period, the yield curve was not able to predict local bear markets reliably, nor was it possible to use the information contained in the yield curve to outperform the stock market.
The aim of this study is to analyze if Turkish firms apply a market timing strategy on their financing decisions and to analyze the persistence of the market timing decision on their capital structure. The sample data of this study contains the 85 initial public offers from 2010 to 2015 in Istanbul Stock Exchange(BIST). Regression analysis method is used for testing the relationship between market timing and equity issues, and short-run and long-run effect of market timing. The year before IPO and the three subsequent years after the IPO considered for analyzing the impact of market timing. The results of this study show that there is a positive relationship between market timing and equity issues. Firms that are decided to go public in “hot” market periods, issue more equites and reduce their leverage ratio sharply right after the IPO, and this relationship shows an impact on capital structure only in short run. This short-term impact of market timing starts vanishing after the second year of going public.
Most of the studies on factor models have been based on the U.S. stock market. Results of these studies have shown that conventional factor models explain the vast part of its return variation and that each of these models strengthens the descriptive power of the traditional CAPM. However, when applying factor models on the stock markets of emerging countries, studies have yielded rather mixed results. Even though most of them find that conventional factor models explain a major part of their returns, they argue that other country-specific risk factors should also be considered, in order derive a model that describes their returns in the most comprehensive way. For this reason, this paper investigates the significance of both common and specific risk factors, in order to find which are the most important risk factors that impact the performance of the stock markets in the BRICS countries. The effect of systematic co-moments is also tested.
Empirical evidence shows that the market premium is the most significant risk factor for all of the BRICS countries. Additionally, the value, size and momentum factors are proven to be insignificant, whereas the investment and profitability factors are among the main determinants of stock returns. Specific factors also appear to be more important than the common one. Moreover, the popular extensions of the CAPM and higher-order moments do not substantially strengthen its descriptive power. Additionally, conventional factor models explain a lower part of the return variation of the BRICS countries compared to the U.S. stock market, indicating that they do not possess the same explanatory power in developed and emerging markets.
After the fall of Lehman Brothers, systemic risk which triggers the whole financial system has gained more attentions from researchers. Recently, graph theory is applied to measure this risk. DebtRank algorithm is one of the network based models which illustrates
the on going shock propagation when no default occurs. Knowingly the important of systemic risk, this paper captures the broad picture of a potential interbank network of ASEAN region in the recent period 2013-2017 using DebtRank. This is done by assign a stress of different levels on external assets of banks by two scenarios: simultaneously and individually. Networks are constructed based on probabilities and the desired density of the network is found to be 10% of total possible links. The first case shows that the studied banking system in ASEAN is stable as systemic risk has the falling pattern. However,
the rising figure in 2017 implies the system in this year is less stale than previous years.
Moreover, the declining of loss caused by contagion is due to the decrease of interconnectedness and rise in capital during this period. Furthermore, the total loss calculated in a range of external shocks is a concave curve. In the second scenario, the results show that the most harmful banks are, at the same time, most fragile one. Besides, there are evidence of high dependence of the individual impact on systemic risk of the system and the vulnerability on bank size and connectivity.
The FinTech industry is very dynamic and the multitude of innovative business models created by these new entrants has increased over the past years. FinTechs display a competitive advantage in the field of technology, agility and customer-centricity that traditional banks cannot compete with. However, the regulatory requirements often pose a challenge for FinTechs to grow and expand their business models. Nevertheless, in recent years the phenomenon of BaFin licensed FinTechs such as N26 or Solaris Bank became apparent in Germany.
Since this topic is of high actuality no studies can be found on the topic of licensed FinTechs yet. Hence, this research paper will examine how the business models of these BaFin licensed FinTechs is constructed and what impact financial licenses by the BaFin can have on these business models and their positioning in the financial services industry. The focus thereby lies on FinTechs with a BaFin license active in the B2B-sector only. Based on an interview series conducted with relevant experts from different licensed FinTechs the findings show that the financial license allows these firms to become an independent entity, expand their product offering and strengthen their market position in the financial services industry.
Die folgende Arbeit untersucht das Geschäftsmodell des Fintech N26 hinsichtlich seiner Wettbewerbsfähigkeit gegenüber der Commerzbank.
Dabei wird eine deskriptive Fallstudienanalyse der benannten Forschungsobjekte nach dem Strukturierungsrahmen des Business Model Canvas durchgeführt, sodass Rückschlüsse auf die Wettbewerbsfähigkeit möglich sind. Die qualitative Vorgehensweise soll dazu dienen der Forschungslücke bezüglich der neuen Fintechs zu schließen.
Aus der detaillierten Auseinandersetzung geht die Unterschiedlichkeit der Geschäftsmodelle hervor. Das innovative Fintech N26 kreiert durch die Implementierung von Finanzdienstleistungen anderer Unternehmen eine ganzheitlich funktionelle Banking Plattform, die digital über eine App gesteuert werden kann. Dadurch schafft N26 ein besonders benutzerfreundliches, flexibles und mobiles Banking. Die Commerzbank differenziert sich durch die persönliche Beratungs- und Betreuungsleistung in stationären Filialen. Das Leistungsprogramm der Bank ist durch Qualität und Quantität ausgezeichnet.
In seinem Geschäftsmodell ist die klassische Bank besonders am Kunden bzw. Absatzvolumen orientiert. Im Kontext der Digitalisierung verliert die Präsenzstruktur jedoch an Signifikanz. Es ergibt sich bei der Commerzbank, dass eine Geschäftsmodellinnovation erforderlich ist, um die Wettbewerbsfähigkeit zu gewährleisten. Im Rahmen der Abschlussarbeit geht hervor, dass das Fintech N26 seine Wettbewerbsfähigkeit gegenüber der Commerzbank durch seine Nutzerorientierung schafft. Dabei sind die Elemente des Geschäftsmodells zueinander so konsistent gestaltet, dass diese den Interessen und Ansprüchen des Zielkundensegments, Generation Y, optimal zugeschnitten sind. Folglich sichert das Fintech die Wettbewerbsfähigkeit im Retail Banken Markt durch Value Innovation.
Bezahlbarer Wohnraum Welcher Anteil des privaten Haushaltseinkommens wird für Wohnen ausgegeben?
(2020)
In this research Cash Conversion Cycle was used to examine working capital management in the supply chain of the automotive industry during 2006–2016. The results of this study reveal patterns and trends in working capital developments over time, which can be used for further managerial analyses and strategy planning as for the single company and for the supply chain level. There was no substantial change in the overall cycle time for the whole industry, but rather the changes happened among the supply chain representatives. During the economic downturn bigger companies took over the credit risk and capital costs, by extending their days sales outstanding. Overall trends show strong regional differences in the levels of Cash Conversion Cycle and its elements.
Are Real Estate Investment Trust considered a safe investment choice in times of financial shock?
(2021)
This paper focuses on real estate investment trust (REIT), one specific asset class in the real estate sector, and intends to answer the following research question: are real estate investment trusts a safe choice for investors, particularly during financial shocks? This study re-examines the relationship between public-traded U.S. REIT and other asset classes by implementing statistical dependence analysis and OLS regression to determine whether and how they follow other markets in times of ambiguity. This study's final results indicate that public-traded U.S. REIT investment should not be considered a safe investment during crisis times because it follows the equity market.
This thesis analyzes the stock prices of German companies during the Covid-19 pandemic. The first part of the thesis concentrates on the theoretical background on stock prices, their value, and how investors make an investment decision, which is all covered by academic literature. In the second part of this thesis, the author answers the question, whether investors experienced herding behavior on the German stock market during the Covid-19 pandemic, by conducting multiple empirical tests. Here, the methodology uses daily closing prices and cross-sectional absolute deviations to conduct regression models, which test specifically for herding behavior. The results imply that investors did not significantly herd around the market during the observation period from 1st June 2019 to 31st May 2021. Due to the never-before-experienced global health crisis, analysts and investors faced great uncertainty. They focused more on private information, because the opinions of others changed rapidly, just like the surroundings during the pandemic. This paper provides key insights about investors herding behavior during the last two years in Germany.