Credit default swap (CDS) prediction model & trading strategy

This project focuses on the study of different explanatory models for the behavior of CDS security, such as Fixed-Effect Model, GLS Random-Effect Model, Pooled OLS and Quantile Regression Model. After determining the best fitness model, trading strategies with long and short positions in CDS have been developed. Due to some specifications of CDS, I conclude that the quantile regression is the most efficient model to estimate the data. The P&L and Sharpe Ratio of the strategy are analyzed using a backtesting analogy, where I conclude that, mainly for non-financial companies, the model allows traders to take advantage of and profit from arbitrages.

Post-merger innovative performance in the renewable energy sector

Due to global warming and shrinking fossil fuel resources, politics as well as society urge for a reduction of green house gas (GHG) emissions. This leads to a re-orientation towards a renewable energy sector. In this context, innovation and new technologies are key success factors. Moreover, the renewable energy sector has entered a consolidation stage, where corporate investors and mergers and acquisitions (M&A) gain in importance. Although both M&A and innovation in the renewable energy sector are important corporate strategies, the link between those two aspects has not been examined before. The present thesis examines the research question how M&A influence the acquirer’s post-merger innovative performance in the renewable energy sector. Based on a framework of relevant literature, three hypotheses are defined. First, the relation between non-technology oriented M&A and post-merger innovative performance is discussed. Second, the impact of absolute acquired knowledge on postmerger innovativeness is examined. Third, the target-acquirer relatedness is discussed. A panel data set of 117 firms collected over a period of six years has been analyzed via a random effects negative binomial regression model and a time lag of one year. The results support a non-significant, negative impact of non-technology M&A on postmerger innovative performance. The applied model did not support a positive and significant impact of absolute acquired knowledge on post-merger innovative performance. Lastly, the results suggest a reverse relation than postulated by Hypothesis 3. Targets from the same industry significantly and negatively influence the acquirers’ innovativeness.