We found that over the past 15 years, private equity has outperformed both the S&P 500 and the MSCI World. There is a decreasing edge over public equities that can be partly explained by the increasing multiples and risk that PE funds assume.n While public equities are prone to large swings, private companies were able to conduct business as usual and operate without the public pressure that frequently resulted in ad-hoc strategic changes detrimental to company's long-term per-formance.n Geographic specialization adds no value and has no impact on the performance of buyouts. But the current study supports the outperformance of industry-specialized funds. Industry specialization results in higher return multiples.

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

This paper re-examines empirical lead-lag relationships in stock portfolios sorted by size, analyst coverage and institutional ownership across seven major developed markets. We find that lead-lag relationships continue to exist in a majority of countries. A simple trading strategy that exploits the return predictability based on lead-lag relationships yields significant abnormal returns in several markets. However, the abnormal returns quickly decline when transaction costs are introduced and become insignificant for one-way transaction costs of more than 40 basis points. Thus, lead-lag relationships are probably not exploitable in practice and will continue to exist in the future.

For any corporation the chain from a business idea to a winning business model is highly challenging -even in high-potential markets. In the following work a mix of scientific methods like lean management, traditional and new established ones is used to investigate the process of business model generation. Therefore a theoretical framework on existing business modelling tools is used to develop a methodology for business model generation. In a case study, this methodology is implemented in the ABB building control product line searching for winning business models in the area of energy management solutions and its feasibility is evaluated afterwards resulting in three business models. Finally, it is shown to what degree even product lines in large corporations can act like innovative and agile start-ups.