Contributions in Merlin

We use a survey approach to learn about valuation professionals’ choices and implementations of valuation techniques in practice. Most use both multiples and DCF, but implement DCF in a way that almost turns it into a multiples exercise. Confusion reigns with respect to interest tax shields and the WACC. Higher educational levels do not reduce the confusion. The survey design allows us to control for a respondent’s professional subgroup(e.g., consulting), education, experience, and valuation-purpose characteristics. We find that profession matters more than education; different professions have different valuation cultures. Other factors are less important. The relative unimportance of education raises questions about the role, benefit, and optimal mode of higher level finance education.

null

Social decision-making is increasingly studied with neurocomputational modeling. Here we discuss how this approach allows researchers to better understand and predict behavior in social settings. Using examples from the study of resource distributions and social learning, we illustrate how this methodology provides a flexible way to quantify social values and beliefs, identify specific motives and cognitive processes underlying social choice and learning, and arbitrate between competing theories of social behavior. We also critically discuss open questions and potential problems associated with this methodology.

Peer code review is a practice widely adopted in software projects to improve the quality of code. In current code review practices, code changes are manually inspected by developers other than the author before these changes are integrated into a project or put into production. We conducted a study to obtain an empirical understanding of what makes a code change easier to review. To this end, we surveyed published academic literature and sources from gray literature (e.g., blogs and white papers), we interviewed ten professional developers, and we designed and deployed a reviewability evaluation tool that professional developers used to rate the reviewability of 98 changes. We find that reviewability is defined through several factors, such as the change description, size, and coherent commit history. We provide recommendations for practitioners and researchers. Preprint [https://pure.tudelft.nl/portal/files/45941832/reviewability.pdf]. Data and Materials [https://doi.org/10.5281/zenodo.1323659].

We introduce time-varying systematic yield risk (SYR) and systematic liquidity risk (SLR) measures for sovereign bond markets of the major European Monetary Union (EMU) country members. Using daily sovereign bond data, our analysis shows that trend components of both types of risk are strongly positively correlated. Vector auto-regression and generalized impulse response analysis reveal that shocks to the SLR has significant impact on SYR lasting up to 5 days, whereas shocks to the SYR has no significant impact on SLR. Since mid-2015, both risks are gradually increasing and as of 2018, they are at their highest levels over the last five years.

Previous experiments have shown that the possibility to punish liars does not per se increase honesty in principal-agent relationships. In this study, we first establish a punishment mechanism that substantially enhances honest behavior and trust in a sender-receiver game: the possibility to impose severe sanctions that are cost-free for enforcers. Adopting this effective mechanism, we investigate how variations in the probability of detecting lies affect sender and receiver. We find that high honesty levels persist under such punishment mechanism even when the detection probability is significantly reduced. Furthermore, the relationship between monitoring and honesty does not follow a linear trend, as a moderate monitoring level proves to be less effective in enhancing honesty than high or very low levels. The punishment mechanism has an even more robust effect on receivers, showing similarly high levels of trust independently of the detection probability. Our analysis of subjects’ beliefs provides further insights into the mechanics behind these behavioral patterns.

Analyzing data streams is a vital task in data science. Often, data comes in different shapes such as triples, tuples, relations, or matrices. Traditional stream processing systems, however, only process data in one of these formats. To enable the processing of streams combining different shapes of data, we developed a system that parses SPARQL queries using the Apache Jena parser and transforms them to Apache Flink topologies. With a custom data type and tailored functions, we enabled the integration of matrices in Jena and therefore allowed to mix graphs, relational, and linear algebra in an RDF graph. This provided a proof of concept that queries may be written for static data and – with the usage of the streaming engine Flink – can easily be run on data streams, even if they contain multiple of the aforementioned types.

null