Optimized Software Licensing – Combining License Types in a License Portfolio

Although software licenses usually range among the most expensive items within the IT budget, they still lack the necessary attention by many companies. Therefore, most companies inadequately have implemented their software asset management neglecting further potential for cost reduction, which can be obtained by optimizing the use of different license types. This paper shows how possible savings can be realized by combining different types of licenses in a license portfolio. The model presented is based on the most common license types considering different user groups as well as their behavior. Additionally to cost risks, the risk of service quality is also taken into consideration. The following examples illustrate the model’s high relevance and show how it can be applied in practice. Accepted after two revisions by Prof. Dr. Hasenkamp. This article is also available in German in print and via http://www.wirtschaftsinformatik.de: Gull D, Wehrmann A (2009) Optimierte Softwarelizenzierung – Kombinierte Lizenztypen im Lizenzportfolio. WIRTSCHAFTSINFORMATIK. doi: 10.1007/11576-009-0182-x.     

What Drives Micro-Angel Investments?

Despite of the significant role of informal venture capital in the financing of new entrepreneurial ventures, there is little research explaining the factors determining the propensity of individuals to make microangel investments. Building on two theoretical frameworks, a social psychological theory of planned behavior and an economic theory on the determinants of demand for risky assets in household portfolios, we develop a set of hypotheses predicting the propensity of individuals to make informal investments in new businesses owned by others. In our analysis we test whether the determinants of micro-angel investments are similar when investing in a business owned by a close family member versus more distant business. The hypotheses are tested using data from 6007 interviews of Finnish adults carried out in the Global Entrepreneurship Monitor program in 2000–2002. The findings show that the theoretical frameworks have more power in explaining investments in firms not owned by close family members. The study provides new understanding of the differences in the drivers of different types of micro-angel investments.     

MARKOWITZ'S PORTFOLIO OPTIMIZATION IN AN INCOMPLETE MARKET

In this paper, for a process S , we establish a duality relation between K_p , the     - closure of the space of claims in     , which are attainable by "simple" strategies, and     , all signed martingale measures     with     , where   p ≥ 1, q ≥ 1  and     . If there exists a     with     a.s., then K_p consists precisely of the random variables     such that ϑ is predictable S -integrable and     for all     . The duality relation corresponding to the case   p = q = 2  is used to investigate the Markowitz's problem of mean–variance portfolio optimization in an incomplete market of semimartingale model via martingale/convex duality method. The duality relationship between the mean–variance efficient portfolios and the variance-optimal signed martingale measure (VSMM) is established. It turns out that the so-called market price of risk is just the standard deviation of the VSMM. An illustrative example of application to a geometric Lévy processes model is also given.     

Mean Reversion in the Short Horizon Returns of UK Portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.     

Customer and Supplier Portfolios: Can Credit Risks be Managed Through Supply Chain Relationships?

While supply chain risk has been the subject of an ever‐increasing amount of research attention, the importance of credit risk has been less studied, at least by supply chain researchers. Yet, there may be risks inherent within a firm's portfolios of supply chain relationships that are manifested in the credit risk of a firm. Moreover, portfolio characteristics may serve as a signal to the external market regarding these risks. While customer and supplier portfolio characteristics may impact the firm's exposure to risks, the specific issue of how portfolio characteristics relate to credit risk has rarely been examined by supply chain scholars. This research bridges extant works in supply chain management and finance to relate supply chain characteristics to a critical reputational outcome, namely credit ratings. In this research, we utilize a sample of firms that recently underwent an initial public offering to empirically examine the theoretical predictions of Resource Dependence Theory regarding the relationships between different supply chain portfolio characteristics and credit risk.