Explaining the demand for structured financial products: survey and field experiment evidence

In many countries structured investment products are popular among retail investors. We explain the demand for these products using unique field data where we let subjects freely design their “favorite” structured product. Results suggest that the supply with capital protected products (guarantee certificates) might indeed be demand-driven. This does not seem to be the case for other product categories where marketing and sales practices might play a more important role. In a survey among financial practitioners we find furthermore that a demand for capital protected products can be explained by loss aversion and saving motifs, e.g. for buying a house.     

Zum Einfluss der Inhaberführung auf die Betriebsrat-Geschäftsführer-Beziehung – Eine theoretische und empirische Analyse in mittelständischen Unternehmen

This paper examines the quality of the relationship between business managements and works councils in small and medium-sized enterprises and strives for answering the question, which factors determine the quality. As ownership is an important determining factor of the establishment of works councils in small and medium-sized businesses, especially the impact of ownership on this relationship has been investigated. Agency theory and stewardship theory give hints that the owner management can have different impacts on this relationship. In order to test these contradictory hypotheses we have estimated a Heckman selection model to investigate which factors determine the quality of the relationship between business managements and works councils. Our results based on data generated from a survey in small and medium-sized enterprises conducted by the IfM Bonn in the years 2005 and 2006 indicate that ownership has a negative influence on the establishment of works councils, but a positive one on the quality of the relationship between business management and works council. Thus, the hypotheses predicting the existence of stewardship behaviour among owners and the existence of agency behaviour among managers can be confirmed.     

Conservative accounting as a signaling device in debt covenants

This paper provides a signaling explanation for benefits of conservative accounting in a debt contracting setting. It develops a model in which firms want to finance more or less risky projects through debt, which is private information. Creditors price the risk on average riskiness, which implies a relatively high interest rate for low-risk firms. These firms can signal their low-risk type by implementing a conservative accounting system. The accounting signal is used in a debt covenant that stipulates early repayment in case of a bad signal. Thus, the low-risk firms benefit from a lower interest cost, but at the cost of higher risk of being forced to liquidate a profitable project. The analysis establishes conditions for separating and pooling equilibria and determines their individual and social welfare implications. It shows that conservative accounting by low-risk firms can even increase social welfare if it mitigates underinvestment.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Digital Finance and FinTech: current research and future research directions

Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction—delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.     

Are sponsors blamed for edging off? Consumer reactions to sponsorship terminations

Firms regularly terminate sponsorships, even without publicly known misconduct by the sponsee such as athlete doping. Consumer reactions to these sponsorship terminations by firms have not been studied despite being a regular occurrence. Using a set of experimental studies, this paper analyzes consumer reactions to these sponsorship terminations (i.e., early and non-renewal) that were not caused by a sponsee’s misconduct, the underlying process that causes the reactions, and the role of several moderating factors (trust, power balance, and locus of control). Our findings reveal that sponsorship terminations have a negative effect on sponsors’ brand images—particularly early terminations that occur before the end of a contract—because consumers perceive these sponsorship terminations as unfair. The results also suggest that a termination is particularly harmful for the sponsor’s perceived fairness if the sponsor is powerful and if the termination decision is under the sponsor’s control. Further, the termination effect is particularly strong for firms that consumers trust.     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross‐contamination. We therefore developed a stochastic model to quantitatively describe cross‐contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross‐contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to ?0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.     

Unterstützung dualer Karrieren im Spitzensport

The longstanding focus of elite athletes on top sports entails disadvantages hampering their later entry to the world of work. In order to successfully contribute their outstanding personality traits to a later job, athletes are thus particularly required to care for their employability. Contributing to this, the author carried out a competency balancing with top athletes. This coaching method significantly decreased the athletes’ need for self-information. Evaluation further showed indications of increased clarity and certainty of occupational goals as well as work satisfaction.