Contributions in Merlin

Open offices are cost-effective and continue to be popular. However, research shows that these environments, brimming with distractions and sensory overload, frequently hamper productivity. Our research investigates the use of virtual reality (VR) to mitigate distractions in an open office setting and improve one’s ability to be in flow. In a lab study, 35 participants performed visual programming tasks in four combinations of physical (open or closed office) and virtual environments(beach or virtual office). While participants both preferred and were in flow more in a closed office without VR, in an open office, the VR environments outperformed the no VR condition in all measures of flow, performance, and preference. Especially considering the recent rapid advancements in VR, our findings illustrate the potential VR has to improve flow and satisfaction in open offices

Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies’ domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors’ domain preference.

Background Chronic cocaine use has been consistently associated with decision-making impairments that contribute to the development and maintenance of drug-taking. However, the underlying cognitive processes of risk-seeking behaviours observed in chronic cocaine users (CU) have so far remained unclear. Here we therefore tested whether CU differ from stimulant-naïve controls in their sensitivity to gain, loss, and probability of loss information when making decisions under risk. Method A sample of 96 participants (56 CU and 40 controls) performed the no-feedback version of the Columbia Card Task, designed to assess risk-taking in relation to gain, loss, and probability of loss information. Additionally, cognitive performance and impulsivity were determined. Current and recent substance use was objectively assessed by toxicological urine and hair analysis. Results Compared to controls, CU showed increased risk-seeking in unfavourable decision scenarios in which the loss probability was high and the returns were low, and a tendency for increased risk aversion in more favourable decision scenarios. In comparison to controls, CU were less sensitive to gain, but similarly sensitive to loss and probability of loss information. Further analysis revealed that individual differences in sensitivity to loss and probability of loss information were related to cognitive performance and impulsivity. Conclusion Reduced sensitivity to gains in people with CU may contribute to their propensity for making risky decisions. While these alterations in gain sensitivity might directly relate to cocaine use per se, the individual psychopathological profile of CU might moderate sensitivity to loss information.

We investigate the effect of regulatory enforcement actions on banks' reputation by estimating the effect of non-compliance with laws and regulations among lead arrangers on the structure of syndicated loans. Consistent with a regulatory reputational stigma, a punished lead arranger increases her loan share to entice participants to continue to co-finance the loan. Consequently, when punished lead arranger initiates a new syndicated loan, then this loan tends to be more concentrated and co-funded by participants with previous collaboration with the lead arranger. However, the observed share increases by punished lead arrangers are seemingly mitigated by extending the loan guarantees, performance pricing provisions, and covenants.

Wer sein Geld klug investieren will, muss sich nicht nur mit Produkten beschäftigen, sondern vor allem mit sich selbst. Capital erklärt in fünf Lektionen, wie aus jedem ein besserer Anleger werden kann.

Multi-group Bayesian structural equation modeling (MG-BSEM) gained considerable attention among substantive researchers investigating cross-group differences and methodologists exploring challenges in measurement invariance testing. MG-BSEM allows for greater flexibility by applying elastic rather than strict equality constraints on item parameters across groups. This, however, requires a specification of user-defined prior variances for cross-group differences in item parameters. Although prior selection in general Bayesian settings is well-studied, guidelines with respect to tuning the normal prior variances in MG-BSEM approximate measurement invariance (AMI) analysis are still largely missing. In a Monte Carlo simulation study we find that correctly specifying prior variances results in more precise credibility intervals (CI) and posterior standard deviations, while prior misspecification has little influence on point estimates. We compared the BIC, DIC, and PPP fit measures and found in our simulation scenarios that the DIC measure was most effective, when a proper threshold for model selection was applied.

Als die Schweizerische Nationalbank im September die neue Hunderternote in Umlauf brachte, fragte sich die NZZ, ob nun das Ende des Bargelds bevorstehe. Welche Pläne haben die Nationalbank und private Finanzdienstleister? Mit Thorsten Hens, Professor fr Finanzökonomie an der UZH....

We analyze how banks’ allocations of mortgage credit across regions change when an online platform enables them to offer to regions where they have no branches, staff or legacy. Unique data from an online platform with offers from different banks to each mortgage application yield three novel findings. First, banks offer more and cheaper credit to borrowers in less competitive offline markets. Second, banks offer more credit to more distant locations, where house prices appear less over-heated, and past price growth is less correlated with that in their existing portfolio. Third, over time offers become more automated, lowering operational costs.

This thesis primarily investigate the improvement of financial indicators based on the dynamically varying covariances. In previous research and papers, financial indicators are usually calculated by convariance matrix based on i.i.d Gaussian assumptions. Here I employ two indicators, financial turbulence and absorption ratio and estimate them based on related covariance matrix obtained by 7 models, i.i.d. Gaussian model, multivariate student’s t model, DCC-GARCH based on multivariate normal model, DCC-GARCH based on multivariate student’t model, Go-GARCH based on multivariate normal model, Go-GARCH based on NIG model, MCD covariance estimator. I refer to the turbulenceresistant portfolio and portfolio based on absorption ratio to estimate the performance of the portfolios to assess the financial indicators accordingly. Then I evaluate the improvement of the DCC-GARCH models on the two financial indicators. Keywords: financial indicators; financial turbulence; absorption ratio; covariance matrix; DCC-GARCH model; Go-GARCH model.