Cumulative prospect theory (CPT) is widely considered to be the most successful descriptive theory for decision making under risk and uncertainty. Sophisticated methods have been developed to reliably elicit CPT parameters on an individual basis. The aim of this paper is to analyze whether such methods are suited to be applied in real world situations, particularly in the context of investment counseling for retail investors. Specifically, we examine whether CPT parameters elicited via standardized computer tools are successful in predicting an individual’s preference for different structured financial products. Surprisingly, we find only low predictive power of the elicited CPT parameters on the WTP. Using a second set of experiments, we examine possible explanations for the low prediction quality. Overall, we have to conclude that it is too much of a leap to draw conclusions about the attractiveness of complex financial products from CPT parameters elicited via simple lotteries.

We examine the developments of depositor knowledge, attitudes, and behavior throughout the recent financial crisis and discuss their impact on bank run risk. Based on a self-collected data set surveying depositors before (2007), at (2008), and after the peak of the crisis (2009), we observe a worrying dynamic pattern. At the peak, depositors knew more about deposit insurance, placed more importance on deposit security, and slightly raised their deposits. However, in the aftermath of the crisis the enhanced depositor knowledge proved to be non-permanent while the increased importance of deposit security and the exposure of depositors persisted. The proportion of completely uninformed, strongly involved, and highly exposed depositors, who carry the highest risk of triggering a bank run, was reduced around the peak of the crisis but rebounded strongly afterwards, even exceeding pre-crisis levels. These findings point to a higher bank run risk in the aftermath than during the financial crisis.

We investigate whether and how business credit information sharing helps to better assess the default risk of private firms. Private firms represent an ideal testing ground because they are smaller, more informationally opaque, riskier, and more dependent on trade credit and bank loans than public firms. Based on a representative panel dataset that comprises private firms from all major industries, we find that business credit information sharing substantially improves the quality of default predictions. The improvement is stronger for older firms and those with limited liability, and depends on the sharing of firms’ payment history and the number of firms covered by the local credit bureau office. The value of soft business credit information is higher the smaller the firms and the lower their distance from the local credit bureau office. Furthermore, in spatial and industry analyses we show that the higher the value of business credit information the lower the realized default rates. Our study highlights the channel through which business credit information sharing adds value and the factors that influence its strength.