In subjective expected utility (SEU), the decision weights people attach to events are their beliefs about the likelihood of events. Much empirical evidence, inspired by Ellsberg (1961) and others, shows that people prefer to bet on events they know more about, even when their beliefs are held constant. (They are averse to ambiguity, or uncertainty about probability.) We review evidence, recent theoretical explanations, and applications of research on ambiguity and SEU.In the last 40 years the leading theories of choice in economics and psychology have been the expected utility (EU) theory of von Neumann and Morgenstern (1947) and the subjective expected utility (SEU) theory of Savage (1954). Empirical violations have led to reexaminations of both kinds of theory. In Weber and Camerer (1987), we reviewed the evidence, axioms, and application of alternatives to EU. Here we do the same for SEU.EU assumes that the probabilities of outcomes are known. If preferences follow a set of simple axioms, they can be represented by a real-valued utility function--preferred choices have higher utility numbers--and the utility of a choice is the expected utility of its possible outcomes, weighted by their probabilities.In SEU, probabilities are not necessarily objectively known, so SEU applies more widely than EU. (Indeed, it is hard to think of an important natural decision for which probabilities are objectively known.) In SEU, decision makers choose acts, which have consequences that depend on which of several uncertain "states" occurs. People are *Thanks to

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We investigate whether loan growth affects the riskiness of banks in 14 major western countries under "regular conditions". Using Bankscope data from more than 10,000 individual banks during 1997-2005, we test three hypotheses on the relation between past loan growth and loan losses, bank profitability, and bank solvency. Our empirical evidence supports the view that loan growth leads to a peak in loan loss provisions three years later, to a decrease in relative interest income, and to lower capital ratios. Further analyses reveal that loan growth also has a negative impact on risk-adjusted interest income. These results suggest that loan growth represents an important driver of bank risk. JEL classification: G20, G21Keywords: bank lending, loan losses, bank profitability, bank solvency * Daniel Foos is Doctoral Student and Research Assistant at the Department of Banking and Finance, University of Mannheim, Email: foos@bank.BWL.uni-mannheim.de. Lars Norden is Assistant Professor at the Department of Banking and Finance, University of Mannheim, and currently visiting the Finance Department, Kelley School of Business, Indiana University, Email: lnorden@indiana.edu. Martin Weber is Professor of Business Adminstration, Banking and Finance at the Department of Banking and Finance, University of Mannheim, and at the Centre for Economic Policy Research (CEPR), Email: weber@bank.BWL.uni-mannheim.de. We wish to thank José Luis Peydró-Alcalde, Wolf Wagner, as well as participants at the 14 th Annual Meeting of the German Finance Association Meetings in Dresden, the 2 nd Conference on Banking Regulation, Integration and Financial Stability at the Centre for European Economic Research (ZEW) in Mannheim, the research seminar at the University of Mannheim for useful comments and suggestions. In addition, we are grateful to Julia Hein and Jeanette Roth for their support on data issues. Martin Betzwieser provided excellent research assistance. 2The activity of lending to customers represents a core function of banks, and is an integral part of the academic literature that explains why banks exist (see Diamond 1984, Bhattacharya andThakor 1993). Some financial systems have been classified as "bank-based" because most of the funds needed for investment are channeled from households to firms through financial intermediaries (e. In addition to macro-economic factors (economic growth, monetary policy, etc.) that matter for all banks there are many bank-specific reasons for an increase or decrease in lending. Either new profitable lending opportunities may arise, like new loan products, lending channels, or lending segments (commercial vs. retail lending, internet-based lending, student loans, etc.), or the expansion to new geographical markets (other regions or countries) occurs. Mechanisms to increase lending are lowering interest rates, loosening credit standards, or both combined. Moreover, a bank may rely on organic internal growth or external growth by means of mergers and acquisitions (M&A). In any of these cases, it...

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We analyse the relationship between credit default swap (CDS), bond and stock markets during 2000-2002. Focusing on the intertemporal co-movement, we examine monthly, weekly and daily lead-lag relationships in a vector autoregressive model and the adjustment between markets caused by cointegration. First, we find that stock returns lead CDS and bond spread changes. Second, CDS spread changes Granger cause bond spread changes for a higher number of firms than vice versa. Third, the CDS market is more sensitive to the stock market than the bond market and the strength of the co-movement increases the lower the credit quality and the larger the bond issues. Finally, the CDS market contributes more to price discovery than the bond market and this effect is stronger for US than for European firms.

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models-the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data-seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.

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