note that parts of this paper were written when Sven Jakusch was working at Ernst & Young Wirtschaftspruefungsgesellschaft GmbH, however, any views, statements or opinions expressed in this paper are solely those of the authors and not related to Ernst & Young. 1 Heterogeneity in Risk Preferences-Evidence from a Maximum Likelihood Approach Heterogeneity in Risk Preferences-Evidence from a Maximum Likelihood Approach Zhu (2006) and Kaustia (2010)). Other studies find that individual investors systematically miss the merits of diversification (Kumar and Goetzmann (2008)), are attracted by stocks that can be characterized by low expected returns and highly positive skewness (Mitton and Vorkink (2007), Kumar and Goetzmann (2008), Kumar (2009b)), tend to more familiar investments (Barber and Odean (2008), Keloharju et al. (2012)), trade more after an increase in stock market prices (Cohen et al. (2002), Dhar and Kumar (2002), Hvidkjaer (2006)) and are succumb to various cognitive traps that negatively affect their performance (e.g. DeBondt (1998), Barber and Odean (2000) and Barber et al. (2009)). Empirical evidence suggests that these trading patterns, if emerging concurrently, have the potential to affect cross-sectional dependence in returns (see e.g. Grinblatt and Han (2005b) and Han and Kumar (2010) for the impact of trading pattern and Kumar (2007) for evidence on portfolio choice), variations in market volatility and prices (

Abstract. Shortcomings revealed by experimental and theoretical researchers such as Allais (1953), Rabin (2000) and Rabin and Thaler (2001) that put the classical expected utility paradigm von Neumann and Morgenstern (1947) into question, led to the proposition of alternative and generalized utility functions, that intend to improve descriptive accuracy. The perhaps best known among those alternative preference theories, that has attracted much popularity among economists, is the so called Prospect Theory by Kahneman and Tversky (1979) and Tversky and Kahneman (1992). Its distinctive features, governed by its set of risk parameters such as risk sensitivity, loss aversion and decision weights, stimulated a series of economic and financial models that build on the previously estimated parameter values by Tversky and Kahneman (1992) to analyze and explain various empirical phenomena for which expected utility doesn't seem to offer a satisfying rationale. In this paper, after providing a brief overview of the relevant literature, we take a closer look at one of those papers, the trading model of Vlcek and Hens (2011) and analyze its implications on Prospect Theory parameters using an adopted maximum likelihood approach for a dataset of 656 individual investors from a large German discount brokerage firm. We find evidence that investors in our dataset are moderately averse to large losses and display high risk sensitivity, supporting the main assumptions of Prospect Theory.

Abstract. This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research question, we implement a frequently used nonlinear probit model in the style of Hey and Orme (1994) and base our analysis on a simulation stud. In detail, we simulate trading sequences for a set of utility models and try to identify the underlying utility model and its parameterization used to generate these sequences by maximum likelihood. We find that for a very broad classification of utility models, this method provides acceptable outcomes. Yet, a closer look at the preference parameters reveals several caveats that come along with typical issues attached to financial data, and that some of these issues seems to drive our results. In particular, deviations are attributable to effects stemming from multicollinearity and coherent under-identification problems, where some of these detrimental effects can be captured up to a certain degree by adjusting the error term specification. Furthermore, additional uncertainty stemming from changing market parameter estimates affects the precision of our estimates for risk preferences and cannot be simply remedied by using a higher standard deviation of the error term or a different assumption regarding its stochastic process. Particularly, if the variance of the error term becomes large, we detect a tendency to identify SP T as utility model providing the best fit to simulated trading sequences. We also find that a frequent issue, namely serial correlation of the residuals, does not seem to be significant. However, we detected a tendency to prefer nesting models over nested utility models, which is particularly prevalent if RDU and EXP O utility models are estimated along with EU T and CRRA utility models.