Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Background: The Domain-Specific Risk Taking scale (DOSPERT) is a widely used instrument that measures perceived risk and benefit and attitude toward risk for activities in several domains, but does not include medical risks.

Objective: To develop a medical risk domain subscale for DOSPERT.

Methods: Sixteen candidate risk items were developed through expert discussion. We conducted cognitive telephone interviews, an online survey, and a random-digit dialing (RDD) telephone survey to reduce and refine the scale, explore its factor structure, and obtain estimates of reliability.

Participants: Eight patients recruited from UIC medical center waiting rooms participated in 45-60 minute cognitive interviews. Thirty Amazon Mechanical Turk workers completed the online survey. One hundred Chicago-area residents completed the RDD telephone survey.

Results: On the basis of cognitive interviews, we eliminated five items due to poor variance or participant misunderstanding. The online survey suggested that two additional items were negatively correlated with the scale, and we considered them candidates for removal. Factor analysis of the responses in the RDD telephone survey and non-statistical factors led us to recommend a final set of 6 items to represent the medical risk domain. The final set of items included blood donation, kidney donation, daily medication use for allergies, knee replacement surgery, general anesthesia in dentistry, and clinical trial participation. The interitem reliability (Cronbach’s α) of the final set of 6 items ranged from 0.57-0.59 depending on the response task. Older respondents gave lower overall ratings of expected benefit from the activities.

Conclusion: We refined a set of items to measure risk and benefit perceptions for medical activities. Our next step will be to add these items to the complete DOSPERT scale, confirm the scale’s psychometric properties, determine whether medical risks constitute a psychologically distinct domain from other risky activities, and characterize individual differences in medical risk attitudes.

Risk behavior can be capricious and may vary from month to month. We study 62 clients of a private bank in Northern Italy. The individuals are of special interest for several reasons. As active traders, they manage the value-at-risk (VaR) of a portion of their wealth portfolios. In addition, they act alone, i.e., without input from a financial adviser. Based on VaR-statistics, we find that, in general, the subjects become more risk-averse after suffering losses and more risk-seeking after experiencing gains. The monthly gains and losses that alter investor risk behavior represent true changes in wealth but are “on paper” only, i.e., not immediately realized. Our results allow several interpretations, but they are not at odds with a house money effect, or the possibility that overconfident investors trade on illusions. Rapidly shifting risk behavior in fast response to unstable circumstances weakens individual risk tolerance as a deep parameter and key construct of finance theory.

Risk attitudes implied by valuations of risk-increasing assets depart markedly from those implied by valuations of risk-reducing assets. For instance, many are unwilling to pay the expected value for a risky asset or for its perfect hedge. Although nearly every theory of risk preference (and logic) demands a negative correlation between valuations of bets and hedges, we observe positive correlations. This inconsistency is difficult to expunge.

This study compares Chinese people’s trust and trustworthiness, risk attitude, and time preference before and after the onset of the COVID-19 pandemic in China. We compare the preferences of subjects in two online experiments with samples drawn from 31 provinces across mainland China before and after the onset of the pandemic. We test two competing hypotheses regarding trust and trustworthiness. On the one hand, the outbreak as a collective threat could enhance in-group cohesion and cooperation and thus increase trust and trustworthiness. On the other hand, to the extent that people expect their future income to decline, they may become more self-protective and self-controlled, and thus less trusting and trustworthy and more risk averse and patient. Comparing before and after the onset, we found that the subjects increased in trustworthiness. After the onset, trust and trustworthiness (and risk aversion and present bias too) were positively correlated with the COVID-19 prevalence rate in the provinces. Subjects with more pessimistic expectations about income change showed more risk aversion and lower discount rates, supporting the speculation concerning self-control.

Using college student samples, two studies were conducted to validate the Chinese version of the Domain-Specific Risk-Taking (DOSPERT) Scale. The results replicated important findings reported by Weber et al. (2002) in the Chinese culture. Risk-taking and risk perception were domain-specific, whereas perceived-risk attitudes were relatively stable across domains, supporting the risk-return model of risk taking. Results of both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) showed that the ethical, recreational, health/safety, and gambling domains were preserved in the Chinese version of DOSPERT and that the items from social and investment domains formed one factor. This result may be explained by Weber and Hsee's (1998) cushion hypothesis. Other possible reasons for this cross-cultural difference in the factor structure were also discussed.

This paper proposes a revised version of the original Domain-Specific Risk-Taking(DOSPERT) scale developed by Weber, Blais, and Betz (2002) that is shorter andapplicable to a {broader range of ages, cultures, and educational levels}. Italso provides a French translation of the revised scale. Using multilevelmodeling, we investigated the risk-return relationship between apparent risktaking and risk perception in 5 risk domains. The results replicate previouslynoted differences in reported degree of risk taking and risk perception at themean level of analysis. The multilevel modeling shows, more interestingly, thatwithin-participants variation in risk taking across the 5 content domains of thescale was about 7 times as large as between-participants variation. We discussthe implications of our findings in terms of the person-situation debate relatedto risk attitude as a stable trait.

Personal finance is a challenging topic which can benefit from a scientific approach to individual financial planning. This paper presents an individual asset liability management (iALM) model for life cycle planning which uses the methodology of dynamic stochastic optimisation and incorporates ideas from both classical and behavioural finance. Its implementation is in the form of a decision support tool for use by financial advisers or wealth managers. The investment universe is given by a set of indices for major asset classes and their returns are simulated forward over the lifetime of a household. On the liability side the foreseen cash flows of incomes and outgoings are simulated and punctuated by life events such as illness and death. The household's utility function is constructed for each time period over a range of monetary values in terms of household financial goals and preferences. Taxes and pension savings are treated using the tax shielded saving accounts specific to a national jurisdiction in terms of constraints in the optimisation sub-models. The paper goes on to present an analysis of iALM model recommendations for a representative UK household, together with an evaluation of the sensitivity of the financial plan generated to changes in market environments such as the 2007–9 crisis. The promise of this new technology is to bring modern decision support tools to individual investors in order to facilitate custom designed consumption, savings and investment policies.