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Based on an online experiment with a sample of finance professionals and participants from the general population (acting as clients), we examine drivers and motives of clients’ choices to delegate investment decisions to agents. We find that clients favor delegation to investment algorithms, followed by delegation to finance professionals compensated with an aligned incentive scheme, and lastly to finance professionals receiving a fixed payment for investing on behalf of others. We show that trust in investment algorithms or finance professionals, and clients’ propensity to shift blame on others increase the likelihood of delegation, whereas clients’ own decision-making quality is associated with a decrease in delegation frequency. Peer reviewed

Incentivized experiments in which individuals receive monetary rewards according to the outcomes of their decisions are regarded as the gold standard for preference elicitation in experimental economics. These task-related real payments are considered necessary to reveal subjects' "true preferences". Using a systematic, large-sample approach with three subject pools of private investors, professional investors, and students, we test the effect of task-related monetary incentives on risk preferences elicited in four standard experimental tasks. We find no systematic differences in behavior between subjects in the incentivized and non-incentivized regimes. We discuss implications for academic research and for applications in the field.

There is an ongoing debate about the replicability of neuroimaging research. It was suggested that one of the main reasons for the high rate of false positive results is the many degrees of freedom researchers have during data analysis. In the Neuroimaging Analysis Replication and Prediction Study (NARPS), we aim to provide the first scientific evidence on the variability of results across analysis teams in neuroscience. We collected fMRI data from 108 participants during two versions of the mixed gambles task, which is often used to study decision-making under risk. For each participant, the dataset includes an anatomical (T1 weighted) scan and fMRI as well as behavioral data from four runs of the task. The dataset is shared through OpenNeuro and is formatted according to the Brain Imaging Data Structure (BIDS) standard. Data pre-processed with fMRIprep and quality control reports are also publicly shared. This dataset can be used to study decision-making under risk and to test replicability and interpretability of previous results in the field. Design Type(s)data integration objective • data validation objective • behavioral data analysis objectiveMeasurement Type(s)brain activity measurementTechnology Type(s)magnetic resonance imagingFactor Type(s)sex • experimental condition • ageSample Characteristic(s)Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

By running a battery of incentivized and non-incentivized experiments with fund managers from four countries in the European Union, we investigate the impact of fund managers' cognitive skills and economic preferences on the dynamics of the mutual funds they manage. First, we find that fund managers' risk tolerance positively correlates with fund risk when accounting for fund benchmark, fund category, and other controls. Second, we show that fund managers' ambiguity tolerance positively correlates with the funds' tracking error from the benchmark. Finally, we report that cognitive skills do not explain fund performance in terms of excess returns. However, we do find that fund managers with high cognitive reflection abilities generate these returns at lower risk.