Improving Investment Decisions with Simulated Experience

We apply a new and innovative approach to communicating risks associated with financial products that should support investors in making better investment decisions. In our experiments, participants are able to gain "simulated experience” by random sampling of a previously described return distribution. We find that simulated experience considerably improves participants' understanding of the underlying risk-return profile and prompts them to reconsider their investment decisions and to choose riskier financial products without regretting their higher risk-taking behavior afterwards. This method of experienced-based learning has high potential for being integrated into real-world applications and service

3D-nanoprinted on-chip antiresonant waveguide with hollow core and microgaps for integrated optofluidic spectroscopy

Here, we unlock the properties of the recently introduced on-chip hollow-core microgap waveguide in the context of optofluidics which allows for intense light-water interaction over long lengths with fast response times. The nanoprinted waveguide operates by the antiresonance effect in the visible and near-infrared domain and includes a hollow core with defined gaps every 176 µm. The spectroscopic capabilities are demonstrated by various absorption-related experiments, showing that the Beer-Lambert law can be applied without any modification. In addition to revealing key performance parameters, time-resolved experiments showed a decisive improvement in diffusion times resulting from the lateral access provided by the microgaps. Overall, the microgap waveguide represents a pathway for on-chip spectroscopy in aqueous environments

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

How Persistent are the Effects of Experience Sampling on Investor Behavior?

Investor behavior was shown to be considerably different when the risk-return tradeoff is presented by experience sampling as opposed to a descriptive communication. We analyze the persistency of this difference in a setting in which investors are faced with multiple decisions over time and are consequently able to adjust the risk level they initially chose. For this we use an experimental setting with repeated investment decisions over multiple trading days, and we also test a new form of risk simulation in which wealth paths over time are presented rather than just final outcomes. After investors’ initial decisions, for which we confirm previous findings, we do not find persistent differences of simulation-based learning on investors’ risk-taking behavior. With regards to trading volume, only a simulation in which investors see wealth paths and not only final outcomes leads to lower trading frequency soon after the initial asset allocation

Improving investment decisions with simulated experience

We apply a new and innovative approach to communicating risks associated with financial products that should support investors in making better investment decisions. In our experiments, participants are able to gain "simulated experience" by random sampling of a previously described return distribution. We find that simulated experience considerably improves participants’ understanding of the underlying risk-return profile and prompts them to reconsider their investment decisions and to choose riskier financial products without regretting their higher risk-taking behavior afterwards. This method of experienced-based learning has high potential for being integrated into real-world applications and services

Why does myopia decrease the willingness to invest? Is it myopic loss aversion or myopic loss probability aversion?

Myopic loss aversion, Prospect theory, Repeated investing, Experimental economics, D81, G11,

Measuring the time stability of prospect theory preferences

Prospect Theory is widely regarded as the most promising descriptive model for decision making under uncertainty. Various tests have corroborated the validity of the characteristic fourfold pattern of risk attitudes implied by the combination of probability weighting and value transformation. But is it also safe to assume stable Prospect Theory preferences at the individual level? This is not only an empirical but also a conceptual question. Measuring the stability of preferences in a multi-parameter decision model such as Prospect Theory is far more complex than evaluating single-parameter models such as Expected Utility Theory under the assumption of constant relative risk aversion. There exist considerable interdependencies among parameters such that allegedly diverging parameter combinations could in fact produce very similar preference structures. In this paper, we provide a theoretic framework for measuring the (temporal) stability of Prospect Theory parameters. To illustrate our methodology, we further apply our approach to 86 subjects for whom we elicit Prospect Theory parameters twice, with a time lag of one month. While documenting remarkable stability of parameter estimates at the aggregate level, we find that a third of the subjects show significant instability across sessions