What Can We Learn about Investors by Simulating Stock Markets?

Simulators are widely used for training in many fields, including finance. However, their potential in gaining insights into biases, mistakes, and success stories in investing remains largely untapped. In experimental research, simulators allow for analysing dynamic decision-making, such as continuous buying and selling. As computational technologies have advanced, researchers have developed microworlds—gamified, computerized environments designed to replicate complex, real-life decision-making scenarios. These microworlds offer a controlled yet dynamic setting where researchers can systematically manipulate variables and observe behavior in real-time.

At ETH Zurich, we developed the Zurich Trading Simulator (ZTS), a powerful tool designed to mimic the fast-paced environment of financial asset trading (see Ref. [1] for details). ZTS provides a dynamic, tick-by-tick display of asset prices, simulating real market fluctuations. Researchers can introduce external variables such as news events and adjust payoff structures to investigate how these factors influence trading decisions. The simulator’s flexibility allows for a wide range of experimental designs, from simple market models to complex scenarios involving bubbles, crashes, and competitive trading environments.

ZTS is implemented as an oTree app, a platform widely used for behavioral experiments in economics and psychology. It is released under an Open Science license, promoting transparency, reproducibility, and collaboration within the research community. The code is freely available on GitHub, making it accessible to both academic researchers and industry practitioners interested in studying decision-making behavior in financial contexts.

Key Research Findings Using ZTS

Using ZTS, we conducted a series of experiments to examine the psychological and behavioral mechanisms that drive financial decision-making:

1. The Impact of Social Trading Platforms:

In one study [2], we investigated how social comparison influences trading behavior. Participants could see the performance of other traders, mimicking features of real-world social trading platforms. We found that upward social comparison—where individuals compare themselves to traders with significantly higher earnings—led to increased trading activity and risk-taking. However, despite these heightened efforts, participants reported lower satisfaction with their own performance. This suggests that while social comparisons can motivate traders to take more risks, they may also undermine their emotional well-being.

2. Physiological Signals as Predictors of Financial Performance:

In another experiment described in Ref. [3], we measured skin conductance levels (SCL)—a physiological indicator of anticipatory arousal linked to goal-directed behavior—while participants engaged in a simulated market bubble-and-crash scenario. Remarkably, we discovered that fluctuations in these bodily signals could predict financial outcomes: participants who exhibited stronger anticipatory arousal tended to make more profitable decisions, while those with lower arousal responses were more prone to losses. This finding highlights the role of embodied cognition in financial decision-making, suggesting that bodily responses can provide early indicators of an individual’s capacity to navigate volatile markets.

3. The Complex Role of Overconfidence and Incentives:

Overconfidence is often cited as a prevalent cognitive bias in financial markets, leading investors to overestimate their knowledge, underestimate risks, and overtrade. Our research explored how overconfidence interacts with monetary incentives. We found that offering performance-based bonuses increased trading activity, but interestingly, this did not translate into improved trading performance or higher earnings compared to other incentive structures. This suggests that while financial rewards can stimulate activity, they do not necessarily enhance decision quality, underscoring the complex and sometimes counterintuitive effects of incentives on behavior.

Further Reading

For readers interested in psychological mechanisms that play an important role in stock markets, Chapter in Ref. [4] presents a good overview.

References

1. Andraszewicz, S., Friedman, J., Kaszás, D., & Hölscher, C. (2023). Zurich Trading Simulator (ZTS)—A dynamic trading experimental tool for oTree. Journal of Behavioral and Experimental Finance, 37, 100762. https://doi.org/10.1016/j.jbef.2022.100762

2. Andraszewicz, S., Kaszás, D., Zeisberger, S. & Hölscher (2023). The influence of upward social comparison on retail trading behaviour. Scientific Reports, 13, 22713, https://doi.org/10.1038/s41598-023-49648-3

3. Wichary, S., Allenbach, M., von Helversen, B., Kaszás, D., Sterna, R., Hölscher, C., & Andraszewicz, S. (2023). Skin conductance predicts earnings in a market bubble-and-crash scenario. https://doi.org/10.31219/osf.io/ybu8z

4. Andraszewicz, S. (2020). Stock Markets, Market Crashes, and Market Bubbles. In: Zaleskiewicz, T., Traczyk, J. (eds) Psychological Perspectives on Financial Decision Making. Springer, Cham. https://doi.org/10.1007/978-3-030-45500-2_10

Originally posted by Dr. Sandra Andraszewicz on LinkedIn.