Mobile trading application users have rocked the financial world and are becoming a noteworthy for their ability to contribute to financial uncertainty, often at great risk to their personal wealth. While participation in meme stock culture likely contributes to this risky behavior, other factors such as personal risk appetite and enjoyment could also explain a user’s willingness to engage in risky actions on these platforms. In this paper, we describe the results of an experiment whereby participants engaged in a simulated financial trading task designed to mimic the Robinhood trading app. We took a mixed method approach to investigating users’ experiences, using time-series machine learning clustering as well as questionnaire measures. We identified distinct clusters of users based on app usage data which reflected degrees of risky behavior and found that these features were associated with a user’s perceived risk appetite and the degree to which they enjoyed the simulated technology. Taken together with past evidence that suggests that risk appetite and enjoyment are associated with application use, we posit that these factors play a role in explaining risky behavior on mobile trading platforms, which has implications for financial application design and future research on financial technology applications.