Agency Trading 1 Case – Trading Data Supplement: to download the file, click here.
Hedging 1 Case – Trading Data Supplement; to download the file, click here.
The RIT Decision Cases have been designed to complement finance curricula at both the undergraduate and graduate levels. The RIT Decision Cases simulate risks and opportunities associated with financial securities, market dynamics and investment or risk management strategies. The cases focus on specific decision tasks, presented in an easy-to-understand manner so that students can explore, learn, and practice strategies that achieve their desired goals. The RIT Decision Cases also sequence from introductory (generally one source of risk) to capstone cases for which the decision maker must manage several, potentially correlated, risks.
The RIT Decision Cases are designed to be run using multiple iterations which implement a range of potential scenarios. Reflecting our mission to integrate theory and practice, most cases have a start-up decision-support template that applies the relevant theory. Those models include RTD and/or API links to the order-driven market(s) in real time – both to pull information from the market(s) into one’s decision model and (optionally) to direct decisions automatically back into the market(s).
Students use their decision-support models to derive and submit their decisions to the market(s). The market(s) aggregate their decisions (and the AI order flow) providing immediate feedback on the outcome of the students’ strategies. Note that, in most cases, they are not exogenous (price takers). The results generated by millisecond market clearing reflect the endogenous (behavioral) uncertainty generated by the students’ decisions. This feedback allows them to adapt their strategies after each iteration and, in so doing, derive a robust strategy that works well across a range of potential outcomes. In effect, the RIT Decision Cases are designed to apply finance theory in a setting in which participants learn how to make good decisions when faced with uncertainty about outcomes. This simulation approach to learning is ideal for understanding the risks and opportunities associated with financial securities and inherent in most investment or risk management strategies.