General Information
Instructor(s)
- Funds Management (r)
- Management Analytics (r)
- Risk Management and Financial Engineering (c)
Target Audience
RSM2303 is recommended for the ‘Risk Management and Financial Engineering’ major and the ‘Funds Management’ major. The course learning objectives have general applicability but are particularly relevant for developing skills for risk management, investment strategies, and securities trading.
Format
12 sessions
Most classes will be held in the Finance Lab.
Course Mission
- Reviewing institutional details about markets, securities, trading & risk management strategies
- Understanding sources of uncertainty associated with decisions required to implement specific tasks
- Developing modeling skills (e.g. forecasting, Monte Carlo simulation, coding, algorithms, etc.)
- Enhancing decision-making skills associated with trading, investing, and risk management
Course Scope
We will use probabilistic modeling and stochastic simulation as tools for guiding risk-informed decisions in complex environments with material uncertainty about the future. The RIT Market Simulator platform (order-driven market/matching engine) and the associated real-time RIT Decision Cases facilitate deriving robust strategies for the decisions that arbitrageurs, portfolio and risk managers make in real time, including managing liquidity risk, market risk, crash risk, model risk and real economy risks. Decision models for each RIT case are linked to data from the simulated market, that is, data generated by the class participants and (optionally) the AI order flow. The markets aggregate participants’ decisions and provide immediate feedback, allowing you to adapt strategies given the range of potential outcomes experienced in the multiple replications of the case. You will develop decision-support ‘models’ to process information and apply finance theory to guide your decisions.
Being a social science, the risks associated with finance decisions are complex due to model and parameter uncertainty, and complex signal extraction issues for varying signal-to-noise ratios associated with information. There is pedagogical value to sequencing skill acquisition from mastering single skills (dealing with one risk) and then adding in additional risks as we sequence through a set of cases on a particular topic to manage situations in which several risks can interact. This simulation-based learning is analogous to flight simulator training for pilots.
You will be allowed to implement some decisions automatically, using your preferred code e.g. Python or Excel/VBA. Automated systems can be efficient but fragile — we will make risk management decisions manually first (practicing at lower speeds and incrementally) to fully understand the scope of potential outcomes; then use algorithms to implement decision strategies automatically.
Schematic of the simulation-based learning approach using the RIT Decision Cases:
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