Cases
Overview
Rotman Interactive Trader (RIT) cases have been designed to complement finance curricula at both the undergraduate and graduate levels. Each RIT case simulates the risks and opportunities associated with particular securities or strategies. RIT cases are designed to focus on specific financial concepts and present them in an easy to understand manner so that students can explore, learn, and practice strategies that achieve their desired goals. The RIT cases also sequence from introductory (generally 1 source of risk) to richer cases for which the decision maker has to manage several risks. Note that most cases have an associated Excel model that applies the relevant theory and links to the orderdriven market in real time. This reflects our mission to integrate theory and practice.
The RIT cases are designed to be run iteratively using multiple replications which implement a range of potential scenarios. 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. The same strategy can yield different results depending on the final realization of the scenario. Participants learn from the immediate feedback on the success of their strategies and practice until they find a robust strategy that works well across the whole range of potential outcomes. In effect, the RIT 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.
Current Topics and Lists of Associated RIT Cases
(left click on a case title to open or close its description)

SellSide Roles and Managing Liquidity and Market Risk
Agency Trading
Agency Trading 1  VWAP Strategies
The first agency trading case is designed to introduce traders to orderdriven markets, to order types and to VWAP strategies. For example, one can illustrate how using limit orders instead of market orders allows the trader to capture the bid ask spread instead of paying the bid ask spread. The market is designed to be extremely liquid so students will not be exposed to liquidity risk.
Agency Trading 2  Limited Liquidity
The second agency trading case builds on the AT1 case by adding liquidity risk. In this simulation, the market will be extremely illiquid so students should use limit orders to execute their trades at desirable prices (that is, avoid price impact). Students will also be under a time constraint, and will potentially need to use some market orders in order to receive order fills in a timely manner.
Liability Trading
Liability Trading 1  Trading as a Principal
The first liability trading case introduces students to taking on price and liquidity risk by accepting a large block trade and requiring them to unwind the position in the open market. While closing the position, they will cause price impact due to limited (but reasonably high) liquidity in the market.
Liability Trading 2  Orders in Illiquid Markets
The second liability trading case is considerably more difficult because it forces students to trade directly with each other in order to unwind their positions. A time constraint is also added, requiring that the trades be closed out by the middle and end of the trading session.
Liability Trading 3  Dynamic Order Arrival
The third liability trading case is a dynamic version of the LT2 case; students will receive their orders for different stocks at unknown intervals.
Liability Trading 4  Liability Trading Capstone
The fourth liability trading case adds multiple marketplace functionality and requires students to seek best execution while weighing different commission and passive order rebate schedules.
Arbitrage
Price Discovery 3  Arbitrage Pricing
The third price discovery case builds on the previous cases by adding a second company and an ETF. The ETF can be priced on an arbitragefree basis using the market values of the two individual companies. Students should observe how the riskiness and distribution for the ETF is considerably different from the individuallypriced companies.
Liability Trading 4  Liability Trading Capstone
The fourth liability trading case adds multiple marketplace functionality and requires students to seek best execution while weighing different commission and passive order rebate schedules.
Algorithmic Trading 1 Algorithmic Arbitrage
The first algorithmic trading case introduces students to algorithmic trading by providing a simple example of exploiting an arbitrage opportunity for one stock traded on two different exchanges.
MarketMaking
Liability Trading 4  Liability Trading Capstone
The fourth liability trading case adds multiple marketplace functionality and requires students to seek best execution while weighing different commission and passive order rebate schedules.
Algorithmic Trading 2  Algorithmic Market Making
The second algorithmic trading case is considerably more difficult because it forces students to build on skills learned in the Algorithmic Arbitrage (ALGO1) case and motivate students to build a marketmaking algorithm that generates profits by capturing the bidask spread.
SmartOrder Routing
Liability Trading 4  Liability Trading Capstone
The fourth liability trading case adds multiple marketplace functionality and requires students to seek best execution while weighing different commission and passive order rebate schedules.
Algorithmic Trading 3  Smart Order Routing
The third algorithmic trading case will challenge students to build an algorithm to manage the liquidity risk associated with block trades/tender offers.

Price Discovery and Law of One Price
Price discovery in equity markets with asymmetric information
The Price Discovery 0 case demonstrates the concept of informational efficiency as students attempt to determine the fair price for a takeover bid. Students have asymmetric information which is updated over time but there is no aggregate uncertainty.
The Price Discovery 1 builds on the Price Discovery 0 (PD0) case to demonstrate the concept of informational efficiency as students attempt to determine the fair price for a newly issued stock. As it happens in PD0, students have asymmetric information which is updated over time but there is no aggregate uncertainty.
The second price discovery case also demonstrates informational efficiency by giving students private price estimates and confidence intervals associated with those forecasts. The fair value of the equity is based on the intersection of all students' information.
Law of one price and arbitrage
The third price discovery case builds on the previous cases by adding a second company and an ETF. The ETF can be priced on an arbitragefree basis using the market values of the two individual companies. Students should observe how the riskiness and distribution for the ETF is considerably different from the individuallypriced companies.

Fixed Income
Fixed Income security pricing, characteristics of bonds, interest rate risk
Fixed Income 1  Treasury Bills
The first fixed income case illustrates how to calculate the fair value (present value of future cash flows) of a riskfree treasury bill when interest rates are known. Trading is based on identifying a mispriced treasury bill.
The second Fixed Income case introduces a yield curve and government coupon bonds (nominally risk free). Students trading the bond learn about coupon payments, accrued interest, dirty and clean prices.
Fixed Income 3  Interest Rate Risk
The third fixed income case builds on the previous fixed income cases by adding interest rate risk.
Overview of credit analysis, default risk
The fourth fixed income case presents students with risky corporate bonds with a chance to default and requires them to price the bonds accordingly. An arbitrage condition exists where students can build a portfolio of bonds with known default risk.
Yield measures and the term structure of Interest rates
The fifth fixed income case challenges students' understanding of bond pricing based on news and benchmark interest rates derived from 4 nontradable government zerocoupon bonds. Students have to price 3 tradable government coupon bonds based on the benchmark rates and news. The news, which will be released throughout the case, may have an impact on the benchmark rates, and thus on the fair prices of the tradable coupon bonds.
Credit Risk
The sixth fixed income case challenges students' understanding of credit risk and it introduces them both to a structural model (Merton's model) and to the use of the Altman Zscore.

Equity Valuation
Price discovery, asymmetric information, relative valuation, structured products, M&A:
The Price Discovery 0 case demonstrates the concept of informational efficiency as students attempt to determine the fair price for a takeover bid. Students have asymmetric information which is updated over time but there is no aggregate uncertainty.
The Price Discovery 1 builds on the Price Discovery 0 (PD0) case to demonstrate the concept of informational efficiency as students attempt to determine the fair price for a newly issued stock. As it happens in PD0, students have asymmetric information which is updated over time but there is no aggregate uncertainty.
Price Discovery 2  Asymmetric Information
The second price discovery case also demonstrates informational efficiency by giving students private price estimates and confidence intervals associated with those forecasts. The fair value of the equity is based on the intersection of all students' information.
Price Discovery 3  Arbitrage Pricing
The third price discovery case builds on the previous cases by adding a second company and an ETF. The ETF can be priced on an arbitragefree basis using the market values of the two individual companies. Students should observe how the riskiness and distribution for the ETF is considerably different from the individuallypriced companies.
Equity Valuation 1  Relative PE Valuation
The first equity valuation case introduces students to basic equity valuation by applying a fixed P/E ratio to the realized earnings of a company to determine the associated stock valuation. Trading is based on identifying mispriced stocks according to that relative valuation criterion.
Equity Valuation 2  DDM Valuation
The second equity valuation case requires students to use the Gordon Dividend Discount Model to value the equity traded in the case. Students must model annual EPS, dividends, and the appropriate discount rate in order to derive a valuation for the company.
Merger & Acquisitions 1  Takeover Arbitrage
The first mergers & acquisitions case requires students to calculate the arbitragefree price of a company that has received a takeover offer. The probability of the deal succeeding is dynamically updated through time and students must value the security based on the probability weighted outcomes.
DCF Valuation
Equity Valuation 3  DCF Modeling
The third equity valuation case requires students to develop a DCF model to value a company and then identify mispricing opportunities on the market.
Model uncertainty/risk
(To be released soon)

Derivative Pricing

Futures and Forward Contracts/Prices
 Structure of Futures markets
 How Futures/Forwards prices are determined
 Margin requirements
 The costofcarry
 Arbitrage
 News trading
Futures 1  Equity Index Futures
The first futures case is designed to introduce students to financial futures that track an index. Students can take long or short positions based on their view on whether the market as a whole is going to rise or fall in response to news releases.
Futures 2  CostofCarry (Contango)
The second futures case facilitates learning about how futures contracts are priced based on the costofcarry. The case uses the contango relationship between physical crude and crude futures and provides arbitrage opportunities when the spread is sufficiently wide.
Commodities 1  Crude Oil Futures
The first commodities case allows students to profit from trading crude oil futures based on their assessment of the price impact of news releases. This is standard directional trading (in a futures market) based on relevant news that might affect the underlying.

The second commodities case expands on the previous commodities case by providing students with a quantitative model that they can use to estimate the price shocks caused by forecasted supply and demand differentials for Natural Gas (NG). Students trade NG futures to profit from their price forecasts for the underlying NG.
Foreign Exchange Trading 1  Covered Interest Rate Parity
The first FX case introduces students to the covered interest rate parity. They will have to find arbitrage opportunities by observing the relationship between interest rates and the spot and forward currency values of two countries.

Options
 Structure of option markets and options payoffs
 Properties of stock and index options
 Strategies involving options

The first options case introduces students to call and put options. They can practice understanding payoffs and identifying mispriced options.
Options 2  Options Strategies
The second options case introduces students to Options Strategies and requires them to build long and short straddles, strangles, condors and butterflies.
Options 3  Trading Volatility
The third options strategies case introduces students to using options strategies to speculate on the volatility of the underlying. Students should seek out mispriced options (using putcall parity) and evaluate the volatility smile to determine which options positions can be used to exploit differentials between the implied and realized volatilities.

Futures and Forward Contracts/Prices

Portfolio Management
Diversification, portfolio choice for longhorizon objectives, Monte Carlo
Portfolio Management 1  Diversification
The Portfolio Management 1 case requires students to allocate a sum of wealth across a diversified portfolio of ETFs. Students can use a MonteCarlo analysis spreadsheet to evaluate the distributions, returns, and risks associated with different portfolios and then allocate theirs accordingly.
Portfolio Management 2  Rebalancing
The second portfolio management case is similar to the PM1 case, except it allows students to rebalance their portfolio intermittently. These rebalancing points present students with the opportunity to enhance (or destroy) value by making wise risk and reward based decisions.
Performance metrics and portfolio optimization
Portfolio Management 3  Optimization
The third portfolio management case will introduce students to the Modern Portfolio Theory. Students will have to either minimize the variance given the level of risk or maximize the Sharpe Ratio. They will be allowed to rebalance their portfolios every 5 years. A tutorial document is available for this case and will show the students how to perform the analysis using Excel.
Portfolio choice subject to regulatory capital adequacy requirement
The case will challenge students in managing their VaR exposure while allocating their funds to three different ETFs. Exceeding the VaR limit described in the case brief will result in fines that will reduce their overall portfolio performance. An Excel file is provided to instructors and can be used to compute and graph the fines, portfolio returns, and total returns for each participant.

Risk Management
Hedging using Futures
Hedging 1  Hedging with Futures
The first hedging case requires students to use an index future to hedge their position in a basket of equities. The case introduces the students to the concept of hedging tracking error, portfolio beta, and hedging costs.
Hedging using Options (Portfolio Insurance)
Hedging 2  Portfolio Insurance
The second hedging case allows students to use various put or call options across multiple months to hedge their position in a single stock. The students use this portfolio insurance strategy to protect their underlying equity position from downside risk
DeltaNeutral Hedging
Hedging 3  DeltaNeutral Hedging
Hedging 3 requires the students to act as a financial institution who is buying/selling blocks of options for individual equities from their clients. When trades are made, students are then responsible for hedging their position and remaining relatively 'delta neutral'.
Introducing production and price risk, crop hedging
Agricultural Hedging 1  Price and Production Risk
The first agricultural hedging case allows students to manage risks associated with a farmer's wheat production. Students must forecast yields (production level) and use domestic or international wheat futures contracts to hedge their price risk. While international contracts are more liquid than domestic, they come at the cost of being undeliverable in kind. Students must decide whether they wish to use a hedge that tracks well due to liquidity but is cashsettled or a perfectly correlated domestic hedge at a higher cost, and evaluate their performance.
Agricultural Hedging 2  Hedge Ratio
The second agricultural hedging case requires students to assume a role of a grain (canola) merchandiser. Students will implement different hedge ratios to understand implications of hedging using futures and underlying spot contracts as well as associated storage costs.
VaR based on regulatory capital adequacy requirement
The case will challenge students in managing their VaR exposure while allocating their funds to three different ETFs. Exceeding the VaR limit described in the case brief will result in fines that will reduce their overall portfolio performance. An Excel file is provided to instructors and can be used to compute and graph the fines, portfolio returns, and total returns for each participant.

Commodities
Crude Oil Futures
Commodities 1  Crude Oil Futures
The first commodities case allows students to profit from trading crude oil futures based on their assessment of the price impact of news releases. This is standard directional trading (in a futures market) based on relevant news that might affect the underlying.
NG Futures
The second commodities case expands on the previous commodities case by providing students with a quantitative model that they can use to estimate the price shocks caused by forecasted supply and demand differentials for Natural Gas (NG). Students trade NG futures to profit from their price forecasts for the underlying NG.
Location Arbitrage
Commodities 3  Location Arbitrage
The Commodities Trading 3 Case will introduce students to the risks and opportunities associated with the concept of 'transportation arbitrage'. Students will be allowed to buy crude oil and transport it across different locations. In order to analyze risks and opportunities associated with their strategy, students need to forecast the price for the time at which the oil will arrive at the destination market.
Product Arbitrage
Commodities 4  Product Arbitrage
The Commodities Trading 4 Case will introduce students to the risks and opportunities associated with the concept of 'production arbitrage'. Students will be allowed to buy crude oil and refine it into two products, Heating Oil and RBOB Gasoline.

Commodities Capstone Case
 News trading
 Costofcarry (storage, transaction costs)
 Location arbitrage
 Refinery arbitrage
Commodities 5  Commodity Capstone
The fifth commodities case requires students to 'juggle' a magnitude of arbitrage and asset pricing strategies to generate profits. Students can take positions based on fundamental views of crude oil, or they can engage in locational, product, or storage arbitrage.
Additional Files
RITAT1VWAPTradingDataSupplement