Portfolio Management Simulation

Apply portfolio theory to portfolio construction and rebalancing

Students apply CAPM and mean-variance optimisation to estimate expected returns, analyse portfolio risk and construct an equity portfolio across 25 global companies.

3 - 6-hour team-based simulation
Analysis and trading
In-class, online or hybrid delivery

Two roles. One changing market.

Each team manages a global equity portfolio from a distinct client perspective. Hedge Fund teams compete to generate higher Alpha, while Pension Fund teams compete to achieve a higher Sharpe Ratio.

Apply CAPM and portfolio optimisation

Estimate expected returns, analyse covariance and determine portfolio weights.

Construct and rebalance a portfolio

Translate model outputs into equity selections, trades and revised allocations.

Evaluate risk-adjusted performance

Compare return and volatility using Alpha or Sharpe Ratio within the relevant mandate.

At a glance

Duration

3 to 6 hours

The total time depends on the selected session format.

Format

Team-based web simulation

Students collaborate to construct and manage an equity portfolio.

Level

Undergraduate, postgraduate and MBA

Suitable for portfolio, asset and investment management teaching.

Syllabus fit

CAPM, portfolio theory and optimisation

Covers expected return, beta, covariance, diversification, mean-variance optimisation, Alpha, Sharpe Ratio and rebalancing.

Prerequisites

Core portfolio concepts

Students need a basic understanding of CAPM, Sharpe Ratio and mean-variance optimisation.

Professor tools

Manage and control the session

Use participant management, timeline controls, class alerts and live decision monitoring.

Delivery

Classroom, online or hybrid

Run it in one session, across several classes or as a homework-supported activity.

Optional assessment

Team-level decision evidence

Use portfolio choices, trades and performance results to support academic judgement.

The core of portfolio management: Risk-return trade-off

Diversification, risk measurement, optimisation and rebalancing work together to create a well-managed portfolio that balances risk and return.

Portfolio Management simulation gameflow and stages

1. Intro:

10 minutes

Students begin by watching the introductory video, which explains the purpose of the simulation, the market environment and how performance will be measured. They review their assigned client mandate, understand whether they are managing a Hedge Fund or Pension Fund portfolio, and learn how their team’s objective differs from that of the other role.

This stage also introduces the simulation structure, the information available to students and the decisions they will make during each quarter. It gives teams time to understand their responsibilities, organise how they will work together and build anticipation before the timed analysis begins. Professors can use this stage to confirm that everyone has access, clarify the distinction between Alpha and Sharpe Ratio, and answer operational questions without influencing investment decisions.

2. Analysis, Quarter 1:

1 hour 30 minutes

This is the main analytical stage of the simulation. Teams begin by reviewing their mission, required actions, client mandate and performance objective. They explore the guidance materials, understand how the supplied Excel model works and familiarise themselves with the historical market data available for the 25 companies.

Students then download the historical data and use the Excel model to calculate and interpret measures such as expected returns, beta, covariance, volatility and portfolio weights. They apply CAPM and mean-variance optimisation to examine the relationship between risk and expected return, while also considering the qualitative case information and the priorities of their assigned client.

Once the analysis is complete, teams decide how to construct their initial portfolio. They select the securities they want to hold, determine how much capital to allocate to each position and consider whether to retain cash or, where permitted, use leverage. They then enter the market and execute their Quarter 1 purchases based on the strategy produced through their analysis.

The professor’s role is to monitor progress, help students interpret the model and test their assumptions without recommending particular stocks or portfolio weights. Useful questions might include why a team has selected a particular level of concentration, how covariance influenced its allocation or whether the proposed portfolio is consistent with its mandate.

3. Quarter 2, Quarter 3 and Quarter 4:

Quarter 2: 30 minutes
Quarter 3: 15 minutes
Quarter 4: 15 minutes

During the later trading quarters, students move from initial portfolio construction to active monitoring and rebalancing. At the beginning of each quarter, teams review their updated portfolio performance, examine new company and market news, and assess whether the assumptions behind their original strategy still hold.

Each new information release may affect some securities positively and others negatively. Teams must interpret the news carefully, consider how it changes expected returns or risk exposure, and decide whether to retain their current holdings, reduce a position, increase an allocation or introduce a new security. They must avoid reacting automatically to short-term movements and instead determine whether the new information justifies a genuine change in strategy.

Students can monitor their portfolio return, volatility and Sharpe Ratio throughout the simulation. Hedge Fund teams can also evaluate their Alpha, while Pension Fund teams review their Sharpe Ratio.

Quarter 2 provides more time for teams to review the first major set of results and reconsider their strategy. Quarters 3 and 4 move more quickly, requiring students to prioritise the most important information and make increasingly time-sensitive trading decisions. By Quarter 4, teams are making their final adjustments and balancing the potential benefit of further trades against the risk of disrupting a portfolio that is already performing well.

Professors can coach selectively by asking what has changed, why a trade is necessary and how the proposed decision affects the team’s risk profile. They should avoid directing students towards a preferred investment or revealing which news items are likely to have the greatest impact.

4. Reflection:

No fixed time limit

Once trading has ended, students review the full history of their portfolio, including their initial allocation, subsequent purchases and sales, changes in portfolio value and final performance metrics. They can examine how the portfolio developed over time and identify which decisions had the greatest effect on the final result.

Hedge Fund teams review their Alpha alongside return, volatility and Sharpe Ratio, while Pension Fund teams focus on Sharpe Ratio together with return and volatility. Teams should compare themselves only with others managing the same client mandate, as the two groups are pursuing different objectives.

The professor uses this stage to lead a structured debrief. Rather than focusing only on the winning teams, the discussion should examine how different strategies produced different outcomes. Professors can compare concentrated and diversified portfolios, discuss the use of cash or leverage, identify the most consequential trades and explore how teams responded to the news released during each quarter.

Useful debrief questions include:

Which assumptions shaped the initial portfolio?
Which news item caused the most important change in strategy?
Did the team rebalance for a clear analytical reason or react to recent performance?
Was the final return achieved with an appropriate level of risk?
Which decision would the team repeat in a second run?
Which decision would it change?

The leaderboard and performance metrics provide evidence for discussion, but they should not be treated as the sole measure of learning. Professors can use the reflection stage to connect the results back to CAPM, diversification, volatility, Alpha, Sharpe Ratio, portfolio optimisation and the importance of monitoring and rebalancing.

Why professors use it

It connects technical portfolio concepts with visible choices, changing information and role-specific results.

Interpret CAPM and optimisation outputs

Students calculate expected returns and portfolio risk, then decide how those outputs should influence security selection and portfolio weights.

Make diversification and rebalancing concrete

Students see how concentration, covariance, volatility and new information affect the performance of an actual portfolio over time.

Create a structured performance debrief

Holdings, trade history, return, volatility, Alpha and Sharpe Ratio give professors evidence for comparing strategies and questioning decisions.

What the experience looks like

The Professor Admin Panel supports setup, timing, participant management and monitoring. The Student Interface guides teams from analysis to portfolio construction, trading and final performance review.

Control the simulation timeline

View each stage and its duration, start automatic progression and adjust the pace where additional working time is needed.

Access the delivery resources

Open Setup & Facilitation, Teaching Notes and the Gameflow Diagram directly from the Admin Guide.

Run and manage the session

Play starts automatic progression. Pause holds the timer in the current stage. Stop locks student screens.

Case materials students work with

Students receive quantitative market information and role-specific investment guidance. Historical data and the portfolio model support the calculation of expected return, covariance, volatility and portfolio weights. The mandate defines the client objective that should guide the team’s strategy.

Client mandate

Teams receive either a Hedge Fund or Pension Fund mandate that defines their objective, available choices and performance measure.

CAPM and portfolio-analysis guidance

Students use CAPM guidance to estimate beta and expected return before considering how each security contributes to the portfolio.

7 mins

Student Intro Video

Students watch an introductory video explaining the simulation objectives, their assigned mandate, how performance is measured and what they need to do before the timed activity begins.

Assess portfolio decisions, not just final returns

Assessment is optional. Professors may run the Portfolio Management Simulation as an ungraded applied activity focused on practice, comparison and debriefing.

Where assessment is appropriate, platform-generated portfolio data, trades and performance measures can support academic judgement. Simulation scores should be interpreted alongside the team’s analysis and decisions rather than automatically replacing the professor’s judgement.

What professors can assess

• Portfolio construction
• CAPM and portfolio modelling

How to interpret the results

• Compare teams within the same client mandate
• Read return alongside risk and the role-specific score

What professors can assess:

Portfolio construction: Assess how the team translated its analysis into security selection, portfolio weights, diversification, concentration and cash allocation. For Hedge Fund teams, the use and consequences of permitted borrowing may also support discussion.
CAPM and portfolio modelling: Review how the team used historical data, beta, expected return, covariance and volatility to support its initial portfolio. Consider the assumptions behind the model as well as its numerical output.
Optional individual assessment report: Students may be asked to submit a short written report outside the platform once they have reviewed and reflected on their outcomes. This is not automatically captured or graded by the simulation.

How to interpret the results:

Compare teams within the same client mandate: Compare Hedge Fund teams with other Hedge Fund teams and Pension Fund teams with other Pension Fund teams. The groups pursue different performance objectives.
Read return alongside risk and the role-specific score: Use return, volatility and the relevant role score together. A high return may also reflect concentration, leverage or greater market exposure.

Set up the simulation and choose your delivery format

The simulation progresses automatically after it is started. Coaching is optional, and professors can intervene when additional time, clarification or class communication is useful.

Step 1

Assign teams and mandates

Place students in teams of 3 to 5. Odd-numbered teams represent Hedge Funds and even-numbered teams represent Pension Funds.

Step 2

Configure the timings

Review the default timeline and adjust stage durations where the class schedule requires a different pace.

Step 3

Start and manage the simulation

Select Play to begin automatic progression. Pause provides more working time. Stop suspends the activity and logs students out.

Step 4

Monitor progress and prepare the debrief

Review team inputs, portfolio decisions and performance information. Coaching should help students test assumptions without recommending a preferred security or trade.

Professor Resources

Everything needed to prepare, run and debrief the Portfolio Management Simulation is organised within the resources below.

CAPM
Mean-Variance Optimisation
Efficient Frontier
Security Market Line
Portfolio Theory
Diversification
Beta and expected return
Covariance and volatility
Alpha
Sharpe Ratio
Portfolio monitoring
Rebalancing
Active and passive investment strategies

Creators of the Portfolio Management Simulation

These industry professionals were involved in the inception, creation, development, testing and optimisation of the simulation.

Former Practice Specialist in McKinsey’s Corporate Finance team in Germany and Finance professor at Aberdeen University.

Gerhard Kling

Investment banking, capital markets, PE, and corporate ratings experience at Morgan Stanley, S&P, and HPS Investment Partners. Holds a PhD in Quantitative Finance.

Gerhard Wörtche

Over 15 years of experience across advisory, PE, VC, family offices, and entrepreneurship. Covered M&A transactions from multiple angles and holds a PhD in Corporate Finance.

Olaf Rottke

Senior investment banker at Morgan Stanley with experience in M&A and TMT coverage across New York and San Francisco. Studied business at Kellogg.

Bharat Venugopal

Structured finance, credit derivatives, and NPL expertise. Portfolio Director at a private equity impact fund and active VC investor. Holds CFA and FRM qualifications.

Raushan Kretschmar

FAQs

The two roles represent different client mandates. Hedge Fund teams seek to generate Alpha, while Pension Fund teams focus on risk-adjusted performance measured through the Sharpe Ratio. The same security or portfolio can therefore be attractive to one role and inappropriate for the other. This gives professors a direct way to show that portfolio quality cannot be judged without reference to the investor’s objective, tolerance for volatility and permitted use of cash or leverage.

The model provides a disciplined quantitative baseline using expected return, beta, covariance, volatility and mean-variance analysis. It does not remove the need for judgement. As new company and market information arrives, teams must decide whether the assumptions behind the model still hold and whether a rebalance is justified by their mandate. Professors can use the gap between model output and final holdings to discuss estimation risk, qualitative information and the danger of treating an optimiser as an automatic answer.

Rebalancing forces teams to revisit earlier assumptions after new information and performance have changed the portfolio. They must decide whether to hold, sell, add, retain cash or use leverage where their role permits it. This makes the exercise about an investment process rather than a single optimisation result. The sequence also supports discussion of discipline, conviction, concentration, turnover and the difference between reacting to news and making an evidence-based portfolio adjustment.

The ranking should be interpreted alongside the role mandate, holdings, trades and risk measures. A concentrated position may produce strong realised performance in one run while exposing the client to risk that was not justified ex ante. Professors can ask whether the team understood the volatility and diversification consequences, whether the position was consistent with its Alpha or Sharpe objective and whether the same process would remain defensible under a different market outcome. The leaderboard is a starting point for analysis, not proof of superior decision-making.