How to build a hedge fund course: a complete guide for lecturers

Central question: What makes a hedge fund different from a conventional long-only fund, and who bears the economic and operational risks?

A strong first session removes the mythology. Hedge funds are pooled investment vehicles with broad strategy flexibility, but the label covers very different mandates, liquidity terms and risk profiles. Lecturers should give students the ecosystem before the strategies: who supplies capital, who manages it, how assets and cash move, which service providers create controls, and how a fund is launched, scaled, closed or liquidated.

What this concept covers

• Hedge funds versus mutual funds, UCITS-style funds, private funds and proprietary trading.
• Manager, investor, administrator, custodian, auditor, legal counsel and prime-broker roles.
• Master-feeder, onshore/offshore and managed-account structures at a conceptual level.
• Fund launch, capital raising, capacity, redemptions, gates, closure and liquidation.
• Why strategy flexibility does not remove fiduciary, governance, operational or liquidity constraints.

Learning outcomes

• Map the main economic and control relationships in a hedge fund structure.
• Explain why investor liquidity and strategy liquidity must be considered together.
• Distinguish investment risk from operational and counterparty risk.
• Describe the fund lifecycle from launch through growth, redemption pressure and closure.

Supports Course Outcomes 1-2 as appropriate to assessment design.

How to teach it

• Build a fund ecosystem map before introducing strategy names.
• Give groups three fund descriptions and ask which service-provider relationships matter most in each.
• Contrast a liquid equity long/short fund with a less-liquid credit or relative-value fund.
• Use a short allocator memo to make students state which risks sit outside the return series.

Case-style example

Aurora Absolute Return Fund launches with $250 million, a 1.5% management fee, 15% performance fee, quarterly redemptions on 60 days notice and an 8% volatility target. A university endowment is comparing it with a daily-liquid multi-asset fund. Students map the manager, administrator, custodian, prime broker and investor relationships, then recommend which terms require deeper diligence. One group can defend the flexibility; another can argue the liquidity mismatch is already a warning. Students discover that fund structure is part of investment risk.

Where students struggle

Students often treat a hedge fund as a strategy rather than an organisation. They may know the phrase long/short but not who verifies NAV, lends securities, holds collateral or controls cash movements.

Reading and a quick check

Use Baker and Filbeck, Chapters 1-3 and 5, then ask students to draw the cash, asset and information flows for a hypothetical fund without notes.

Where it leads

The structure creates the incentives and liquidity terms that shape manager behaviour, which is the focus of Concept 2.

Central question: How do fees and liquidity terms change manager incentives and the investor's realised economics?

Headline returns are not investor returns. Management fees, performance fees, high-water marks, hurdles, share classes, lock-ups, notice periods, gates and side pockets determine who is paid, when capital can leave and how risk is shared. This topic works best when taught numerically and then connected to incentives rather than as legal vocabulary.

What this concept covers

• Management and performance fees, crystallisation and fee bases.
• High-water marks, hurdle rates and loss recovery.
• Lock-ups, redemption frequency, notice periods, gates and side pockets.
• Founder share classes and fee negotiation at a conceptual level.
• Convex incentive effects, asset gathering, risk-taking and investor alignment.

Learning outcomes

• Calculate simple fee outcomes across gains, losses and recovery periods.
• Explain how high-water marks affect incentives after a drawdown.
• Compare liquidity terms with the liquidity of underlying positions.
• Critique whether a fee and redemption package aligns the manager and investor.

Supports Course Outcomes 1-2 as appropriate to assessment design.

How to teach it

• Run a three-year fee calculation before discussing incentives.
• Ask students to compare identical gross returns under two fee structures.
• Introduce a drawdown and ask whether the manager has incentives to close and restart.
• Use a term-sheet critique where students must trade a lower fee against a longer lock-up.

Case-style example

Helix Capital starts at NAV 100 with a 1.5% management fee, 20% performance fee and high-water mark. Gross returns are +12%, -8% and +10% over three years; a competing share class charges 2% and 15% with a 4% hurdle. Students calculate approximate investor outcomes, identify when incentive fees crystallise, then choose which share class is better for a long-horizon allocator. Different assumptions about fee timing can support different answers. Students discover that contract design changes both net return and behaviour.

Where students struggle

Students often quote 2-and-20 without calculating the path dependency of fees. They also mistake better liquidity terms for an unqualified benefit even when the strategy itself is illiquid.

Reading and a quick check

Pair Baker and Filbeck, Chapter 9, with Ben-David, Birru and Rossi (2026) in the reading list. Quick check: ask what happens to incentives after a large loss when a high-water mark is far above current NAV.

Where it leads

Once incentives are clear, students can compare the different strategy families that managers use to generate return.

Central question: Where is the return supposed to come from, and what market exposure is being accepted to earn it?

Hedge fund strategy labels are useful only if students can translate them into positions, exposures and economic mechanisms. The goal is not to memorise a taxonomy. It is to ask what is long, what is short, what must converge or reprice, which risks are hedged, and what would cause the trade to fail.

What this concept covers

• Equity long/short, market-neutral and short-bias approaches.
• Event-driven and activist strategies.
• Global macro and managed directional exposures.
• Relative value, fixed-income arbitrage and convertible or volatility-linked trades.
• Multistrategy portfolios, factor exposures and the difference between labels and actual positions.

Learning outcomes

• Classify a strategy from its positions and return mechanism rather than its marketing label.
• Identify intended alpha sources and unintended beta or factor exposures.
• Explain which market conditions favour or damage a strategy.
• Compare liquidity, leverage and implementation constraints across strategy families.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Give students position books rather than strategy names and ask them to infer the strategy.
• Use a return decomposition exercise to show that two managers with different labels may share factor exposures.
• Ask each group to write one sentence beginning: This trade makes money if...
• End with a failure-mode matrix: thesis wrong, timing wrong, financing fails, liquidity disappears or crowding reverses.

Case-style example

Meridian Alternatives shows three sleeves: Sleeve A is 130% long and 70% short equities with net beta 0.25; Sleeve B is long cash Treasuries and short related futures with 20 basis points of expected convergence; Sleeve C buys announced merger targets at a 6% spread. Students identify each strategy family, state the return mechanism and rank the main risks. A debate arises over whether Sleeve B is low risk because it is hedged. Students discover that low directional beta can coexist with large financing and liquidity risk.

Where students struggle

Students often equate market neutrality with low risk and strategy diversification with exposure diversification. They need to look through labels to positions, financing and correlations.

Reading and a quick check

Use Baker and Filbeck, Chapters 11-15. Quick check: give one unfamiliar trade and ask students to describe return source, hedge, financing need and failure mode in four lines.

Where it leads

The first strategy deep dive is long/short equity, where fundamental company analysis and short mechanics become visible.

Central question: How should a manager build a defensible long or short thesis when accounting evidence, valuation and market narrative disagree?

Long/short equity is the closest bridge between conventional security analysis and hedge fund investing. Students should connect business quality, financial statements, valuation, catalysts, short interest, borrow cost and position sizing. A short thesis is not simply the inverse of a long thesis because losses can be unbounded, borrow can be recalled and crowding can overwhelm the fundamental view.

What this concept covers

• Long and short thesis construction, catalysts and variant perception.
• Income statement, balance sheet and cash flow evidence.
• Quality of earnings, cash conversion, leverage and working-capital signals.
• Short selling mechanics, securities lending, borrow cost, recall and squeeze risk.
• Gross and net exposure, position sizing and asymmetric loss profiles.

Learning outcomes

• Use financial-statement evidence to support or reject an investment thesis.
• Distinguish a weak company from an attractive short at the current price.
• Calculate simple gross and net exposure and position P&L.
• Explain how borrow cost and squeeze risk alter a short recommendation.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Have students produce paired long and short memos on the same sector.
• Separate the fundamental thesis from the catalyst and the trade construction.
• Use a short-squeeze case to distinguish being right on value from surviving the path.
• Require students to identify evidence that would falsify their own thesis.

Case-style example

Northline Software trades at $48. A hedge fund is considering a $20 million long in Northline and a $15 million short in RivalWorks at $30. Northline has 18% revenue growth, 82% recurring revenue and free-cash-flow margin of 14%; RivalWorks reports 21% growth but receivables are up 45%, cash conversion is weak and borrow costs 5% a year. Students compute gross and net exposure, estimate P&L if Northline rises 12% and RivalWorks falls 10%, then debate whether accounting risk justifies the short. Students discover that thesis quality and trade mechanics are separate decisions.

Where students struggle

Students can over-rely on ratios, confuse accounting deterioration with overvaluation, or assume a short position behaves symmetrically with a long. They also underestimate the role of path, liquidity and borrow availability.

Reading and a quick check

Use the GameStop case later in the course and a short equity-analysis note here. Quick check: ask for three facts that support the short and three facts that could still make the trade fail.

Where a simulation fits

The Financial Statement Analysis Simulation fits here as a focused fundamental-analysis exercise. It gives each student a company case, three-statement data, ratio work and five reporting periods. For a Hedge Fund course, the lecturer should frame the share-price prediction as evidence-based thesis revision rather than a full valuation model.

Where it leads

After a security-level strategy, students can compare event-driven, macro, relative-value and derivatives-based approaches where the unit of analysis changes.

Central question: How do event, macro and relative-value trades convert a market view or pricing discrepancy into a controlled position?

This concept broadens the course beyond equity selection. The emphasis should stay on trade architecture: catalyst or convergence, instruments, hedge ratio, financing, optionality, liquidity and scenario outcomes. Students do not need to become derivatives specialists, but they should be able to see why apparently small spreads can require large gross exposures and why options create nonlinear payoffs.

What this concept covers

• Merger arbitrage and event-driven catalysts.
• Activist positions and governance interventions.
• Global macro exposures across rates, currencies, equities and commodities.
• Relative-value and basis trades, including cash-futures logic.
• Futures, forwards and options as instruments for directional, hedge and volatility exposures.

Learning outcomes

• Describe the catalyst or convergence mechanism behind an event or relative-value trade.
• Identify the instruments and hedge that create the intended exposure.
• Compare linear and nonlinear payoff structures at a conceptual level.
• Evaluate whether expected spread or catalyst return compensates for financing, break and liquidity risk.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Use a merger spread calculation and a simple probability-weighted break case.
• Diagram a Treasury cash-futures basis trade without requiring a full fixed-income model.
• Give groups the same macro view but different instruments and ask which implementation is most robust.
• Use payoff sketches for an option-based volatility position.

Case-style example

Orion Relative Value can buy $120 million of a Treasury cash bond and short an economically related futures position. The expected convergence is 22 basis points over three months, repo financing costs 5.0% annualised and the trade requires $4 million of initial equity plus variation margin. A second opportunity is a merger-arbitrage spread offering 7% if the deal closes and a 24% loss if it breaks. Students calculate rough carry and probability-weighted outcomes, then choose which risk they would rather underwrite. Students discover that narrow spreads can hide financing and tail risk.

Where students struggle

Students often call a hedged trade risk-free or treat options as merely another security. They need to ask what remains unhedged, how margin changes, and whether the hedge survives stress.

Reading and a quick check

Use Baker and Filbeck, Chapters 12-14, plus the 2024 and 2026 Federal Reserve readings on Treasury basis activity. Quick check: ask students to explain a basis trade to a non-specialist in four sentences.

Where it leads

With the main strategy families visible, the course can move from individual trades to portfolio construction.

Central question: How should multiple positions be combined so that the portfolio earns intended alpha rather than accidental market risk?

Portfolio construction is where strategy ideas become a mandate. Students need to move beyond selecting securities and decide weights, concentration, cash, gross and net exposure, beta, covariance and diversification. The teaching goal is not to worship optimisation. It is to show how a model disciplines trade-offs and where judgement must override unstable inputs.

What this concept covers

• Expected return, beta, covariance, volatility and correlation.
• CAPM as a benchmark for expected return and beta interpretation.
• Mean-variance logic, efficient frontier and diversification.
• Gross, net and factor exposure at portfolio level.
• Concentration, cash, active risk and position sizing.

Learning outcomes

• Calculate and interpret core portfolio inputs and exposures.
• Construct a portfolio that reflects a stated mandate and risk objective.
• Explain why diversification can fail when correlations change.
• Defend a portfolio weight using both model output and investment judgement.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Start with five securities and force students to choose weights before showing optimisation output.
• Compare the optimised portfolio with a deliberately robust alternative.
• Require an assumptions note identifying unstable inputs.
• Use the simulation immediately after students can explain beta, covariance, volatility and role-specific objectives.

Case-style example

Beacon Hedge Partners has five investable securities with expected returns of 6%, 8%, 9%, 11% and 13% and betas of 0.4, 0.7, 0.9, 1.2 and 1.5. The mandate targets portfolio beta below 0.5, volatility near 8% and no single position above 30%. Students use a supplied covariance matrix to propose weights, then compare the model portfolio with a simpler diversified alternative. One group can maximise expected return; another can prioritise robustness. Students discover that optimisation is an input to judgement, not the judgement itself.

Where students struggle

Students may treat historical covariance as stable, optimise to implausibly precise weights, or focus on return without checking concentration and beta. A technically optimal portfolio can be economically fragile.

Reading and a quick check

Use Baker and Filbeck, Chapters 23 and 27 as performance context, then the Ardia et al. (2024) reading later. Quick check: ask students what input they trust least and how the portfolio changes if it is wrong.

Where a simulation fits

The Portfolio Management Simulation is the strongest direct fit. Teams analyse historical data, use CAPM and portfolio modelling, construct portfolios and rebalance as market conditions change. Hedge Fund teams pursue a higher-alpha mandate while Pension Fund teams pursue a different risk-adjusted objective, which creates a useful contrast for debriefing.

Where it leads

Portfolio construction creates the exposures that must then be financed and kept liquid, especially when leverage is used.

Central question: When does leverage improve capital efficiency, and when does financing structure become the dominant risk?

Hedge fund leverage is broader than borrowing cash. Gross notional exposure can arise through prime-broker financing, repo, derivatives and short positions. Students should learn to connect leverage to margin, haircuts, collateral, counterparty terms, liquidity and forced deleveraging. This concept is particularly important because funding can change faster than the underlying investment thesis.

What this concept covers

• Gross and net leverage and why definitions differ by instrument.
• Prime brokerage, securities financing, margin and collateral.
• Repo and haircut mechanics at a conceptual level.
• Derivatives notional versus economic exposure.
• Funding liquidity, margin spirals, counterparty concentration and forced deleveraging.

Learning outcomes

• Calculate simple gross and net leverage measures.
• Explain how margin and haircut changes affect required cash.
• Identify funding-liquidity risk in otherwise market-neutral trades.
• Evaluate whether a position can survive a stress period without forced sale.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Use a balance-sheet style leverage worksheet rather than formulas alone.
• Stress both asset prices and financing terms at the same time.
• Ask students which positions would have to be sold first in a margin call.
• Use LTCM as a later case to connect leverage, liquidity and crowded relative-value trades.

Case-style example

Northstar Macro has $500 million NAV, $900 million of long exposure and $750 million of short exposure. It holds $70 million cash and faces a stress in which long positions fall 6%, shorts rise 4% and financing counterparties raise margin requirements by $35 million. Students calculate gross and net exposure, estimate the first-order P&L and assess whether existing cash covers the margin call. One group can defend keeping the book intact; another can reduce leverage before prices recover. Students discover that solvency, liquidity and conviction are different questions.

Where students struggle

Students often quote leverage ratios without defining the denominator, ignore derivative notional, or assume a market-neutral book is funding-neutral. The practical question is whether the fund can finance the position through stress.

Reading and a quick check

Use Kruttli et al. (2025) and the Federal Reserve Treasury-exposure note. Quick check: ask students which matters more in a funding shock - expected long-run convergence or immediate collateral capacity - and why.

Where it leads

Financing risk leads directly into the wider risk-management framework: volatility, drawdowns, stress and tail behaviour.

Central question: What risk measures are useful for a hedge fund, and what important risks do they fail to capture?

Hedge funds can have nonlinear payoffs, stale or smoothed marks, concentrated books and strategy-specific liquidity. Volatility remains useful but is not enough. Lecturers should combine statistical measures with scenarios, drawdowns, position-level limits and liquidity analysis, then ask students what happens when historical relationships change.

What this concept covers

• Volatility, downside deviation and maximum drawdown.
• Value-at-risk as a framework and its limitations.
• Scenario analysis and stress testing.
• Tail risk, convexity, gap risk and correlation breakdown.
• Liquidity-adjusted thinking, concentration limits and risk budgets.

Learning outcomes

• Interpret volatility and drawdown in context.
• Design a stress scenario tied to actual portfolio exposures.
• Explain why apparently stable returns can conceal nonlinear or illiquid risk.
• Recommend risk limits that connect to strategy and investor liquidity.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Give the class a low-volatility strategy with a hidden short-option payoff and ask what metric misses it.
• Compare historical and hypothetical stress scenarios.
• Require a risk dashboard with no more than six measures.
• Make students state the action linked to each limit breach.

Case-style example

Sentinel Volatility Fund reports 7% annualised volatility, a 1.1 Sharpe Ratio and maximum monthly loss of 2.5% over three years. Its main strategy earns a small premium by selling options, but a supplied stress scenario implies an 11% portfolio loss if implied volatility doubles and correlations rise to 0.8. Students compare the benign history with the forward stress, choose a position limit and decide whether the track record justifies the risk. Students discover that smooth historical returns can be compensation for rare losses.

Where students struggle

Students can turn risk management into a list of ratios. The hard part is connecting a measure to an exposure, a limit, an action and a plausible failure mode.

Reading and a quick check

Use Baker and Filbeck, Chapters 16-18. Quick check: ask students for one risk that standard deviation captures reasonably well and one it does not.

Where it leads

Once risk is measured, students can evaluate whether reported performance represents skill, exposure, luck or measurement bias.

Central question: How much of a hedge fund return is genuine alpha, and what benchmark or model should be used to decide?

Hedge fund performance can look compelling under the wrong benchmark. This concept should teach students to decompose return into market and factor exposures, interpret Sharpe Ratio and drawdown, choose a comparison set and recognise database biases. The core teaching principle is that a single alpha estimate is model-dependent, not a permanent property of a manager.

What this concept covers

• Absolute return, excess return, alpha and beta.
• Sharpe Ratio and risk-adjusted comparison.
• Single- and multi-factor attribution at a conceptual and applied level.
• Benchmark choice, style drift and peer-group limitations.
• Survivorship, backfill, self-reporting, smoothing and model misspecification.

Learning outcomes

• Calculate simple Sharpe Ratio and CAPM alpha.
• Select a defensible benchmark or factor model for a strategy.
• Distinguish skill from systematic exposure and leverage.
• Identify data and measurement biases that can inflate apparent performance.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Give students the same track record and three possible benchmarks.
• Make them defend which benchmark is least wrong.
• Introduce a missing failed fund to demonstrate survivorship bias.
• Compare reported monthly volatility with position-level or stress evidence.

Case-style example

Delta Ridge returned 11% last year with 8% volatility. The risk-free rate was 4%, the equity market returned 9% and the fund beta was 0.35. A peer market-neutral index returned 7%. Students compute a simple Sharpe Ratio and CAPM alpha, then decide whether the market-neutral peer or CAPM is the better benchmark. Two defensible interpretations should emerge because the strategy also carries value and momentum exposures. Students discover that alpha is conditional on the model used to define beta.

Where students struggle

Students often treat positive alpha as proof of skill and Sharpe Ratio as comparable across all strategies. Non-normal returns, smoothing and benchmark choice can change the conclusion.

Reading and a quick check

Assign Ardia, Barras, Gagliardini and Scaillet (2024). Quick check: ask students to write the sentence, My alpha estimate is only meaningful if..., and complete it with the key model assumption.

Where a simulation fits

The Portfolio Management Simulation provides a practical debrief because the leaderboard combines return and volatility and separates Hedge Fund and Pension Fund mandates. Lecturers can ask why a high return may reflect concentration, leverage or market exposure rather than superior decision quality.

Where it leads

Performance evidence informs rebalancing, capacity and crowding decisions, but those decisions also depend on market impact and the behaviour of other investors.

Central question: When should a successful trade or strategy be resized, rebalanced or closed because capacity and crowding have changed?

Portfolio monitoring is not a mechanical quarterly rebalance. Hedge fund managers must decide whether new information changes expected return, risk, liquidity or the thesis itself. Capacity matters because a strategy that works at $200 million may not work at $5 billion, and crowded trades can turn correlated when many funds must exit together.

What this concept covers

• Thesis monitoring versus price monitoring.
• Rebalancing, turnover and transaction costs.
• Fund size, strategy capacity and market impact.
• Crowding, common ownership and correlated deleveraging.
• Stop-losses, position limits, catalyst failure and exit discipline.

Learning outcomes

• Distinguish thesis change from market noise.
• Evaluate whether a position should be resized based on expected return and risk.
• Explain how capacity can reduce future performance.
• Identify crowding indicators and plausible exit bottlenecks.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Use a sequence of market updates and force a decision after each one.
• Give teams the same new information but different starting positions.
• Ask students to write explicit sell or resize rules before revealing the outcome.
• Debrief turnover as a cost, not a sign of activity.

Case-style example

Quartz Equity Opportunities owns four crowded technology longs that represent 42% of NAV and 68% of the portfolio's predicted factor risk. One holding rises 25% after earnings, lifting its weight from 12% to 16%; another misses guidance but the long-term thesis is unchanged. Trading each 1% of NAV costs an estimated 8 basis points. Students decide which positions to rebalance, compute the approximate turnover cost of their plan and defend whether concentration is intentional alpha or accidental crowding. Students discover that rebalancing is a thesis-and-risk decision, not a calendar rule.

Where students struggle

Students may rebalance automatically back to target weights or, at the other extreme, never reduce a winning position. Both approaches avoid the real judgement about expected return, risk budget, liquidity and costs.

Reading and a quick check

Use Lu, Naik and Teo (2024) for capacity and persistence context. Quick check: ask when a manager should let a winning position become larger rather than rebalance it.

Where a simulation fits

The Portfolio Management Simulation fits again after the main run because teams buy, sell and rebalance as market news changes. The debrief should focus on whether each change was driven by updated evidence, mandate discipline or performance chasing.

Where it leads

A manager may have a credible strategy and good portfolio discipline yet still be uninvestable if operations, governance or controls are weak.

Central question: How should an allocator decide whether a skilled-looking manager is operationally and institutionally investable?

Manager selection is a second investment problem. Allocators need to assess people, process, portfolio construction, risk, terms, operations, valuation, custody, service providers, governance and regulatory context. This section should make clear that operational due diligence is not paperwork after the investment decision; it can invalidate the investment thesis.

What this concept covers

• Investment due diligence: team, philosophy, process, edge and portfolio evidence.
• Operational due diligence: valuation, NAV, cash controls, administrators, auditors and service-provider independence.
• Key-person, business continuity, cyber and counterparty risks at a conceptual level.
• Governance, conflicts, valuation policies and investor reporting.
• Regulatory portability: use local rules and official sources rather than teaching one jurisdiction as universal.

Learning outcomes

• Build a due-diligence framework that integrates investment and operational evidence.
• Identify red flags that can override an attractive return record.
• Assess whether liquidity and valuation policies fit the strategy.
• Recommend conditions, monitoring actions or reasons to reject a manager.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Give students an attractive track record with several operational red flags.
• Split the class into investment and operational diligence teams, then force a joint recommendation.
• Require an evidence request list rather than yes/no questions.
• Discuss how jurisdiction changes disclosure, marketing and investor-protection rules.

Case-style example

Cypress Credit Opportunities reports 14% annual returns with 6% volatility and monthly liquidity. Forty percent of assets, however, are Level 3 or model-valued, the fund administrator is affiliated with the manager, one senior portfolio manager controls both trade approvals and valuation overrides, and the key-person clause covers only the founder. Students rank the red flags, request five additional pieces of evidence and decide whether to invest, invest conditionally or reject the fund. Students discover that operational weaknesses can dominate a strong historical return series.

Where students struggle

Students often separate operational diligence from investment diligence and assume reputable service providers remove the need for controls. They should ask who can move cash, who values hard-to-price assets and how conflicts are resolved.

Reading and a quick check

Use Baker and Filbeck, Chapters 6, 18-20, and a current local regulatory source chosen for the jurisdiction. Quick check: ask for the single operational red flag that would make the class stop diligence immediately and why.

Where it leads

The final concept turns the perspective fully toward the allocator and asks what role hedge funds should play in an institutional portfolio.

Central question: When does a hedge fund allocation improve an institutional portfolio after fees, liquidity needs and governance capacity are considered?

The course should finish with the allocator's decision rather than a celebration of strategies. Pension funds, endowments, family offices and other investors use hedge funds for different purposes: diversifying equity risk, seeking absolute return, accessing specialist trades or reducing drawdown. The same fund can be attractive for one portfolio and unsuitable for another because objectives, liquidity, governance and fee tolerance differ.

What this concept covers

• Portfolio role: diversifier, alpha source, downside control or specialist exposure.
• Strategic versus opportunistic allocation and liquidity budgeting.
• Manager concentration, funds of funds and implementation routes.
• Net-of-fee return expectations and governance capacity.
• Investment committee decision-making, monitoring criteria and exit conditions.

Learning outcomes

• Assess whether a hedge fund strategy fits a stated institutional objective.
• Compare managers on net return, risk, liquidity, transparency and operational quality.
• Recommend an allocation size consistent with portfolio and liquidity constraints.
• Defend an investment committee recommendation with explicit monitoring and exit criteria.

Supports Course Outcomes 2-10 as appropriate to assessment design.

How to teach it

• Give students a full institutional portfolio and three candidate hedge funds.
• Require a no-invest option so allocation is not assumed.
• Use a committee format with return, risk, liquidity and governance objections.
• Finish with an individual one-page defence so group work produces attributable evidence.

Case-style example

Elmbridge Endowment has $2 billion in assets, a 5% annual spending requirement and a policy limit of 20% in investments redeemable less frequently than quarterly. It is considering three $100 million hedge fund allocations: a 9% target-return equity long/short fund with monthly liquidity, a 10% target-return relative-value fund with a one-year lock-up, and a 7% target-return market-neutral fund with quarterly liquidity. Students recommend zero, one or two allocations, explain the portfolio role and state three monitoring triggers. Students discover that the best manager is not always the best portfolio allocation.

Where students struggle

Students can default to chasing the highest target return or treating low correlation as permanent. The institutional decision must connect role, liquidity, fees, governance burden and downside behaviour.

Reading and a quick check

Use Mirabile as the allocator-oriented complement and the most recent readings on funding, performance and AI. Quick check: require a one-sentence portfolio role before allowing any allocation recommendation.

Where a simulation fits

The Portfolio Management Simulation provides a useful contrast because Hedge Fund and Pension Fund teams pursue different objectives. It is not a manager-selection simulation, so use it to reveal mandate-dependent portfolio choices rather than to imply that it covers operational due diligence.

Where it leads

This concept closes the lifecycle. Students should now be able to move from fund structure and strategy logic through trade construction, portfolio risk and performance to a defensible institutional allocation decision.

10-week course structure

A 10-week version should compress adjacent topics without deleting the difficult decisions. Combine structure with terms, bring short selling into long/short analysis and use the Portfolio Management Simulation as the central applied session.

14-week course structure

A 14-week version creates room for a separate short-selling/event session, a portfolio workshop before the simulation and deeper risk, performance and due-diligence work. Use the extra sessions for application and debrief rather than adding unrelated strategy labels.

The Portfolio Management Simulation is the primary applied fit for this course. It is a 3-hour team-based web simulation that can be delivered in one session, split into shorter blocks, assigned as homework or used in hybrid form. Teams of 3-5 receive either a Hedge Fund or Pension Fund mandate, analyse historical market data and construct portfolios from 25 global companies. The model supports expected return, beta, covariance, volatility and portfolio weights. Teams then buy, sell and rebalance as conditions change, and the debrief compares return with volatility and the mandate-specific score.

Why it fits here

• Students already know CAPM, expected return, beta, covariance, volatility and diversification, so the model can be challenged rather than copied.
• The Hedge Fund mandate creates a direct discussion of alpha seeking, active weights and permitted borrowing, while Pension Fund teams provide a useful comparator.
• Recorded portfolio choices, trades and performance make it possible to distinguish decision quality from final return.

What students do

• Review historical price series and role-specific mandate guidance.
• Use CAPM and portfolio-analysis guidance to estimate beta, expected return, covariance, volatility and weights.
• Construct the initial portfolio by choosing securities, weights and cash; Hedge Fund teams may also discuss the consequences of permitted borrowing.
• Respond to market news by buying, selling, retaining or rebalancing positions.
• Compare return and volatility with teams pursuing the same mandate, then explain whether high return came from skill, concentration, leverage or market exposure.

Recommended course placement

• At the end of Session 6 after portfolio construction teaching.
• Across Sessions 6-7 if the class wants preparation before the timed run and a separate debrief.
• As a 3-hour applied class, split 4 x 45 minutes, 3 x 1 hour or 2 x 1.5 hours, or as a hybrid/homework activity.
• Before the performance-attribution session so later teaching can reuse the team evidence.

Knowledge students should have before the simulation

• CAPM and beta.
• Expected return, covariance and volatility.
• Diversification and portfolio weights.
• Alpha and Sharpe Ratio at an introductory level.
• The difference between a mandate objective and simply maximising raw return.

Delivery format

Suggested debrief questions

• Which portfolio decision had the largest effect on risk-adjusted performance?
• Did your team create intended alpha or simply take more market, concentration or leverage exposure?
• Which CAPM or covariance assumption was least robust?
• What new information caused a genuine thesis change rather than performance chasing?
• How would the portfolio differ if you were managing the Pension Fund mandate?
• What would you retain as team evidence, and what individual evidence would you require for summative assessment?

The Financial Statement Analysis Simulation is the secondary fit. It is a 2-3-hour single-player web simulation in which each student acts as a Financial Analyst. Students connect the Income Statement, Balance Sheet and Cash Flow Statement, calculate profitability, liquidity, efficiency and solvency ratios, then reassess company performance across five reporting periods as new financial information and company reports arrive. They submit and revise a share-price prediction. For a Hedge Fund course, that prediction should be treated as an engagement and evidence-revision device, not as a substitute for a DCF or a complete long/short valuation process.

Why it fits here

• Long/short equity depends on evidence quality, cash conversion, balance-sheet risk and the ability to update a thesis as new information arrives.
• The simulation is individual, so it can provide attributable evidence before students move into a group long/short recommendation.
• The five evolving reporting periods make thesis revision and confirmation bias visible.

What students do

• Review a company's three primary financial statements and the supplied Financials & Ratios workbook.
• Calculate required ratios and check whether errors arise from data selection, formulas, entry or interpretation.
• Across five evolving rounds, compare trends, read updated company information and decide whether the original view still holds.
• Submit a revised share-price prediction as a concise expression of evidence-based judgement.
• Use the final reflection, submitted answers and history of decisions to explain why the thesis changed or did not change.

Recommended course placement

• During Session 4 after financial statements and ratio interpretation have been refreshed.
• Before a separate long/short thesis task so students must convert accounting evidence into valuation, catalyst, borrow and risk judgement.
• As one 2-3-hour workshop or divided across teaching sessions; the web-based delivery supports in-class, online or hybrid use.

Knowledge students should have before the simulation

• Basic understanding of the three financial statements is helpful but the simulation is beginner accessible.
• Core profitability, liquidity, efficiency and solvency ratios.
• The distinction between accounting analysis and equity valuation.
• A simple long/short thesis template for the post-simulation extension.

Delivery format

Suggested debrief questions

• Which financial indicator most changed your view of the company?
• Did any ratio move for an accounting reason that did not change economic value?
• Where did you follow the cohort too closely rather than update from evidence?
• What additional valuation evidence would a hedge fund need before taking a long or short position?
• What catalyst, borrow cost or downside scenario is missing from the simulation?
• Which individual evidence would you use for grading, and which leaderboard information would you treat only as context?

Grading with the Portfolio Management Simulation

The simulation involves team-based analysis of historical market data, CAPM and portfolio modelling, portfolio construction, trading and rebalancing under either a Hedge Fund or Pension Fund mandate. The platform-generated portfolio data, trades and performance measures can support academic judgement, but final return or the simulation score should not automatically become the grade.

• Portfolio construction: security selection, weights, diversification, concentration and cash allocation.
• Model use: how beta, expected return, covariance and volatility informed the initial portfolio, including limitations.
• Mandate fit: whether Hedge Fund decisions pursued alpha without ignoring risk and whether teams understood the different Pension Fund objective.
• Rebalancing: whether trades followed new evidence and risk logic rather than performance chasing.
• Individual evidence: a separate written report, assumptions note or viva completed outside the platform when an individual mark is required.

Grading with the Financial Statement Analysis Simulation

The simulation is single-player. Each student analyses the three statements, calculates ratios, interprets five reporting periods and repeatedly submits a share-price prediction. It can therefore supply individual evidence, but the leaderboard or aggregate score should still be interpreted as supporting evidence rather than an automatic mark.

• Calculation quality: correct use of statement data and ratio formulas.
• Interpretation: whether the student explains what changes mean for profitability, liquidity, efficiency, solvency and financial health.
• Evidence revision: when and why the student changes the share-price view as new information arrives.
• Hedge fund extension: a post-simulation long/short thesis that adds valuation, catalyst, borrow or squeeze risk and position sizing.
• Reflection: identification of confirmation bias, cohort following, calculation errors and missing information.