How to build an investment analysis course: a complete guide for lecturers

Central question: What exactly is an investment analyst trying to decide, for whom, and under what market constraints?

Open the course by treating investment analysis as a decision process rather than a collection of formulas. Students need to distinguish the investor objective, the security or portfolio being analysed, the available information, the benchmark and the constraints before they calculate anything.

What this concept covers

• Investment objectives, mandates and risk tolerance.
• Primary and secondary markets, exchanges, dealers, brokers and market makers.
• Asset classes and the distinction between security selection and asset allocation.
• Investment policy statements, benchmarks, liquidity and horizon.
• The analyst workflow: information, forecast, valuation, decision, implementation and review.

Learning outcomes

By the end of this concept, students should be able to:

• Define a decision-relevant investment objective.
• Distinguish security analysis from portfolio construction.
• Identify market participants and trading frictions that matter to an investment decision.
• Translate a client mandate into measurable constraints.
• Frame an investment recommendation around evidence and a benchmark.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Give every group the same security but a different investor mandate.
• Ask students to write the decision before they open a spreadsheet.
• Map the information chain from company disclosure to analyst estimate to trade.
• Introduce the idea that the same asset can be appropriate for one mandate and unsuitable for another.

Case-style example

Northgate University Endowment has £120 million to invest. Its spending rule needs 4% a year, it can tolerate a 15% one-year loss and it must keep £18 million liquid. A family office evaluating the same securities can tolerate a 30% drawdown and has no annual spending need. Students write a one-page mandate for each investor and identify which evidence could lead the two investors to opposite recommendations. They discover that investment quality is conditional on the investor objective.

Where students struggle

Students often begin with the asset and only later ask who owns it. That reverses the professional process. The mandate determines what risk, horizon, liquidity and benchmark are relevant.

Reading and a quick check

Ask students to rewrite a vague objective such as “maximise returns” into a measurable mandate with horizon, benchmark, risk tolerance and liquidity constraints.

Where it leads

The next step is to measure return and risk consistently so that choices can be compared rather than described.

Central question: How should investors measure reward and risk, and why does portfolio context change the answer?

Students need clean return arithmetic before they can evaluate securities or portfolios. This concept connects holding-period returns, annualisation, volatility and correlation to the central investment principle that total portfolio risk is not the weighted average of standalone risk.

What this concept covers

• Holding-period, arithmetic, geometric and annualised returns.
• Expected return, variance, standard deviation and downside measures.
• Covariance and correlation.
• Systematic versus idiosyncratic risk.
• Diversification and concentration risk.

Learning outcomes

By the end of this concept, students should be able to:

• Calculate and compare common return measures.
• Interpret volatility and correlation in portfolio context.
• Separate diversifiable from non-diversifiable risk.
• Explain why a volatile security can still improve a portfolio.
• Evaluate concentration using contribution to portfolio risk.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Use two assets with identical expected return but different correlation with the existing portfolio.
• Have students calculate portfolio risk before discussing diversification.
• Ask groups to identify the risk measure that matches a given mandate.
• Contrast a concentrated stock-picking portfolio with a broad market portfolio.

Case-style example

Pinecrest Pension owns a £40 million equity portfolio with 12% volatility. It can add either Alder Tech, expected return 9%, volatility 24%, correlation 0.20 with the portfolio, or Metro Utilities, expected return 7%, volatility 11%, correlation 0.75. Students calculate a 10% allocation to each candidate, compare resulting portfolio volatility and recommend one. One team may prefer higher expected return, another lower standalone risk. They discover that correlation can dominate standalone volatility in diversification decisions.

Where students struggle

Students frequently rank securities by standalone volatility. The better question is how the security changes total portfolio risk and whether that change is rewarded.

Reading and a quick check

Give a three-security mini-table of returns, volatilities and correlations and ask which asset provides the greatest diversification benefit before any optimisation is run.

Where it leads

With return, variance and covariance in place, students can build the efficient frontier and see how portfolio weights translate into risk-return choices.

Central question: How can an investor combine securities to achieve the best available expected return for a chosen level of risk?

This concept turns diversification into an explicit optimisation problem. The teaching goal is not to make students worship the optimiser. It is to show what the model is solving, which inputs drive the answer and why estimation error can make an apparently optimal portfolio fragile.

What this concept covers

• Portfolio expected return and variance.
• Feasible set and efficient frontier.
• Risk-free asset, capital allocation line and tangency portfolio.
• Sharpe Ratio and risk-adjusted choice.
• Estimation error, constraints and unstable weights.

Learning outcomes

By the end of this concept, students should be able to:

• Construct a simple efficient frontier.
• Identify dominated and efficient portfolios.
• Calculate and interpret the Sharpe Ratio.
• Explain how constraints change an optimal portfolio.
• Challenge implausible optimiser outputs.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Start with three assets so students can see the feasible set.
• Add a no-short constraint and compare the frontier.
• Change one expected-return estimate slightly and show how weights move.
• Require a qualitative defence of the final allocation.

Case-style example

Alder Ridge Fund can allocate among Global Equity (8% expected return, 16% volatility), Government Bonds (4%, 6%) and Infrastructure (7%, 11%). Equity-bond correlation is 0.10, equity-infrastructure 0.55 and bond-infrastructure 0.15. Students build three candidate portfolios, calculate expected return and variance, then compare the highest Sharpe portfolio with a 50/30/20 policy mix. They discover that the mathematically best portfolio can conflict with mandate limits or unstable inputs.

Where students struggle

Students often treat optimisation as a machine that reveals the correct portfolio. In practice, expected returns are estimated with substantial error, so constraints and judgement matter.

Reading and a quick check

Change one expected return by 50 basis points and ask students whether the new optimal weights are economically credible.

Where a simulation fits

The Portfolio Management Simulation fits once students have learned CAPM, Sharpe Ratio and mean-variance optimisation. The full simulation is best placed later, after students can also interpret market information and defend rebalancing decisions. View the Portfolio Management Simulation.

Where it leads

Portfolio theory provides the demand side of investment choice. Asset-pricing models then ask what return should be required for bearing systematic risk.

Central question: What return should an investor require for risk, and when is beta too simple a description of that risk?

CAPM is useful because it forces a disciplined distinction between total risk and priced systematic risk. The concept should then widen into multifactor thinking so students can evaluate whether apparent alpha is compensation for systematic exposures rather than security-selection skill.

What this concept covers

• Beta and the security market line.
• Expected versus required return and Jensen alpha.
• CAPM assumptions and empirical limitations.
• Size, value, profitability, investment and momentum-style factor exposures.
• Benchmark choice and model risk.

Learning outcomes

By the end of this concept, students should be able to:

• Estimate and interpret beta.
• Use CAPM to calculate a required return.
• Calculate and critique alpha.
• Compare CAPM with multifactor explanations.
• Explain why benchmark choice changes measured skill.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Use the same realised return under two different betas.
• Ask whether a positive alpha survives a factor-model adjustment.
• Compare a low-beta defensive stock with a high-beta cyclical stock.
• Emphasise that factors are models of expected return, not guaranteed premia.

Case-style example

Redbridge Asset Management is comparing Luma Health, beta 0.75, and Orbit Components, beta 1.35. The risk-free rate is 3%, expected market return is 8%, and both stocks are forecast to return 9%. Students compute CAPM required returns, implied alpha and then consider that Luma has a strong profitability factor exposure while Orbit has a value tilt. Some teams prefer Luma’s positive CAPM alpha; others question the model. They discover that alpha is benchmark-dependent, not a raw return number.

Where students struggle

Students often interpret beta as total risk or treat positive realised alpha as proof of skill. Keep the benchmark, estimation period and factor exposures visible.

Reading and a quick check

Give students two securities with identical return but different betas and ask which has higher CAPM alpha and what evidence could overturn that conclusion.

Where a simulation fits

The Portfolio Management Simulation uses CAPM guidance, beta, expected return, covariance and mean-variance optimisation. It is a direct application once the model has been taught and its limits made explicit. View the Portfolio Management Simulation.

Where it leads

After students can estimate required return, they are ready to ask how efficiently markets incorporate information and whether predictable patterns survive implementation costs.

Central question: When should an analyst believe a security is mispriced, and what would make that belief investable rather than merely interesting?

This concept is where investment analysis becomes epistemic discipline. Students need to distinguish a price they dislike from an exploitable mispricing. Market efficiency provides the benchmark, anomalies provide empirical challenges and behavioural finance provides mechanisms that may explain departures from fully rational pricing.

What this concept covers

• Weak, semi-strong and strong-form efficiency.
• Event information and the speed of price adjustment.
• Momentum, value and other return patterns.
• Limits to arbitrage, short-selling and implementation risk.
• Overconfidence, extrapolation, loss aversion and herding.

Learning outcomes

By the end of this concept, students should be able to:

• Evaluate evidence for and against market efficiency.
• Distinguish statistical predictability from an implementable strategy.
• Identify behavioural mechanisms that can affect prices.
• Assess limits to arbitrage and short-selling risk.
• State what evidence would falsify an investment thesis.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Use an earnings announcement and ask when the information became public.
• Compare a backtested anomaly before and after costs.
• Run a short debate between efficient-markets and behavioural explanations.
• Require every mispricing claim to include a catalyst and a risk that the analyst may simply be wrong.

Case-style example

Nova Retail trades at £18 after rising 60% in three weeks on social-media attention. Consensus EPS is £0.90, the sector trades at 14x forward earnings and borrow costs for short sellers are 9% a year. Students compare an implied fundamental price near £12.60 with the cost and risk of shorting. One group shorts immediately; another waits for an earnings catalyst. They discover that identifying possible overvaluation is different from designing a trade that can survive timing and financing risk.

Where students struggle

Students often use “inefficient” as a synonym for volatile or surprising. Ask what public information is not reflected in price, why it persists and how an investor could capture it after costs.

Reading and a quick check

Give a one-paragraph anomaly claim and ask students to list the transaction costs, benchmark issues and data-snooping tests needed before they would invest.

Where it leads

If prices can diverge from analyst estimates, students need a disciplined way to extract evidence from company accounts before building valuation forecasts.

Central question: What do the financial statements reveal about economic performance, cash generation and the sustainability of reported earnings?

Security analysis depends on accounting evidence. Rather than teaching ratios as isolated formulas, this concept asks students to connect the income statement, balance sheet and cash flow statement, trace business events across them and distinguish reported growth from high-quality growth.

What this concept covers

• Three-statement linkage and cash versus accrual earnings.
• Profitability, efficiency, liquidity and solvency ratios.
• Working capital, capital expenditure and cash conversion.
• Earnings quality, one-offs, accounting choices and warning signs.
• Forecasting implications for valuation.

Learning outcomes

By the end of this concept, students should be able to:

• Connect changes across the three financial statements.
• Calculate and interpret key ratios.
• Identify signs of weak earnings quality.
• Separate operating improvement from accounting presentation.
• Convert accounting evidence into a forward-looking analyst view.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Ask students to trace one business event across all three statements.
• Use ratios only after students explain the economic driver.
• Compare net income with operating cash flow.
• Require a forecast implication for each red flag identified.

Case-style example

Harbormark PLC reports revenue up 14% to £570 million and net income up 18%, but operating cash flow falls from £62 million to £34 million. Receivable days rise from 41 to 58 and inventory days from 52 to 73. Students calculate the working-capital movements, identify the strongest earnings-quality warning sign and decide whether to cut a growth forecast. Bulls point to new contracts; bears point to cash conversion. They discover why earnings growth without cash support deserves challenge.

Where students struggle

Students can calculate ratios correctly yet fail to explain what business event caused them or how the result should change a forecast. Mark the causal chain, not just the arithmetic.

Reading and a quick check

Give two years of profit, cash flow, receivables and inventory data and ask for one ratio, one interpretation and one valuation implication.

Where a simulation fits

The Financial Statement Analysis Simulation fits directly here. Students work individually as Financial Analysts, connect the three statements, calculate and interpret ratios across changing reporting periods and use the evidence to form a supported share-price prediction. The prediction is an engagement device rather than a full valuation model. View the Financial Statement Analysis Simulation.

Where it leads

Once students can judge the quality and direction of fundamentals, they can convert forecasts into an explicit equity valuation and investment thesis.

Central question: What is the security worth, which assumptions drive that value, and what would have to happen for the market price to converge toward the analyst view?

Valuation should be taught as a range of conditional outcomes, not a single target price. Students should connect accounting forecasts to DCF, dividend or residual-income logic and triangulate with market multiples, while making the investment thesis and catalyst explicit.

What this concept covers

• Free cash flow and dividend-based valuation logic.
• Terminal value, growth and discount-rate sensitivity.
• Comparable-company and precedent-multiple evidence.
• Forecast horizon, scenario analysis and margin of safety.
• Investment thesis, catalyst, target price and thesis-breaking evidence.

Learning outcomes

By the end of this concept, students should be able to:

• Build or interpret a simple equity valuation.
• Triangulate valuation methods rather than average them mechanically.
• Identify the assumptions that drive target value.
• Link financial forecasts to a clear investment thesis.
• State catalysts, risks and falsification conditions.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Make students write the thesis before the target price.
• Use a valuation football field rather than one point estimate.
• Force a bull, base and bear case with different operating assumptions.
• Ask which assumption is both uncertain and value-sensitive.

Case-style example

Lumiere Software generates £42 million of forecast FCFF next year. Base-case FCFF grows 7% for five years, then 3%, with an 8.5% WACC. A peer multiple implies £22 per share, while the base DCF implies £28 and the market price is £24. Students compute a sensitivity to 2% terminal growth and 9.5% WACC, then write Buy, Hold or Sell. Bulls trust recurring revenue; bears question retention. They discover that the recommendation depends on assumptions, not the spreadsheet label.

Where students struggle

Students often hide uncertainty inside precise decimal places. Require a valuation range, an assumptions table and a sentence explaining what would make the analyst wrong.

Reading and a quick check

Ask students to identify the two assumptions that explain most of the gap between market price and target value, then change each independently.

Where a simulation fits

The Financial Statement Analysis Simulation can sit immediately before valuation as evidence preparation. Its share-price prediction should not be presented as a DCF substitute; use it to test whether students can turn reported performance and new information into a defensible directional view. Once students can translate forecasts into a Buy, Hold or Sell recommendation, the Investment Banking Simulation also fits here as a broader applied exercise in valuation, modelling, recommendation logic and professional communication under deadlines.

Where it leads

Equity is only one major security class. Fixed-income analysis changes the emphasis from upside participation to contractual cash flows, rates and default risk.

Central question: How should an investor value contractual cash flows when interest rates, term structure and default risk can all change?

Fixed income gives students a different lens on investment value. The cash-flow promise is more explicit than in equity, but the investor must still interpret yield, duration, convexity, spread and credit quality. The topic also reinforces the distinction between price risk and income return.

What this concept covers

• Bond pricing, yield to maturity and spot-rate logic.
• Duration, convexity and interest-rate sensitivity.
• Yield-curve interpretation.
• Credit spreads, ratings and default risk.
• Total return and reinvestment risk.

Learning outcomes

By the end of this concept, students should be able to:

• Price a plain-vanilla bond.
• Interpret yield and duration.
• Estimate the direction and approximate size of an interest-rate price move.
• Separate interest-rate risk from credit-spread risk.
• Compare bonds on expected return, downside and portfolio role.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Start with a zero-coupon bond, then add coupons and credit spread.
• Show the same yield change for short and long duration bonds.
• Use a steepening or flattening yield curve scenario.
• Ask whether a higher yield compensates for the added credit risk.

Case-style example

Norchester Telecom has a seven-year bond with 5% annual coupon, £100 par and current yield to maturity of 5.8%. Its modified duration is 5.4. A weaker earnings outlook could widen the credit spread by 120 basis points, while a central-bank cut could lower risk-free yields by 75 basis points. Students estimate the first-order price effects separately and together, then recommend Buy or Avoid. They discover that rate and credit moves can offset one another and that yield alone is not expected return.

Where students struggle

Students often treat yield as a guaranteed return or forget that a bond can lose value when rates or credit spreads move. Keep price, income and reinvestment effects separate.

Reading and a quick check

Give a duration and two yield scenarios and ask for an approximate price change, then ask what the calculation misses.

Where it leads

With security-level analysis across equity and fixed income, students can return to the portfolio and decide how to allocate under real-world constraints.

Central question: How should an analyst turn security views into position sizes that respect the investor mandate and the uncertainty of the inputs?

Security selection is incomplete until it becomes a portfolio. This concept moves from expected returns and covariances to position sizing, concentration limits, cash, leverage and practical constraints. It is the natural point to teach that portfolio construction is risk budgeting, not just ranking stocks.

What this concept covers

• Strategic versus tactical allocation.
• Position sizing and risk contribution.
• Long-only, shorting, cash and leverage constraints.
• Optimisation with concentration and turnover limits.
• Scenario, stress and estimation-risk overlays.

Learning outcomes

By the end of this concept, students should be able to:

• Translate security views into portfolio weights.
• Apply concentration and mandate constraints.
• Interpret risk contribution.
• Compare model-optimal and implementable portfolios.
• Defend an allocation under competing return and risk objectives.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Give students the same expected returns but different mandates.
• Add a turnover limit after the optimiser has produced weights.
• Require one deliberate non-model override and a written justification.
• Ask what risk is concentrated even when capital weights look diversified.

Case-style example

Zenith Global Equity has £50 million, a 10% cash ceiling, no single stock above 12% and a tracking-error budget of 6%. The optimiser puts 23% in Aster Semiconductors because of a high expected return and low estimated covariance. Students redesign the allocation under the limits and explain whether to cap Aster at 12% or challenge the covariance estimate. One view maximises model Sharpe; the other prioritises robustness. They discover why constraints often improve implementability rather than merely reduce performance.

Where students struggle

Students can confuse a list of good stocks with a good portfolio. Make them explain position size, interaction with existing holdings and the risk budget.

Reading and a quick check

Show an optimiser output with an implausible 35% position and ask students for three reasons the input or objective function may be causing it.

Where a simulation fits

This is the strongest placement for the Portfolio Management Simulation. Teams manage either a Hedge Fund or Pension Fund mandate, use historical data and an Excel model to analyse expected return, beta, covariance, volatility and weights, then construct and rebalance a portfolio across 25 global companies. View the Portfolio Management Simulation.

Where it leads

Once the portfolio is implemented, the next question is whether active decisions add value after costs and relative to an appropriate benchmark.

Central question: When is active investment skill worth paying for, and how should investors separate genuine alpha from systematic exposures and implementation costs?

This concept connects evidence on market efficiency with the economics of active management. Students compare indexing, factor tilts and discretionary selection, then subtract turnover, fees and market impact rather than evaluating strategies on gross backtests.

What this concept covers

• Indexing and benchmark construction.
• Active share, tracking error and information ratio.
• Factor investing and smart beta.
• Turnover, commissions, spreads and market impact.
• Backtest overfitting and data-snooping risk.

Learning outcomes

By the end of this concept, students should be able to:

• Compare active and passive investment propositions.
• Separate factor exposure from manager alpha.
• Estimate the return drag from turnover and fees.
• Identify backtest overfitting warning signs.
• Recommend a strategy consistent with client objectives and costs.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Give a gross-alpha strategy and make students subtract realistic costs.
• Compare a closet indexer with a concentrated active fund.
• Ask which factor exposure explains the manager track record.
• Use out-of-sample versus in-sample performance to discuss overfitting.

Case-style example

Beacon Quant Fund reports 3.2% annual gross alpha with 180% annual turnover. Estimated all-in trading cost is 55 basis points per 100% of turnover and the fee is 90 basis points. A low-cost factor ETF captures 2.0% of the same gross excess return for 25 basis points. Students calculate net alpha, compare both choices and debate whether Beacon adds security-selection skill. They discover how impressive gross backtests can become ordinary after factor exposure, turnover and fees.

Where students struggle

Students often compare gross active returns with net index returns or ignore that a factor tilt is not necessarily alpha. Make the benchmark and cost base explicit.

Reading and a quick check

Ask students to convert a gross backtest into a client-level net return using turnover, implementation cost and fees, then state which assumptions are hardest to verify.

Where a simulation fits

The Portfolio Management Simulation supports this concept through competing mandates, trading decisions and rebalancing. Use the debrief to separate portfolio outcome from the quality of the process and to ask whether concentration, cash or leverage created the result. View the Portfolio Management Simulation.

Where it leads

Performance must then be evaluated formally so that return, risk, benchmark exposure and allocation decisions can be separated.

Central question: Did the portfolio perform well for the risk taken and the mandate given, and which decisions actually created or destroyed value?

A raw return does not tell a lecturer or investor whether a portfolio was well managed. This concept makes students compare returns with volatility and benchmark exposure, then decompose outcomes into allocation, selection and timing effects where appropriate.

What this concept covers

• Absolute versus benchmark-relative return.
• Sharpe Ratio, Jensen alpha and information ratio.
• Basic performance attribution.
• Rebalancing policy and drift.
• Outcome versus process evaluation.

Learning outcomes

By the end of this concept, students should be able to:

• Calculate and interpret common performance measures.
• Compare portfolios with different risk.
• Attribute return to broad decision categories.
• Design a simple rebalancing rule.
• Separate good process from lucky outcome.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Compare two portfolios with the same return and different volatility.
• Use an attribution table where allocation helped but selection hurt.
• Ask whether a winning concentrated position should be rebalanced.
• Mark the pre-decision rationale alongside the realised outcome.

Case-style example

Harbour Pension earns 9.4% with 8.0% volatility and a 2% risk-free rate; Meridian Hedge Fund earns 12.0% with 17% volatility. Meridian’s beta-adjusted alpha is 1.1%, but Harbour has the higher Sharpe Ratio. Students calculate both Sharpe Ratios, compare the two mandates and recommend whether either manager should rebalance after one holding rises from 8% to 15%. They discover that “better performance” has no meaning without mandate, benchmark and risk.

Where students struggle

Students reward the highest return. Repeatedly ask whether the result was appropriate for the mandate, repeatable and produced by the intended source of risk.

Reading and a quick check

Give return, volatility, beta and benchmark data for two managers and ask students which is better before revealing that their mandates differ.

Where a simulation fits

The Portfolio Management Simulation is especially useful here because Hedge Fund teams are evaluated around Alpha while Pension Fund teams focus on Sharpe Ratio. Compare teams only within the same mandate and use portfolio choices, trades and performance as evidence, not as an automatic grade. View the Portfolio Management Simulation.

Where it leads

The course can now close by adding sustainability, stewardship and changing information tools to an integrated investment recommendation.

Central question: How should financially material sustainability information affect expected return, risk, valuation and portfolio choice without turning ESG scores into automatic investment answers?

The final concept brings the course together. Sustainability can affect cash flows, discount rates, stranded-asset risk, regulation, reputation and investor preferences, but the investment consequence depends on materiality, price and mandate. The goal is evidence-based integration rather than score-following.

What this concept covers

• Financial materiality and sustainability information.
• Climate transition, physical risk and scenario analysis.
• ESG data quality, disagreement and greenwashing risk.
• Stewardship, voting and engagement.
• Portfolio tilts, exclusions and mandate consistency.

Learning outcomes

By the end of this concept, students should be able to:

• Identify financially material sustainability information.
• Connect sustainability risks to cash flow, discount rate or scenario assumptions.
• Evaluate ESG data limitations.
• Compare exclusion, integration and stewardship approaches.
• Defend an integrated portfolio recommendation.

Supports the course outcomes on analysis, valuation, portfolio judgement and evidence-based recommendation.

How to teach it

• Give the same ESG issue to a long-horizon pension fund and a short-horizon hedge fund.
• Require a financial transmission mechanism for every sustainability claim.
• Compare two ESG ratings that disagree and ask why.
• Close with a full investment committee-style recommendation.

Case-style example

Greenwave Materials is expected to return 8.5% but faces a possible £35 million carbon-compliance cost within five years. Brownstone Logistics is expected to return 9.2%, has lower near-term transition cost but higher fuel-price exposure. A pension fund has a 2050 net-zero commitment; a market-neutral fund does not. Students build one downside scenario for each company and recommend portfolio weights for both mandates. They discover that sustainability evidence matters through financial channels and investor objectives, not through a universal score.

Where students struggle

Students can either ignore sustainability as non-financial or treat high ESG scores as proof of a good investment. Require materiality, valuation impact and price to stay connected.

Reading and a quick check

Ask students to convert one sustainability issue into an explicit change to revenue growth, margin, capex, discount rate or scenario probability, then defend the direction and magnitude.

Where a simulation fits

The ESG and Sustainable Finance Simulation fits naturally here. Students examine a real sustainability challenge from multiple stakeholder perspectives, connect ESG evidence to financial viability and negotiate an approval-committee recommendation. Use it after students can discuss materiality, stewardship and the difference between responsible-investment principles and automatic score-following.

Where it leads

Students should now be able to move from mandate and evidence to valuation, position sizing, portfolio implementation and performance review while defending the assumptions that connect each stage.

10-week course structure

A 10-week version should compress foundations and combine some application without removing the core risk-return, valuation and portfolio decisions. Use the Financial Statement Analysis Simulation in the middle of the course and a shortened Portfolio Management application near the end.

14-week course structure

A 14-week version creates space to separate CAPM from factor models, give Financial Statement Analysis its own debrief, deepen fixed-income analysis and run Portfolio Management across two sessions with a fuller performance review.

The Investment Banking Simulation is the strongest bridge between valuation work and a defendable professional recommendation in this course. Students work in teams through a live transaction environment that combines analysis, financial modelling, pitching, strategic advisory, proposal development and deal execution under time pressure.

For Investment Analysis, use the simulation to move students from a model output to an argued view. They should explain what the valuation says, what it does not say, which assumptions matter most and how new information changes the recommendation.

Why it fits here

• Students already know DCF, market-multiple and recommendation logic.
• They can connect security analysis to a professional pitch or advisory note.
• The simulation makes deadlines, evolving information and execution pressure visible.
• It gives a richer applied bridge before later portfolio-construction work.

What students do

Transaction analysis

Teams work through case materials, market information and model outputs to form a valuation and transaction view.

Financial modelling

Students use DCF and related finance tools to test value, structure proposals and compare strategic options.

Pitching and advisory

Teams prepare positions, communicate recommendations and compete to secure or support transactions.

Negotiation and execution

Students respond to new information, deadlines and counterparty behaviour, then revise their recommendations when needed.

Recommended course placement

• After Session 7 when students can defend an equity-valuation view.
• Across Sessions 7 to 9 if the course wants a larger applied workshop.
• Before the main investment report, pitch or recommendation memo.

Knowledge students should have before the simulation

• Basic DCF and valuation logic.
• How to compare valuation methods and defend a range.
• How to turn financial analysis into a recommendation.
• Awareness of buy-side, sell-side and advisory perspectives.

Delivery format

Suggested debrief questions

• Which valuation input most affected your recommendation?
• What changed once new information or counterparties entered the process?
• What did the model show clearly, and what still required judgement?
• How would you convert the simulation outcome into an investment recommendation for a lecturer-marked assessment?
• Where did time pressure improve or weaken analytical quality?

The Portfolio Management Simulation is the primary applied simulation for this course. Teams manage a global equity portfolio from one of two client perspectives: Hedge Fund teams aim to generate stronger Alpha, while Pension Fund teams aim for a higher Sharpe Ratio. The different objectives make mandate fit visible rather than treating the highest raw return as the universal winner.

Students analyse historical market data across 25 global companies, use CAPM and mean-variance optimisation to estimate expected return, beta, covariance, volatility and portfolio weights, then construct an initial portfolio and rebalance it as new market information arrives over later quarters.

Why it fits here

• Students already know CAPM, Sharpe Ratio, covariance and mean-variance optimisation.
• They can interpret the optimiser as a baseline rather than an automatic answer.
• They have learned market efficiency and fundamental analysis, so new information can be judged rather than simply chased.
• The simulation creates a natural bridge from security analysis to performance evaluation.

What students do

Client mandate

Each team receives either a Hedge Fund or Pension Fund mandate with a distinct performance objective and permitted choices.

Portfolio analysis

Teams review historical data and use the supplied Excel model to estimate expected returns, beta, covariance, volatility and portfolio weights.

Construct and rebalance

Teams choose securities, allocate capital, decide on cash and where permitted leverage, then buy, sell or rebalance as market information changes.

Evaluate risk-adjusted performance

Teams compare return and volatility with the role-appropriate measure: Alpha for Hedge Fund teams and Sharpe Ratio for Pension Fund teams.

Recommended course placement

• Start after Session 9 portfolio-construction teaching.
• Use Session 10 for analysis and the initial allocation.
• Use Session 11 for later quarters, rebalancing and performance debrief.
• Follow with an individual memo explaining which decisions should be repeated or changed.

Knowledge students should have before the simulation

• CAPM and beta.
• Sharpe Ratio and risk-adjusted performance.
• Mean-variance optimisation, covariance and portfolio weights.
• Diversification, concentration and the difference between model output and portfolio judgement.

Delivery format

Suggested debrief questions

• Which model input had the greatest influence on the initial allocation?
• Where did qualitative news justify overriding the optimisation model?
• Did concentration or leverage improve return at the cost of mandate fit?
• Which rebalance was most defensible even if it did not improve realised performance?
• How should a lecturer separate good process from a lucky outcome?
• Would the same portfolio be suitable for the other client mandate?

Grading with the Financial Statement Analysis Simulation

Students participate individually as Financial Analysts, calculate and interpret ratios and revise a share-price prediction as financial information changes. The prediction is not a full valuation model, so grade the evidence chain rather than closeness to a particular price.

• Accuracy of ratio calculation and financial-statement linkage.
• Quality of written interpretation and identification of meaningful trends.
• Whether revisions to the share-price view are consistent with new evidence.
• Recognition of earnings-quality or cash-conversion warning signs.
• Individual reflection on an error, changed assumption or missing information.

Grading with the Investment Banking Simulation

Teams use valuation, modelling, transaction analysis and recommendation logic in a competitive, role-based environment. Mark the quality of the analytical chain, professional communication and response to new information rather than only the final competitive outcome.

• Accuracy and relevance of valuation, modelling and transaction analysis.
• Clarity of the recommendation, rationale and supporting evidence.
• Quality of pitch, memo or proposal content produced during the exercise.
• How well teams adapt to new information, deadlines and counterparty behaviour.
• Individual reflection or oral defence on one material judgement made during the simulation.

Grading with the Portfolio Management Simulation

Teams manage either a Hedge Fund or Pension Fund mandate, use CAPM and mean-variance analysis, construct an equity portfolio and rebalance across changing market information. Compare teams within the same mandate and mark decision quality alongside performance.

• Mandate fit and clarity of portfolio objective.
• Use of expected return, beta, covariance, volatility and optimisation evidence.
• Diversification, concentration, cash and where relevant leverage decisions.
• Quality of rebalancing in response to new information.
• Interpretation of Alpha or Sharpe Ratio and the difference between process and realised outcome.
• Post-simulation memo and individual defence of one material decision.

For the team-based simulations, the platform records portfolio choices, trade decisions, comparative outcomes and where relevant recommendation or proposal outputs. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument. For Financial Statement Analysis, individual submissions provide more attributable evidence, but the lecturer still sets and applies the academic marking criteria.