How to build a financial modelling course: a complete guide for lecturers

Central question: How should a financial model be designed so that another person can follow, test and use it for a decision?

Financial modelling is not the same as filling a spreadsheet with formulas. The first teaching task is to make students define the decision, the users, the outputs and the model boundary before they build anything. This creates a common architecture for assumptions, calculations, statements, checks and outputs, and it gives lecturers a basis for marking robustness rather than cosmetic complexity.

What this concept covers

• Purpose and scope statements before model build.
• Separation of inputs, calculations and outputs.
• Consistent time axes, units, signs and formula conventions.
• Transparent assumptions, source notes, version control and change logs.
• Model checks, error flags and a clear output dashboard.

Learning outcomes

• Design a model map from a written brief.
• Apply consistent spreadsheet conventions and documentation.
• Explain why a modelling choice improves auditability or usability.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Give students a badly organised workbook and ask them to diagnose five risks before touching a formula.
• Require a one-page model architecture before any build begins.
• Use ICAEW modelling principles as a shared review checklist.

Case-style example

Arcadia Services is considering a £35 million acquisition. Students receive a workbook with 14 mixed-purpose tabs, hard-coded prices inside formulas, two conflicting tax assumptions and no model checks. Their task is to redesign the architecture into Inputs, Historicals, Forecast, Debt, Valuation and Outputs, then justify three controls. One team may prioritise speed; another may prioritise auditability. Students discover that a well-structured model is a decision-control system, not merely a calculation engine.

Where students struggle

Students often confuse complexity with quality. They may add tabs, colours and formulas without defining who will use the model or how an error would be detected.

Reading and a quick check

Assign the ICAEW Financial Modelling Code and ask students to identify three design choices they would insist on before reviewing anyone else’s model.

Teaching activity

Run a model-redesign clinic: groups critique an intentionally weak workbook, agree a common standard and produce an architecture diagram before Session 2.

Where it leads

Once the model has a purpose and architecture, students can populate it with historical financial information that is clean enough to forecast.

Central question: Which historical numbers belong in the model, which need adjustment and what do the statements reveal before forecasting starts?

A forecast is only as credible as the historical base. Students need to reconcile the income statement, balance sheet and cash-flow statement, distinguish accounting presentation from economic drivers and normalise one-off items before projecting the future. This session links financial statement analysis to modelling rather than treating the historicals as a mechanical copy-and-paste exercise.

What this concept covers

• Statement linkage and reconciliation.
• Normalisation of exceptional items and non-recurring costs.
• Revenue, margin, working-capital and capex history.
• Ratio and trend analysis as a diagnostic tool.
• Source tagging and treatment of missing or inconsistent data.

Learning outcomes

• Reconstruct a clean historical base from published information.
• Separate recurring economics from accounting noise.
• Identify historical relationships that should or should not drive a forecast.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Give two years of statements plus notes and ask students to build a three-year historical schedule.
• Require a bridge from reported to normalised EBITDA.
• Ask students to explain which historical ratio is informative and which would be misleading if projected.

Case-style example

Cedar Retail reports revenue of £210 million and EBITDA of £28 million, including a £5 million property gain. Inventory days rose from 61 to 84, while receivable days stayed at 19. Students rebuild normalised EBITDA, calculate working-capital trends and decide whether the property gain should inform the forecast. One view says the weaker cash conversion is temporary; another sees structural inventory pressure. Students discover that normalisation changes both the forecast base and the story the model tells.

Where students struggle

Students can reproduce statements without understanding which relationships are economically meaningful. They also miss sign conventions and cash-flow reconciliation errors.

Reading and a quick check

Use a short financial statement forecasting note, then ask students to explain why a single reported EBITDA number is insufficient for a forward model.

Where a simulation fits

The Financial Statement Analysis Simulation fits after students have built historicals. It gives individual practice analysing income statements, balance sheets and cash-flow statements across five reporting periods, so lecturers can ask students to carry the most decision-relevant ratios and trends into a forecast model.

Financial Statement Analysis

Where it leads

With a clean historical base, students can turn business logic into explicit operating drivers and forecast assumptions.

Central question: What should drive the forecast, and how can students avoid turning historical growth rates into unsupported predictions?

Good financial models make assumptions explicit and connect them to operational evidence. Rather than projecting every line by a percentage, students should identify the few drivers that explain revenue, margin, customer economics and cost behaviour. This is where the course moves from spreadsheet technique to financial judgement.

What this concept covers

• Revenue build by price, volume, customers, capacity or market share.
• Fixed, variable and semi-variable cost behaviour.
• Margin bridges and operating leverage.
• Base, upside and downside assumptions.
• Evidence hierarchy: company data, industry data, management guidance and lecturer-supplied case information.

Learning outcomes

• Translate a business model into forecast drivers.
• Distinguish a driver from a plug or unsupported growth assumption.
• Defend the evidence behind a base-case forecast.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Give the same company to teams but vary the evidence pack.
• Ask students to build revenue from two alternative driver structures and compare the implications.
• Require an assumptions register with source, owner and confidence level.

Case-style example

Northstar Fitness has 140 clubs, 310,000 members, monthly fees of £31 and annual churn of 24%. Management plans 18 new clubs but expects mature-club utilisation to soften. Students forecast revenue using member growth, churn, pricing and openings, then compare it with a simple 12% top-line growth assumption. Both can initially produce similar totals. Students discover that a driver-based model exposes capacity, churn and execution risk that a single growth rate hides.

Where students struggle

Students often anchor on management guidance, use arbitrary percentages or create excessive detail without knowing which assumptions actually move value.

Reading and a quick check

Assign Green and Zhao’s review of earnings forecasting and ask students to identify where human judgement enters even when statistical tools improve prediction.

Teaching activity

Run an assumptions workshop in which students rank forecast drivers by value impact and evidence quality, then defend the top five.

Where it leads

The driver schedules now need to flow through an integrated income statement, balance sheet and cash-flow statement.

Central question: How do operating assumptions flow through all three financial statements without breaking accounting and cash logic?

The integrated three-statement model is the central technical spine of the course. Students need to see how revenue, costs, working capital, capital expenditure, financing and tax interact rather than building three independent statements. The purpose is not to reward a particular spreadsheet layout but to make the balance sheet balance and cash reconcile for the right economic reasons.

What this concept covers

• Income statement, balance sheet and cash-flow linkage.
• Retained earnings, depreciation, working capital and financing flows.
• Cash sweep or minimum cash logic.
• Balancing mechanisms that are explicit rather than hidden plugs.
• Error checks for balance sheet, cash movement and sign consistency.

Learning outcomes

• Build a linked forecast that balances across periods.
• Trace one assumption through profit, cash and the balance sheet.
• Diagnose a broken model systematically.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Build one forecast year live and have students complete the next two.
• Deliberately break three formulas and ask groups to locate the root cause.
• Use trace precedents/dependents and check rows as teaching tools, not afterthoughts.

Case-style example

Tern Manufacturing starts with £12 million cash, £48 million debt and £22 million inventory. Revenue is forecast to rise 9%, EBITDA margin from 14% to 15%, capex at £11 million and receivable days from 42 to 50. Students complete the three statements and diagnose why a naïve model shows both higher profit and higher cash. One team expects cash improvement; another expects a squeeze. Students discover that growth can consume cash through working capital even while accounting profit rises.

Where students struggle

Students commonly build statements in isolation, use cash as a balancing plug or forget that non-cash charges and balance-sheet movements affect cash differently.

Reading and a quick check

Use Benninga and Mofkadi’s corporate-finance modelling sections, then ask students to trace a £1 increase in depreciation through all three statements.

Teaching activity

Use a model-debugging lab: each group receives a different broken integrated model and must document the error, fix and control that would have caught it.

Where it leads

Once the core model works, students can add the supporting schedules that explain cash conversion and reinvestment.

Central question: How should a model represent the operating investment needed to support growth, rather than assuming profit automatically becomes cash?

Working capital and capital expenditure turn an earnings forecast into a cash-flow forecast. This concept is especially important because weak models often bury cash conversion in a percentage-of-sales plug. Students should model receivables, inventory, payables, capex and depreciation explicitly enough to understand the operational trade-offs.

What this concept covers

• Receivable, inventory and payable days.
• Deferred revenue and other operating balances where relevant.
• Maintenance versus growth capex.
• Depreciation schedules and asset roll-forwards.
• Free cash flow and cash conversion.

Learning outcomes

• Forecast operating working capital from defensible drivers.
• Separate maintenance and growth investment where evidence allows.
• Explain why cash flow can diverge from EBITDA and net income.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Use a seasonal company and ask why year-end balance-sheet ratios can mislead.
• Build a capex and depreciation roll-forward.
• Ask students to quantify cash released by a five-day improvement in receivables.

Case-style example

Marlow Foods forecasts revenue of £160 million, receivable days of 46, inventory days of 72 and payable days of 38. Management proposes a £14 million automated warehouse expected to reduce inventory days to 58 after one year. Students calculate the working-capital release, model capex and depreciation, and compare project NPV with liquidity pressure. One view favours immediate investment; another delays it. Students discover that a value-creating project can still create a near-term financing problem.

Where students struggle

Students frequently forecast working capital as a fixed percentage without understanding the operating cycle, or double-count capex effects by mishandling depreciation.

Reading and a quick check

Use a cash-flow modelling chapter and ask students to reconcile EBITDA to free cash flow with every major bridge item labelled.

Where a simulation fits

The Capital Budgeting Simulation fits once students can model project cash flows. Students compare projects using NPV, IRR, profitability index and payback under a fixed $10 million capital budget, which makes it useful for showing how model outputs support capital-allocation choices rather than replace them.

Capital Budgeting

Where it leads

The cash-flow schedules allow the model to support a financing schedule and test whether the proposed capital structure remains sustainable.

Central question: How should a model represent debt, interest, repayment and covenant pressure without hiding circularity or refinancing risk?

Financing schedules connect operating performance with liquidity and capital structure. Students should model opening debt, drawdowns, mandatory amortisation, cash sweeps, interest and maturity profiles, while understanding that debt terms are decisions rather than fixed spreadsheet inputs. The modelling challenge is technical, but the learning objective is to judge how much financial risk the business can carry.

What this concept covers

• Debt tranches, opening balances, draws and repayments.
• Cash interest, margins, reference rates and fees.
• Cash sweeps and minimum cash.
• Covenant and coverage metrics.
• Circularity between interest, cash and debt, with transparent handling.

Learning outcomes

• Build a debt roll-forward and interest schedule.
• Test liquidity and covenant headroom under downside assumptions.
• Explain financing choices in terms of flexibility, price and risk.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Model a simple term loan first, then add revolving credit and optional repayment.
• Show two methods for handling interest circularity and discuss transparency.
• Have lenders and borrowers propose different debt structures from the same forecast.

Case-style example

Bromley Logistics has £75 million opening debt at SONIA plus 350 bps, £8 million minimum cash and forecast EBITDA of £24 million. A downside case cuts EBITDA to £18 million and raises working-capital use by £6 million. Students build the debt schedule, interest and cash sweep, then decide whether to refinance with a longer maturity at 525 bps. One team accepts higher pricing for headroom; another keeps cheaper debt. Students discover that cheapest debt is not always the most resilient structure.

Where students struggle

Students hide circular references, mix debt balances and interest periods, or model repayment without testing the cash required to fund it.

Reading and a quick check

Assign a debt-schedule modelling section and ask students to show exactly which cells create circularity and how their chosen method controls it.

Where a simulation fits

The Debt Financing Simulation fits after the debt schedule. Borrower and lender teams negotiate amount, pricing, maturity, repayment, seniority, security and covenants, which gives students live inputs they can translate into financing schedules and downside headroom.

Debt Financing

Where it leads

With operating and financing cash flows linked, students can value the business and test how assumptions translate into enterprise and equity value.

Central question: How do forecast assumptions, cost of capital and terminal value combine into a valuation range that is useful for a decision?

DCF modelling should teach judgement, not false precision. Students need to move from an integrated forecast to unlevered free cash flow, estimate a defensible discount rate, choose terminal assumptions and show how changes affect enterprise and equity value. The model is strongest when it makes uncertainty visible.

What this concept covers

• Unlevered free cash flow.
• WACC and discounting mechanics.
• Perpetuity-growth and exit-multiple terminal value.
• Enterprise-to-equity value bridge.
• Sensitivity tables and valuation ranges.

Learning outcomes

• Build a DCF from the integrated forecast.
• Defend WACC and terminal assumptions.
• Interpret sensitivity rather than reporting a single value.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Require both terminal-value methods and a reconciliation.
• Ask students to calculate what percentage of enterprise value comes from terminal value.
• Use peer review to challenge WACC, margins and growth assumptions.

Case-style example

Helix Software forecasts unlevered free cash flow rising from £18 million to £32 million over five years. Students value it using 9.5% WACC and 2.5% terminal growth, then test 8.5%-10.5% WACC and 1.5%-3.0% growth. A strategic buyer argues for the high case while a cautious investor uses the low case. Students must recommend a range and identify the assumption worth diligence. They discover that valuation debate is often an assumptions debate in disguise.

Where students struggle

Students report a precise valuation without explaining why the forecast, WACC or terminal value deserves confidence. They may also mix levered and unlevered cash flows.

Reading and a quick check

Assign Green, Hand and Zhang on DCF errors and ask students to identify two modelling judgements that can create a plausible but misleading valuation.

Where a simulation fits

The Investment Banking Simulation fits after valuation and again as a multi-session capstone. Across four rounds, teams connect financial modelling, DCF, financing, advisory work, transaction proposals and changing information. The public format is 16-32 hours, so it should be spread across multiple meetings or blended with independent work rather than forced into one class.

Investment Banking

Where it leads

Valuation becomes the base for transaction models, starting with how an acquisition changes the buyer, seller and combined company.

Central question: How does an acquisition change value, ownership, financing and earnings, and which deal assumptions matter most?

M&A modelling links valuation to transaction structure. Students should model purchase price, consideration, financing, synergies and pro forma earnings, while recognising that accretion is not the same as value creation. The model should help answer whether the deal economics remain attractive under alternative synergy and financing cases.

What this concept covers

• Purchase price and premium.
• Cash, shares and mixed consideration.
• New debt, foregone cash interest and share issuance.
• Synergy timing, implementation cost and tax effects.
• Pro forma EPS and accretion/dilution.

Learning outcomes

• Build a simplified merger model.
• Separate EPS accretion from economic value creation.
• Test the effect of synergies, premium and consideration mix.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Start with a no-synergy case so students see the financing mechanics.
• Add synergy scenarios only after the base deal works.
• Require a short memo explaining why an accretive deal could still destroy value.

Case-style example

Orion Media, with 80 million shares at £12, considers buying Valeo Digital for £420 million. Valeo has £34 million EBITDA and 22 million shares at £15. Orion can pay cash funded at 6.5% or issue shares, and expects £18 million annual pre-tax synergies after £25 million one-off integration cost. Students model accretion/dilution and value creation under both structures. Teams can defend either route. Students discover that financing optics and fundamental value can point in different directions.

Where students struggle

Students often equate EPS accretion with a good acquisition, overlook share count mechanics or treat synergies as certain and immediate.

Reading and a quick check

Use a transaction-modelling chapter from Pignataro as a supplement, then ask students to explain why a zero-premium all-share deal can still be dilutive.

Where a simulation fits

The M&A Simulation fits after a merger model. Buyer and seller teams work from company analysis and negotiate transaction terms including valuation, consideration and synergies, giving students a direct reason to test how a changed term moves the model.

M&A

Where it leads

The acquisition model prepares students for the more leveraged transaction structure of a sponsor-backed buyout.

Central question: What purchase price, leverage and operating plan can a sponsor support while still earning a credible return?

An LBO model is a compact integration of operating forecasts, debt schedules, valuation and return analysis. Students should understand sources and uses, debt capacity, cash sweeps, sponsor equity, exit assumptions, IRR and MOIC, then test which return drivers are operational and which depend on financial engineering or multiple expansion.

What this concept covers

• Sources and uses and purchase accounting at a simplified level.
• Debt tranches, amortisation and cash sweep.
• Sponsor equity and management rollover where relevant.
• Exit enterprise value, equity bridge, IRR and MOIC.
• Downside cases for EBITDA, leverage and exit multiple.

Learning outcomes

• Build or complete an LBO model from a company forecast.
• Identify the main drivers of sponsor return.
• Defend a maximum bid rather than merely target a return.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Show the same company under 4.0x and 6.0x leverage.
• Require a no-multiple-expansion case.
• Have teams state a walk-away price before a competitive exercise.

Case-style example

Beacon Components is offered at 9.0x £30 million EBITDA. Debt markets will provide either 4.5x leverage at 7.0% or 5.5x at 8.5%. Management forecasts EBITDA of £42 million in year five. Students model both structures, a 9.0x exit and an 8.0x downside exit, then set a maximum bid for a 20% target IRR. One team chooses cheaper lower leverage; another accepts costlier debt to reduce equity. Students discover that leverage amplifies both return and fragility.

Where students struggle

Students optimise the spreadsheet to hit an IRR target, forget debt repayment capacity or rely on multiple expansion to rescue weak operations.

Reading and a quick check

Use Benninga for core cash-flow logic and Pignataro for transaction mechanics; close by asking which assumption contributes most to equity returns.

Where a simulation fits

The Leveraged Buyout Simulation fits directly after the LBO model. PE firms normalise and forecast target companies, calculate equity and enterprise value, assess debt capacity, select lenders and submit bids against a target return while lenders and sell-side advisers pursue different objectives.

Leveraged Buyout (LBO)

Where it leads

LBO modelling leads naturally to equity issuance and IPO modelling, where valuation, dilution and investor demand interact.

Central question: How should a company and its advisers translate forecast performance into an offer valuation, share count, proceeds and ownership outcome?

IPO modelling combines valuation with capital raising. Students need to connect pre-money equity value, new primary shares, existing secondary shares, gross proceeds, fees, dilution and post-money ownership. The lesson is not to predict the exact offer price, but to understand how valuation and deal structure affect capital raised and shareholder outcomes.

What this concept covers

• Equity value range from multiples or DCF.
• Primary versus secondary shares.
• Offer size, price, share count and gross proceeds.
• Fees, net proceeds and dilution.
• Post-money market capitalisation and ownership.

Learning outcomes

• Build an IPO proceeds and dilution schedule.
• Explain the trade-off between valuation, capital raised and investor demand.
• Compare primary and secondary objectives.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Give teams the same company but different capital needs.
• Ask one team to maximise proceeds and another to prioritise aftermarket flexibility.
• Run sensitivity to offer price and new-share count.

Case-style example

Luma Health has 120 million pre-IPO shares and needs at least £180 million net new capital. Advisers propose an equity value range of £900 million-£1.1 billion and estimated fees of 5% of gross primary proceeds. Students model offer price, new shares, dilution and post-money value, then compare a larger primary raise with a tighter offer. One side protects existing ownership; another prioritises cash headroom. Students discover that IPO structure is a financing decision, not only a valuation exercise.

Where students struggle

Students confuse enterprise value and equity value, ignore primary versus secondary shares or calculate dilution using inconsistent pre- and post-money share counts.

Reading and a quick check

Use the Roku IPO case or a short equity-capital-markets note, then ask students to rebuild the ownership table from offer assumptions.

Where a simulation fits

The IPO Simulation fits after students can model equity value, new shares, proceeds and dilution. Underwriter and investor teams value companies, set ranges, participate in roadshows and book building, then make pricing and allocation decisions.

Initial Public Offering (IPO)

Where it leads

After students can build transaction models, the course should make uncertainty explicit through scenarios and sensitivity analysis.

Central question: Which uncertainties matter enough to change the decision, and how should a model distinguish a sensitivity from a coherent scenario?

Sensitivity analysis varies one or two assumptions to show model exposure; scenario analysis tells a coherent alternative story across multiple drivers. Students should learn both, then use them to identify breakpoints, financing stress and decision thresholds. The model should help a decision-maker ask what has to be true, not only what the base case produces.

What this concept covers

• One- and two-way data tables or equivalent sensitivity tools.
• Scenario design across linked operating and financing assumptions.
• Break-even and reverse stress testing.
• Covenant and liquidity downside tests.
• Decision thresholds for price, leverage or capital allocation.

Learning outcomes

• Design scenarios that are internally coherent.
• Identify the assumptions with the greatest decision impact.
• Use reverse stress testing to define a break point.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Ban arbitrary “+/-10% everywhere” sensitivities and require economic logic.
• Ask students to construct a downside from named business events.
• Use a tornado-style ranking or sensitivity matrix for the five highest-impact assumptions.

Case-style example

Evergreen Data Centres expects 12% annual revenue growth, 52% EBITDA margin and £70 million annual capex. Students build a base case, then a downside with 6% growth, 48% margin and £85 million capex, plus a reverse stress test for the minimum EBITDA that preserves covenant headroom. One group cuts valuation but keeps financing; another recommends lower leverage. Students discover that a coherent downside can change capital structure even when the business remains fundamentally attractive.

Where students struggle

Students create mechanical sensitivities that do not correspond to a plausible business scenario, or they show large tables without explaining which cells alter the decision.

Reading and a quick check

Use the ICAEW review guidance and ask students to state the three assumptions they would reverse-stress first and why.

Where a simulation fits

The Leveraged Buyout Simulation fits directly after the LBO model. PE firms normalise and forecast target companies, calculate equity and enterprise value, assess debt capacity, select lenders and submit bids against a target return while lenders and sell-side advisers pursue different objectives.

Leveraged Buyout (LBO)

Where it leads

The final concept turns the completed model into reviewable evidence and a defended recommendation.

Central question: How can a modeller prove that the workbook is internally consistent, transparent enough to review and useful for a decision?

A financial modelling course should finish with review and communication, not with the last formula. Students need to test logic, data, formulas, scenarios and outputs, document limitations and present a recommendation that distinguishes model evidence from unresolved judgement. This is also the right place to assess individual ownership of group work.

What this concept covers

• Structural, data, analytical and detailed formula review.
• Checks, error flags, version control and source documentation.
• Peer review and independent challenge.
• Output dashboards and decision summaries.
• Oral defence of assumptions, limitations and recommendation.

Learning outcomes

• Review a model systematically and document findings.
• Separate a modelling error from a debatable assumption.
• Communicate the decision, risks and evidence without hiding uncertainty.

Supports the course outcomes most directly associated with this stage of the modelling lifecycle.

How to teach it

• Use paired model review with a structured checklist.
• Require a change log showing corrections and effect on outputs.
• Finish with a short investment committee or CFO-style oral defence.

Case-style example

Apex Renewables submits a valuation model showing £640 million enterprise value and 22% sponsor IRR. An independent review finds a tax-rate link copied incorrectly in two years, a hard-coded exit multiple and a circularity switch left off. Students correct the workbook, quantify the new £585 million value and 18% IRR, then decide whether the investment recommendation changes. One team still invests at a lower price; another walks away. Students discover that review can change both the number and the decision.

Where students struggle

Students treat review as proofreading, focus on formatting, or assume that a balanced balance sheet proves the model is correct.

Reading and a quick check

Assign Poon et al. on spreadsheet quality assurance and the ICAEW Twenty Principles, then ask students to classify review findings as data, logic, formula, control or judgement issues.

Where a simulation fits

The Investment Banking Simulation fits after valuation and again as a multi-session capstone. Across four rounds, teams connect financial modelling, DCF, financing, advisory work, transaction proposals and changing information. The public format is 16-32 hours, so it should be spread across multiple meetings or blended with independent work rather than forced into one class.

Investment Banking

Where it leads

This is the capstone: students should now be able to build, review and defend a model from source data through forecast, valuation, transaction structure and recommendation.

10-week course structure

A 10-session version should compress adjacent build stages rather than remove model review or transaction application. Use more structured templates and place the Investment Banking capstone across independent study plus the final two teaching touchpoints.

14-week course structure

A 14-session version creates space to separate statement integration, cash conversion, capital investment and scenario work. Use the extra time for deeper review, transaction-model comparison and capstone debrief rather than adding spreadsheet tricks that do not change financial judgement.

The Leveraged Buyout Simulation places students inside a competitive buyout process after they can already build a basic LBO. PE teams normalise and forecast three target companies, calculate Equity Value and Enterprise Value, assess debt capacity and submit acquisition bids against a target return. Lenders propose debt packages and sell-side advisers position targets through a roadshow, so the model has to interact with financing terms and competitive bidding rather than sit in isolation.

Why it fits here

This is the strongest direct fit because the verified activity already uses the same building blocks as the course: historical normalisation, P&L and cash-flow forecasting, FCFE-based valuation, debt capacity, lender selection, sponsor equity, enterprise-value bids and target IRR. Students can therefore be assessed on whether their deal decision follows from a coherent model and a defensible downside view.

What students do

PE firms

• Normalise and forecast target-company financials.
• Calculate Equity Value and Enterprise Value and assess acquisition attractiveness.
• Review competing lender packages, select financing, determine sponsor equity and submit an Enterprise Value bid.

Lenders

• Assess borrower/target risk and construct debt terms for PE teams.
• Compete to be selected by sponsor teams while balancing pricing and credit risk.

Sell-side advisers

• Analyse and position target companies.
• Run the roadshow and manage how the sell-side story meets bidder scrutiny.

Deal process

• Move through briefing, analysis, roadshow, bidding, structuring and debriefing.
• Submit models and Approval Memorandums or equivalent team outputs where the lecturer uses them.

Recommended course placement

• At the end of Session 9 after sources and uses, debt schedules, IRR and MOIC.
• Across Sessions 9-10 if the course wants a larger applied block.
• Before a final investment committee memo so students convert a live bid into written analysis.

Knowledge students should have before the simulation

• Basic DCF and company valuation.
• Historical normalisation and forecast design.
• Sources and uses, debt capacity and interest schedules.
• Enterprise Value, Equity Value, IRR, MOIC and exit assumptions.

Delivery format

Suggested debrief questions

• Which forecast or valuation assumption set the maximum price your team could pay?
• How did the available debt terms change sponsor equity and downside risk?
• Did competition push any team above its stated walk-away price?
• Which part of the return came from operating improvement, deleveraging or exit assumptions?
• What would you re-run in the model before an investment committee approved the bid?

The Investment Banking Simulation is the broadest Financial Modelling capstone in the current catalogue. It runs for 16-32 hours in a web-based, team format across four rounds. Students connect DCF, debt financing, derivatives, buy-side and sell-side advisory work, proposals and transaction decisions, with model and memo outputs submitted as the process develops. Because of its duration, it should be scheduled across several meetings or blended with independent work.

Why it fits here

Financial Modelling sits inside the transaction process rather than outside it. Across the rounds, teams build or update financial analysis, financing and valuation work as deadlines, confidential information and transaction priorities change. That gives lecturers a strong basis for asking whether the model remained logically consistent when the decision context changed.

What students do

Round 1 - financing and acquisition analysis

• Lender teams propose debt packages.
• PE teams begin models, assumptions and Enterprise Value work, then select financing and submit bids in the buy-side phase.

Round 2 - financing, derivatives and sell-side information

• Teams extend financing analysis; sell-side roadshows and data-room information feed into buy-side models and Approval Memoranda.

Round 3 - integrated advisory work

• Teams combine analysis, financial modelling, strategy, pitching, negotiation and deal structuring across a broader service set.

Round 4 - final transaction work

• Teams complete later-round advisory work, including fairness-opinion style FCFF DCF analysis and restructuring-related decisions where the public simulation workflow calls for them.

Recommended course placement

• Launch after Session 10, when students have already built DCF, financing and transaction models.
• Use Sessions 11-12 as scheduled touchpoints and allocate substantial independent or lab time between them.
• For a 2-hour-per-session course, do not promise completion inside one class; plan a blended capstone or select a shorter simulation instead.

Knowledge students should have before the simulation

• Basic finance and DCF.
• Integrated financial statements and forecast assumptions.
• Debt financing and capital structure.
• Enterprise Value and Equity Value.
• Ability to turn a model into a transaction recommendation.

Delivery format

Suggested debrief questions

• Which model assumption changed most when new information arrived?
• Where did a financing or advisory role create a different model priority?
• What did the model answer well, and what still required judgement or diligence?
• Which correction or new input had the largest effect on the transaction recommendation?
• How would you turn the team output into an individually attributable assessment?

Grading with the Investment Banking Simulation

The Investment Banking Simulation should be assessed as a multi-round transaction-modelling exercise, not as a single leaderboard result. Team models, Approval Memoranda, financing terms and transaction outputs can support the lecturer’s judgement across the four rounds.

• Technical consistency and evolution of model/valuation outputs.
• Use of financing and transaction information in recommendations.
• Ability to update analysis when new information or deadlines change the case.
• Quality of written approval/advisory material where used.
• Individual defence of one material assumption or decision.

Grading with the Leveraged Buyout (LBO) Simulation

The LBO simulation can be graded through the logic linking target forecasts, valuation, debt capacity, lender selection, sponsor equity, bid and target return. Role outcomes differ, so use role-relevant criteria rather than one common scoreboard measure.

• Forecast and normalisation quality for target companies.
• Coherence of debt terms, lender selection and capital structure.
• Bid discipline relative to valuation and downside.
• Ability to explain IRR/MOIC drivers and model limitations.
• Individual post-simulation revision to the model or investment view.

Grading with the Initial Public Offering (IPO) Simulation

The IPO simulation is most useful for assessing whether students can connect comparable valuation, offer structure, roadshow information, book building, pricing and allocation.

• Valuation range and rationale.
• Consistency of primary/secondary share and dilution calculations.
• Use of demand information in price decisions.
• Trade-off between proceeds, investor demand and ownership.
• Individual explanation of how the final book changed the model.

Grading with the Capital Budgeting Simulation

Capital Budgeting is a shorter individual exercise around NPV, IRR, profitability index, payback and allocation under a fixed $10 million budget. Use the platform outcome as evidence of decision process, then assess the explanation.

• Correct project cash-flow treatment.
• Use and interpretation of NPV and IRR.
• Capital-rationing logic under the fixed budget.
• Recognition of conflicts between decision rules.
• Short individual rationale for the chosen portfolio.

Grading with the Financial Statement Analysis Simulation

Financial Statement Analysis is individual and uses five reporting periods. It is useful as an early diagnostic of whether students can extract trends from the statements before forecasting; the share-price prediction is an engagement/reflection device, not a substitute for equity valuation.

• Accuracy of ratio and trend interpretation.
• Identification of recurring versus event-driven changes.
• Link from historical evidence to forecast assumptions.
• Clarity about what the statements do not prove.
• Individual forecast-driver note after the exercise.

Grading with the Debt Financing Simulation

Debt Financing places borrower and lender teams into a refinancing negotiation over amount, pricing, maturity, repayment, seniority, security and covenants. Assessment should reward how students translate terms into a sustainable financing schedule.

• Debt-term economics and modelling accuracy.
• Liquidity and covenant headroom under base/downside cases.
• Negotiation rationale from the assigned role.
• Consistency between model capacity and agreed terms.
• Individual note explaining one rejected financing alternative.

Grading with the M&A Simulation

The M&A simulation is a short buyer-seller negotiation that can be linked directly to a merger model. The public activity has no need for a formal platform score; lecturers should assess the valuation and transaction logic behind agreed terms.

• Valuation and synergy assumptions.
• Consideration mix and financing consequences.
• Ability to update the merger model as terms change.
• Negotiation rationale and treatment of integration risk.
• Individual explanation of whether the final deal creates value.

Evidence and moderation: The platform records what each team decided, the terms they agreed and comparative outcomes across groups. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument. Use an individual note or oral defence for attribution, apply a published rubric to model logic and judgement, and moderate across groups where different roles produced different opportunities.