Financial Modeling for Beginners

1.1 What is Financial Modeling?

Financial modeling is the process of creating a numerical representation, typically in a spreadsheet, of a company's financial performance and operations. This representation, or "model," combines a company's historical financial data with a set of assumptions about its future to create a forecast of its earnings, expenses, and overall financial health.

The purpose of a financial model is to serve as a powerful decision-making tool. Company executives and finance teams use models to:

• Make Budgeting and Investment Decisions: Assess the financial impact of new projects, such as opening a new factory or retail location.

• Allocate Capital: Determine how to best deploy existing resources and prioritize projects to maximize value.

• Manage Risk: Test various scenarios to understand the potential risks and returns of strategic choices.

• Value a Business: External analysts and investors use models to estimate a company's valuation to decide whether to acquire, invest in, or lend to the business.

Ultimately, a financial model is only as good as the inputs and assumptions that go into it. Its true value emerges when the quantitative analysis is woven into a clear narrative, helping stakeholders understand not just what the numbers are, but why a recommendation is being made.

1.2 The African Context: A Tool for Raising Capital and Managing Risk

While financial modeling is a universal business tool, its application in the African context—particularly for startups and high-growth companies—is often more high-stakes and externally focused.

The Model as a "Funding Passport"

For an entrepreneur in Lagos, Nairobi, or Cape Town seeking venture capital, the financial model is arguably the most critical document in their data room. It is the primary tool used to translate a compelling vision into a mathematical representation of profitability. It directly answers the investor's core question: "If we invest in you now, what assurances do we have that we will get a return on our investment?".

There are many cases where an "amazing storyteller" with a promising startup gets investors excited, only to see that interest evaporate once the financial model is reviewed. A well-thought-through model complements the founder's story and signals credibility, substantially increasing the chances of a successful fundraise. Startups with the best-constructed models tend to secure funding with greater ease.

A Tool to Proactively De-Risk an "African" Investment

The African startup ecosystem is unique and cannot rely on a "copy-paste" funding model from other regions. Investors often perceive the continent as having a higher risk profile, citing concerns over currency volatility, macroeconomic instability, and infrastructure challenges.

A robust financial model is the founder's most powerful tool to proactively address and quantify these risks. It demonstrates foresight and proves that the business plan is resilient. By building a model that includes and "stress-tests" these specific regional challenges—for example, "What is the impact on our profit if the Naira devalues by 30%?" or "What happens to our cash flow if logistical hurdles delay our inventory?"—a founder can show an investor that they have a credible plan to navigate the very risks the investor is worried about. This transforms the model from a simple forecast into a sophisticated risk management and strategic planning tool.

1.3 The Modeler's Toolkit: Best Practices and Excel Foundations

A financial model's design is critical. A poorly structured model is difficult to follow, hard to maintain, and inspires little confidence. Professional-grade models adhere to a strict set of formatting and structural rules to ensure clarity, flexibility, and transparency.

Best Practice: The Structure

1. Separate Inputs, Calculations, and Outputs: This is the most important rule. All key assumptions (inputs) should be aggregated in one worksheet or a dedicated section. These inputs should be separate from the formulas (calculations) and the final financial statements (outputs).

2. Color-Coding: This is the universal language of financial modeling. It allows any user to understand the model's logic at a glance.

   • Blue Font: Used for hard-coded numbers. This includes all historical data and all future assumptions (e.g., growth rates, commodity prices, inflation). A blue font tells the user, "This is an input; you can change this value."

   • Black Font: Used for all formulas and calculations. This includes all links to other cells or other worksheets. A black font tells the user, "Do not type over this cell; it is a calculation."

   • Green and Red Fonts: Often used for final outputs, such as positive (green) or negative (red) net income, to draw the user's attention.

3. Clarity and Consistency: Models must be easy to read. This is achieved through:

   • Clear Labeling: All rows and columns should be clearly labeled.

   • Consistent Formats: Use the same date format, currency symbols, and number of decimals across the entire model. This prevents confusion and allows for quick cross-referencing.

   • Logical Flow: The model should be structured logically, typically with inputs first, followed by supporting schedules (like debt and fixed assets), and then the main three financial statements.

Essential Excel Functions (Beginner Level)

While professional models use dozens of complex functions, a beginner can build a powerful model using a few key tools.

• SUM: Adds all numbers in a range of cells.

• MIN / MAX: Returns the smallest or largest value in a range. These are often used to create "plugs" in a model, such as ensuring a cash balance never drops below zero or that a revolving credit line is not paid down by more than its outstanding balance.

• AVERAGE: Calculates the average of a range, often used to analyze historical margins.

• IF: The most basic conditional logic function. It checks whether a condition is met and returns one value if TRUE and another if FALSE.

• SUMIF(S) / COUNTIF(S): Used to sum or count cells that meet one or more criteria. This is extremely useful for aggregating data.

• EOMONTH: The End of Month function. Given a start date, EOMONTH(date, months) returns the last day of the month, a specified number of months in the future. It is the standard function used to create the date headers (e.g., 12/31/2024, 12/31/2025...) for a time-series forecast.

Module 2: The Engine - Building the 3-Statement Model

2.1 Introduction to the Three Statements

The foundation of nearly every financial model is the 3-statement model, which projects a company's three core financial statements. These statements are intrinsically linked and provide a complete picture of a company's financial health.

1. The Income Statement (IS): Also known as the Profit & Loss (P&L) statement. It details a company's revenues and expenses over a period of time (e.g., a quarter or a year) to show its profitability. It answers the question: "How profitable was the business during this period?"

2. The Balance Sheet (BS): Also known as the Statement of Financial Position. It provides a snapshot of a company's financial position at a specific point in time. It lists what a company owns (Assets) and what it owes (Liabilities), with the remainder being Shareholders' Equity. Its "golden rule" is that it must always balance: $Assets = Liabilities + Equity$

3. The Cash Flow Statement (CFS): Tracks the actual movement of cash in and out of the business over a period of time. It is critical because profitability (Net Income) is not the same as cash. The CFS reconciles the Net Income from the Income Statement to the change in the Cash balance on the Balance Sheet.

2.2 The Golden Rules: How the Statements are Linked

The 3-statement model is "integrated," which means the statements are dynamically linked. A change in one statement will automatically flow through and affect the other two. Understanding these links is the single most important technical skill for a beginner.

• Link 1 (IS → BS): Net Income from the bottom of the Income Statement flows into the Balance Sheet as Retained Earnings, which is a component of Shareholders' Equity. This link ensures that a company's cumulative profits are captured in its net worth.

• Link 2 (IS → CFS): Net Income from the Income Statement is the starting point for the Cash Flow Statement (using the indirect method).

• Link 3 (Supporting Items): Several items link all three statements, often through supporting schedules:

  • Depreciation: This non-cash expense appears on the IS (reducing Net Income). Because it is a non-cash expense, it is added back to Net Income on the CFS in the Cash Flow from Operations section.

  • Capital Expenditures (CapEx): This is the cash spent on Property, Plant & Equipment (PP&E). CapEx does not appear on the IS. It is a cash outflow in the Cash Flow from Investing section on the CFS. This outflow then increases the PP&E asset on the Balance Sheet.

  • Changes in Working Capital: Changes in accounts like Accounts Receivable or Inventory (Assets) and Accounts Payable (Liabilities) on the Balance Sheet are adjusted for on the CFS to reflect the actual cash received or spent.

  • Debt: When a company raises debt, the cash inflow appears in the Cash Flow from Financing section on the CFS, and the Total Debt liability on the Balance Sheet increases. The Interest Expense on that debt is recorded on the Income Statement.

• Link 4 (The Final Check): The Cash Flow Statement calculates the Ending Cash Balance for the period. This Ending Cash Balance is the Cash asset on the Balance Sheet for the same period. This is the final link that makes the model "balance." If the Balance Sheet's "Golden Rule" (Assets = Liabilities + Equity) holds true, the model is mechanically sound.

The table below illustrates this flow of data.

Table 1: Visualizing the 3-Statement Links (Simplified Skeleton)

2.3 African Context: Why This "Plumbing" is Critical

This 3-statement "plumbing" is not just an accounting exercise. In volatile African economies, it is the only way to find the truth behind a company's performance.

The case of MTN Nigeria provides a perfect example. The company operates in Nigeria, an economy subject to significant currency volatility with the Naira (NGN). In its financial results for the first nine months of 2025, MTN Nigeria reported a massive net forex gain of N55.6 billion. This was a complete reversal from the same period in 2024, when it recorded a staggering net forex loss of N904.9 billion.

A beginner looking only at the Income Statement would be dangerously misled:

• In 2024, they would think the company was collapsing.

• In 2025, they would think the company's underlying operations were surging.

Both conclusions are wrong. This "forex gain/loss" is a non-cash accounting entry that arises from re-valuing the company's foreign currency-denominated debt (e.g., USD-denominated loans) into Naira for reporting purposes. It has little to do with how many data plans or MoMo (Mobile Money) subscriptions MTN sold.

This is where the 3-statement model reveals the truth. Following Link 2, the Net Income (which includes this giant non-cash gain) is the starting point for the Cash Flow Statement. Then, following Link 3, all non-cash items are added or subtracted. In this case, the N55.6 billion gain would be subtracted from Net Income in the Cash Flow from Operations section.

This adjustment strips away the "paper" noise of the Naira's volatility, allowing an analyst to see the real, underlying operational cash flow being generated by the business. For any company operating in Nigeria, Ghana, Kenya, or South Africa, this skill of separating accounting profits from cash flow is the most fundamental and critical part of financial modeling.

2.4 Practical Walkthrough: Setting Up the Historicals

The first step in building a model is to input the company's historical financial statements. These are found in the "Annual Reports" or "Investor Relations" section of a company's website.

Key African Company Data Sources:

• MTN Nigeria: (NGX-listed) Annual and quarterly reports.

• Safaricom PLC: (NSE-listed) Annual reports, which provide detailed performance breakdowns for Kenya and Ethiopia, especially for M-PESA.

• Anglo American Platinum: (JSE-listed) Annual reports and production reports.

• Okomu Oil Palm PLC: (NGX-listed) Annual and quarterly reports detailing agricultural performance.

The process involves laying out 3-5 years of historical Income Statement, Balance Sheet, and Cash Flow Statement data in an Excel template. All this historical data should be formatted in blue font (as it is hard-coded) and checked to ensure that the historical Balance Sheets balance (Assets = Liabilities + Equity).

Module 3: The Drivers - Forecasting the Income Statement and Balance Sheet

3.1 The Art and Science of Forecasting

This module is where the model transitions from history (black and white) to the future (color). A forecast is built on a set of Assumptions—the inputs (in blue font) that drive the Calculations (in black font).

The primary rule of professional forecasting is to never "hard-code" an assumption (like $=B4*1.10 to grow by 10%). All assumptions (growth rates, margins, prices, inflation rates) must be placed in a dedicated "Inputs" section, from which the calculation formulas will link. This allows the user to change any assumption in one place and have the entire model update instantly.

A forecast should not be a simple "percent of sales" exercise. A defensible forecast must be "bottom-up," meaning it forecasts the key operational drivers of the business—such as subscribers, production volumes, or prices.

3.2 Case Study 1 (Telecom): Forecasting MTN Nigeria Revenue

Forecasting revenue for a diversified company like MTN Nigeria, a leading telecom provider in West Africa, requires building separate forecasts for its distinct business segments. The wrong way is to forecast a single growth rate for "Total Revenue."

The right way is a segmented, bottom-up build based on the industry-standard formula:

Revenue = Number of Users/Subscribers x Average Revenue Per User (ARPU).

We will structure our model to forecast MTN's key segments, as detailed in its financial reports:

1. Voice Revenue: Forecasted Subscribers x Forecasted Voice ARPU. This is a mature market. Voice revenue growth is slow (10.0% group-wide in Q3 2025), so assumptions here should be modest.

2. Data Revenue: Forecasted Active Data Subscribers x Forecasted Data ARPU. This is the primary growth engine. Management commentary supports high-growth assumptions, driven by aggressive 4G/5G network expansion, rising smartphone penetration (65.1% in Q3 2025), and a 36.3% surge in data traffic.

3. Fintech Revenue: Forecasted Active MoMo Wallets x Forecasted Fintech ARPU. This is the second high-growth engine. Fintech revenue jumped 72.5% in Q3 2025, driven by an aggressive expansion of the agent and merchant network.

This segmented approach creates a far more defensible and intelligent model. For example, the Data Revenue forecast is now driven by two separate assumptions. The growth in subscribers is driven by capital expenditure on network coverage. The growth in ARPU (which reached NGN 4,831) is driven by users consuming more data (average usage hit 13.2GB per month) and higher-value services. This two-variable forecast correctly captures the underlying "story" of the business.

Table 2: Sample Revenue Forecast - MTN Nigeria (Illustrative)

3.3 Case Study 2 (Mining/Agri): Forecasting Revenue

Companies in the extractive and agricultural sectors, such as Anglo American Platinum (AAP) in South Africa or Okomu Oil Palm (OOP) in Nigeria, have a fundamentally different business model. Their revenue is not driven by subscribers but by global commodity markets.

The correct forecasting formula is:

Revenue = Production Volume (tonnes, ounces, etc.) x Realized Price ($/tonne,$/oz, etc.).

• Forecasting Volume: This assumption should be based on management guidance.

  • For AAP, 2025 PGM production was guided to be between 3.0 and 3.4 million ounces. A model would use the midpoint (3.2 million ounces) as the base-case assumption.

  • For Okomu Oil Palm, management stated a plan to "double its production of CPO to around 80,000t per annum by 2025" as new plantations are acquired and mature.

• Forecasting Price: This is the most volatile assumption and should be a key input for sensitivity analysis.

A critical error for beginners is to forecast a commodity company's revenue in its local currency (e.g., Naira). Palm oil and platinum are global commodities priced in U.s. dollars (USD). The company's reported revenue in Naira (NGN) or Rand (ZAR) is a result of the global price and the local exchange rate.

Okomu Oil Palm's 2024 results illustrate this perfectly. The company reported a 194% increase in rubber sales prices and a 73% jump in total revenue. However, management explicitly states this was fueled by the "depreciation of the Naira".

Therefore, the correct way to model revenue for an African commodity exporter is a three-step process:

1. Forecast Production Volume (in tonnes or ounces).

2. Forecast Global USD Price (per tonne or ounce).

3. Forecast NGN/USD or ZAR/USD Exchange Rate.

4. Calculated Revenue (Local) = (Volume) x (USD Price) x (FX Rate)

This structure correctly isolates the three key drivers: operations (Volume), the global market (USD Price), and the local economy (FX Rate).

Table 3: Sample Revenue Forecast - Anglo American Platinum (Illustrative)

3.4 Forecasting Expenses and CapEx

Operating Expenses (OpEx)

The simple method for forecasting operating expenses like Cost of Goods Sold (COGS) and Selling, General & Administrative (SG&A) is as a percentage of revenue, based on historical averages.

However, in high-inflation African economies, this method is flawed and can lead to dangerously optimistic forecasts. A company's revenue (like Okomu's) may be tied to global USD prices, but its costs (like salaries, rent, and fuel) are tied to local inflation.

Okomu's 2024 report states this explicitly: the company faced an "exceptionally challenging" environment in Nigeria with 34.8% inflation, which led to a "nearly 80% increase in costs, year on year". This cost growth outpaced its 73% revenue growth, leading to a "margin squeeze."

A more sophisticated model would therefore separate costs:

• Variable Costs: Forecast as a % of Revenue (e.g., sales commissions).

• Fixed Costs: Forecast as a % of a separate Inflation Rate assumption (e.g., salaries, rent, fuel).

This structure correctly models the margin pressure that management is actively warning about.

Capital Expenditures (CapEx)

CapEx should almost never be forecast as a simple percentage of sales. CapEx is "lumpy" and driven by discrete, multi-year strategic projects.

• Telecom (MTN): CapEx is driven by the aggressive rollout of 4G/5G infrastructure and Fibre-to-the-Home (FTTH) networks. MTN Nigeria's 2025 capex spending was N757.4 billion, a 248% surge. These figures should be built into the model based on management's investment plans.

• Mining (AAP): CapEx in mining is massive and notoriously high-risk. Major mining projects (over $1 billion) overrun their initial budgets by 79% on average, with schedule delays over 50%. Mining megaprojects are "notorious for overshooting". A professional model for AAP would have a Sustaining CapEx (to maintain current mines) and a separate Growth CapEx (for new projects). This Growth CapEx line item would be a key variable in a "Stress Test" scenario to see if the project remains profitable even with a 40-80% cost overrun.

Module 4: The Pay-Off - Valuation Part 1: Discounted Cash Flow (DCF) Analysis

4.1 The "Intrinsic" Value: What is a DCF?

Once the 3-statement forecast is built, it can be used to value the company. The first and most common method is the Discounted Cash Flow (DCF) analysis.

The core idea of a DCF is that a company's value is not based on its current assets or a market multiple, but on the sum of all the cash flows it is expected to generate in the future.

Because cash received in ten years is worth less than cash received today (due to inflation and risk—the "time value of money"), those future cash flows must be "discounted" back to their present-day value. $\text{Intrinsic Value} = \frac{\text{Cash Flow}_{\text{Year 1}}}{(1+r)^1} + \frac{\text{Cash Flow}_{\text{Year 2}}}{(1+r)^2} + \dots + \frac{\text{Terminal Value}}{(1+r)^n}$ Where r is the discount rate.

4.2 Calculating Unlevered Free Cash Flow (UFCF)

We do not discount Net Income, because accounting profits are misleading. Instead, we use Unlevered Free Cash Flow (UFCF), also known as Free Cash Flow to the Firm (FCFF).

UFCF represents the total cash generated by the company's core business operations before any debt payments (interest or principal) are made. This makes it the cash flow available to all capital providers (both debt and equity holders).

The standard calculation for UFCF is:

• EBIT (Earnings Before Interest and Taxes, from the Income Statement)

• Minus: Taxes on EBIT (This is a theoretical "unlevered" tax, calculated as EBIT * (1 - Tax Rate))

• Plus: Depreciation & Amortization (Adding back the non-cash expenses from the IS)

• Minus: Capital Expenditures (CapEx) (The cash spent on long-term assets, from the CFS)

• Minus/Plus: Change in Net Working Capital (The cash impact of changes in AR, Inventory, AP, etc., from the BS/CFS)

• = Unlevered Free Cash Flow (UFCF)

4.3 The Discount Rate (WACC) in an African Context

The most important and most debated input in a DCF is the discount rate r. For a UFCF-based DCF, the correct discount rate is the Weighted Average Cost of Capital (WACC).

WACC represents the blended, average rate of return that all of a company's investors (debt and equity) require to compensate them for their risk.

This is where the African context becomes paramount. The WACC is the mathematical quantification of risk. Research shows that the average WACC for projects in Africa is significantly higher than in developed markets. One study on the electricity sector found the average WACC in Africa to be 15.6%, compared to just 5.1% in the USA and 4.2% in Western Europe.

This is not just an academic number. This disparity is caused by "high instability leading to high-risk premiums". Because the WACC is the denominator in the DCF formula, a higher WACC mechanically and dramatically lowers the calculated present value of the company. This is the financial market's way of saying that a future dollar of cash flow from a high-risk region is worth less today than a future dollar from a stable, low-risk region.

4.4 Practical Walkthrough: Building the WACC for MTN Nigeria

Calculating a realistic WACC is what separates a professional emerging-market analyst from a beginner.

The formula is: $\text{WACC} = \left(\frac{E}{V} \times \text{Cost of Equity}\right) + \left(\frac{D}{V} \times \text{Cost of Debt} \times (1 - \text{Tax Rate})\right)$

• E/V and D/V are the proportions of Equity and Debt in the company's capital structure.

• Cost of Debt is the company's effective interest rate on its borrowings, which can be estimated from its annual report.

• Tax Rate is the corporate tax rate in Nigeria (approx. 30%).

• Cost of Equity is the most complex component.

The Cost of Equity is calculated using the Capital Asset Pricing Model (CAPM): $\text{Cost of Equity} = \text{Risk-Free Rate} + (\text{Beta} \times \text{Equity Risk Premium})$

1. Risk-Free Rate (Rf): The yield on a long-term (e.g., 10-year) local government bond. For MTN Nigeria, this would be the yield on an FGN (Federal Government of Nigeria) bond.

2. Beta: A measure of the stock's volatility relative to the market. A Beta of 1.0 means it moves with the market.

3. Equity Risk Premium (ERP): This is the single most important input for an African valuation. It is the excess return investors demand for investing in the stock market over the risk-free rate.

A common mistake is to use a single ERP (e.g., 6%) for all countries. The correct approach is to build up a country-specific ERP: $\text{ERP} = \text{Mature Market Premium} + \text{Country Risk Premium (CRP)}$

Data from valuation expert Prof. Aswath Damodaran provides the necessary components as of January 2025:

• Mature Market Premium (MMP): 4.33% (This is the premium for a stable market like the U.S.)

• Country Risk Premium (CRP) for Nigeria: Based on its Caa1 sovereign rating, Nigeria has an "Adjusted Default Spread" of 7.426%.

This means the Total Equity Risk Premium for Nigeria = 4.33% + 7.426% = 11.759%.

An analyst using a generic 6% ERP would massively overvalue MTN Nigeria. The professional model, using an 11.76% ERP, will result in a much higher Cost of Equity and a higher WACC, which correctly and realistically accounts for the specific risks of operating in that market.

Table 4: Sample WACC Calculation - MTN Nigeria (Illustrative)

4.5 Calculating Terminal Value and Using the Right Excel Functions

A forecast cannot continue forever. After a 5- or 10-year explicit forecast period, a Terminal Value is calculated. This represents the value of all the company's cash flows from that point into perpetuity, assuming it grows at a stable, long-term (terminal) rate (e.g., 2-3%).

Advanced Excel: XNPV vs. NPV

Once the UFCF and WACC are forecasted, they must be discounted.

• Beginner Method: The NPV function. NPV(rate, value1, [value2],...). This function is flawed because it assumes all cash flows arrive at the exact end of each period (e.g., exactly on 12/31), which is unrealistic.

• Professional Method: The XNPV function. The formula is =XNPV(rate, values, dates). This function is far superior because it uses a specific array of dates corresponding to each cash flow. It correctly discounts cash flows based on their exact timing (e.g., 365 days, 730 days, etc.), resulting in a more precise and defensible valuation. The XIRR function works the same way for calculating the Internal Rate of Return.

Module 5: The Market - Valuation Part 2: Comparable Company Analysis (Comps)

5.1 The "Relative" Value: What are Comps?

The second main valuation method is Comparable Company Analysis (Comps). This is a relative valuation method, meaning it derives a company's value by comparing it to similar, publicly-traded companies (its "peer group").

The logic is simple: if Company A is a direct competitor to Company B and Company C, it should be valued on similar terms.

The Process:

1. Select a Peer Group: Identify companies that are similar in industry, size, and geography.

2. Gather Financial Data: For the peer group, collect their Market Capitalization, Net Debt, Revenue, EBITDA, and Net Income.

3. Calculate Valuation Multiples: The two most common multiples are:

   • P/E (Price / Earnings): Market Capitalization / Net Income

   • EV/EBITDA (Enterprise Value / EBITDA): (Market Cap + Net Debt) / EBITDA.

4. Apply the Multiples: Calculate the median or mean multiple of the peer group (e.g., the peer group trades at an average of 8.0x EV/EBITDA). Then, apply this multiple to your target company's EBITDA to find its implied Enterprise Value.

5.2 Comps Case Study 1: Valuing Safaricom (Kenya) - The Fintech Problem

This method seems simple, but the African context presents unique challenges. Let's try to value Safaricom (Kenya).

Safaricom is a telecom company, but its main growth driver is M-PESA, its world-renowned fintech platform. M-PESA revenue grew 19.4% in FY24, while mobile data grew 24.9%. Safaricom itself trades at an EV/EBITDA multiple of approximately 6.5x-7.3x and a P/E of ~13.7x.

Now, let's assemble its Pan-African telecom peer group:

Table 5: Pan-African Telecom Comps Table (Illustrative LTM Data)

This table presents a "problem." The multiples are inconsistent. MTN trades at a very low 3.5x EV/EBITDA, while Airtel Africa trades at 8.7x. Airtel's P/E is 30x, more than double its peers. A beginner would be stuck—which multiple is "correct"?

The answer is that a simple "Comps" analysis is misleading here. The market is not valuing these companies as single entities. Analyst reports on Safaricom reveal the professional approach: a Sum-of-the-Parts (SOTP) analysis.

This method, which is common for Africa's diversified conglomerates, involves:

1. Valuing the "GSM Business": The mature but stable telecom (voice, data) business is valued using a lower, mature telecom multiple (e.g., 5.0x EV/EBITDA, similar to Vodacom).

2. Valuing the "Financial Services Business": The high-growth M-PESA (fintech) platform is valued separately, using a much higher multiple (e.g., 12.0x-15.0x EV/EBITDA) more appropriate for a fintech company.

3. Summing the Parts: The implied values of the two segments are added together (along with subtracting net debt) to arrive at a total value for Safaricom.

This SOTP approach is the only way to correctly capture the value of a company like Safaricom, where a high-growth fintech "startup" is embedded within a mature utility.

5.3 Comps Case Study 2: Valuing Anglo American Platinum (South Africa)

A similar problem exists in the mining sector. Let's try to value Anglo American Platinum (AAP).

An amateur might create a peer group of "JSE-Listed Miners." This would be a critical mistake.

Table 6: JSE Mining Comps Table (Illustrative LTM Data)

The data clearly shows that not all miners are valued equally. The market is segmenting them by their primary commodity.

• Iron Ore/Coal (Kumba, Exxaro): Trade at very low 2.0x-4.0x EV/EBITDA multiples.

• PGM/Gold (Sibanye, Harmony): Trade at higher 6.0x-7.0x multiples.

The first lesson is that the peer group for AAP must be other PGM producers like Sibanye, not all miners.

The second, more advanced lesson involves the "EBITDA" in the multiple. The data is for the "LTM" (Last Twelve Months). However, 2024 was a terrible year for PGM prices. The PGM basket price declined by 24%, and Anglo American's annual profit plunged 94%.

This means the "LTM EBITDA" is cyclically depressed. The market, which is forward-looking, is not applying its 7.2x multiple to this depressed historical number. It is applying that multiple to a future, normalized, or recovering EBITDA. Using a simple LTM multiple at the bottom of a commodity cycle will lead to a nonsensical valuation. This demonstrates the "art" of valuation: an analyst must understand the industry cycle and not apply multiples blindly.

Module 6: Conclusion - Telling the Story with Scenario Analysis

A finished financial model is not an answer. It is an engine for finding answers. The final step is to use the model to perform Scenario Analysis (testing different sets of assumptions) and Sensitivity Analysis (testing the impact of changing one single variable).

This is where we use our African-context models to answer real-world strategic questions.

6.1 Case 1: MTN Nigeria - Quantifying Currency Risk

• The Question: Management commentary is dominated by the volatility of the Naira. The company swung from a N514.9 billion loss to a N750.2 billion profit, driven primarily by "a strengthened naira". How sensitive is the business to the exchange rate?

• Model Action: A Sensitivity Table is built in Excel.

  • Input Variable: NGN/USD Exchange Rate (e.g., in steps of 250, from 1,000 to 2,000).

  • Output Variables: Net Forex Gain/Loss, Net Finance Costs, and Profit After Tax.

• The Story: The resulting table will quantify management's warnings. It will show, in billions of Naira, exactly how much profit is gained or lost with every 100-Naira move in the NGN/USD rate. This demonstrates the massive, outsized impact of this macroeconomic factor on the company's reported bottom line.

6.2 Case 2: Anglo American Platinum - Commodity and FX Risk

• The Question: What is the combined impact of weak commodity prices and a volatile South African Rand (ZAR)?

• Model Action: We don't need to guess. Anglo American's own investor presentation provides the sensitivities. A 10% change in the ZAR/USD exchange rate impacts 6-month EBITDA by $113 million. This is a real-world risk: management confirmed that "weak commodity prices" led to a 94% plunge in profit and forced job cuts.

• The Story: A scenario block is built in the model:

  • Scenario 1: Base Case (e.g., $1,500/oz PGM price, 18.00 ZAR/USD)

  • Scenario 2: 2024 Price Shock (e.g., $1,200/oz PGM price, 19.00 ZAR/USD)

• The model will instantly show EBITDA and Free Cash Flow turning negative, proving why management was forced to restructure, cut costs by R12 billion, and lay off thousands of workers. This is a powerful, real-world lesson in using a model for strategic risk management.

6.3 Case 3: Okomu Oil Palm - Inflation vs. Growth

• The Question: Okomu's 2024 results show 73% revenue growth. This looks fantastic. But the same report shows 34.8% inflation and a "nearly 80% increase in costs". Is the company actually growing in real terms, or just running to stand still?

• Model Action: The model's forecast, which correctly separates USD-linked revenues from NGN-inflation-linked costs, is used to calculate the EBITDA Margin over the 5-year forecast period.

• The Story: The model will forecast a "Margin Squeeze." It will show that even as nominal Naira revenue climbs, the EBITDA margin will shrink if local cost inflation (salaries, fuel) grows faster than the combined global USD palm oil price and Naira devaluation. This analysis highlights the critical difference between "nominal" growth (growing in high-inflation currency) and "real" (inflation-adjusted) growth, which is the on-the-ground reality for most Nigerian businesses.

6.4 Final Word: From Beginner to Modeler

This curriculum has guided the learner through a complete journey. It began with understanding why a model is a high-stakes "passport to funding" in the African context. It then provided the technical skills to build the "engine": the 3-statement model, with its critical links [Module 2].

It moved from basic forecasting to industry-specific, driver-based forecasting for Africa's key sectors—Telecom (Subscribers x ARPU) and Mining (Volume x Price x FX Rate) [Module 3]. It demonstrated how to build a professional-grade valuation, not just by copying a formula, but by understanding and quantifying risk—specifically the high Country Risk Premium that defines an African WACC [Module 4].

Finally, it showed that simple "Comps" are not simple, and that professional analysis in Africa requires advanced techniques like Sum-of-the-Parts (SOTP) for diversified companies like Safaricom and cycle-aware analysis for commodity producers like AAP [Module 5]. By using these models to answer real-world questions about currency, inflation, and price shocks, the learner has transitioned from a beginner to a true financial modeler, capable of speaking the essential language of investment, growth, and risk [Module 6].