Financial Modeling Guide

What is Financial Modeling Used For?

Financial modeling is a tool to analyze a particular company’s historical performance and relevant market data on comparable companies operating in the same (or adjacent) industry to project its financial performance.

By forecasting the operating and financial performance of a company (or project), financial models are practical for various use-cases and guide decision-making, such as in the context of performing a valuation or capital budgeting analysis.

The following list contains the top ten most common types of financial models:

1. 3-Statement Model (Income Statement, Cash Flow Statement, Balance Sheet)
2. Discounted Cash Flow Analysis (DCF Model)
3. Comparable Company Analysis (Trading Comps Model)
4. Precedent Transactions Analysis (Transaction Comps Model)
5. Merger Model (M&A Accretion/Dilution)
6. Leveraged Buyout (LBO) Model
7. Initial Public Offering (IPO) Model
8. Sum-of-the-Parts Model (SOTP Valuation)
9. Capital Budgeting Model (Capital Investment Model)
10. Lender Model (Credit Risk Analysis)

What are the Financial Modeling Best Practices?

Financial modeling best practices refer to industry-standard modeling conventions and tips to adhere to when building models in Excel.

Following these general guidelines and industry best practices ensures that the financial models built on the job are intuitive, error-proof, and structurally sound.

Like many computer programmers, people who build financial models can get opinionated about the “right way” to do it.

In fact, there is surprisingly little consistency across Wall Street around the structure of financial models.

One reason is that financial models can vary widely in purpose, which, along with the context of the analysis, determines the required level of granularity and structure of the model.

For example, if your task was to build a discounted cash flow (DCF) model to be used in a preliminary pitch book as a valuation for one of 5 potential acquisition targets, it would likely be a waste of time to build a highly complex and feature-rich model.

The time required to build a super complex DCF model isn’t justified, given the purpose of the financial model (and the context of the analysis).

On the other hand, a leveraged finance model used to make thousands of loan approval decisions for various loan types under various scenarios requires a lot of complexity.

What are the Different Types of Financial Models?

The most common types of financial models include the following:

In particular, the core 3-statement financial model is designed to better grasp the operating drivers of a given company and its business model.

Creating a robust projection model requires analyzing a company’s historical performance and the relevant industry trends (and developments) to create a defensible 3-statement forecast, which underpins most valuation models (e.g. DCF analysis), strategic planning, and capital budgeting analyses.

^{Financial Modeling Example – Apple 3-Statement Model (Source: WSP Financial Modeling Course)}

Financial Modeling Course – Excel Template

Before we delve into the financial modeling best practices and conventions – are you interested in learning how to build a basic 3-statement model in Excel?

If so, fill out the form below to access our free introductory financial modeling course, including the Excel template that goes along with the tutorial.

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What are the Examples of Financial Modeling?

Understanding the purpose of the model is key to determining its optimal structure. There are two primary determinants of a model’s ideal structure:

1. Granularity
2. Flexibility

Let’s consider the following 5 common types of financial models built in corporate finance.

How to Structure a Financial Model

Component 1. Granularity in Financial Modeling

One critical determinant of a financial model’s structure is the concept of granularity.

Granularity refers to how detailed a model needs to be. For example, imagine you are tasked with performing an LBO analysis for Disney.

If the purpose is to provide a back-of-the-envelope floor valuation range to be used in a preliminary pitch book, it might be appropriate to perform a “high-level” LBO analysis, using consolidated data and making very simple assumptions for financing.

If your model is a key decision-making tool for financing requirements in a potential recapitalization of Disney, a far higher degree of accuracy is crucial. The differences in these two examples might involve things like:

• Forecasting revenue and cost of goods segment by segment, and using price-per-unit and #-units-sold drivers instead of aggregate forecasts
• Forecasting financials across different business units, as opposed to looking only at consolidated financials
• Analyzing assets and liabilities in more detail (i.e. leases, pensions, PP&E, etc.)
• Breaking out financing into various tranches with more realistic pricing
• Looking at quarterly or monthly results instead of annual results

Practically speaking, the more granular a model, the longer and more difficult it will be to understand. In addition, the likelihood of errors increases exponentially by having more data.

Concerning the importance of abiding by a consistent structure of a financial model — from the layout of the worksheets to the layout of individual sections, formulas, rows, and columns — the matter is particularly critical for in-depth, granular models.

In addition, integrating formal error and “integrity” checks can mitigate the risk of modeling errors.

Component 2. Flexibility in Financial Modeling

The other main determinant for how to structure a financial model is its required flexibility. A model’s flexibility stems from how often it will be used, by how many users, and for how many different uses. A model designed for a specific transaction or for a particular company requires far less flexibility than one designed for heavy reuse (often called a template).

As you can imagine, a template must be far more flexible than a company-specific or “transaction-specific model”. For example, say you are tasked with building a merger model.

If the purpose of the model is to analyze the potential acquisition of Disney by Apple, you would build in far less functionality than if its purpose was to build a merger model that could handle any two companies.

Specifically, a merger model template might require the following items that are not required in the deal-specific model:

1. Adjustments to the Acquirer’s Currency
2. Dynamic Calendarization (Set Target’s Financials to Acquirer’s Fiscal Year)
3. Insertion of Placeholders (Income Statement, Balance Sheet, and Cash Flow Statement)
4. Net operating loss analysis (neither Disney or Apple have NOLs)

The placeholders are for the line items that do not appear on Disney or Apple financials

Put together, granularity and flexibility largely determine the structural requirements in financial modeling.

Structural requirements for models with low granularity and a limited user base are quite low.

There is a trade-off to building a highly structured model per usual, which boils down to time.

In short, simply don’t if you don’t need to build in bells and whistles

But as you add granularity and flexibility into the model, structure and error-proofing become increasingly critical.

The table below shows the granularity and flexibility levels of common investment banking models.

What is the Purpose of Financial Modeling?

Before building a financial model, the first step must be to understand the purpose of the analysis and end-goal.

A financial model is a tool designed to aid decision-making, irrespective of its granularity and flexibility.

Therefore, a proper financial model must have a presented output and conclusion, which should be intuitive and easy to understand.

For instance, if an investment banking analyst submits a valuation model to their direct supervisor, or an associate, the process of auditing the model should be relatively easy, assuming the model was built properly and abides by the standard modeling best practices and industry conventions.

Otherwise, the associate is likely to be frustrated because auditing the model is time-consuming and the “flow” of the model is difficult to comprehend.

While constructing a financial model, constantly remind yourself that another person will soon review your work.

Since virtually all financial models will help decision-making within various assumptions and forecasts, an effective model will allow users to easily modify and sensitize various scenarios and present information in various ways.

How to Format a Financial Model

Thus far, we’ve established a simple framework for structuring models, so it is now time to discuss specific features of model architecture, error proofing, flexibility, and presentation, where we’ll lay out the key elements of an effectively structured model.

In particular, these best practices and industry conventions will go a long way to improving the transparency of the model.

As a financial model becomes more complex – due to higher granularity and flexibility – the model naturally becomes less transparent.

The financial modeling best practices and practical tips described in the next sections will help fix this, starting with the following guidelines around formatting financial models.

Guidelines for Color Coding in Financial Modeling

Just about everyone agrees that color-coding cells based on whether the cell contains a hard-coded number or a formula are critical. Without color coding, it is extremely difficult to visually distinguish between cells that should be modified and cells that should not (i.e. formulas).

A properly built financial model will further distinguish between formulas that link to other worksheets and workbooks, as well as cells that link to financial data services, like Capital IQ and FactSet.

While different investment banks have different house styles, blue is typically used to color inputs, and black is used for formulas (or calculations).

The table below shows our recommended color coding scheme.

While everyone agrees that color coding is important, keeping up with it can be a pain in native Excel. It’s not easy to format cells based on whether they are inputs or formulas, but it can be done. One option is to use Excel’s “Go To Special”.

Alternatively, color coding is dramatically simplified with a third-party Excel add-in like Capital IQ or Factset.

These tools allow you to “auto-color” an entire worksheet in one click.

How to Make Financial Models Easier to Audit

Inserting comments (Shortcut “Shift F2”) in cells is critical for footnoting sources and adding clarity to data in a model.

For example, a cell with an assumption of revenue growth from an equity research report should include a comment regarding the research report.

So how much commenting do you need?

Always err on the side of over-commenting.

No managing director (MD) at an investment bank will complain that a financial model contains too many comments.

Conversely, a financial model with inputs and calculations, where the rationale is not straightforward to understand, is prone to criticism.

Additionally, if you’re on a conference call and someone asks how you came up with the number in cell AC1238, and you are not sure, you’ll regret not commenting.

What are the Sign Convention Rules in Financial Modeling?

The decision on whether to use positive or negative sign conventions must be made before the model is built.

Models in practice are all over the place on this one. The modeler should choose from and clearly identify one of the following 3 approaches:

Convention 1. Income = Positive; Expenses = Negative

• Advantage: logical, consistent, makes subtotal calculations less error-prone
• Disadvantage: Doesn’t align with conventions used by public filings, % margin calculations appear negative

Convention 2. Expenses Positive; Non-Operating Income Negative.

• Advantage: Consistent with public filings, % margin calculations appear positive
• Disadvantages: Negative non-operating income is confusing, subtotal calculations are error-prone, proper labeling is critical

Convention 3. Expenses Positive (Except Non-Operating Expenses)

• Advantage: Avoids negative non-operating income presentation; margins evaluate to positive
• Disadvantage: Presentation not internally consistent. Proper labeling is critical.

Our recommendation is Convention 1. The reduced likelihood of error from easier subtotaling alone makes this our clear choice. In addition, one of the most common mistakes in modeling is forgetting to switch the sign from positive to negative, or vice versa when linking data across financial statements. Convention 1, by virtue of being the most visibly transparent approach, makes it easier to track sign-related mistakes.

How to Maintain Consistency in Financial Modeling

Hard-coded numbers (constants) should never be embedded into a cell reference.

The danger here is that you’ll likely forget there is an assumption inside a formula. Inputs must be separated from calculations.

Most investment banking models, like the 3-statement model, rely on historical data to drive forecasts.

Financial data should be presented from left to right. The right of the historical columns are the forecast columns. Hence, the left section font color is blue, while the projection section on the right is black.

While building a financial model in Excel, remember the rule: “One Row, One Calculation”. 

The formulas in the forecast columns should be consistent across the row.

How to Ensure Simplicity in Financial Models (“BASE”)

In financial modeling, roll-forward schedules (“BASE” or “Cork-Screw”) refer to a forecasting approach that connects the current period forecast to the prior period.

This approach is useful in adding transparency to how schedules are constructed. Maintaining strict adherence to the roll-forward approach improves a user’s ability to audit the model and reduces the likelihood of linking errors.

There is a temptation when working in Excel to create complicated formulas.

While it may feel good to craft a super complex formula, the obvious disadvantage is that no one – including the author after being away from the model for a bit – will understand it.

Because transparency should drive structure, complicated formulas should be avoided at all cost. A complicated formula can often be broken down into multiple cells and simplified. Remember, Microsoft doesn’t charge you extra for using more cells! So take advantage of that. Below are some common traps to avoid:

1. Simplify IF statements and avoid nested IFs
2. Consider using flags

For instance, “IF” statements in financial models – while intuitive and well understood by most Excel users – can become long and difficult to audit.

There are several excellent alternatives to IF that top-notch modelers frequently use. They include using Boolean logic along with various reference functions, including MAX, MIN, AND, OR, VLOOKUP, HLOOKUP, and OFFSET.

Below is a real-world example of how an IF statement can be simplified. Cell F298 uses any surplus cash generated during the year to pay down the revolver, up until the revolver is fully paid down.

However, if deficits are generated during the year, we want the revolver to grow. While an IF statement accomplishes this, a MIN function does it more elegantly:

Example 1. Revolver Formula Using IF Statement

Example 2. Revolver Formula Using Excel MIN Function

The revolver formula using MIN as an alternative to IF also holds up better when additional complexity is required. Imagine there’s a limit on the annual revolver draw of $50,000. Look at how we have to modify both formulas to accommodate this:

Example 3. Revolver Formula Using IF Statement (Modified)

Example 4. Revolver Formula Using Excel MIN and MAX Function

While both formulas are challenging to audit, the formula using IF statements is more difficult to audit and is more vulnerable to getting completely out of hand with additional modifications. It uses nested (or embedded) IF statements, with which our feeble human brains have a hard time once there are more than one or two.

Fortunately, Excel has made this a bit easier in 2016 with the introduction of the IFS function, but our preference for relying on more elegant functions remains.

How to Manage Date Complexities in Excel

Flags refer to a modeling technique most useful for modeling transitions across phases of a company, project or transaction over time, without violating the “one row/one calculation” consistency rule.

Imagine you’re building a model for a company contemplating bankruptcy. Each phase of the restructuring process has its own distinct borrowing and operating characteristics.

In our example below, the company’s revolver “freezes” once it goes into bankruptcy, and a new type of borrowing (“DIP”) acts as the new revolver until the company emerges from bankruptcy.

Additionally, a new “Exit” facility replaces the DIP. We insert 3 “flags” in rows 8-10 to output “TRUE/FALSE” based on the phase we’re in. This enables us to build very simple, consistent formulas for each revolver, without having to embed IF statements into each calculation.

In cell F16, the formula is =F13*F8. Whenever you apply an operator (like multiplication) on a TRUE, the TRUE is treated like a “1” while a FALSE is treated like a “0.” This means the pre-bankruptcy revolver is the de facto revolver when the pre-bankruptcy flag evaluates to TRUE and becomes 0 once the flag evaluates to FALSE (starting in column I in our example below).

The main benefit is that with the use of just an extra 3 rows, we’ve avoided having to insert any sort of conditional tests within the calculations. The same applies to the formulas in rows 20 and 204 — the flags have prevented a lot of extra code.

How to Name Cells and Ranges

Another way many modelers reduce formula complexity is by using names and named ranges.

That said, we strongly caution against using names and named ranges.

Why? As you’re probably beginning to sense, there is always some sort of trade-off with Excel. In the case of names, the trade-off is that when you name a cell, you no longer know exactly where it is without going to the name manager.

In addition, unless you are proactively deleting names (you aren’t), Excel will retain these names even when you delete the named cell. The result is that a file you’re using today to build a DCF contains dozens of phantom names from previous versions of the model, leading to warning messages and confusion.

What are Common Mistakes in Financial Modeling?

Do Not Calculate on the Balance Sheet – Link from Supporting Schedules Instead

In investment banking, your financial models frequently involve financial statements. Ideally, your calculations are done in schedules separate from the output you’re working towards.

For example, you shouldn’t perform any direct calculations on the model’s balance sheet.

Instead, balance sheet forecasts should be determined in separate schedules and linked to the balance sheet, as illustrated below.

This consistency enhances transparency in the model, making it easier to audit.

Never Re-Enter the Same Input in Different Cells

For example, if you’ve inputted a company name in the first worksheet of the model, reference that worksheet name — don’t re-type it into the other worksheets.

The same goes for years and dates entered into a column header or discount rate assumptions used in various places in the model.

A more subtle example of this is hard coding subtotals or EPS when you can calculate it. In other words, calculate whenever possible.

Always Link Directly to a Source Cell – Easier to Audit “Daisy Chained” Data

The one major exception is when “straight-lining” base period assumptions.

For this, go ahead and daisy chain. The reason is that straight-lining base period assumptions are an implicit assumption, that can change, making it possible for certain years in the forecast to ultimately end up with different assumptions than other years.

Avoid Formulas Containing References to Multiple Worksheets

Compare the two images below – it is far more difficult to audit the formula in the first image because you’ll need to bounce around to different worksheets to view the precedent cells.

Therefore, bring the data from other worksheets into the active worksheet where the calculation is made whenever possible.

Link Assumptions into Standalone Cells in the Calculation and Output Sheets

If you’re working with larger models and have assumptions that need to be referenced from a separate worksheet, consider linking assumptions directly into the worksheet where you’re using them, and color code them as a distinct worksheet reference link.

In other words, don’t have an input reference embedded into a calculation (i.e. =D13*input!C7).

Instead, use a clean reference =input!C7 and a separate cell for the calculation. While this creates a redundant cell reference, it preserves the visual auditability of the model tab and reduces the likelihood of error.

Avoid Linking External Files

Excel allows you to link to other Excel files, but others might not have access to the linked-to files, or these files may get inadvertently moved.

Therefore, avoid linking to other files whenever possible.

If linking to other files is necessary, be vigilant about color-coding all cell references to other files.

One Sheet vs. Multiple Sheet Financial Model: Which is Better?

In short, a financial model presented on a single long sheet is preferable over many short sheets.

A long worksheet means a lot of scrolling and less visual compartmentalizing of sections.

On the other hand, multiple worksheets significantly increase the likelihood of linking errors.

There’s no hard and fast rule about this, but the general bias should be toward a longer sheet over multiple, shorter worksheets.

The dangers of mis-linking across worksheets are quite real and hard to mitigate, while the issues of cumbersome scrolling and lack of compartmentalization associated with long worksheets can be drastically mitigated with Excel’s split screen functionality, clear headers, and links from a cover sheet or table of contents.

Why Group Rows Instead of Hiding Them?

As a general financial modeling best practice, do not hide rows. Instead, group the rows (and do it sparingly).

A model often has rows with data and calculations that you do not want to show when the model is printed or when you paste the data into a presentation. In this situation, it’s often tempting to hide rows and columns for a “cleaner” presentation of results.

The danger is that when the model is passed around, it is very easy to miss (and potentially paste over) the hidden data.

How to Structure a Financial Model

In short, group the inputs (i.e. the assumptions) that drive the financial model together in one section.

Nearly every financial modeling expert recommends a standard that isolates all of the model’s hard-coded assumptions (things like revenue growth, WACC, operating margin, interest rates, etc…) in one clearly defined section of a model — typically on a dedicated tab called “Inputs”.

These should never be commingled with the model’s calculations (i.e. balance sheet schedules, financial statements) or outputs (i.e. credit and financial ratios, charts and summary tables).

In other words, think of a model comprised of three clearly identified and physically separated components:

The advantages of using one sheet are as follows.

• Consistent, reliable architecture: Once a model is built, the user has only one place to go to change any assumptions. This creates a consistent distinction between areas in the model that the user works in vs. areas the computer works in.
• Error mitigation: Storing all assumptions in one place makes it far less likely that you’ll forget to remove old assumptions from a prior analysis and inadvertently bring them into a new analysis.

Yet despite these advantages, this practice has never been widely adopted in investment banking.

One reason is simply poor practice. Some models would benefit from an input/calculation/output separation but are often built with no forethought given to structure. Imagine building a house without any pre-planning.

Sure, you’ll avoid the pain of all that planning, but you’ll encounter unforeseen problems and end up redoing work or adding complexity by working around what’s already been done. This problem is rampant in investment banking models.

Another reason is that many investment banking models are simply not granular enough to merit the additional audit trail and legwork.

The analyses bankers perform are often broader than in-depth, at least initially.

For example, a pitch book might present a valuation using 4 different valuation models, but none of them will be overly granular.

Common investment banking analyses like accretion dilution models, LBO models, operating models, and DCF models usually don’t delve into detail beyond the limits of public filings and basic forecasting. In this case, moving back and forth from input to calculation to output tabs is unnecessarily cumbersome.

As long as you’re diligent about color coding, placing assumptions on the same sheet and right below calculations is preferable in smaller models, because your assumptions are visually right next to the output, making it easy to see what’s driving what.

The other consideration is the number of users of a model. The advantages of the “inputs together” approach grow with the number of intended users of a model. When you have many users, your model will inevitably be used by people with a wide range of modeling proficiency.

In this case, a consistent and reliable structure that prevents users from getting into the guts of the model will reduce error. In addition, it will also reduce the time a user spends in the model.

A user can simply locate the area for inputs, fill them in, and the model (in theory) will work.

That said, despite attempts by IB teams to standardize models, many investment banking models are essentially “one-offs” that get materially modified for each new use.

Apart from comps models, which are becoming templates, most models are used primarily by their original authors (usually an analyst and associates) who understand the model well.

The bottom line on keeping inputs all together: Unfortunately, there’s no established benchmark for when it makes sense to separate assumptions. The ideal approach depends on the scope and goal of the model.

For a simple 1-page discounted cash flow analysis not intended for frequent reuse, it is preferable to embed inputs throughout the page.

However, for a large fully integrated LBO model with many debt tranches to be used as a group-wide template, the benefits of keeping all inputs together will outweigh the costs.

No Spacer Columns Between Data

Elevator Jumps

In long worksheets, dedicating the leftmost column to placing an “x” or another character at the start of schedules will make it easy to quickly navigate from section to section.

Annual vs. Quarterly Model: How to Adjust for Periodicity

Most investment banking models are either quarterly or annual. For example, a U.S. equity research earnings model will always be a quarterly model because one of its key purposes is to forecast upcoming earnings, which are reported by firms quarterly.

Similarly, a restructuring model is usually a quarterly model (or even a monthly or weekly model) because a key purpose of this model is to understand the cash flow impact of operational and financing changes over the next 1-2 years.

On the other hand, a DCF valuation is a long-term analysis, with at least 4-5 years of explicit forecasts required. In this case, an annual model is appropriate.

There are also models for which both quarterly and annual periods are useful. For example, a merger model usually needs a quarterly period, because a key goal is to understand the impact of the acquisition on the acquirer’s financial statements over the next 2 years.

However, attaching a DCF valuation to the combined merged companies may also be desirable. In this case, a possible solution is to roll up the quarters into an annual model and extend those annual forecasts further out.

When determining a model’s periodicity, remember the following:

• The model must be set up with the smallest unit of time desired, with longer periods aggregated (“rolled up”) from those shorter periods. If you’re building an integrated financial statement model in which you want quarterly and annual data, forecast the quarterly data first.
• Keep the quarterly and annual data in separate worksheets. It is easier to audit what’s going on when periods aren’t commingled. Additionally, commingling quarterly and annual data in one worksheet will either A) force you to violate the one row/one formula consistency best practice, or B) you will have to jump through some crazy hoops to maintain the consistency.