This article provides a detailed analysis of the complexities involved in revenue modeling.

Why is Revenue Modelling Important?

From a financial modeler’s point of view, the revenue number may be the most important input in the financial model. This is because revenues form the top line of the financial model. Expenses are subtracted from revenues to arrive at profits. Profits are then adjusted to arrive at cash flows. All investment and financing decisions are taken based on this cash flow. Hence, if a firm has made a big error while forecasting revenues, they will face a trickle-down effect of their mistake. All the subsequent calculations will be incorrect, and the company may end up making some really bad decisions since they did not have the correct information at hand.

Why is Revenue Modelling Challenging?

Revenue modeling is important. However, it is also extremely challenging. This is because there are multiple ways of arriving at the revenue number. Some examples have been listed below:

• No of Units Sold* Selling price = Revenues
• Total Market Size * Market Share = Revenues
• Previous Years Revenue * Growth Factor = Revenues
• Revenues Per Store * No of Stores = Revenue

Each of the above formulae provides a different way of looking at the same number. For instance, the first method is very production oriented. At the same time, the second way of arriving at revenue focuses on competition. This method is external oriented and brings the management’s attention to the change in market share.

The third method derives the number based on the previous year’s actuals. This method is used if the past forecasts of the company have proven to be accurate and only minor adjustments need to be made to arrive at the new number. Lastly, the fourth formula expresses the revenues as a factor of the number of stores. This viewpoint encourages the company to increase the number of stores in operation if the revenue has to be maximized.

Therefore, a financial modeler has to decide an approach which is most suitable for their specific case. This is what makes revenue modeling extremely challenging.

Different Methods of Creating a Revenue Model

The above-mentioned methods are amongst the most basic techniques which can be used for revenue modeling. In real life, companies use much more complex parameters to be able to accurately model revenue. Some examples have been provided below:

• Focusing on opening new stores in order to increase revenues would be an incorrect strategy if the size of the store is not considered. Therefore, many financial modelers distinguish between small and large stores while creating financial models. Therefore, instead of using a store as a single unit, modelers tend to use units such as square footage which provide a better estimate

• However, opening a small store in a prime location could still generate more sales than opening a big store in a remote location. It is for this reason that many companies forecast their sales using revenue per square foot from comparable stores

• Also, many companies ensure that new store calculations are listed separately. This ensures that stakeholders are made aware of whether the sales were generated by already established stores or by newer stores. This provides information about how much revenue can be expected from a new store immediately after it becomes operational.

Key Considerations to be Taken into Account While Revenue Modelling

There are some factors which negatively affect the revenue model. They need to be managed to ensure that the model and the results that it gives are accurate.

1. Influenced by Stakeholders: Many times, revenue numbers are inflated in order to massage the egos of the higher management. The higher management may put in a lot of pressure, and the ground level resources may give unreasonable forecasts under this pressure. This needs to be avoided. Companies must ensure that the numbers which get reported are accurate and realistic; otherwise, the results of the model become inaccurate. It should be understood that the process of giving sales targets is not the same as collecting data for the financial model.

2. Longer Duration: Companies should refrain from making revenue predictions about the distant future. Empirical records show that revenue is a highly sensitive number, and it varies widely over the years because of factors which cannot be predicted too far off into the future. Hence, companies should ensure that detailed revenue modeling is only done for the short term. For long term numbers, the data can simply be extrapolated. This would provide a rough estimate without actually putting in too much effort.

3. Impact on the Stock Price: Companies should refrain from giving overly optimistic revenue guidance. This should be the case, especially when their stocks are traded on the exchange. This is because when companies miss their revenue guidance, their stocks experience a loss in value. Revenue guidance should be seen as a company’s word, and attempts should be made to fulfill the promise without resorting to devious methods.

Once the revenue modeling is complete, the next step in the process refers to the modeling of expenses. This process is challenging because there are several different types of costs, and they all behave in different manners.

In this article, we will first have a brief overview of the cost modeling process. Once that is done, we will also have a look at the various important decisions which are undertaken during the development of the cost model.

How is Cost Modeling Done?

The process of cost modeling has been identified, and the important steps are listed below:

1. Segmenting the Expenses: The cost modeling process usually begins by listing down the expenses. Once the expenses are listed down, they are categorized. In financial accounting, expenses are categorized based on the format provided by the regulatory bodies. However, on the other hand, during the cost modeling exercise, expenses are grouped together if they have similar drivers.

   For instance,

   1. Electricity expenses are directly related to the amount of square footage controlled by the company. Other things being equal, the electricity bill will only increase or decrease if the area under the control of the company changes.

   2. Cleaning and facility management expenses are also heavily dependent on square footage.

   Hence, these two can be placed in a similar group. However, advertising expenses are totally different. They have no relation to square footage. Instead, advertising expenses depend upon the sales which the company intends to generate. Hence, advertising expenses must be placed in a separate group.

2. Identifying Cost Drivers: After the costs have been segmented into various groups, the next step is to list down their drivers. These drivers are usually intermediate inputs. Hence, they should also be automatically calculated based on the inputs mentioned in the model.

   For instance, if the company wants to increase its sales, it will also have to increase its production. The production will only be increased by creating new facilities which will lead to an increase in the square footage. Therefore, the model must be capable of converting the company’s goal i.e., a higher sales target into an intermediate input such as increased square footage. The range of these variables must be defined to ensure that the model does not incorrectly extrapolate the data and give the wrong results.

3. Focus on Total Cost of Ownership: Once the relationships between costs and their drivers have been identified, they can be input into the model. However, it is important to ensure that the total cost of any decision is segregated for decision making.

   For instance, if a company plans to expand its manufacturing facilities in order to make more products, then the total cost of this decision needs to be accounted for. The analysis must depict the rent, depreciation, and many other expenses to give the decision maker a sense of the total impact of their decision. In most cases, the total impact will be visible in one place only.

   Most operational decisions related to costs aren’t very complicated. The few which are complicated need to be taken into account while developing the cost model.

4. Allocate Costs to Each Supplier: A good financial model must take into account that the costs do not remain the same for all suppliers. Some suppliers always provide better terms and service as compared to others.

   For instance, some suppliers provide just in time delivery, longer credit duration. The products supplied by many suppliers are found to be faulty less often. Hence, the cost of exchanging the products or getting them repaired under warranty is saved.

   It would not be feasible to allocate the costs exactly for each supplier since large companies deal with thousands of vendors at the same time. However, in order to simplify the process, companies can group their suppliers into different groups. The costs can then be apportioned differently accordingly. This information comes in extremely handy when companies are about to make cost decisions. They can forecast the monetary value of the inefficiencies of the suppliers and make decisions accordingly.

5. Create a Standard Costing System: The final objective of cost modeling is to be able to create a standard costing system. This is where all the costs of the company are collated and expressed in the per unit form. This helps companies keep track of the expected costs.

   The financial model can then be used to keep track of the actual costs as compared to standard costs. The variances can then be brought to the notice of the management. Analysis of these variances allows companies to adjust their standard cost estimates. This process continues until the amount of variance is negligible, and the model has been perfected.

The conclusion is that cost modelling is linked to various different variables. There are some costs which are linked to sales, some are linked to each other whereas some more are linked to a totally different variable. All these complications need to be taken into account while creating a cost model.

In this article, we will have a closer look at what circular reference error is and how it can be managed to ensure minimal impact on the model.

What is the Circular Reference Error?

In simple words, circular references mean when the output of an equation is also a part of the input. In excel terms, this means that the formula in a cell points to itself, either directly or indirectly. Financial modelers never really directly link the input cell to the output cell. Generally, the input cell is linked to the output cell through a chain of complex formulas. This chain is difficult to recognize. In many cases, the error may be because of incorrect modeling. However, in many cases, the circular reference would actually be appropriate and an important part of the model.

Let us use an example to understand how circular references can inadvertently get built in a financial model. It is a known fact that interest expense is a part of the profit and loss statement. Hence, interest expense is used to calculate the net profit after tax.

However, this net profit after tax is then used as an input to the cash flow statement. This cash flow statement then projects the amount of cash that the company is expected to have on hand. The cash balance then helps determine the amount of loan or overdraft that a company would have to take to meet its working capital requirements. Now, the amount of loan taken is the determining factor in the amount of interest expense that will be incurred.

It is clear from the above example that the interest expense in the profit and loss statement ends up being an input for the interest expense in the cash flow statement. In such cases, MS Excel will throw a circular reference error. Many circular references like interest expense follow well known and well-documented patterns. However, there are many others which are very difficult to trace. Financial modelers end up spending a lot of time trying to understand the root of the problem and whether the circular reference is valid.

How is Circular Reference Error Managed?

In finance, many calculations are indeed circular. The Microsoft Excel tool has been built to recognize this issue. Hence, once the financial modelers recognize the source of the circular reference and consider it to be valid, he/she can easily correct the error. The two most common ways of correcting the error have been listed below.

• Microsoft Excel has created a functionality called Iterative Calculation. If this functionality is enabled, the system no longer throws the circular reference calculation. This functionality can be activated by following the path mentioned below:

• Menu—>Options—>Formulas—>Enable Iterative Calculations

• Even after the circular reference is activated, the model throws an error every time a downstream calculation takes place. Financial modelers often use the “IF” condition formula to avoid these errors. This means that they have a cell where they pass the values “ON” or “OFF” to the model. The “IF” formula is designed to forward the actual value if the model is set to ON. However, if the model is set to OFF, the value zero is passed on. This is done to avoid too many errors during the building phase of the model.

• Circular reference errors can be incredibly complex. For instance, there is a possibility that an erroneous value may be passed into the cell as an input. To prevent this, Microsoft Excel has created a functionality called Jumpstart. Jumpstart is a two-part formula. It checks the correctness of the value before passing it into the model.

The bottom line is that a financial modeler needs to have the skill required to understand which circular references are a part of the model and which have come in due to calculation errors. Once the correct circular references have been identified, the modeler must use standard Excel solutions to get rid of the problem. There are many solutions which can be found by conducting a google search. However, many of these solutions cause a problem with other calculations down the road. Hence, it is better only to implement methods which have been tried and tested.

In this article, we will have a closer look at what an accretion/dilution model is and how it is used to enable decision making.

What is an Accretion/Dilution Model?

In simple words, an accretion/dilution model measures the effect of the acquisition on the earnings per share of the acquiring company. This means that if the acquiring company had an EPS of $1 prior to the merger and has a proposed EPS of $1.25 after the merger, the merger is said to be accretive. On the other hand, if the EPS of the acquired entity reduces to $0.75, then the merger is said to be dilutive.

The strange thing about the accretive/dilutive model is that it focuses on the short term earnings. Firstly, it is a known fact that investment bankers do not place much trust in the earnings of any company. Instead, they focus on cash flow. Hence, many investors have started focusing their attention towards the cash EPS number instead of book EPS while taking into account the accretive or dilutive effect of the merger.

Also, the EPS is a short term measure, whereas investment bankers are generally focused on the long term value creation potential of any company.

However, it seems like the earnings of a company are under a lot of immediate scrutiny after a merger. Hence, an increasing EPS number makes for good PR and increases investor confidence. This is why a lot of emphases is laid down on the accretion/dilution model during the decision making phase.

Many companies are known only to pursue mergers which are found to be accretive. However, it is important to know that accretion or dilution do not have much impact on the long term value creation. Therefore, using the accretion/dilution analysis as a proxy for shareholder wealth creation may not be an accurate thing to do.

How Sources of Funding Affect Accretion and Dilution?

Acquisition transactions can be financed in many ways. For instance, there could be an all-stock deal. Alternatively, there could also be an all-cash deal. However, in most cases, part of the consideration is paid in cash, whereas the other part is paid in stock.

An accretion/dilution analysis taken into account the funding pattern as well while calculating the effect of the merger.

• In case of an all-stock merger, the financial modeler has a tough task trying to figure out the number of shares of the new entity which will be left outstanding once the merger has taken place. The total earnings of the firm are projected in the combined financial statements. However, these earnings get divided into the number of shares outstanding. This denominator has to be derived based on a number of factors. Typically a number of scenarios are considered.

  For instance, what would be the effect of the merger, if two shares of the existing entity are swapped for one of the new ones? Similarly, the effect of a one-to-one merger will also be one scenario. Based on this analysis, the firm determines the highest compensation that it can provide to the other party without diluting its own earnings or cash flow per share.

• Similarly, in case of an all-cash merger, the company has to take into account increased interest expenses.

  Even if the company has cash lying on its balance sheet, it will have to forego the interest being earned on that cash. That loss also needs to be accounted for. In this case, the denominator i.e., the number of shares outstanding, will not change. However, the numerator i.e., the earnings to be apportioned amongst the shares will change because the effects of interest expenses also have to be taken into account.

• Financial modeler has to consider the various purchase prices which are possible during the merger process.

  For each purchase price, the amount of debt after the transaction has to be derived. Then the expense related to this debt needs to be worked out, and the income statement needs to be adjusted accordingly.

  Just like in the above case, the financial modeler can use the accretion/dilution analysis to predict the maximum amount that can be offered to the counterparty without diluting its own EPS.

• Lastly, transactions are commonly funded using a mixture of debt as well as equity. From a financial modeling point of view, these transactions are exceptionally difficult to model. This is because, in such cases, both the numerator and the denominator of the transaction change. This adds a lot of complexity to the situation.

  The financial modeler needs to create a model which helps decision-makers figure out the best debt to equity mix. These transactions are difficult to model since the cost of debt is not linear, it keeps on increasing as the firm adds more and more debt, and the transactions become risky.

The bottom line is that the accretion/dilution analysis has limited use when a longer time period is considered. However, it is widely used in analyzing the short term impact of the merger.

In the world of banking and high finance, modelling or financial modelling is a term used to describe the process of forecasting and estimating risk and return as well as predict how the future would be in financial aspects.

Financial Modelling is critical to the success of the finance industry as it not only helps bankers and financiers have a grip on their investments and portfolios but also helps them to peer into the future to understand how their past and present choices make the difference between success and failure in the future.

Indeed, without modelling, it would be impossible for bankers and investments wizards to offer new investment and financial products to their customers as they cannot make profits unless they know for sure how the investment is going to turn out in the future.

This is the reason why Bankers and Investment Specialists swear by their models as it helps them to be in business.

Moreover, even Sovereign Debt is modelled and ratings of nations prepared based on the models developed by the bankers. High Finance cannot do without Financial Modelling as a tool.

Where Models Fail Spectacularly and How to Improve Modelling Using Feedback

Having said that, there are numerous occasions in which Financial Models have spectacularly failed as was evident during the Great Recession of 2008 when the entire financial system was close to collapse as bankers and financiers failed to not only anticipate the crisis but also to estimate the extent of damage done to the Global Economy on account of their investment decisions.

As financial models work by extrapolating the present indicators and risks into the future, anticipating what happens next is an important element of modelling.

However, it is not always the case that such predictions and models are accurate as more often than not, the probabilistic scenarios do not work out as planned and more importantly, the Known Unknowns of the economic and financial system wreak havoc on even the best modelled plans.

This is the reason why in recent years, bankers and financiers have turned to ways and means to improve their modelling using sophisticated tools and technology.

Indeed, it is believed that as technology improves so will the ability to model the future as well.

Moreover, in the present times, Algorithmic Modelling has taken over with the result that it is mostly Artificial Intelligence that is used.

How Does Modelling Work and Why Models are Critical to Bankers and Financiers

So, how are models prepared and how do they work and help the bankers and financiers?

To start with, modelling is done based on past returns for the risk appetites and the present value of such investments and the probabilistic returns in the future for such investments.

This means that data from the past and the present is used to model the future returns and whether the investments would turn out the way they are modelled.

This requires knowledge of not only risk and return patterns but also information about a broad range of indicators that help bankers understand how their decisions regarding investments would turn out in the future.

In other words, models are useful as they help bankers and their clients understand what a certain sum of money invested now would yield in the future and how such investments can be broadly impacted by the Known and Unknown risks.

Indeed, this is where astute modellers succeed as they not only have the data at their disposal but also use Gut Feeling to make informed decisions about the Known and the Unknown risks that can impact the investments. So, the past informs the present which in turn shapes tomorrow.

Why Contemporary Modelling is Often Criticized for Ethical and Social Reasons

Having said that, there has been much criticism in recent years of the models and the tools and technologies used by the bankers and financiers to prepare models.

These criticisms are aimed at how models these days are over reliant on technology and how machine learning driven modelling, though rigorous does not take into account the Human Element.

In other words, there are many who believe that modelling has become too mechanistic and disconnected from the everyday realities of the majority.

However, the fact that bankers and financiers are in the business of making money for their clients and for themselves is touted as the reason why the above criticism is invalid.

On the other hand, there is some element of truth to the naysayers as Debt Rating and Sovereign Risk and Return Models impact the lives of Billions of People and hence, there is an ethical and social case to be made for responsible modelling.

In other words, while models for the rich and wealthy can be free from emotional factors of modelling, the same cannot be said of the financial models which are prepare for nations and which impact the lives and livelihoods of the poor and needy.

Concluding Thoughts

Last, it is a fact that models often incorporate feedback about the past investments and the present values so that there can be better forecasts.

However, this does not always work as intended as some of the decisions might be the victims of conscious and unconscious bias of the modellers.

This is where a thorough review of the models is often needed as multiple layers of checks and balances ensure objectivity and transparency.

Moreover, cross checking enhances the reliability of models as well.

To conclude, Financial Modelling is an extremely critical element of Banking and Finance now and in future.

Accuracy

Microsoft Excel has some serious limitations when it comes to the accuracy of the financial model. There are famous spreadsheet errors that have costed companies billions of dollars. JP Morgan is the best example of this. The bank used to rely extensively on Excel-based financial models in order to manage their portfolio. In 2012, the bank lost close to $6 billion, and later it was revealed that the bank’s team could not make the right decisions because they were blindsided by the erroneous data, which was provided by their Excel-based model. The exact cause has not been revealed. However, there is anecdotal evidence that the numbers on the spreadsheet were not accurately copied from one sheet to another. This led to a gross miscalculation and, finally, to a huge loss.

Excel is actually plagued by a wide variety of software limitations. If a company uses an Excel-based financial model, it has to be aware of the possible human errors, capacity limitations, and development errors that may be built into the model. Also, a small mistake in the financial model can have a cascading effect later on. These mistakes are difficult to detect and can have catastrophic consequences.

Simulation

As the complexity of the financial model increases, the utility of Excel decreases inversely. This is because Excel is basically a two-dimensional model. However, in reality, data needs to be processed in multiple dimensions. This is because there are hundreds of variables that may affect the financial results of a company in real life.

Excel is not capable of handling these situations. For instance, some kind of analysis, such as Monte Carlo simulation, uses large volumes of data. It is a known fact that Excel is not the best tool for Monte Carlo analysis. Investment banking firms have started using other software which is more reliable.

Difficulty in Collaboration

Basic versions of the Excel sheet only allow one user to work on the sheet at a time. This becomes a major limitation given the fact that in real-life, financial models are so huge and complex that they are seldom managed by a single person. Therefore, if Excel is being used, a person needs to complete work on the Excel sheet. Once the task is completed, they need to e-mail that sheet to a different person. The second person may send the sheet back with more changes. Reconciling different versions of the same financial model becomes a major challenge if Excel is used.

Advanced versions of Excel allow multi-user functionality. This allows for better and more seamless collaboration. However, licenses for these versions are very expensive. Hence, when a company does a cost-benefit analysis, they generally find out that they are better off buying other financial modeling software since it works out cheaper and provides better features as compared to Excel.

High Requirement of Human Skill

Companies that use Microsoft Excel have realized that it turns out to be expensive since it also requires very skilled people, and these people have to be paid higher salaries.

An Excel sheet is blank and unstructured. Hence, creating a financial model in that sheet requires a lot of skill on the part of the financial modeler. These modelers need to be good at programming as well as an understanding of the financial function. This is not the case when other, more advanced software is used for financial modeling. A lot of the features which have to be programmed into Excel are available as out of box functionalities within other software. This reduces the dependence on human skills.

Also, if a person is working on multiple projects in Excel, managing the workflow becomes a tedious task. The end result is numerous spreadsheet which looks confusing and has similar names. Advanced software allows users actually to spend time analyzing the data. The administrative tasks such as importing and exporting data are performed automatically by the software.

The only advantage of Excel is that it is unstructured and allows the modeler to create the model in any way that they see fit. Hence, it is still the tool of choice for types of models that are not easily available in out of the box financial modeling software.

The bottom line is that MS Excel is only suitable for financial models that aren’t too complex or big. Continuing to use Excel despite its limitations, can have significant financial consequences.

Backward Looking or Forward-Looking?

Normally, there are different branches of finance where the calculations are either forward-looking or backward-looking. For instance, financial reporting is all about backward-looking calculations. It is all about keeping score of what happened in the past and reporting the same to different stakeholder groups viz. tax authorities, shareholders, suppliers, etc.

At the same time, managerial accounting and costing are forward-looking. This is the financial process where budgets are created in order to keep track of events which are likely to happen in the future. All the figures mentioned in these plans are expected figures and not actual figures pertaining to past events.

The problem with financial modeling is that it has to be backward-looking as well as forward-looking at the same time. Some elements of financial modeling have to be taken from financial reports, whereas others have to be taken from costing plans.

Financial Modelling: A Hybrid between Backward & Forward-Looking Statements

During financial modeling, output variables are determined. Then steps are taken to define the relationship between output variables and their underlying causal factors. For instance, revenue can be considered to be an output variable which a financial modeler may be interested in.

A financial modeler is required to evaluate past financial statements of the firm. This is done to ascertain the hidden drivers which influence revenue growth. The chain of causality is seldom simple.

The causal factors which affect revenue may themselves be affected by other causal factors. Therefore, a financial modeler is supposed to look at the backward-looking financial statements with great attention to detail. This needs to be done to unearth the hidden parameters which affect the actual numbers. This is what makes financial modeling much more complex as compared to financial accounting

The financial modeler has to look backward to unearth the causal links and create a model. However, once this model is created, the financial modeler now has to look forward. This is because after the inputs have been clearly identified, the financial modeler is supposed to identify the possible variations in these inputs.

It needs to be ascertained whether the inputs will vary all at once or whether only some factors will vary at the same time.

The financial modeler is then supposed to predict the values of important variables such as interest rates, tax rates, and so on. These assessments have to be made based on the knowledge of current affairs. Some extreme scenarios also need to consider for stress testing purposes. This is what adds to the complexity of financial modeling.

Financial Modelling: Underlying Assumptions

Another problem with financial modeling is that there a lot of assumptions which are hidden and which the modeler may not even be aware of. Some of these assumptions are based on empirical values and hence may not be completely true. This is because these assumptions could be found to untrue if black swan events occur.

For instance, prior to subprime mortgage, all financial models were built on the assumption that loan defaults could not happen in large numbers all across the countries. This is the reason that mortgages from Texas would be pooled along with mortgages from other far-flung states like Wisconsin since it was assumed that all the mortgages couldn’t go bad at the same time.

However, when the subprime mortgage actually occurred, home prices started falling all across the country, and this led to mortgage defaults all across as well. The models weren’t really equipped to foresee this situation because of the underlying assumption. As a result, there was carnage in the markets!

Financial Modelling: Level of Detail

Another issue which increases the complexity of financial modeling is the level of detail which needs to be built in the model. Ideally, decision-makers would like to view the information in as much granularity as possible.

Therefore, ideally, the model should have the ability to allow the user to drill down the data from the aggregate to the granular level. This ability needs to be designed by the financial modeler. Therefore a financial modeler is expected to have a better understanding of how the numbers work at a bird’s eye level as well as at a granular level.

The above-mentioned points just provide some of the highlights about what makes financial modeling challenging. Also, it needs to be understood that apart from understanding the financial details, a financial modeler also needs to be an expert in using technology. This is because understanding the process is not enough. It needs to be expressed in the form of a reusable model, and that requires the use of technology as well. Since the job requires a person to be an expert in so many fields, financial modeling jobs are some of the highest-paying jobs in the entire finance domain.

Why Should a Financial Model be Tested?

Calculations which are thrown up by the financial model, form the basis of many decisions made. As a result, it is essential for a financial modeler to ensure that the results are reliable. However, it is important to understand that a financial model has several thousand calculations which run at the same time. Hence, it is impossible to guarantee that the results given out by the model will be accurate and precise. However, if the model is tested rigorously at different stages of its lifecycle, most of the mistakes will be weeded out.

If testing is not done rigorously, it is likely that errors will be discovered at later stages. Errors detected by the decision-makers significantly undermine the confidence in the model. Hence, in this case, prevention is definitely better than cure.

Testing the Model Vs. Auditing the Model

When a financial model is being created, the words testing and auditing will be used almost simultaneously. This often creates confusion in the minds of financial modelers who begin to assume that both testing and auditing are the same processes. However, the reality is different.

Testing refers to the correctness and accuracy of each calculation which has been built into the model. On the other hand, auditing just checks whether the model created by the team matches the business requirements. For instance, if the users asked for a ten year DCF model, the auditors would check whether the model was created for ten years and incorporates all the other assumptions which were specified by the business. Testing, on the other hand, would include checking each and every calculation which led to the numbers which were used in the cash flow model.

Who Should Test the Model?

Testing requires the user to have a thought process different from the people who built the model. As a result, financial models should be tested by someone who has not been a part of the build team. However, in order to thoroughly test the model, the user needs to be a subject matter expert. In an ideal scenario, the user should have worked with other financial models and hence should be well aware of the intended functionality of the model.

When Should the Model be Tested?

Financial models are never built all at once. Instead, they are built in several stages. During the first stage, a basic functioning financial model is provided to the users. In later iterations, more and more functionalities are added to the model. Hence, testing needs to be done more rigorously as the stages progress. Finally, the model must not be provided to the business until all the known issues have been fixed. This approach allows us to break down the humungous task of testing the model into smaller, more manageable tasks.

Common Types of Tests

There are various functionalities which need to be tested while testing a financial model. Some of them have been listed below:

1. Input Testing: Every financial model begins with inputs from the end-user. It is therefore, essential to make sure that the model does not take faulty inputs. An intelligent model has validations built-in which alert the user if unusual values are filled in. Hence, testing the model for assumptions would means purposely entering wrong values as inputs and checking whether the system alerts the user. Wrong values generally include exceptionally large or small values. They also include negative values or even zero. A well-designed model will not allow the values to be entered. A poorly designed model will take in these faulty inputs and give out faulty outputs. The user will then have to manually verify each one of their inputs to zero down the root cause of the problem.

2. Numerical Testing: The numerical testing included checking each and every calculation which is performed by the model. The financial system already has some internal checks and balances. For instance, if the model includes a balance sheet, then it is supposed to balance automatically.

3. Testing can also be done by comparing the two models. This usually happens when a newer model replaces an older version. In such cases, the results given by the newer model are compared with, the older model in order to find out the flaws.

4. Technical Testing: Lastly, financial models need to be checked for system compatibility too. Financial models can be extremely heavy spreadsheets or applications. As a result, they may require a certain amount of computing power to operate. If this power is not provided, the entire system may crash. Technical testing is, therefore, necessary to ensure that the financial model will work as intended taking into consideration the technical specifications of the user’s computers.

Hence, it would be fair to say that the testing phase of financial modeling is almost as important as the build phase. Neglecting this phase may save some time and effort in the short run. However, it is likely to cause a lot of pain in the long run.

The major steps which need to be taken in order to create a working financial model have been listed in this article below:

1. Step #1: Start With Historical Facts: If the company preparing the financial model has been in existence for some time, it would be a wise move to start with its historical financial statements. This is because an analysis of the past statements often reveals hidden trends which may shape up the future.

   However, it needs to be understood that past trends are only indicative, and the future could be very different. However, in many cases, financial models are developed for start-up companies where there are no past details available. In such cases, details of a comparable company can be used, or this step may have to be skipped.

2. Step #2: Isolate The Parameters: The purpose of a financial model is to accurately forecast the revenues and the expenses which may occur in the future. However, it is important to understand that these revenues and expenses do not function in isolation.

   These revenue and expense numbers are actually the result of an interaction between several underlying parameters. Therefore, if an attempt is made to predict the numbers without understanding the parameters involved, it is likely that the predictions will not be very accurate.

   Hence, understanding the key parameters which influence the business is of vital importance. These parameters may be specific to the industry or even to a specific organization.

   For instance, companies which use commodities as their input or output must be mindful of the effect of fluctuations in commodity prices.

   For example, an increase or decrease in steel and cement prices will have a huge impact on the real estate industry. Similarly, if a company derives a major part of its revenue from exports, then it may be vulnerable to fluctuations in currency rates.

   At the end of this stage, a financial modeler must have identified all the relevant parameters which are likely to impact their business. These parameters must be isolated and provided as an input to the user. This will provide the user with the ability to vary the parameters one at a time and validate the results.

   The individual effect of each parameter on the breakeven level and the profitability can be identified if the model has been designed well.

3. Step #3: Identify Cost Behaviours: A profit and loss statement shows a static view of the expenses involved. However, the reality is that not all expenses behave in the same manner when the volume of production increases or decreases.

   For instance, depreciation charge remains the same, regardless of the output that a machine produces. Similarly, labor charges remain more or less fixed in the short run, regardless of whether they are used for production or not. However, there are costs such as raw materials, which vary directly with the level of production. Also, there are costs such as electricity which may increase with the increase in production. This is because successive units of electricity are more expensive as compared to previous ones.

   It is important that this behavior of different costs has been fed into the financial model. This will ensure that the model gives reliable results when it is simulated to know the expected profitability at different production levels.

4. Step #4: Identify Inter-relationships Amongst Parameters: A financial modeler must ensure that their model is logical at all times. For this reason, it is important that they identify the inter-relationships between various parameters and also model them.

   For instance, a rise in price would have an inverse relationship with the quantity sold. Similarly, a rise in one expense may sometimes reduce or even eliminate other expenses. The problem is that the relationship between parameters is often complex and non-linear. Identifying and modeling them accurately is an art which needs to be learned over several years!

5. Step #5: Provide a Range for all Parameters: More measures need to be taken to ensure the logical accuracy of the model. It is for this reason that all parameters which have been identified need to be given a range. If the results of the financial model go beyond a certain range, it should throw an error.

   Companies, then need to run thousands of iterations of these tests to ensure that all possible errors have been identified and even rectified in the process. The end result would be a sturdy and dependable model which can be used for decision making.

6. Step #6: Scenario Analysis: Lastly, the financial model should be built in such a manner that it does not give only one result. The reality is that the future is highly uncertain, and decision-makers would be better off if they are provided several scenarios.

   For example, the best-case scenario when revenues are the highest and the costs are the lowest. The worst-case scenario when costs are the highest and revenues are the lowest.

The bottom line is that financial modeling is an extremely complex task. These steps provide a broad guideline to accomplish the task. However, the reality is that there are several more tasks which need to be undertaken based on the specific financial model being created.

This article will provide more information about scenario analysis and its application in the financial modeling domain.

What is Scenario Analysis?

A financial model is nothing but a collection of inputs and outputs. If the inputs to the model are changes, the output to the model changes by default! This process of changing inputs and checking how the business would perform under different situations is called a scenario analysis.

Financial modelers usually provide the ability to change a single input at a time. This is done in order to allow the financial modeler to isolate the effects of that single input.

However, in reality, inputs do not change in isolation. Inputs change in correlation with other inputs. For instance, an increase in interest rates is often accompanied by fewer sales and more bad debts. This combination of inputs which occur simultaneously is called a scenario.

A good financial model allows simulations and scenario analysis to be easily performed. This is because a good financial model requires the user to enter inputs only once.

Hence, by varying the inputs at that one location, scenario analysis can be performed. If the inputs have to be entered at multiple places, it is quite possible that the user may forget to enter the input at different locations within the model and as a result, the model may end up giving less than optimal results.

When Should You Perform Scenario Analysis?

Scenario analysis needs to be performed only after the model has been completed. This is because the model needs to be tested for accuracy and consistency under normal circumstances before it can be assigned the complicated task of projecting the effect of different scenarios on financial statements.

How is Scenario Analysis Performed?

Each scenario has assumptions. Some of the assumptions are explicit, whereas other assumptions are implicit. It is the job of a financial modeler to ensure that the end-user is made fully aware of all the assumptions in the model. This is the reason why most financial models have two modes.

1. One mode allows the user to choose a pre-defined scenario. Here, the user cannot edit individual inputs. They can only choose the scenario type like the best case, worst case, or most likely scenario. Once the user chooses the scenario, the inputs to the model are automatically filled in.

   Also, there is usually a separate worksheet where documentation is provided regarding the nature of the assumptions. This saves the user’s time since they do not have to enter inputs manually. Also, this makes it possible to draw standard-reports easily. The standard model is for users who are not experts. It prevents them from entering inputs which are inconsistent and drawing invalid conclusions.

2. The second mode, which is commonly referred to as an “Expert mode,” is where the model allows users to change individual assumptions. For instance, the user can select one of the above-mentioned scenarios as the base case. They can then vary individual inputs in the base case to see its impact on the financial model.

It is the job of the financial modeler to ensure that the layout of the results of scenario analysis is similar. This is essential to ensure continuity and consistency for the end-user.

Shortcomings of Scenario Analysis

• Analysis Paralysis: Scenario analysis is a good tool for obtaining quick references while making decisions regarding the business. However, in many companies, there is an extreme emphasis on data. This often leads to an analysis paralysis wherein companies are not able to act because they spend too much of their time and resources collecting data.

  It needs to be understood that any action needs to be taken swiftly. This is because the market is moving extremely quickly, and companies which take too long to respond are often left behind. Also, business users should keep in mind that the financial models created are a part of a make-believe process. The results provided are close estimates at best and by no means, perfect!

• Inability to Predict Growth Drivers: Also, it needs to be understood that the future of a business or an industry cannot be guessed in purely financial terms. Looking at the different financial figures over and over again distracts companies from underlying technological changes which may actually be driving growth in the industry.

  For instance, no amount of financial modeling by Nokia would have helped the company identify the challenge it is likely to face from the Apple iPhone.

  In most industries, growth is driven by technological innovation which makes it possible to create a better product or to create the same product more cheaply. This cannot be done via scenario analysis.

The bottom line is that scenario analysis is an extremely valuable tool. It can help the company understand and predict a wide range of circumstances and be prepared for them.

However, it should not be considered infallible. Scenario analysis also has a few shortcomings. The financial modeler must be aware of these shortcomings as well.