Sensitivity Analysis in Financial Modeling

Sensitivity Analysis in financial modeling is a tool that analysts use to understand and interpret how any independent variable affects the values of a dependent variable when they are subject to certain conditions and assumptions.

Apart from using the analysis in finance, it is also used in fields like engineering, geography, biology, etc. However, in finance, it is also called What-if analysis. The outcome adds to the credibility of the model since the variables are tested in different scenarios, providing a range of possibilities. Microsoft Excel is commonly used for this purpose. To learn more about financial modeling, refer to the best financial modeling courses.

Here are the main components of the sensitivity analysis in financial models.

• Input variables – The input variables are the independent ones that you will have to use in order to determine their effects on the dependent ones. You will use different types of input variables, like interest rates, growth rates, or inflation, that are related to different scenarios, which will pose the “what-if” question. 
• Target variables – They are the ones that affect changes in the input variables. Through this analysis, in the models, we can determine what happens to the particular scenario and the target variables, like the Internal Rate of Return (IRR) or Net Present Value (NPV), when the input variables change. 

Apart from the above, in sensitivity analysis in financial models, there are other technical components as well. They are as follows:

• Microsoft Excel – This is the most widely used application because not only is it easy to use, but it also has all the features necessary to explain the points well, like charts and graphs, formulas, the facility to link, add new rows and columns in the sheets, and so on, that makes the work faster and easy to understand.
• Assumptionsor baseline scenarios – Assumptions are fundamental and should be relevant to the scenarios. They will include any current or historical data that will support the process. 
• Alternative scenarios – It is not necessary to stick to a particular type of assumption or scenario. You can pick up any variation in the scenario, too, and incorporate it to assess how it affects the outcome. 

Thus, the above are the main components that an analyst has to keep in mind. 

As we have already mentioned above, assumptions are an essential component of the analysis. However, it is equally necessary to know what kind of assumption we should use in order to make the analysis effective while doing the financial modeling. 

• Fixed Value assumptions – The name itself suggests that there is a fixed component. Therefore, we should assign a particular value to one of the independent variables, which will remain fixed. The rest can change. In this situation, we derive an outcome based on one fixed value and other values and get an insight into the robustness of the process. 
• Range assumptions – Range itself implies a minimum and maximum value. In this case, the independent variables have a range, and the model is tested within that range. This helps to capture the flexibility and variability of the values and how or to what extent the resultant outcome changes within sensitivity analysis in financial models. 
• Distribution assumptions – Here, the independent variable will have a probability distribution, which can be normal or uniform. This process portrays the uncertainty more realistically, and you can explore a wide range of outcomes based on the probability of such a variable happening. 

Such assumptions act as a guide for analysts and stakeholders to make decisions that account for uncertainties and their impact on a situation.

The next big question is, why do we select such assumptions as those defined above? Do they really influence our sensitivity analysis in financial models in a significant way? Why do we not keep one particular standard kind of assumption instead of altering them so much? The answer is given below:

• Reliability – Clear and defined assumptions that explore many forms make the model reliable because the analyst is sure of the fact that different scenarios have been thoroughly explored and the outcome is reliable. These assumptions are basically some beliefs and estimates and are the building blocks of the process. 
• Transparency – When we define the assumptions clearly, it makes the model transparent, and analysts are able to interpret it quickly because we do not just incorporate the assumptions; we need to explain why we have considered them. This makes the model credible and authentic. 
• Risk Identification – Assumptions are the best way to point out the possibility of risk or uncertainty and to what extent they influence the outcome. The management and stakeholders can successfully make relevant decisions by choosing the correct scenario that will lead to the best results. For that same reason, assumptions should be accurate and well-defined, or else they have far-reaching negative implications for the business. 

Thus, as you must have already derived from the above points, the assumptions you put during sensitivity analysis in financial models should be such that they empower the management to do strategic planning and help in designing risk management techniques that will lead to the best use of resources through minimization of cost and wastage.

Since the main components of the sensitivity analysis in financial models are the variables, it is crucial to select them properly and carefully. There are some essential factors to consider in order to do that. 

• Step 1 - A modeler should know that while creating a model, the factors that influence the most are revenue, cost, profits, competition level, market, and political scenario, capital structure, and so on. These factors should be selected with care and proper research. 
• Step 2- The variables should cover every possible scenario and should preferably have a wide range that will include all such cases. Thus, you can always select extreme values, but they should be realistic. 
• Step 3 - The quality and availability of authentic data play an essential role in financial modeling. So, the data and information collected should be correct and taken from authentic sources. In this context, experts and professionals who have excellent experience in research and data handling will be able to contribute good insight into the process and give suggestions that will provide valuable input to the range selection. 
• Step 4 - The best tool for sensitivity analysis in financial models is Microsoft Excel because of its built-in features, like Goal Seek, Formulas, data tables, charts, and so on. However, other tools are available in the market, which you can always explore, some of which are Crystal Ball, @RISK, etc, that have advanced and flexible features capable of handling complex models. 
• Step 5 - The final step is to feed the data and analyze the results. This means you should be able to identify which variable impacts the scenario in what way and up to what extent. You can repeat the process by changing variables and using new sets of data. The more variation and range you are able to cover, the better the chance of getting clarity and proper understanding regarding which is the best scenario for improvement. 

Thus, by following the above ideas, one can perform a sensitivity analysis in financial models that will be accurate and useful. To learn more about financial modeling, refer to the best financial modeling courses.

As we have already discussed, this concept is valuable from the point of view of both management and stakeholders. It also gives knowledge about both micro and macroeconomic levels related to the national or global scenario. But let us summarise them with a few points.

• The analysis helps in designing contingency plans for better risk management and making the business environment strong enough to navigate any contingency or sudden setbacks. 
• Management can set realistic goals that will guide the business toward growth and expansion through the optimum use of resources because the analysis derives outcomes from possibilities. This helps in optimizing performance. 
• It aids in financial planning and budgeting. It is used to forecast revenue and profits through demand and supply analysis, changes in customer taste and preference, and overall market conditions. The cash flow projections are possible that impact the working capital and expense management directly. 
• As market conditions keep changing, it becomes necessary to assess the credit risk of borrowers or evaluate the cost of capital while designing debt-related portfolios. Sensitivity analysis in financial models helps in selecting the best source and keeping the leverage levels within manageable limits. 
• As modelers, you should be able to design the analysis that will consider the impact of factors like interest rate fluctuations, exchange rate fluctuations, inflation, political disturbances, etc. Such risk exposure identification guides the development of hedging strategies, which are used to minimize or control risk. 
• Since it is necessary to evaluate the returns from investment to ensure funds are appropriately utilized, sensitivity analysis also helps in the valuation of financial assets and instruments like stock and bonds and the impact of changes in their valuation on the business performance. 
• Finally, the concept of sensitivity analysis in financial models is also successfully applied while selecting projects for heavy investment and determining the cost and revenue levels. For this purpose, we calculate the NPV or IRR and find the sensitivity of cost or earnings on the latter, which helps to decide the best option. 

In this context, we should also understand why we use this concept of sensitivity analysis in financial models and its importance in the financial world. 

• After going through the above details, it is pretty easy to conclude that it is a vital tool that aids in financial decision-making by evaluating the impact of different variables on the model and expected outcomes. 
• Risk management is an essential factor in business. The analysis contributes to managing risk by making informed choices and optimum use of resources. 
• The economic and financial landscape is rapidly changing, giving rise to new and varied situations that should be considered while bringing in necessary changes, innovation, and upgradation in business processes. The management can use sensitivity analysis to assess how and up to what extent these changes should be implemented. 
• Since the process is flexible and does not stick to any fixed rule or information, it can be updated as and when required. 
• This process also points out any mistake or misinterpretation in the original benchmark that was considered. Based on that, the management can make changes or update the model with the latest information.