Bottom Up Financial Modeling is a technique used by both financial analysts and investment professionals to make meaningful decisions supported by detailed financial data. It is a specialized type of financial modeling that requires sophisticated skill sets and relies on both experience and quantitative analysis. Advanced techniques for bottom up financial modeling make it possible for investments of all types to be analyzed and presented for review in an organized, professional manner.
This blog post will provide an overview of advanced techniques for bottom up financial modeling, highlighting the benefits they provide.
Definition of Bottom Up Financial Modeling
Bottom Up Financial Modeling is an approach to analyzing investments and valuations at an individual or micro-level rather than through a macro-level assessment of an entire sector or group of investments. It involves building a financial model that can produce detailed, granular insights into the underlying value of investments. The end result is specialized data that can be used to make more informed investment decisions.
Overview of Advanced Techniques
Advanced techniques for Bottom Up Financial Modeling include the following:
- Identifying key drivers for analysis
- Performing computed analysis for scenario modelling and sensitivity analysis
- Incorporating historical data for trend analysis and forecasting
- Integrating financial and economic data as parameters
- Testing assumptions in the financial model
- Creating management dashboards
By combining detailed, bottom up analytics with the advanced techniques outlined above, financial analysts and investment professionals are able to make more comprehensive and accurate decisions.
- Identify key drivers for analysis
- Perform computed analysis for scenario modelling and sensitivity analysis
- Incorporate historical data for trend analysis and forecasting
- Integrate financial and economic data as parameters
- Test assumptions in the financial model
- Create management dashboards
Breakdown of Different Model Outputs
Creating a comprehensive financial model requires a great deal of data, and attention to detail. An effective model can provide the information needed to make important decisions regarding a business and its financials. The outputs of the model are the most important elements and understanding the different types outputs can ensure their accuracy.
Development of Forecasted Income Statements
The purpose of developing an income statement is to track the performance of the business, over a period of time. These statements compare the company's income to its expenses and other financial metrics such as profit or loss. Typically, income statements will include elements such as sales, cost of goods sold, operating expenses, depreciation, and interest. These figures are then aggregated to determine the net income for the period.
Calibration of Balance Sheet Metrics
Balance sheets provide a snapshot of a company's financial health at any given time by listing all of its assets, liabilities, and equity. This information is then used to calculate various financial ratios, such as the current ratio and the debt to equity ratio. To ensure accuracy in the output of these metrics, it is important to calibrate the balance sheet.
Calculations of Cash Flow Statements
Cash flow statements are used to track the cash that flows in and out of a company over a period of time. The statement consists of three sections: operating activities, investing activities, and financing activities. By understanding the different activities that are included in each section, and the calculations necessary to create an effective cash flow statement, a business can gain insight into its overall financial health.
Weighted Cost of Capital
The weighted cost of capital (WACC) is a central consideration for any financial model. This calculation represents the average cost of funds used by a company to finance their business. As such, it must be taken into account when evaluating various investments and decisions.
Defining the Weighted Cost of Capital
The weighted cost of capital for a company is the average cost of funding a business. It is the weighted mix of the cost of equity, the cost of debt, and any other sources of financing. To calculate the WACC, the cost of each financing instrument must be weighed by its proportion of the firm’s capital.
Modeling Cost of Capital Implications
When creating a financial model, understanding the weighted cost of capital is crucial. This measurement must be incorporated into all calculations, as the WACC will affect cash flow and return on investment. Additionally, when modeling various business scenarios, it is important to consider how the WACC will be affected.
- What cost of capital implications will occur when the proportion of debt changes?
- How will different cost of capital assumptions affect the outcome of the model?
- How may the cost of capital affect cash flow, dividends, and shareholder value over time?
Analyzing Value Creation
Analyzing the impact of a company’s weighted cost of capital ultimately allows for better decision-making when it comes to investments. By understanding how the cost of capital affects profitability, potential investments can be evaluated for value creation potential. This can be accomplished by comparing the WACC before and after a given investment.
The weighted cost of capital plays a major role in any financial model. Careful evaluation of the WACC is key when evaluating investment decisions with an eye towards value creation. Advanced modeling techniques should be used to ensure the most accurate possible WACC is used throughout the model.
Sensitivity analysis is a major component of bottom up financial modeling. It consists of examining the impacts of model inputs and assumptions on the results of the model, by testing different magnitudes of these inputs. In other words, it seeks to uncover what potential changes in a model’s inputs would have as output. This is important to know in order to have a full understanding of the dynamics of a model.
Overview of Different Methodologies
The way in which a sensitivity analysis is conducted, depends primarily on the acceptability of using all the inputs of the model simultaneously. For example, if it is difficult to adjust multiple inputs simultaneously, then the analyst must use a sequenced approach. In this approach, the model is run multiple times with each range adjustment, which could be done increasing or decreasing one variable per run. On the other hand, changing multiple inputs simultaneously can save analysis time, and this is known as the simultaneous approach. This approach usually requires software and is the preferred method.
Testing Output Variables
The primary goal of sensitivity analysis is to assess the output variables of the model, or the end result of the financial process. This is done in two parts. The first part is to test the variables against the initial model, using the results to separate which variables need farther testing. This can be done by measuring the change in the ratio of the output to its corresponding input against the change in the input. After the key variables have been identified, the analyst must move to the second part and run the model with these variables and measure the output.
Refining and Optimizing Forecasting
Sensitivity analysis is essential for bottom up financial modeling. Not only does it provide an understanding of changes in the financials that depend on the inputs, it allows the analyst to ‘refine and optimize’ the model to the current market situations. This can be done by searching for the magnitudes of the inputs that will produce the optimal new results. Through this, sensitivity analysis allows the user to more precisely forecast the likely outcomes of a financial project.
The proper estimation of a company's value is a difficult task, and when using bottom-up financial modeling, there are a few techniques that are effective in this endeavor. Below are some of the techniques that can be useful in valuing a business when conducting bottom-up financial modeling.
One of the most common techniques for valuing a business is to compare it to similar public companies. This is done by analyzing factors such as sales, net income, market capitalization, and other statistics, and then making assumptions about how the company being valued would perform relative to these comparative firms. This technique can give a good indication of a company’s value, but the accuracy of this approach depends on the accuracy of the assumptions being used.
Digging into Historical Analysis
While making predictions about the future based upon comparables can be useful, looking into the company’s past performance can often be just as useful. By looking at the company’s financial history and examining trends, it can give a better indication of the current level of performance, as well as help to provide a more accurate picture of the company’s future prospects.
Utilizing Precedent Transactions
Another useful tool in valuing a company when using a bottom-up financial model is to use precedent transactions. This involves comparing the company to other companies that have been recently acquired or sold and applying the same valuation ratios to the company under analysis. This technique can be useful but can also be prone to inaccuracy due to the fact that the circumstances of all of the various companies involved will not be exactly the same.
The process of consolidating inputs and output of multiple smaller models into a cohesive whole is essential to the success of any system of bottom-up financial modeling. In this section, we will discuss several techniques for combining separate model components, establishing baseline assumptions and merging the outputs of model sections.
Combining Separate Model Components
The most effective approach to combining separate model components is to build in fidelity between the discreet components. This ensures that the models fit together like pieces of a puzzle, with none of the pieces missing or overlapping. It also allows for easy validation and troubleshooting during the reconciliation process, ensuring that the model works as intended.
The primary challenge associated with combining separate model components is establishing a consistent data schema that makes sense within the context of the entire model. It’s critical to ensure that line items, assumptions and outputs are consistent across the model. Most of the standardising and solving for the unexpected should happen at this stage to prevent any issues further down the line.
Establishing a Baseline Assumptions
Before diving into the detail of integrating multiple model components together, it’s essential to establish a baseline set of assumptions regarding the theoretical premise behind the model and how it will fit together. This ensures that the model as a whole is consistent with the set of rules and assumptions outlined at the start of the process.
In establishing a baseline set of assumptions, it’s important to understand not only the specific elements of each model component but also the broader theoretical framework which informs them. Once the rules are understood, it’s a matter of mapping those rules to each individual component of the model, thereby forming the basis of the unified model.
Merging Outputs of Model Sections
The act of merging the outputs of each of the model sections together can be done in multiple ways. One of the most common is to use the sum of the outputs of each model as the output of the combined model. This is the simplest option, as it eliminates the need for manual adjustments and reconciliation.
An alternative methodology would be to take the lowest result between the separate components, or the highest. Depending on the nature of the model and the nature of the outputs, it can be beneficial to create a bespoke merging methodology that fits the needs of the organization or financial project better. This allows for a more robust set of results which reflects the actual financial reality of the project in question.
Financial modeling is a process used to collate and arrange key assumptions about an organization's operations in order to project the status of the organization's finances for the future. Bottom up financial modeling is used to build an improved understanding of the components that play a role in projecting company performance, but it requires a diligent approach and advanced techniques due to its detailed and comprehensive nature. This blog post has provided a comprehensive review of the advanced techniques available to build a reliable bottom up financial model. After discussing the most important financial statements to consider in the model, methods for creating detailed assumptions, strategies for estimating growth and the importance of incorporating sensitivity analysis, it is clear that bottom up modeling can be a powerful tool for forecasting a company’s performance.
Review of Advanced Techniques
In terms of building a financial model, understanding the techniques available is essential before attempting to build out a preliminary model. Specifically, we discussed common techniques such as building assumptions from scratch, deriving assumptions from public data sources, macros and leveraging backcasting to make conservative and realistic assumptions. We also reviewed how to ensure the accuracy of those assumptions over time.
Reflection on Modeling Best Practices
It is also important to remain cognizant of the inherent risks associated with building a bottom up financial model in terms of accuracy and reliability. Building a model requires a detailed and thoughtful approach, using the most accurate empirical data available. Additionally, it is important to consider the timeframe of the model and how to incorporate sensitivity analysis to ensure that reliable data can be used to effectively project future performance. Overall, bottom up financial modeling is a powerful tool for businesses to understand the drivers of their performance and determine the effectiveness of internal decisions. By becoming aware of and leveraging the advanced techniques discussed in this post, businesses can gain a more holistic understanding of their financials and utilize the insights to drive their overall performance.