As a financial modeller, you have to explain very technical concepts to audiences with little to no background in finance. This is a unique skill, as it requires both technical expertise and soft skills. You have to ensure the client can grasp complex concepts, without going into an exhausting amount of detail; only then can you sell your solution. One of the best strategies to outline the key concepts and benefits is with this financial modelling case study, which we’ll outline here.
This particular case study is from the steel industry. It demonstrates how the modeller themselves has to spend a lot of time understanding the technical data and then translate this into layman’s terms. Concurrently, the modeller also has to understand the business context in order to make this translation comprehensible, and indeed, actionable.
In essence, they need to have a robust understanding of the business and how it will be impacted by changes. This is the foundation of an effective business model, action plan, and risk analysis, which were delivered here for this major steel manufacturer. Before we get into this financial modelling case study, we’ll outline some key concepts.
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What is the purpose of financial modelling?
Financial modelling is about decision making. These projections enable personnel to make better decisions, primarily through having a deeper understanding of the business and how both internal and external factors may affect its operation. This includes forecasts for expected cash inflows and outflows and the implications of various funding options. A financial model will aid due diligence by estimating the financial impact of a particular activity, thus helping to manage risk. Financial models can also provide a benchmark for incremental performance reviews.
However, not all decisions are created equal and for each of these forecasts, there is a specific type of financial model. It is important to understand that financial modelling is not an exact science (albeit a very technical discipline) and the results obtained are not set in stone. Instead, financial modelling is a framework or a set of guidelines for decision-making as opposed to a crystal ball. But that’s not to say a good financial modeller can’t make some very accurate predictions. Often, calculating exact dollar and cent outcomes is not the key goal, but instead, exact predictions of the magnitude and direction of movements in the event of a change in assumptions or circumstances.
Why is financial modelling important in decision making?
To illustrate how financial modelling can enhance decision-making, it’s useful to outline the key types of models. With these introductions, you can get a more tangible sense of how financial modelling could benefit a business according to its specific circumstances. Below we’ll outline five principal groups.
- Profitability and price planning: These models are used to analyse how a company can deploy its resources in order to profit. For a retail business, for example, this may comprise identifying the optimal product mix or pricing strategy to maximise profitability. Such models are used for forecasts about the immediate future, as the variables tend to be too volatile to make long-term projections.
- Liquidity planning: Taking on debt is part of growing a business, but with debt, comes risk. Liquidity planning enables companies to keep a handle on their cash flow to track their solvency. This model will also take into account external factors like interest rates and currency fluctuations. The model can simulate extreme economic scenarios to test the company’s solvency.
- Credit planning: Most businesses operate by offering clients credit. However, with credit comes risk and businesses need to decide how much credit they wish to extend. Financial modelling can help businesses make this call using information from credit rating agencies and publicly declared financials of clients, if they exist.
- Company valuation: This type of financial model requires complex analyses of several variables in order to estimate the value of a company. These models are often created by investment banks and private equity firms.
- Valuation of financial instruments: These models are used to value bonds, futures, options, and other types of financial instruments. These models are the backbone of technology-based trading as they automate complex calculations, such as fair value. Many of the most sophisticated of these algorithms are still in their infancy, but it’s not inconceivable that they may one day replace human traders.
This is but a sample of the types of financial models used by analysts. However, these outlines should illustrate in more specific terms the benefits of financial modelling and how they can help companies plan better business models and manage risk. Now, let’s have a look at this financial modelling case study to see these concepts in action.
A financial modelling case study from the steel industry
A major steel manufacturer sought the services of a financial modeller to evaluate the different funding options to expand one of their facilities. There were several factors to be taken into account beyond extending the space; equipment needed to be commissioned and imported over an 18 month period. At the same time, the facility needed to conduct business as usual to ensure regular cash flow alongside this foreseen expense escalation.
There were various loans and loan tenors available, alongside an existing overdraft facility. The question for the company board was what would be the optimal mix of funding in order to minimise costs and avoid the need for refinancing or additional loans. To analyse the options and variables, they drafted a financial modeller to carry out liquidity planning. For the purposes of this financial modelling case study, we’ll describe the methodology used to create this type of model and the results achieved.
As a starting point, existing management accounts and budgeting gave an insight into the business as usual cash flow. Escalation dates and factors had to be applied in order to estimate the potential future cash flow situation. In addition, the expansion project capital requirements were estimated and foreign exchange implications analysed, and payment terms and dates determined.
Forecasts were calculated using opening account balances and the predicted changes to historic trends. These estimates had to take into account fluctuations in working capital and the new expansion capital outflows. These were overlaid on existing business cash flow models in order to determine the predicted monthly cash position.
Different loans with their specific interest rates and repayment profiles were then applied to this model to forecast a series of outcomes, expressed via the monthly cash position and the ending cash balance, plus the tax implications. From this analysis, a clear option emerged. This financing scenario was tested via sensitivity analysis, where key assumptions and possible changes to existing cash flows were applied.
These financial modelling tools revealed a clear choice for financing. Further to this, the preferred solution was subject to a series of rigorous sensitivity analyses where key assumptions were applied and scenarios modelled. This allowed the board to quantify the risks in regard to foreign exchange rates, fluctuations in interest rates, changes to payment schedules, and changes in operational costs.
This enabled the board to make decisions that were informed as possible in regard to their choice of financing. Of course, these analyses aren’t cast iron – unexpected events like the pandemic are demonstrative of this – but they are an invaluable basis for decision making. Sure enough, in the wake of the pandemic, many financial modellers are incorporating disaster scenarios into their analyses, so that we can be better prepared for the next black swan event.
Find the right financial modeller
As this financial modelling case study demonstrates, mastering financial modelling requires exceptionally strong mathematical and analytical skills. They’ll need to be fully proficient in Excel and VBA, and as we move into the era of machine learning and AI, these essential skills will also need to be complemented by a strong understanding of programming languages.
Their skills as a business analyst need to translate business needs to financial requirements, and perhaps most crucially, they need to be able to communicate this information to key stakeholders. Certainly, financial modelling requires an excellent technical mind, but you have to be able to explain scenarios and their consequences in plain English (or whichever language you may be speaking).
Above all, it’s these soft skills that set apart a good financial modeller from an excellent one. The ability to understand the business, its requirements, stakeholders’ ambitions, and the magnitude of the impact of external events are critical to helping the business be better prepared for whatever the world economy throws at them. You need much more than just good excel or programming skills.
Source exceptional talent with Outvise
Certainly, going to financial modelling consulting firms is one route to finding an outstanding analytical mind. However, with consulting firms come consulting price tags. In light of this, what’s the most cost-effective way to find an outstanding financial modelling analyst? How can you guarantee good value for money, especially since the exercise is about effective financial management?
Since the pandemic, promising and seasoned professionals alike have poured into the freelance market – all businesses need to do is connect with them. Outvise was created to forge links between the most talented freelance professionals and corporations. By creating a specialised, curated professional network, Outvise has facilitated companies to make the most effective, efficient use of freelance talent to drive their projects forward.
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Gerrit Eloff has been working in a multicultural and multinational environment for the majority of his career. A qualified actuary through the institute of actuaries in London with extensive experience working with Executive committee members, board members and shareholders, assisting them in defining strategy based on detailed data analysis and predictive financial models.
He has 20 years of broad financial modelling experience spanning across the full business planning cycle, from initial financial assessment, pricing strategies, business sensitivity analysis, valuations, funding structures to investor negotiation modelling.