It is complicated to understand the nature of the relationships that exist between various financial variables and conclude in the financial statements. Financial modelling, on the other hand, is regarded as one of the most difficult assignments, even in the financial area. There are various reasons for this erroneous assumption of complexity. Some of the causes are discussed farther down in this post.
Generally speaking, there are many disciplines of finance where the computations are either forward-looking or backward-looking, depending on the situation. For example, financial reporting is based entirely on computations that are performed in the past. Keep track of what happened in the past and report the results to various stakeholder groups like as tax authorities, shareholders, suppliers and other parties involved.
The challenge with financial modelling is that it has to be both backward-looking and forward-looking at the same time, which is difficult to accomplish. It is necessary to extract certain parts of financial modelling from financial data, whilst other elements must be extracted from costing plans.
Combination of Backward and Future-Looking Statements in Financial Modelling
Output variables are determined during financial modelling. Following that, efforts are performed to define the link between the output variables and the underlying cause factors. For example, revenue might be thought of as an output variable that a financial modeller might be interested in. A financial modeller is necessary to examine the firm's previous financial statements. This is done to identify the unknown factors that influence revenue growth. The causal chain is rarely straightforward. The causal factors that affect revenue may be influenced by other causal factors. As a result, a financial modeller is expected to scrutinise backward-looking financial statements with considerable care. This must be done in order to uncover the hidden parameters that influence the real figures. This is what makes financial modelling so much more difficult than financial accounting.
To uncover the causal relationships and develop a model, the financial modeller must look backward. However, after this model has been built, the financial modeller must now look ahead. This is due to the fact that, once the inputs have been properly characterised, the financial modeller is expected to detect the probable variances in these inputs. It must be determined if the inputs will change all at once or whether only part of them will change at the same time. The financial modeller is then expected to forecast key factors such as interest rates, tax rates, and so on. These judgements must be made based on current events knowledge. Additionally, some extreme scenarios must be considered for stress testing purposes. This adds to the complication of financial modelling.
Assumptions in Financial Modelling
Another issue with financial modelling is that many assumptions are buried and the modeller may be unaware of them. Some of these assumptions are based on actual data and so may not be entirely accurate. This is due to the possibility that these assumptions will be discovered to be false if black swan events occur.
Prior to subprime mortgages, for example, all financial models were based on the premise that loan defaults could not occur in big numbers across the country. This is why mortgages from Texas were pooled with mortgages from other far-flung places like Wisconsin, because it was considered that all of the mortgages couldn't fail at the same time. However, when the subprime mortgage was issued, property prices began to decline across the country, resulting in widespread mortgage defaults. Because of the underlying assumption, the models were ill-equipped to predict this occurrence. As a result, the markets were a shambles!
The level of detail in financial modelling
Another factor that contributes to the complexity of financial modelling is the level of detail that must be included in the model. Decision-makers would prefer to see information with as much granularity as feasible. As a result, the model should ideally allow the user to dig down the data from the aggregate to the granular level. The financial modeller must create this capability. As a result, a financial modeller is expected to have a deeper understanding of how numbers function at both a high level and at a detailed level.
The points described above are only a few of the highlights of what makes financial modelling difficult. It should also be noted that, in addition to comprehending financial specifics, a financial modeller must also be a technological specialist. This is due to the fact that comprehending the process is insufficient. It must be expressed in the form of a reusable model, which necessitates the use of technology. Financial modelling professions are among of the highest-paying occupations in the whole finance area since they demand a person to be an expert in so many fields.