What makes a financial model more predictive than descriptive?
When a model’s inputs are related more to the causes than to the results of the process being modeled, the model is more predictive and less descriptive than otherwise.
Challenges facing credit and equity analysts
With the currently available financial modelling tools, analysts often have to make forecasts by directly tinkering with a company’s future financial results such as revenue or cost, making the outcome of the forecast more a description of what is already on the analyst’s mind than predictions of unexpected results.
Seasoned analysts make forecasts based on their understanding of a company’s business model, but lacking a powerful tool, it is difficult for them to translate that real world understanding into quantitative projections, particularly toward a long forecasting horizon. Instead, analysts rely on experiences and intuitions to model only top-line total or average quantities that are less detailed and insightful and more short-term than ideally needed.
Analysts are assaulted by new information every day from news headlines, visually griping data, and vocal commentators. Yet, when a change takes place in the market, exactly what and how much will be the impact to a company’s near-term results as well as long-term prospects? What will be the timing of such impact? What actions can a company deploy to counter the impact effectively? Analysts often have to content with the great many views that seem to make sense but stop at providing any answers to these questions.
Asides from the most experienced analysts, it is challenging for most to see through quickly the numbers announced in a company’s new financial results, understand what changes have really taken place, and independently reconcile or refute the stories given by the company’s management.
Assessing a company’s long-term prospect is a critical part of the job for both credit and equity analysts, yet there is a lack of tools available to produce long-dated quantitative projections. Empirical rule-of-thumb financial ratios, indexation and intuitively weighted averages have been used as universal diagnostic indicators of a company’s overall financial health. However, as top-down guess work, these indicators lack the power to identify company-specific risks or to illuminate any details on the timing or any possible remedies of the risks.
Analysts often spend their time much more than they would like in trying to match up all the accounting numbers in a company’s financial statements and to reconcile the numbers between a company’s balance sheet, income statement and cash flow statement. In reality, results in the three financial statements should not be projected separately, but all be derived from one common underlying source.