Forecasts can be qualitative, quantitative, or both. Those that are qualitative are based on expert opinion, as the ECB example in section 2 documented. Quite often qualitative estimates are economic, but they may also involve product develop- ment timetables, estimates on market demand, and sales projections. On the other hand, forecasts might be based on quantitative models, benefiting from experi- mentation to substantiate expert opinion.
As advice based on long years of experience, simulation results should not be taken at face value. Several caveats should be borne in mind, such as the afore- mentioned possible existence of nonlinearities and asymmetries, and the fact that models do not include all variables that impact on real life output (see the
case study on models for fair value estimates of real estate in section 7). Hence, as a matter of policy, quantitative approaches:
● Should always be subject to thorough testing, and
● They must be enriched and substantiated through qualitative approaches (more on this later).
Quantitative analysis has been promoted by advanced modelling procedures developed by mathematicians, physicists, engineers, and economists. It has also significantly benefited from cheaper and cheaper computing power which made it cost-effective to generate complex simulations, serving the main object of com- puting which is:
● Foresight
● Insight
● Analysis, and
● Design.
This is much more important than the automation of numerical calculations. The basic reasons for experimentation are better understanding of the problem; analy- sis of alternatives in terms of risk, cost, and reward; as well as a more objective and better documented basis for decisions.
Forward-looking statements benefit from what has been outlined in the preced- ing paragraphs, because through experimentation management can provide faster response to developing situations. It also acquires the ability to test novel methods or solutions. Both qualitative and quantitative approaches should be used for experimentation reasons. In financial analytics:
● Qualitative results are largely based on fundamental research
● While technical analysis and charting is, to a large extent, quantitative.3 In connection with forward-looking statements, a qualitative approach would typically look into the company’s ability to successfully implement its new strategic direction, including alliances and other factors, such as the ability to focus on products and services which permit it to:
● Take advantage of the most desirable opportunities in the firm’s industry, and
● Implement product rationalizations in a manner that does not disrupt the link to its customers.
Qualitative analysis may as well address governmental and public policy changes that may affect the level of new investments; changes in environmental
regulations; protection and validity of patent and other intellectual property rights; as well as reliance on large customers and significant suppliers (section 6 presents a case study on assessing real estate prices in the housing market, which includes both qualitative and quantitative evaluation).
Serious undertaking of qualitative research should look at the longer term, which is always a challenge. Italy’s Finsiel, the software company, provides an example on the aftermath of failing to do so. In the early to mid-1990s Finsiel:
● Had a billion dollar turnover, and
● Was one of Europe’s biggest information technology services firms.
The company’s problem has been that its business came almost entirely from lucrative technology contracts from Italy’s public sector. Diversification was wanting, and likely future developments were not given due attention.
Eventually, Finsiel’s tight grip on the Italian public sector market was forced open by European Union directives on tendering.
In the mid-1990s, there has also been a negative fallout from Tangentopoli, the targeting of a myriad of scams by Italy’s judiciary. Finsiel’s power in the Italian software market waned, and the Rome-based firm went into decline all the way to becoming an acquisitions target. In March 2005, Finsiel found a new owner, Gruppo Cos, which specializes in call centres – a line of business basically dif- ferent from Finsiel’s historical strength.
On the border line between qualitative and quantitative research lie issues such as the company’s credit rating; ability to provide customer financing when appropri- ate; continued availability of financing, expressed by financial resources in amounts and on terms required to support future business; compliance with covenants and restrictions of the firm’s bank credit facilities; and outcome of pend- ing and future litigation. All these issues impact upon the outcome projected by a forward-looking statement.
Within the perspective of qualitative evaluations mentioned in the preceding paragraphs, issues relating to the projected income statement are predominantly quantitative. Here, the first and foremost subject forecasters should keep in per- spective is that in practically every company profit and loss is a nonlinear algo- rithm of risk factors, because:
● It is the result of all changes in fair value of positions, and
● A reflection of business risks assumed by the firm in the course of its operations.
Moreover, the market values being used may themselves behave in nonlinear fash- ion – particularly in connection to the derivative financial products nearly every firm nowadays has in its portfolio. There is also the effect of neglected risk, market disruptions, imprecise risk metrics, and stress circumstances (see Chapter 16).
Another uncertainty entering into an earnings simulation is that of complex cor- relations across risk types. Whether in the manufacturing industry or (particu- larly so) in banking, correlations and weights are most often subjective, and imprecise. They are what some analysts call ‘tricky things’. Yet, it is not really possible to simulate a business environment without them.
In spite of these limitations and uncertainties entering into the production of a forward-looking statement, senior management must come up with a fairly detailed analysis. A plan is shown in Box 12.1. Every chapter has its challenges which must be addressed, even if at the end management may only publicly release some compound figures. In a growing number of cases, it is also becom- ing necessary to compute projected profit and loss, along the lines of a long sup- plementary list as shown in Box 12.2.
Box 12.1 Main chapters of a profit and loss statement
Operating income Interest income Interest expense Net interest income
Credit loss expense/recovery
Net interest income after credit loss expense/recovery Net fee and commission income
Net trading income Other income
Total operating income
Operating expenses Personnel expenses
General and administrative expenses Depreciation of property and equipment Amortization of other tangible assets
(Continued)