-based
Cash Flow Projection (click to view at full size) In
corporate finance and the
accounting profession,
financial modeling typically entails
financial statement forecasting; usually the preparation of detailed company-specific models used for Applications include: •
Business valuation,
stock valuation, and
project valuation - especially via
discounted cash flow, but including
other valuation approaches •
Scenario planning,
FP&A and
management decision making ("what is"; "what if"; "what has to be done") •
Budgeting:
revenue forecasting and
analytics;
production budgeting;
operations budgeting •
Capital budgeting, including
cost of capital (i.e.
WACC) calculations •
Cash flow forecasting;
working capital- and
treasury management;
asset and liability management •
Financial statement analysis /
ratio analysis (including of
operating- and
finance leases, and
R&D) •
Transaction analytics:
M&A,
PE,
VC,
LBO,
IPO,
Project finance,
P3 • Credit decisioning:
Credit analysis,
Consumer credit risk;
impairment- and
provision-modeling • Management accounting:
Activity-based costing,
Profitability analysis,
Cost analysis,
Whole-life cost,
Managerial risk accounting •
Public sector procurement To generalize as to the nature of these models: firstly, as they are built around
financial statements, calculations and outputs are monthly, quarterly or annual; secondly, the inputs take the form of "assumptions", where the analyst
specifies the values that will apply in each period for external / global variables (
exchange rates,
tax percentage, etc....; may be thought of as the model
parameters), and for internal / company specific
variables (
wages,
unit costs, etc....). Correspondingly, both characteristics are reflected (at least implicitly) in the
mathematical form of these models: firstly, the models are in
discrete time; secondly, they are
deterministic. For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see and . Modelers are often designated "
financial analyst" (and are sometimes referred to,
tongue in cheek, as "number crunchers"). Typically, Accounting qualifications and finance certifications such as the
CIIA and
CFA generally do not provide direct or explicit training in modeling. At the same time, numerous commercial
training courses are offered, both through universities and privately. For the components and steps of business modeling here, see ; see also for further discussion and considerations. Although purpose-built
business software does exist, the vast proportion of the market is
spreadsheet-based; this is largely since the models are almost always company-specific. Also, analysts will each have their own criteria and methods for financial modeling.
Microsoft Excel now has by far the dominant position, having overtaken
Lotus 1-2-3 in the 1990s. Spreadsheet-based modelling can have its own problems, and several standardizations and "best practice"s have been proposed. Here, professional guidelines emphasize transparent, auditable, and well-documented models.
Good practice includes separating input, calculation, and output sheets to enhance traceability and reduce error risk. Practical training providers further highlight consistent
formatting, clear labeling, and
documentation of assumptions as essential for usability and stakeholder confidence.
"Spreadsheet risk" is increasingly studied and managed; (For example, a forecast for growth in revenue but without corresponding increases in
working capital,
fixed assets and the associated financing, may imbed unrealistic assumptions about
asset turnover,
debt level and/or
equity financing. See .) What is required, Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures" — i.e., as mentioned, the problems are treated as deterministic in nature — and thus calculate a single value for the asset or project, but without providing information on the range, variance and sensitivity of outcomes; see . A further, more general critique relates to the lack of basic
computer programming concepts amongst modelers, with the result that their models are often poorly structured, and difficult to maintain. Serious criticism is also directed at the nature of budgeting, and its impact on the organization. ==Quantitative finance==