Considering risk, return and ranking in

business modelling

Overview

In the aftermath of the GFC, today’s and tomorrow’s business leaders are placing an ever-growing reliance on business and financial models

Quantifying decisions is commonplace

But key users / decision-makers always have the necessary skills to objectively critique and review these models?

We consider the “three R’s” of business modelling: risk, return and ranking

Management decisions are based on the model’s

outputs

These decisions may therefore be based on inaccurate

information

The wrong decisions may be made

Expensive

Reputational risk for modeller, bank and company

Financial risk for bank and business

Why does it matter?

Models contain errors: 100% of models contain errors of some kind [Ernst & Young]

90% of all Excel spreadsheets with more than 150 rows of Excel

formulae contain material errors [KPMG]

MBA students with over 250 hours of spreadsheet development

experience had a 24% chance of introducing spreadsheet errors

in to the first worksheet they built [R. Panko, 1998]

Businesses and financiers partially rely on these models: Bad decisions can be made

Intention is to reduce the risks

Key risks and considerations

Risks

Business risks: Uncertainty attached with the operations of an entity wherein the

realisation of future expected returns is unpredictable

Model risk: Model doesn’t actually do what it’s meant to do

Logic may not work in all situations

Model attempts too much, gets too complex

Assumptions may not be used / input incorrectly

Value drivers in model don’t reflect actual business drivers

Key man risk: Model depends on one person

Model risk

Assessed as the consequence of getting it

wrong

Can be broken down into components:

MR = IR x CR x DR

where:MR = model riskIR = inherent riskCR = control riskDR = detection risk

Model risk: concepts

Model Auditing Process of conducting due diligence on a financial model in order to

eliminate errors in the spreadsheet

Concerned with model structure: consistency of formulae

removing circularities

free from material errors, duplication or omissions

general calculation verification of the model

Validation Concerned with model assumptions:

scope is extended to verify input data back to original documentation

tax and accounting treatments are often verified

performed in addition to Model Auditing

Inherent risk

Inherent risk reflects the potential issues with the specifics of the situation:

Nature of the environmentIs the environment naturally “risky”? Will assumptions be difficult to substantiate? Are certain key drivers highly volatile? Are there political / economical / social / technical factors to consider?

Nature of the transaction / project being modelledHas the project been clearly scoped and kept free of ambiguity? Has this ever been done before? Are those providing inputs and guidance on construction subject matter experts? Are there highly technical issues included (e.g. tax, accounting) which may be challenged?

Nature of the modelIs the model being built from scratch or is it being “modified”? Is there a danger of constructing a square peg for a round hole? If the model is pre-existing, how well acquainted is everyone with its mechanics? Has it been reviewed?

Nature of the modellerIs the modeller experienced in (a) modelling and (b) constructing models for the scenario required? If the project requires technical or industry expertise, is the modeller well-equipped?

Control risk

Does the model contain proactive and counteractive measures to raise / prevent issues with key areas of concern (e.g. Balance Sheet not balancing, costs do not add up)

Measures may include:

Checks

Data validation

Controls

Conditional formatting

Sheet protection

Detection risk

What review process (if any) is being employed to check

or verify the model?

How many people are involved in the review process?

Who are the people involved in the review process?

Are key inputs and outputs clearly understood?

Can the results be verified independently?

What methods of review are being used?

Returns

Does the model consider the key outputs?

Are the drivers present and correct?

Are Returns ON Capital getting mixed up with Returns

OF Capital?

Consider common computational errors that affect

returns calculations: Depreciation errors

Rolled-up vs. capitalised interest

Terminal Value

Mixing units up

Conceptual issues: e.g. NPV vs. IRR

Ranking returns

What are the key drivers and outputs?

What are the key constraints?

Direct checking accordingly

Other considerations:

Ranking techniques

Key Factor Analysis

Thank you!

Sydney

Michael Michaelides

+61 2 9222 9222

michael.michaelides@corality.com

Melbourne

Liam Bastick

+61 421 610852

liam.bastick@corality.com