Business Risk and Simulation Modelling in Practice

Business Risk and Simulation Modelling in Practice
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The market approach aims to establish the value of a company based on how similar firms are priced on the stock exchange or through company transactions. Using the market approach, price-related indicators such as price to earnings, sales and book values are utilised. An ever-present problem however, is that different valuation multiples and valuation methodologies tend to provide the analyst with contradictory outputs. The solution to this problem so far has been to claim that the market approach is more art than science, thus providing the analyst with the freedom to alter the multiples at their own discretion to reach a uniform value or range. Valuation: The Market Approach puts an end to this problem, providing the reader with a rational scientific-based understanding and the necessary tools to perform a sound market approach valuation, or if reviewing such valuations, provide the tools to challenge the work of the arts-based senior experts. The book begins with an in-depth review of the basics; which is then applied in a detailed worked example. Step-by-step, the reader’s expertise is built towards a complete understanding and implementation of the market approach, not only on a standalone basis but also in relation to the DCF methodology. The book is aimed at the seasoned professional, but will also be invaluable to students as they apply their academic knowledge to the real world of valuation and M&A. About the author: SETH BERNSTROM is a Director at the Valuations practice of PwC. He has 15 years of experience as a valuation expert with a special focus on private equity, with long-running engagements in Valuation for some of the leading Nordic private equity houses. Additionally, he provides valuation support and valuation-related advisory services to large and medium-sized Nordic and (Nordic-based) global companies. In addition to his regular work at PwC, he also acts as Visiting Lecturer on valuation at KTH Royal Institute of Technology in Stockholm. Furthermore, he often gives lectures and seminars on valuation at other leading Nordic universities, investment banks, companies, and organizations. He holds a Master of Science in Business Administration and Economics from the Stockholm University School of Business.

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Rees Michael. Business Risk and Simulation Modelling in Practice

Preface

About the Author

About the Website

Part I. An Introduction to Risk Assessment – Its Uses, Processes, Approaches, Benefits and Challenges

CHAPTER 1. The Context and Uses of Risk Assessment

1.1 Risk Assessment Examples

1.2 General Challenges in Decision-Making Processes

1.3 Key Drivers of the Need for Formalised Risk Assessment in Business Contexts

1.4 The Objectives and Uses of General Risk Assessment

CHAPTER 2. Key Stages of the General Risk Assessment Process

2.1 Overview of the Process Stages

2.2 Process Iterations

2.3 Risk Identification

2.4 Risk Mapping

2.5 Risk Prioritisation and Its Potential Criteria

2.6 Risk Response: Mitigation and Exploitation

2.7 Project Management and Monitoring

CHAPTER 3. Approaches to Risk Assessment and Quantification

3.1 Informal or Intuitive Approaches

3.2 Risk Registers without Aggregation

3.3 Risk Register with Aggregation (Quantitative)

3.4 Full Risk Modelling

CHAPTER 4. Full Integrated Risk Modelling: Decision-Support Benefits

4.1 Key Characteristics of Full Models

4.2 Overview of the Benefits of Full Risk Modelling

4.3 Creating More Accurate and Realistic Models

4.4 Using the Range of Possible Outcomes to Enhance Decision-Making

4.5 Supporting Transparent Assumptions and Reducing Biases

4.6 Facilitating Group Work and Communication

CHAPTER 5. Organisational Challenges Relating to Risk Modelling

5.1 “We Are Doing It Already”

5.2 “We Already Tried It, and It Showed Unrealistic Results”

5.3 “The Models Will Not Be Useful!”

5.4 Working Effectively with Enhanced Processes and Procedures

5.5 Management Processes, Culture and Change Management

Part II. The Design of Risk Models – Principles, Processes and Methodology

CHAPTER 6. Principles of Simulation Methods

6.1 Core Aspects of Simulation: A Descriptive Example

6.2 Simulation as a Risk Modelling Tool

6.3 Sensitivity and Scenario Analysis: Relationship to Simulation

6.4 Optimisation Analysis and Modelling: Relationship to Simulation

6.5 Analytic and Other Numerical Methods

6.6 The Applicability of Simulation Methods

CHAPTER 7. Core Principles of Risk Model Design

7.1 Model Planning and Communication

7.2 Sensitivity-Driven Thinking as a Model Design Tool

7.3 Risk Mapping and Process Alignment

7.4 General Dependency Relationships

7.5 Working with Existing Models

CHAPTER 8. Measuring Risk using Statistics of Distributions

8.1 Defining Risk More Precisely

8.2 Random Processes and Their Visual Representation

8.3 Percentiles

8.4 Measures of the Central Point

8.5 Measures of Range

8.6 Skewness and Non-Symmetry

8.7 Other Measures of Risk

8.8 Measuring Dependencies

CHAPTER 9. The Selection of Distributions for Use in Risk Models

9.1 Descriptions of Individual Distributions

9.2 A Framework for Distribution Selection and Use

9.3 Approximation of Distributions with Each Other

CHAPTER 10. Creating Samples from Distributions

10.1 Readily Available Inverse Functions

10.2 Functions Requiring Lookup and Search Methods

10.3 Comparing Calculated Samples with Those in @RISK

10.4 Creating User-Defined Inverse Functions

10.5 Other Generalisations

CHAPTER 11. Modelling Dependencies between Sources of Risk

11.1 Parameter Dependency and Partial Causality

11.2 Dependencies between Sampling Processes

11.3 Dependencies within Time Series

Part III. Getting Started with Simulation in Practice

CHAPTER 12. Using Excel/VBA for Simulation Modelling

12.1 Description of Example Model and Uncertainty Ranges

12.2 Creating and Running a Simulation: Core Steps

12.3 Basic Results Analysis

12.4 Other Simple Features

12.5 Generalising the Core Capabilities

12.6 Optimising Model Structure and Layout

12.7 Bringing it All Together: Examples Using the Simulation Template

12.8 Further Possible uses of VBA

CHAPTER 13. Using @RISK for Simulation Modelling

13.1 Description of Example Model and Uncertainty Ranges

13.2 Creating and Running a Simulation: Core Steps and Basic Icons

13.3 Simulation Control: An Introduction

13.4 Further Core Features

13.5 Working with Macros and the @RISK Macro Language

13.6 Additional In-Built Applications and Features: An Introduction

13.7 Benefits of @RISK over Excel/VBA Approaches: A Brief Summary

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This book aims to be a practical guide to help business risk managers, modelling analysts and general management to understand, conduct and use quantitative risk assessment and uncertainty modelling in their own situations. It is intended to provide a solid foundation in the most relevant aspects of quantitative modelling and the associated statistical concepts in a way that is accessible, intuitive, pragmatic and applicable to general business and corporate contexts. It also discusses the interfaces between quantitative risk modelling activities and the organisational context within which such activities take place. In particular, it covers links with general risk assessment processes and issues relating to organisational cultures, incentives and change management. Some knowledge of these issues is generally important in order to ensure the success of quantitative risk assessment approaches in practical organisational contexts.

The text is structured into three parts (containing 13 chapters in total):

.....

• Cognitive. These are biases that are inherent to the human psyche, and often believed to have arisen for evolutionary reasons.

• Structural. These are situations where a particular type of approach inherently creates biases in the results, as a result of the methodology and tools used.

.....

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