Core Themes and Best Practices from 'Principles of Financial Modelling'
Salamat Ali
Assistant Manager ll Quantitative Analyst ll Financial Analyst ll Excel Expert ll Project Manager ll Business Analyst ll Amazon FBA Expert
In starting we will talk about overview of financial modelling, including its objectives, stages and processes.
Financial Modelling:
Financial modelling is the application of mathematical techniques, such as linear algebra and calculus, to business finance, enabling the creation of quantitative models that forecast financial outcomes, manage risk, and inform strategic business decisions. By bridging the gap between mathematical concepts and financial data, financial modelling provides a powerful tool for businesses to optimize performance, maximize value, and drive growth.
Objectives:
"A model is a simplified version of a real-life situation. A financial model is a tool that helps with business and finance decisions. Financial models are used to:
The Stages of Model:
Backward Thinking And Forward Calculation Processes:
Now we will discuss about Models in Decision Support.
We will summaries it into two parts first is benefits of using models and next is challenging is using model.
Benefits of using models:
Providing Numerical Information
A model helps find answers to important questions, which is crucial for big decisions. It also helps us learn and understand the situation better, giving us valuable insights early on.
Capturing Influencing Factors and Relationships
The process of building a model should force a consideration of which factors influence the situation, including which are most important.
Generating Insight and Forming Hypotheses
In this context, the following well-known quotes come to mind:
◾ “Plans are useless, but planning is everything” (Eisenhower).
◾ “Every model is wrong, some are useful” (Box).
◾ “Perfection is the enemy of the good” (Voltaire).
Decision Levers, Scenarios, Uncertainties, Optimization, Risk Mitigation and Project Design
A good model helps us identify what we can control and what we can't. It also shows us what we can partly control, but need to take extra steps for. This helps us make the best decisions and prepare for uncertain outcomes. It also gives us insight into potential risks and helps us find the best solution.
CHALLENGES IN USING MODELS
o The Nature of Model Error (Specification error, Implementation error, Decision error)
o Inherent Ambiguity and Circularity of Reasoning
o Inconsistent Scope or Alignment of Decision and Model
o Inconsistent Scope or Alignment of Decision and Model
o Balancing Intuition with Rationality
o Lack of Data or Insufficient Understanding of a Situation
Modelling is prone to errors, including specification mistakes. Implementation errors can also occur, leading to flawed results. Decision errors can happen when models are misused or misinterpreted. Ambiguity and circular reasoning can lead to confusing conclusions. Models and decisions must align, but often have different scopes. Inconsistent alignment can lead to poor decision-making. Balancing intuition with rationality is a delicate task. Limited data and understanding can lead to model failure and poor decisions.
Now, we will discuss the foundation of modelling best practices and the core competencies required to build good models.
It is probably fair to say that many models built in practice are of mediocre quality, especially larger ones. Typical weakness that often arise include:
o To understand, to audit or validate.
o Function Complexity
o Contain error
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We consider that seven key areas form the core competencies and foundation of best practices:
1. Know the goal and how analysis fits in.
2. Understand the application inside out.
3. Master the tools (like Excel and VBA) to be creative.
4. Design flexible and sensitive models.
5. Build models with good data flow and layout.
6. Make models transparent and user-friendly.
7. Use problem-solving skills to integrate it all.
Tools:
Decision-support Role, Objectives, Outputs and Communication
o What business decisions do I need to make?
o What outputs do I need?
o Do I need to analyze scenarios or risks?
o Are there optimization issues to consider?
o What variables should I include?
o How much detail do I need for variables and time?
o What's the logical flow of inputs, calculations, and outputs?
o What data do I have?
o How can I keep the model simple, but not too simple?
Skills with Implementation Platform
The modeler must have sufficient skill with whichever platform has been chosen (e.g. Excel), including the ability to creatively consider a variety of options and to choose the one which is most appropriate overall. Often, models are implemented in ways that are either insufficiently flexible, or are flexible but are unnecessarily complicated.
Defining Sensitivity and Flexibility Requirements
write in own easy words "The topic of clearly defining the sensitivity requirements is perhaps the single most important area in model design; once these are adequately defined, the appropriate approach to many other aspects of the modelling also become clear. In fact, we use the term “flexibility requirements” to emphasize the wider applicability of this concept, which includes:
· Standard sensitivity analysis
· sensitivity thought processes
· The ability to include new data sets and/or remove old ones
· Being able to update a forecast model with realized figures
· Increasing the scope of validity of a model by turning contextual assumptions
We consider that the core of modelling “best practices” is the creation of models that lie on (or close to) the “best practice frontier”, shown schematically in Figure. In this framework, the core to modelling best practices is:
Defining the nature of the flexibilities required.
Building a model that has the minimum complexity.
Only add flexibility to the model where it's really needed. This is because:
o Making a model more flexible makes it way more complicated.
o It's harder to simplify a complicated model than to add flexibility to a simple one.