Why is Financial Modelling so Complex And Tricky?

It is complicated to understand the nature of the relationships that exist between various financial variables and conclude in the financial statements. Financial modelling, on the other hand, is regarded as one of the most difficult assignments, even in the financial area. There are various reasons for this erroneous assumption of complexity. Some of the causes are discussed farther down in this post.

Generally speaking, there are many disciplines of finance where the computations are either forward-looking or backward-looking, depending on the situation. For example, financial reporting is based entirely on computations that are performed in the past. Keep track of what happened in the past and report the results to various stakeholder groups like as tax authorities, shareholders, suppliers and other parties involved.

The Maze of Assumptions

One of the biggest culprits behind the complexity is the sheer number of assumptions you need to make. Where do you even start? Revenue projections, cost structures, growth rates, interest rates – the list goes on! Each assumption is like a tiny lever, and if you pull the wrong one, the whole model can go haywire.

Take, for instance, a recent example: the electric vehicle (EV) startup, Rivian. Their initial financial models were built on ambitious production and delivery targets. However, supply chain disruptions and manufacturing bottlenecks significantly impacted their actual performance. The assumptions about scaling production quickly proved overly optimistic, leading to substantial financial losses. This highlights how crucial it is to validate your assumptions with rigorous research and sensitivity analysis.

The Importance of Accuracy: A Tightrope Walk

Accuracy is paramount in financial modelling. A small error can snowball, leading to disastrous decisions. It's like building a bridge – if one measurement is off, the entire structure is compromised.

Imagine a real estate developer building a model to assess the viability of a new apartment complex. An inaccurate projection of rental income or construction costs could lead to significant financial losses. This precision requires not just mathematical skills, but also a deep understanding of the industry and market dynamics.

The Tools of the Trade: Excel and Beyond

While Microsoft Excel remains the cornerstone of financial modelling, it's not always enough. Complex projects often require specialized tools and programming languages.

• Financial Modelling Software: Packages like Bloomberg Terminal or FactSet provide access to vast amounts of financial data and sophisticated analytical tools.

• Programming Languages: Python and R are increasingly popular for their ability to handle large datasets, automate tasks, and perform advanced statistical analysis.

• Cloud-Based Platforms: Tools like Google Sheets allow for real-time collaboration and version control, which is crucial for team projects.

However, just knowing the tools isn't enough. You need to understand how to use them effectively and apply them to the specific context of your model.

Common Challenges That Trip Us Up

• Data Gathering and Validation: Finding reliable data and ensuring its accuracy can be a daunting task.

• Forecasting Uncertainty: Predicting future market conditions is inherently challenging, and unexpected events can throw even the best models off track.

• Model Complexity: Overly complex models can be difficult to understand, maintain, and audit.

• Communication of Results: Presenting complex financial information in a clear and concise manner is essential for effective decision-making.

• Keeping Up With Change: Financial markets and regulations are constantly evolving, requiring models to be regularly updated and revised. A recent example of this is the rapid rise of AI and its impact on various industries, requiring models to adjust for potential disruption and growth.

Let's analysis into some real company examples that highlight the complexities and pitfalls of financial modelling:

1. WeWork's Inflated Projections:

• The Scenario: WeWork, the co-working space giant, built its financial models on aggressive growth projections and a "tech company" valuation. Their models assumed rapid expansion and high occupancy rates.

• The Reality: When they prepared for their IPO, the discrepancies between their projected and actual financials became glaringly apparent. Their models failed to account for the high costs of rapid expansion, the volatility of the real estate market, and the company's unsustainable business model.

• The Lesson: Overly optimistic assumptions, especially regarding growth and market penetration, can lead to disastrous financial models. The WeWork debacle underscored the importance of rigorous due diligence and realistic projections.

2. General Electric's (GE) Complex Accounting:

• The Scenario: GE's financial models were notoriously complex, involving numerous business segments, complex accounting practices, and opaque financial reporting.

• The Reality: This complexity made it difficult for investors and analysts to accurately assess the company's financial health. Hidden liabilities and underperforming assets were masked by the intricate financial structure.

• The Lesson: Overly complex financial models can obscure underlying problems and lead to inaccurate assessments of a company's financial performance. Transparency and simplicity are crucial for effective financial modelling.

3. Boeing's 737 MAX Crisis:

• The Scenario: Boeing's financial models for the 737 MAX likely underestimated the potential costs associated with design flaws, regulatory scrutiny, and reputational damage.

• The Reality: The 737 MAX crashes and subsequent grounding resulted in billions of dollars in losses, production delays, and legal settlements. Their financial models failed to account for the catastrophic impact of a major product failure.

• The Lesson: Financial models must consider downside risks and potential black swan events. Sensitivity analysis and scenario planning are essential for assessing the impact of unforeseen circumstances.

4. Tesla's Volatile Projections:

• The Scenario: Tesla's financial models are subject to significant volatility due to the company's rapid growth, technological innovation, and fluctuating market demand.

• The Reality: Predicting Tesla's future performance is incredibly challenging, as it depends on factors such as battery technology, production capacity, and competition from other EV manufacturers. Projections are often adjusted drastically.

• The Lesson: Companies in rapidly evolving industries require flexible and adaptable financial models. Regular updates and scenario planning are crucial for navigating uncertainty.

5. Blockbuster vs Netflix:

• The Scenario: Blockbuster's financial model was based on physical store rentals and late fees. They failed to adapt to the changing landscape of digital streaming.

• The Reality: Netflix, with its subscription-based model and focus on online streaming, disrupted the traditional video rental market. Blockbuster's inability to adapt its financial model led to its demise.

• The Lesson: Financial models must be continuously evaluated and updated to reflect changing market conditions and technological advancements. Failing to adapt can lead to obsolescence.

These examples underscore the importance of:

• Realistic Assumptions: Avoid overly optimistic projections and validate assumptions with data.

• Transparency and Simplicity: Complex models can obscure underlying problems.

• Risk Management: Consider downside risks and potential black swan events.

• Adaptability: Financial models must be flexible and adaptable to changing market conditions.

Making Sense of the Chaos: Tips for Beginners and Pros

• Start Simple: Don't try to build a complex model from the get-go. Begin with a basic framework and gradually add complexity as needed.

• Document Everything: Clearly document your assumptions, formulas, and data sources. This will make your model easier to understand and review.

• Test and Validate: Regularly test your model with historical data and perform sensitivity analysis to assess its robustness.

• Seek Feedback: Ask colleagues or mentors to review your model and provide feedback.

• Stay Updated: Keep abreast of the latest developments in financial modelling and industry trends.

• Focus on the Story: Remember that a financial model is a tool for telling a story about a company's financial future. Make sure your model is clear, concise, and tells a compelling narrative.

Financial modelling is indeed complex and tricky, but it's also incredibly rewarding. By understanding the challenges and mastering the tools and techniques, you can unlock the power of financial modelling and make informed decisions.

Remember, practice makes perfect! So, dive in, experiment, and don't be afraid to make mistakes. After all, that's how we learn.

What are your biggest challenges in financial modelling? Share your thoughts in the comments below!