In the dynamic world of finance, the art of financial Modeling stands as a cornerstone, pivotal in shaping business strategies and investment decisions. As markets evolve and technologies advance, the challenges and intricacies of financial Modeling grow, demanding more sophisticated and adaptable approaches. This article dives into these complexities, guided by insights from Sam Sivarajan, a seasoned expert with a rich law, investment banking, and behavioral science background.

Sivarajan, known for his groundbreaking work and thought leadership, offers a unique perspective on financial Modeling. His journey from a corporate lawyer to an investment banker and finally to a consultant and author in behavioral finance shapes his comprehensive understanding of the field. This article explores his expert views on hardcoding in financial models, the importance of realistic assumptions, and the interplay of automated decision-making and human biases.

Readers will gain insights into Sivarajan's approach to financial Modeling, balancing technical precision with strategic foresight. The article also highlights key takeaways from his books, which address making informed financial decisions. This exploration offers theoretical knowledge and practical wisdom, crucial for anyone navigating the complex terrain of financial Modeling.

Prepare to embark on a journey through the nuances of financial Modeling, where each decision can significantly impact business outcomes.

The Perils of Hardcoding in Financial Models

In financial Modeling, hardcoding stands as a major pitfall. It often leads to models becoming inefficient and unreliable for decision-making. The worst financial models typically feature hardcoded assumptions, making them nearly unusable for practical purposes.

Often created with historical profit and loss figures, these models lack flexibility and transparency. Hardcoding, embedding fixed values directly into formulas, poses significant challenges. It complicates updating and auditing models, leading to potential compliance issues.

Financial models burdened by hardcoding are frequently discarded when the creator leaves an organization. This is due to the difficulty in understanding and modifying these models. Each modeler's unique style further complicates this issue.

As a result, these models become more of a hindrance than a helpful tool. They are often scrapped in favor of starting anew to ensure accuracy and adaptability in financial decision-making processes. This underscores the importance of avoiding hard-coded elements in financial models to maintain their usefulness and integrity.

 

Sam Sivarajan's Diverse Career Journey and Impact on Decision-Making

Sam Sivarajan's career journey is a testament to the power of diverse experiences in shaping effective decision-making. Starting as a corporate lawyer in Toronto, Sivarajan transitioned to an investment banker in London and Vienna, specializing in telecoms and technology deals during the 90s' tech boom and subsequent downturn. This period was marked by significant global activity in these sectors, offering him a front-row seat to emerging technologies and market dynamics.

Upon returning to Canada, he pivoted to wealth management, leading three businesses while completing a doctorate in behavioral science. His fascination with decision-making processes drove this academic pursuit. Today, Sivarajan applies his vast knowledge and experience in consulting, writing, and speaking roles, focusing on aiding individuals and companies in making better decisions.

A notable highlight of his career was in investment banking, particularly during the tech boom. He played a crucial role in pioneering projects like the first satellite IPO in Europe. His team's success hinged on their ability to create robust financial models and make reasoned assumptions about futuristic services like FaceTime and YouTube long before they became mainstream. This experience illustrates the importance of innovative thinking and the ability to adapt to rapidly evolving markets and technologies in financial Modeling and decision-making.

 

Balancing Art and Science in Financial Modeling

In financial Modeling, striking a balance between the art of assumption-making and the science of technical structuring is crucial, especially in high-stakes scenarios. This balance was notably demonstrated in the valuation of telecom licenses during the tech boom of the 90s.

Key factors like market penetration and competitive share were fundamental to these models. In one instance, a decision to bid for a UK telecom license was made based on potential market share and population size, highlighting a rational approach to valuation.

Modeling futuristic services like video conferencing was another challenge. Assumptions had to be made about user behavior and pricing models, which required both creative and analytical thinking. The evolution of services like FaceTime from a charged feature to a bandwidth-inclusive one underscores the dynamic nature of market evolution and the need for adaptable Modeling.

The experience underscores several key lessons for financial modelers:

1. Experience and Diversity: Gaining diverse Modeling experiences is invaluable. It helps in understanding different market contexts and user behaviors.

2. Purpose and Audience: Always remember the model's purpose and who will use it. Understand the balance between knowable facts and necessary assumptions.

3. Scenario Analysis: Employ a scenario-based approach, outlining optimistic, pessimistic, and most likely outcomes.

4. Model Limitations: Acknowledge the limitations of models. They are simplifications of reality and should not be the sole decision-making tool.

5. Strategic Thinking: Good Modeling requires deep strategic thinking about qualitative and quantitative business drivers. It's not just about adjusting numbers to make them work; it's about understanding the real-world implications of those numbers.

So, financial Modeling is an interplay of art and science, requiring a blend of technical skills, experience, strategic thinking, and humility in acknowledging uncertainties and limitations.

 

Realistic Assumptions in Financial Modeling

In financial Modeling, the practicality and realism of assumptions are critical. An example discussed highlights the issue of making assumptions that look good on paper but are impractical in reality. For instance, cutting costs in a department may seem straightforward in a spreadsheet, but the real-world implications are often more complex and substantial.

In another case, the decision to abandon a potential deal was made after realizing that the company was not growing as expected. These scenarios underline the importance of critically examining assumptions and their feasibility.

Key advice for modelers includes:

1. Holistically Assess Assumptions: Look at the entire picture, considering how assumptions align with the business's reality. For instance, in Discounted Cash Flow (DCF) models, if the terminal value exceeds 100% of the total value, it suggests unrealistic expectations about the business's profitability.

2. Tailor Assumptions to Each Business: Understand that different businesses have different drivers. For example, subscriber numbers might be a key revenue driver for a mobile company but not for a manufacturing firm.

3. Challenge and Defend Assumptions: Each assumption should be defendable to an outsider. Test assumptions through scenarios and seek feedback.

4. Iterative Process: Building a model should be an ongoing process of testing and refining assumptions.

5. Technical Skills: Learning macros and Visual Basic for Applications (VBA) can significantly enhance model building.

This approach emphasizes the need for financial modelers to critically evaluate and defend their assumptions, ensuring they align with the business's realities and strategic objectives. It's not just about the numbers but understanding their story and how they translate into real-world scenarios.

 

Decision-Making: Navigating Automatic Choices and Biases

The human brain is incredibly efficient, automating many of the 35,000 decisions we make daily. This efficiency, while beneficial for routine tasks, can be a hindrance in critical decision-making scenarios.

When decisions are made automatically, biases, emotions, and past experiences can unduly influence choices without conscious awareness. This is particularly relevant in business contexts, where decisions like investing or shutting down a business line require deliberate thought.

To improve decision-making:

1. Awareness of Biases: Recognize that biases are inherent and can't be entirely eliminated. They stem from the brain's efficiency in automating decisions.

2. Rational Thinking: Encourage the use of rational thought for crucial decisions. This involves understanding the influence of biases and emotions and actively working through them.

3. Pre-Mortems and Scenario Planning: Conduct pre-mortems to anticipate potential challenges and outcomes. This process helps prepare for various scenarios, especially in unpredictable situations like market downturns.

4. Educational Tools and Frameworks: Use research, evidence, and real-life cases to educate about decision-making impacts. However, knowledge alone isn't enough; practical tools and frameworks are necessary.

5. Systematic Evaluation: In consulting, apply systematic approaches to evaluate options, pros and cons, potential consequences, and impacts on current business operations.

6. Multiple Perspectives: Employ frameworks like Edward Debono's Six Thinking Hats to view decisions from various angles. This method helps avoid groupthink and ensures that decisions are well-rounded, considering logical, emotional, cultural, operational, and strategic factors.

7. Personal Context in Decisions: Understand that even with the same facts, personal preferences, motivations, and circumstances can lead to different, yet rational, decisions.

This approach to decision-making emphasizes the need to balance automatic brain functions with conscious, rational analysis, especially in complex and impactful business scenarios. It highlights the importance of understanding and managing biases and emotions to make more informed and effective decisions.

 

Sam Sivarajan's Journey as an Author

Sam Sivarajan shares his experiences and motivations behind writing his three books: "Making Your Money Work," "Uphill," and "Am I OK?" Each book addresses a unique aspect of decision-making and finance.

1. "Making Your Money Work": Sivarajan's debut book aims to aid readers in making informed financial decisions. It covers fundamental concepts like risk, return, and compound interest.

2. "Uphill": Inspired by the challenges posed by the pandemic, this book broadens the scope of behavioral science principles. It applies these principles to understanding a rapidly changing world and making better decisions in both work and personal life.

3. "Am I OK?": This book serves as a practical guide for advisors and investors. It focuses on essential questions to consider in making key investment decisions.

Sivarajan found the writing process enjoyable and relatively easy but acknowledges that editing was more time-consuming and less enjoyable. He went through multiple edits and drafts over many months.

Opting for self-publishing, he also navigated the learning curve that comes with it. Despite reading his books numerous times, Sivarajan notes the ongoing challenge of deciding the best way to express ideas, emphasizing the subjective nature of writing.

 

Sam Sivarajan's Rapid Fire on Financial Modeling

In a rapid-fire session, Sam Sivarajan shared his preferences and insights on common financial Modeling questions:

1. Circular References: He advises against using circular references in models.

2. VBA Usage: Sivarajan supports Modeling using Visual Basic for Applications (VBA).

3. Model Orientation: He prefers horizontal models.

4. Dynamic Arrays in Excel: Sivarajan is in favour of using dynamic arrays.

5. External Workbook Links: He advises against using external workbook links.

6. Named Ranges: Named ranges are preferred in his approach.

7. Modeling Standards: Initially, he didn't follow formal standards due to early career timing but recognized their importance.

8. Future of Excel: He believes Excel will continue to be relevant for the foreseeable future.

9. AI in Modeling: Sivarajan thinks AI will partially assist in Modeling but won't replace the need for human input in assumptions and logic.

10. Favorite Lookup Function: He uses VLOOKUP, mainly due to familiarity.

11. Sheet and Cell Protection: He advocates for protection in models.

12. Financial Models as Decision Tools: Sivarajan views financial models as one of many tools in decision-making, not the sole or primary one.

He emphasizes the importance of understanding the model's audience and purpose, validating assumptions, and maintaining simplicity. The true value of a model, according to Sivarajan, lies in the thought process behind it rather than its complexity.

Conclusion:

In summary, the insights from financial Modeling expert Sam Sivarajan offer a comprehensive understanding of the field. The discussion highlights the dangers of hardcoding in financial models, emphasizing the need for flexibility and adaptability. Sivarajan's diverse career journey, from law to investment banking and behavioral science, underscores the importance of varied experiences in effective decision-making.

Key takeaways include the need to balance the art of assumption-making with the technical aspects of financial Modeling. Realistic assumptions, critical examination of these assumptions, and their practical implications play a crucial role. Moreover, understanding automated decision-making processes and managing biases can significantly improve business decisions.

Sivarajan's books provide further insight into making informed financial and investment decisions, influenced by his extensive experience and academic background. His rapid-fire responses reveal preferences in Modeling techniques and the belief that financial models are vital but not exclusive tools in corporate decision-making.