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Dismantling the New Crystal Ball – Excel

Financial projection is often presented to us as an exercise in near-surgical precision, an indispensable skill for any serious investor. Intricate spreadsheets are built, full of detailed assumptions, growth rates projected to the third decimal place, optimized margins, and discount rates adjusted for the slightest risk. The result—a concrete figure of intrinsic value or future results—gives us a false sense of control and certainty.

However, this precision is, for the vast majority of companies, a dangerous and harmful illusion. The reality is that the value of any model is directly tied to the quality, quantity, and transparency of the information that feeds it. For the vast majority of companies, this information simply does not exist with the necessary quality.

The illusion crumbles when:

  • The company does not provide clear or consistent guidance, leaving the analyst blind to its future direction.

  • There are constant and publicly unreported structural changes, causing any projection to be based on an outdated snapshot.

  • Revenue and cost sources vary quarterly without coherent explanations, turning the analysis into mere guesswork.

In these cases—which are the norm, not the exception—models are nothing more than sandcastles. The problem is not the DCF method or the Excel tool; the fundamental problem is that the available data is opaque, incomplete, and ambiguous. When this happens, technical rigor becomes a sophisticated simulation that only serves to deceive ourselves.

1. The Generalization of Information Scarcity

Forget the idea that transparency is the norm. The harsh truth is that the vast majority of companies operate with such limited transparency that they cannot be reliably modeled. We are not talking about exceptions; we are talking about the prevailing reality in emerging markets, in most innovative tech sectors, in companies with constantly evolving business models, or in smaller and medium-sized companies that lack the regulatory pressure or resources for exhaustive communication.

This manifests in very specific ways:

  • Absolute lack of key breakdowns: The norm is that companies do not segment revenue by business line, region, or channel, preventing any understanding of true growth drivers. Similarly, structurally different costs are grouped under the same heading, making it impossible to identify inefficiencies or specific profitability. Without these breakdowns, any revenue or cost projection is an unfounded assumption.

  • Vague or nonexistent guidance: the rule, not the exception: Most companies refuse to give clear and quantifiable forecasts about growth, margins, or future expenses. They use evasive, qualitative language full of boilerplate phrases that offer no clues for financial projection. This opacity is not a failure—it’s their modus operandi.

  • Unstable and changing operating models: Today’s business dynamism means that many companies constantly change their business structure (from direct sales to marketplace, from product to service) without providing clarity about the real financial impact. They continually adapt to regulatory policies, tariffs, or geopolitical dynamics that are impossible to predict. How can one model the profitability of a company whose cost structure and revenue sources are redefined each year? The answer is simple: you can't.

Companies such as many Latin American fintechs, African logistics platforms operating in multiple countries with differing regulations, or even certain segments of giants like Meituan (with its complex ecosystem and constant Chinese regulatory intervention) are just examples of this widespread norm of opacity. This is not an isolated problem; it’s the reality of how the business world works for the vast majority of companies.

2. Direct Impact on DCF Models 💥

If the premise is that 95% of companies are unmodelable, then the impact is clear: most financial models built are, at best, an act of faith—and at worst, a source of self-deception.

  • Cash flows impossible to determine: Calculating future cash flows for most companies requires:

    • Revenue and expense projections that are mere guesses.

    • Estimates of capital investment and working capital that are pure speculation.

    • Assumptions about tax rates, reinvestment, and amortization that lack solid foundations.
      When these variables—the backbone of the DCF—are invisible or subject to speculative interpretation, the model loses all solidity. It’s not just that precision is compromised; the usefulness of the analysis is nullified. You're discounting cash flows that are the product of your imagination.

  • Unmodelable and irrelevant margin volatility: Attempting to model an operating margin for most companies is a pointless task. If there’s no breakdown by business unit, if rates and subsidies change without notice, or if the company operates in multiple jurisdictions with volatile conditions, margin projection is a fantasy. It’s like trying to guess the movements of a subatomic particle—it’s inherently unpredictable. The number you put in the model is your wish, not a prediction based on reality.

  • Excel as a bias validator: Since solid information is scarce in the vast majority of companies, any number entered into a model is a purely subjective judgment. This transforms DCF into an exercise in bias validation, where the analyst does not seek the truth but adjusts the figures until the resulting value “fits” their pre-existing expectations or the current narrative. The model's sensitivity is so extreme that a small change in an arbitrary assumption can drastically alter the result—proving the absolute lack of robustness of these models for 95% of companies.

3. Reactions to the Dominant Narrative 🎢

The most visible consequence of this omnipresent uncertainty is the irrational volatility in the stock prices of most companies. Each new quarterly report acts as a massive emotional trigger: “surprises,” good or bad, cause disproportionate and illogical price movements.

This dynamic has critical pedagogical implications:

  • The market reaction is based almost exclusively on the narrative, not fundamentals: For the vast majority of companies, the lack of credible data forces the market to operate on stories and perceptions. Influencers, financial media, and the companies themselves build narratives (of disruption, exponential growth, “the next big thing”). When fundamental information is scarce, the market clings to the story, and any change in it—not in the numbers—is what triggers price movements.

  • Analysts and media swing from euphoria to panic with every rumor or isolated data point: This is standard behavior. A slightly positive figure unleashes euphoria and outlandish price targets. A negative one, even a minor one, provokes panic and extreme devaluations. This pendulum effect is the norm, not the exception, for most companies, because the lack of fundamental anchoring makes any piece of information magnified.

  • The investor seeking consistency becomes a victim of an emotional roller coaster: For the investor looking for a long-term value logic, most companies are an emotional trap. Sharp drops induce panic and selling at the worst moment, while irrational surges foster euphoria and buying at the top. The lack of transparency not only impedes rigorous analysis, but turns investing into a psychological game where most people lose.

4. Rational Approaches

If we accept that 95% of companies cannot be reliably modeled, the strategy is not to abandon analysis but to abandon the attempt to model them. Instead, the intelligent investor focuses on two paths:

4.1 Focus on the 5% of truly modelable companies

These are companies with stable business models, transparent information, clear and consistent guidance, and a predictable operating history. They are the minority, and it is there where quantitative tools have real value.

4.2 For the remaining 95%, adopt a purely qualitative and extreme risk management approach:

  • Judge management quality as the sole criterion: If numbers don’t help, the only hope is the people in charge. Analyze whether the management team has demonstrated:

    • Exceptional adaptability to adverse contexts.

    • Efficient capital allocation (despite opacity).

    • Coherent communication in their strategy, even if limited.

  • Observe indirect external indicators: crumbs of information: For these companies, clues must be sought outside of financial reports:

    • Participation of strategic partners or major shareholders with privileged access: their investment can be a signal of internal validation.

    • Institutional capital movement: If large funds with greater resources invest or divest, it could be a sign.

    • Changes in user or traffic dynamics publicly observable: Social media metrics, web traffic (Alexa, SimilarWeb), or app downloads may offer hints, even if not directly financial.

  • Invest as a game of very low probabilities and high uncertainty: For 95% of companies, investing is not a science—it’s a speculation game with high uncertainty. The edge does not lie in “knowing more” (because you can’t), but in being extremely patient, having a very high risk tolerance, and understanding that most of these investments will fail. The key is to invest small amounts and diversify heavily, assuming that only a minimal fraction will bear fruit.

5. Recommendations for the Investor 🎓

For those learning to invest, the fundamental lesson is that 95% of what is taught in finance about modeling applies only to a minority of companies. The reality of the market is much more opaque and volatile for most opportunities.

✅ What you should do:

  • Ruthlessly question data before even thinking of modeling. If the information isn’t pristine, close the Excel file.

  • Prioritize (almost exclusively) companies with transparent reports and stable business models for your first investments. Learn within the 5% that allows rigorous analysis.

  • Develop exceptional qualitative judgment. For 95% of companies, your only weapon is understanding management, the competitive environment, and strategic direction without the help of reliable numbers.

  • Build very broad scenarios, accepting that they are mere speculations. For the 95%, any “single forecast” is nonsense.

❌ What you should avoid:

  • Believing a spreadsheet is a crystal ball for most companies. It’s a self-deception artifact.

  • Assuming more complexity in Excel equals greater intelligence or precision for the 95%. It just adds layers to the illusion.

  • Seeking certainties in environments built on ambiguity. Certainty is a chimera for most investments.

Conclusion

Modeling future business results is a powerful tool, yes—but only when applied to the small percentage of companies that offer solid and transparent data. For the overwhelming majority of companies, the attempt to predict the future becomes a dangerous illusion and a waste of time. Responsible financial education should not only teach how to model but, more importantly, when not to model. It should train investors to understand that for 95% of companies, investing is a high-speculation game where rigorous quantitative analysis is unfeasible and where caution and humility are the most valuable virtues.


 

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