02 Jan 2025 7 min read

The Problem with CAGR

Biotech investing is not for the faint-hearted. This field, ruled by unpredictability, is a theatre of sharp turns: clinical trials that vault or vaporize fortunes, regulatory whims that recast the rules, and groundbreaking science that reshapes entire markets. Investors, yearning for clarity in the chaos, often cling to deceptively simple metrics like the Compound Annual Growth Rate (CAGR).

But here lies the trap. CAGR seduces with its elegance—a tidy number to sum up the past and forecast the future. Yet it smooths the jagged edges of biotech's turbulent paths, masking risks and concealing fragility. Nassim Taleb would label this the "delusion of control," while Benoît Mandelbrot would recognize it as a failure to embrace the fractal-like volatility intrinsic to such industries.

CAGR assumes a steady ascent, oblivious to the pitfalls, false summits, and the occasional freefall. Two firms with identical CAGR might tell vastly different stories—one of measured strides, the other of harrowing setbacks followed by improbable recoveries. The metric's fixation on endpoints blinds us to these critical details.

Even worse, its reliance on Gaussian distributions underestimates the power of "fat tails"—those rare, catastrophic, or transformative events that shape biotech more than the everyday grind. Trial failures, regulatory shocks, and "black swan" breakthroughs all loom larger than CAGR’s tidy narrative allows. Investors relying solely on such metrics risk misreading the terrain entirely.

There is hope, though. Scenario planning, probabilistic modeling, and stress-testing can reveal vulnerabilities obscured by simplistic measures. Tools like Real Options Analysis—allowing for pivots and exits—offer a framework attuned to the sector's mercurial nature. And perhaps most importantly, a relentless focus on the science, competitive landscape, and intellectual property strength remains non-negotiable.

The lesson? Biotech demands a more nuanced lens. Those measuring its complex storms with linear tools like CAGR risk missteps. A sharper toolkit and deeper curiosity are the true prerequisites for navigating this volatile, high-stakes arena.

1. The Insidious Allure and Limitations of CAGR:

CAGR, calculated as: $$\text{CAGR} = \left[ \left( \frac{\text{Ending Value}}{\text{Starting Value}} \right)^{\frac{1}{\text{Number of Years}}} - 1 \right]$$ provides a seemingly objective measure of historical growth. However, its application in the volatile biotech sector is fraught with limitations:

Linearity Assumption

CAGR assumes a smooth, linear trajectory of growth, a stark contrast to the reality of biotech development. Clinical trial failures, unexpected regulatory hurdles, and the emergence of unforeseen competitors can cause sudden and significant deviations from a linear path. For instance, a single successful drug approval can lead to exponential revenue growth, while a clinical trial failure can cause a precipitous decline, rendering CAGR an inaccurate reflection of the true growth trajectory.

Example:

For a company with:

Year 1 Revenue: $100M
Year 5 Revenue: $200M
n=5 years

$$
\text{CAGR} = \left( \frac{200}{100} \right)^{\frac{1}{5}} - 1 = 14.87%
$$

At first glance, this suggests a steady 14.87% annual growth rate. However, this number ignores the volatility within those five years. Consider the intermediate revenues:

Year 3 Revenue: $50M (trial delay).
Year 4 Revenue: $150M (partial recovery).

The Reality Behind the Numbers

The 14.87% CAGR creates a false impression of steady growth. In reality:

Year 3: Revenue plummets by 50%, a dramatic decline caused by delays in clinical trials or regulatory bottlenecks.
Year 4: A partial recovery raises revenues, but this is far from a smooth return to form.

By averaging the start and end points, CAGR effectively erases the story in between, hiding both the risks (Year 3's sharp decline) and the resilience (Year 4's recovery).

Hindsight Bias

When applied retrospectively, CAGR can create an illusion of predictable growth. Successful companies may appear to have exhibited consistent, steady growth based on CAGR calculations. However, their success may be attributable to a series of fortunate events, strategic adaptations, and perhaps even a degree of luck, rather than a predictable and consistent growth trajectory.

Ignoring Path Dependence

CAGR focuses solely on the starting and ending values, disregarding the crucial impact of the sequence of events. Two companies with identical CAGR values may have vastly different risk profiles. One might have achieved growth through a series of small, incremental successes, while another may have experienced a catastrophic failure followed by an unexpected breakthrough. CAGR fails to capture these crucial differences in the path to success, masking the underlying fragility of the latter company.

Example

Two companies with identical CAGR can have vastly different risk profiles:

CAGR ignores the fragility of Company B’s trajectory.

Revenue trajectories for two companies:

Company 1 Steady Growth:

CAGR=20%

Company 2 Volatile Growth:

Alternating growth: +40%, -20%.

Both yield:

CAGR=20%

Reality: The volatility of the second company is masked, leading to misjudged risk.

Masking Volatility

By smoothing out fluctuations, CAGR hides the true nature of risk and the potential for extreme events to significantly impact outcomes. This can lead to underestimating the potential for catastrophic losses and overestimating the stability of the company's growth trajectory.

2. The Fractal Nature of Biotech Growth and Mandelbrot's Critique:

Benoît Mandelbrot, a pioneer in fractal geometry, demonstrated that many natural phenomena exhibit self-similarity at different scales. This concept, applicable to financial markets, also resonates within the biotech sector.

Self-Similarity in Biotech: Biotech growth trajectories often display self-similar patterns. Small fluctuations in research, development, or market dynamics can have amplified effects, leading to periods of rapid growth followed by sudden downturns. A minor setback in a key clinical trial, for instance, can trigger a cascade of negative events, impacting investor confidence, delaying timelines, and ultimately hindering the company's long-term prospects.
Irregularity and the Limitations of CAGR: The biotech landscape is characterized by inherent irregularity. Clinical trial outcomes, regulatory approvals, and competitive pressures introduce significant uncertainties, making it difficult to predict long-term trajectories. The emergence of a novel competitor, a sudden shift in regulatory policy, or an unexpected safety concern can dramatically alter the course of a company's development. CAGR, with its assumption of linear growth, fails to adequately capture this inherent irregularity, smoothing over these complexities and masking the true nature of risk.

3. Taleb's Critique and the Role of "Black Swan" Events:

Nassim Nicholas Taleb, in his seminal work "The Black Swan," emphasizes the importance of rare, unpredictable, and high-impact events in shaping outcomes. The biotech sector is particularly susceptible to such "black swan" events:

Unexpected Scientific Breakthroughs: The discovery of a novel therapeutic target or a breakthrough in gene editing technology can dramatically alter market dynamics and render existing therapies obsolete.
Regulatory Shifts: Changes in regulatory policies, such as shifts in approval pathways, changes in reimbursement policies, or increased scrutiny of drug safety, can have a profound impact on a company's prospects.
Pandemics: Unforeseen global health crises, such as the COVID-19 pandemic, can create both significant opportunities and unprecedented challenges. The pandemic accelerated the development of mRNA vaccines, but also highlighted the potential for supply chain disruptions and the emergence of new variants.

CAGR, by focusing on historical data and assuming normality, is ill-equipped to anticipate or account for the impact of "black swan" events. This can lead to overconfidence and a dangerous underestimation of risk. Investors may be lulled into a false sense of security by seemingly consistent growth rates, failing to adequately consider the potential for rare but devastating events to derail even the most promising companies.

4. The Gaussian Fallacy and its Implications for Biotech:

Traditional financial models often rely on the Gaussian (normal) distribution, which assumes that most outcomes cluster around the mean and extreme events are rare and insignificant. However, in reality, many events in complex systems, including biotech, exhibit "fat tails," meaning that extreme events are more frequent and impactful than predicted by the Gaussian distribution.

Clinical Trial Failures

A clinical trial failure, while potentially rare, can have a devastating impact on a biotech company's valuation and even threaten its survival. The potential for such catastrophic events is not adequately captured by CAGR, which focuses on average growth rates and underestimates the likelihood and potential impact of these "fat tail" events.

Consequence

This reliance on Gaussian assumptions can lead to a significant underestimation of risk and misallocation of capital. Investors may underestimate the likelihood of extreme events and overestimate the expected returns, leading to suboptimal investment decisions.

Moving Beyond CAGR

To more accurately assess investment potential in the biotech sector, it is crucial to move beyond simplistic metrics like CAGR.

Scenario Planning

Developing multiple plausible scenarios, including "black swan" events, allows for a more comprehensive understanding of potential outcomes and their associated probabilities.

Example: Scenarios should consider potential delays in clinical trials, unexpected safety concerns, the emergence of superior competitors, changes in regulatory policy, and shifts in market dynamics.

Real Options Analysis

This framework views research and development as a series of options, allowing for flexibility and the ability to adapt to changing circumstances.

Example: A biotech company may have the option to discontinue development of a particular drug candidate if early clinical trials yield disappointing results, or to pursue a different indication if initial data suggests a higher probability of success in a specific patient population. Real Options Analysis helps to quantify the value of these flexibility options, providing a more realistic assessment of the investment's potential.

Focus on Scientific Merit and Competitive Advantage

A deep understanding of the underlying science is paramount. Evaluating the scientific validity and potential impact of a company's technology, considering factors such as novelty, clinical relevance, and the strength of the intellectual property, is crucial. Assessing the competitive landscape, analyzing the potential for intellectual property protection, and evaluating the scientific rigor of preclinical and clinical data are all critical factors in determining the long-term viability of a biotech investment.

Incorporate Probabilistic Modeling

Utilizing probabilistic models that account for uncertainty and volatility, such as Monte Carlo simulations, can generate a range of potential outcomes, providing a more realistic assessment of risk and return.

Stress-Testing for Fragility

Conducting "what-if" analyses to assess the potential impact of adverse scenarios, such as clinical trial failures, regulatory setbacks, and the emergence of strong competitors, can help to identify potential vulnerabilities and inform risk mitigation strategies.

Conclusion

Biotech investing defies simple metrics. The industry thrives on volatility, innovation, and unpredictability, where triumphs and disasters often stem from singular, high-impact events. Yet the widespread reliance on tools like CAGR and TAM betrays a desire to simplify what cannot—and should not—be simplified. These metrics, while alluringly clear, obscure the risks and nuances that define the sector, leaving investors ill-equipped to navigate its complexities.

Understanding biotech’s landscape demands more than linear calculations or glossy pitch decks. It requires embracing the industry's fractal nature, recognizing the influence of tail risks, and appreciating the non-linear trajectories that mark its path. Investors who move beyond conventional measures—who use scenario planning, probabilistic modeling, and real options frameworks—are better positioned to assess true potential while mitigating fragility.

The biotech industry doesn’t reward complacency or shortcuts. Success lies in the ability to decode complexity, anticipate volatility, and recognize opportunities within uncertainty. For those who rise to this challenge, the rewards—both scientific and financial—can be transformative. For those who don’t, the industry’s storms are unforgiving. In biotech, the tools you choose to measure its turbulence can mean the difference between merely surviving and truly thriving.