Standard factor models (value, quality, momentum) are counterproductive for biotech stocks. Dan Rasmussen's research found that value must be redefined as market cap relative to R&D spend, where more spending is "cheaper," completely flipping the traditional logic used in other sectors.

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A funding paradox exists where capital-efficient medical service platforms struggle to raise funds while high-risk, cash-intensive therapeutic companies secure large rounds. This is because investors understand the traditional drug development model but are unclear on how to value a medical service.

A significant portion of biotech's high costs stems from its "artisanal" nature, where each company develops bespoke digital workflows and data structures. This inefficiency arises because startups are often structured for acquisition after a single clinical success, not for long-term, scalable operations.

Standard quant factors like expanding margins and avoiding capital raises are negative signals for development-stage biotech firms. These companies must burn cash to advance products, rendering traditional models useless. The only semi-reliable quant metric is Enterprise Value to Cash.

Despite decades of evidence, there is no agreement on why factors like "value" (cheap stocks outperforming) work. The debate is split between rational risk-based explanations (Fama's view that they are inherently riskier) and behavioral ones (Shiller's view that investors make systematic errors). This uncertainty persists at the core of quant investing.

Instead of hiring dozens of PhDs to analyze clinical trials, a quantitative firm can use the 13F filings of top specialist biotech hedge funds as a proxy for deep domain expertise. This "approved list" from experts can be modeled as a quantitative factor that has been shown to outperform.

In an scientifically inscrutable sector, the percentage of a company owned by dedicated biotech funds serves as a reliable proxy for quality. A complete lack of specialist ownership is a major red flag, suggesting the company is likely marketed to uninformed investors and may have poor science.

One of the few working quantitative models in biotech is to systematically purchase stocks after they have crashed on bad news. This low-batting-average, high-slugging-percentage approach is terrifying but can work by getting favorable odds on a recovery, provided the company has sufficient cash runway to survive.

Despite significant stock price increases (e.g., 3-4x for some names), the current biotech rally is not a sign of an overheated market. Many small-cap companies are still trading at a fraction of their potential value based on their pipelines, suggesting the rally is a recovery from deeply distressed, sub-cash valuations.

A massive disconnect exists where scientific breakthroughs are accelerating, yet the biotech market is in a downturn, with many companies trading below cash. This paradox highlights structural and economic failures within the industry, rather than a lack of scientific progress. The core question is why the business is collapsing while the technology is exploding.

The "takeout candidate" thesis often fails because corporate development teams at large firms won't risk their careers on optically cheap but unprofitable assets. They prefer to overpay for proven, de-risked companies later, making cheapness a poor indicator of an impending acquisition.