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.

Verdad Capital's research shows biotech stocks heavily owned by multiple specialist funds significantly outperform those with none. This "consensus" among experts acts as a powerful quality screen in a sector where traditional financial metrics are useless, as stocks with zero specialist ownership generate near-zero returns.

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.

Private VCs with board seats operate deterministically, using their influence to 'make sure' a drug succeeds. Public fund managers operate probabilistically, accepting imperfect information in exchange for liquidity. They must calculate the odds of success rather than trying to directly shape the outcome.

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 biotech investing, the collective wisdom of specialists is more valuable than any single expert's contrarian bet. Stocks owned by multiple specialists perform better, suggesting that an individual specialist's unique, high-alpha idea is more likely to be wrong than right.

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.

The fundamental purpose of any biotech company is to leverage a novel technology or insight that increases the probability of clinical trial success. This reframes the mission away from just "cool science" to having a core thesis for beating the industry's dismal odds of getting a drug to market.

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.