Investors bet against new drug launches because the shift from a research-focused culture to a commercial one is seen as an 'unnatural transition.' Companies are graded harshly on early results, creating a predictable valuation dip that hedge funds exploit, as seen with Portola Pharmaceuticals.

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.

It's a fool's errand to predict specific trial results. A robust quantitative approach to biotech focuses on underlying drivers and base rates. It positions a portfolio so the random, unpredictable nature of trial events plays out favorably over time, guided by factors like valuation and specialist ownership.

Investors without a scientific background can de-risk biotech portfolios by avoiding early-stage "science projects" (Phase 1-2). Instead, they should focus on companies that have completed Phase 3 trials. This strategy shifts the primary risk from unpredictable scientific development to more analyzable commercial execution.

Early-stage biotech companies are vulnerable to short selling in public markets because their experiments run for 12-24 months, creating long periods without news flow. With no catalysts to drive buying ("no bid"), hedge funds can short the stocks until data is released, highlighting a structural disadvantage of being public too early.

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.

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.

Market dynamics, like investor fixation on AI or predatory short-selling, pose a greater risk to biotech firms than clinical trial results. A company can have a breakthrough drug but still fail if its stock—its funding currency—is ignored or attacked by Wall Street.