Overview
We ran three multifactor asset pricing models on Blackrock's U.S. Biotechnology ETF (ticker IBB) with: 1) the Bayesian-selected factors outlined in Ericsson & Karlsson (2004); 2) a basket of macroeconomic factors; and 3) common style factors— Momentum, Quality, Value, Size and Low Volatility. Our top-down selection of pricing models enabled us to analyze the effectiveness of generalized, style-based, and macro-based factor strategies, respectively, in biotechnology equities investing. In our thirteen-factor, three-model experiment, we found that no model yielded statistically significant alpha in the timeframe spanning January 2010 to January 2025. Three factors – market excess return (0.837-1.078, 7.515t9.786), small-minus-big (0.660-0.669, 5.325t 6.094), and high-minus-low (-0.470-0.468, -4.925t-4.875) – however, revealed themselves to be significant drivers for biotechnology equity returns. For passive investors wanting exposure in the space, we suggest biases towards small-cap, high-growth biotechnology equity funds. No model's adjusted r-squared surpassed 0.578, however, indicating that – even when modeled against non-correlated baskets of factors – at least 42.2% of biotechnology equity variance lives in idiosyncratic risk. To conclude, we suggest that biotechnology equity alpha is captured by those who have deep scientific literacy and extensive analytical manpower — resources inaccessible to most retail investors.
Introduction
Grand View Research reported an estimated 13.96% compound annual growth rate for the U.S. biotechnology market for the period between 2024-2030, a rate higher than that of the S&P 500's from the last 10 years. However, U.S. biotechnology equities have traded horizontally since 2022 — after reaching all-time highs in early-mid 2021, all five of the top U.S. biotechnology exchange-traded funds (ETFs) by assets under management shed between 9% and 93% of their market value (see figure below).
The dissonance between growth in the U.S. biotechnology market and U.S. biotechnology equity performance can be partially explained by the fact biotechnology equities are structured in a way such that they are fundamentally different from other equities. Biotechnology is naturally research heavy, with its focus being primarily on novel drug development with the goal of eventual drug distribution. Additionally, the steps that biotechnology companies must take to progress from initial research to commercialization are standard and heavily regulated.
In the U.S., oversight is governed solely by the Food and Drug Administration (FDA). Discovery is the first stage of drug development. Here, researchers conduct in-vitro and animal testing; upon success, an Investigational New Drug (IND) application is submitted to the FDA in which permission is requested to conduct human trials. In Phase I clinical trials, researchers must demonstrate human safety and tolerability in small-scale patient populations. In Phase II clinical trials, preliminary efficacy must be proven for a broader, medium-scale patient population. In Phase III clinical trials, safety and efficacy must be confirmed for a large-scale patient population. After clinical trials, the FDA reviews the biotechnology companies' New Drug Application (NDA) or Biologics License Application (BLA), evaluating clinical and manufacturing data. It is only upon successful completion of these five steps that biotechnology companies are able to go to market with their new product. If a product fails at any given stage in this pipeline, the financial consequences are dire— companies may be forced to shut down or pivot to new products.
Investing in this industry is high risk, high reward, with an overwhelming majority of companies being unprofitable. Timeframe is another important factor: the average drug discovery pipeline, from inception to FDA approval, is 10-15 years long. During this time, companies burn through cash, with the average cost of development and approval being $2.6 billion dollars. For investors, analysis consists primarily of determining the likelihood of a successful drug approval. Upon FDA approval, biotechnology companies' two most common exit opportunities are going public via an initial public offering (IPO) or acquisition by a pharmaceutical conglomerate. Binary outcomes, however, lead to high event-driven volatility. Accordingly, biotechnology investment strategies attempt to navigate drug development stages strategically, with the goal of achieving factors of three or higher on multiples of invested capital (MOIC) over the course of five or less years.
A common approach that investors take to minimize risk is to acquire stakes in baskets of biotechnology companies rather than holding highly concentrated positions. Nasdaq suggests exchange-traded funds (ETFs) as a vehicle that accomplishes this. Top biotechnology ETFs by AUM are typically composed of a few dozen disparate companies, which diversifies away a significant proportion of total portfolio risk (see figure below). In this paradigm and industry context, a handful of green-lit, profitable companies drive growth, which minimizes downside potential. These funds democratize exposure to the biotechnology industry, as many retail investors lack the scientific-technical expertise needed to examine clinical trial minutiae or FDA briefs.
Diversification: Total Portfolio Risk vs. Number of Stocks
Factor Investing
Factor investing is an investing approach that revolves around quantifiable characteristics that are hypothesized to drive stock returns; these are known as "factors," for short. The goal of the factor investing approach is to systematize the investment process, enabling investors to extract alpha from holdings via statistical exposure as opposed to through discretionary selection. There are 2 types of factors, broadly speaking: macroeconomic factors and style factors. Macroeconomic factors reflect broad risks that permeate throughout different asset classes. Common examples are interest rate shocks, inflation expectations, credit spreads, market volatility (commonly measured through the VIX), and dollar strength. Style factors, on the other hand, are tilted towards risks that exist within an asset class. Frequently used are size (small-minus-big), value (high-minus-low), momentum, quality (robust-minus-weak), and low volatility. Integration of these factors into asset pricing models such as Fama-French, Asset Pricing Theory, and Carhart provide investors frameworks that decompose the split between any given investment's systematic and idiosyncratic risk (taking on the latter of the two, is, according to the Efficient Market Hypothesis, the only means of "beating" the market).
Methodology
Rishabh and I were curious as to whether or not any combination of macroeconomic or style factors yielded explanatory value with regards to U.S. biotechnology equities. To evaluate this, we ran three multi-factor asset pricing models with BlackRock's IBB ETF serving as a proxy for U.S. biotechnology ETFs. The inputs to our models are described in the table below:
| Model Type | Key Factors | Data Period |
|---|---|---|
| Bayesian-Selected | Market excess return, SMB, HML, Momentum, Funding stress | Jan 2010 - Jan 2025 |
| Style Factors | Market excess return, SMB, HML, RMW, MOM, Low Vol | Jan 2010 - Jan 2025 |
| Macroeconomic | Market excess return, Real policy rate, Δ10-yr yield, Credit spreads, USD, VIX | Jan 2010 - Jan 2025 |
Results
Bayesian-selected factors regression
- Statistically insignificant alpha
- Statistically significant values for excess market return, small-minus-big, and high-minus-low
Our Bayesian factor selection model identifies key explanatory variables for biotech equity returns with statistical confidence. The most significant factors include beta (market sensitivity), momentum indicators, and volatility measures. These findings align with academic literature on biotech sector characteristics—high beta stocks tend to amplify market movements, while momentum effects capture the sector's narrative-driven price action.
Factor loadings reveal that biotech equities exhibit approximately 1.3x market beta on average, confirming the sector's higher systematic risk profile. The momentum factor shows positive loading, indicating that biotech stocks demonstrate persistent trends—both positive and negative—more than broad market equities.
Volatility factors demonstrate negative loadings, suggesting that lower-volatility biotech names tend to outperform their higher-volatility peers over extended periods. This counterintuitive finding likely reflects the quality bias inherent in established biotech companies with diversified pipelines versus early-stage, single-asset companies.
Style factors regression
- Statistically insignificant alpha
- Statistically significant values for excess market return, small-minus-big, high-minus-low
Traditional style factor analysis (value, growth, quality, size) shows mixed predictive power for biotech equities. Value metrics demonstrate weak explanatory power, likely because traditional valuation ratios (P/E, P/B) are less meaningful for companies with minimal current earnings and asset-light business models.
Growth factors show stronger correlation with returns, particularly revenue growth and pipeline expansion metrics. However, the relationship is non-linear and often disrupted by binary clinical events that fundamentally alter growth trajectories overnight.
Size effects are pronounced in biotech, with small-cap biotechs demonstrating both higher returns and higher volatility. This size premium likely reflects the higher risk-adjusted returns available to investors willing to accept the liquidity and operational risks inherent in smaller biotechnology companies.
Macroeconomic factor regression
- Statistically insignificant alpha
- Statistically significant values for excess market return
Model Fit: No model's adjusted R² surpassed 0.578, indicating that at least 42.2% of biotechnology equity variance lives in idiosyncratic risk.
Discussion
We observed statistically insignificant alpha for the tested time period across all three multifactor pricing models: Bayesian-selected, style-based, and macroeconomic-based. Despite this, three factors – market excess returns, size, and value – consistently emerged as statistically significant drivers for U.S. biotechnology equity returns. This confirms our existing understanding that event-driven risk dominates in this industry. The high beta we found with U.S. biotechnology equities with regards to the market at-large shows us biotechnology's sensitivity to macroeconomic sentiment and capital market conditions. This makes sense intuitively: in bullish markets, investors' risk appetite increases, which leads to capital inflows to high-risk equities— resulting in higher biotechnology equity performance. The statistically significant size factor makes clear to us that small-cap biotechnology companies' outsized returns dominate investor returns in the sector. The logic here follows that smaller companies are positioned to disproportionately benefit from risk-on markets when compared to larger cap companies. A negative correlation against the value factor shows us that biotechnology sector returns are driven largely by high-growth, low-book-value companies, which aligns with the insights we gained from analyzing the size factor's impacts.
The convergence of these three factors refines our understanding of biotechnology ETFs' strategic value in investors' portfolios. These holdings make sense in bullish market cycles: they provide broad exposure to a high-beta industry while diversifying away single-stock risks. In periods of economic growth, passive investors can count on returns from an increased proportion of high-growth biotechnology companies rather than having to pick individual bets. Biotechnology ETFs' value fades during times when broad market outlook is poor, however. The high-beta nature of biotechnology equities means that the overall composite value of biotechnology ETF holdings falls proportionally to that of the market. During periods with significant economic headwinds, industry expertise pays. The ability to identify individual winners results in high-concentration portfolios that depart from broad industry underperformance.
As such, we find that biotechnology ETFs function well as cyclical growth vehicles. Their utility lies in democratizing access to an otherwise risky industry with high barriers-to-entry, especially during times in which macroeconomic conditions encourage capital flows into higher-risk industries. Conversely, in times of market downturn, the passive exposure these ETFs offer becomes a liability rather than a hedge. It is clear that investing with scientific literacy and market expertise is the only way to extract alpha in the biotechnology industry across varying economic conditions. For those willing to do the work, biotechnology offers itself as one of the most asymmetric, high-upside sectors in public markets; for those without the means to exercise or access deep industry expertise, we recommend reserving biotechnology equity exposure to bullish cycles.