Relative Price & Value of pre-Phase III Pipelines for the 23 Largest Drug & Biotech Companies – Updated View

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Richard Evans / Scott Hinds / Ryan Baum


203.901.1631 /.1632 / .1627 richard@ / hinds@ /


November 20, 2014

Relative Price & Value of pre-Phase III Pipelines for the 23 Largest Drug & Biotech Companies – Updated View

  • We use patent data to estimate the amount and quality of innovation in companies’ pre-phase III (aka ‘hidden’ pipelines); we then determine whether companies’ share prices accurately reflect what’s in these hidden pipelines. Since inception (November 2012), companies that screen as >= 20% undervalued have outperformed their peers by 1.4x (cap wtd) to 1.6x (equally wtd)
  • Because of large misvaluations in hidden pipelines, shares of VRTX, BMY, SNY, and GSK all appear at least 20 percent undervalued. Conversely shares of ALXN, BIIB, CELG, GILD, NVO, REGN, and SHPG all appear at least 20 percent overvalued
  • For more information on our pipeline valuation methods, and for related R&D productivity metrics covering the 23 largest publicly-traded companies (by R&D spending) please visit

Where we’re BULLISH (changes highlighted): Biopharma companies with undervalued pipelines (e.g. VRTX, BMY, SNY, GSK); Biopharma companies with pending major product approvals (e.g. TSRO, ALKS, HLUY, EBS, BMY, BVRX, CBST); ABBV and ENTA on sales prospects in Hep C; CFN, BCR, CNMD and TFX on rising hospital patient volumes; XRAY and PDCO on rising dental patient volumes and rising average dollar values of dental products and services consumed per visit; CNC, MOH and WCG on bullish prospects for Medicaid HMOs; and, DVA and FMS for the likely gross margin effects of generic forms of Epogen

Where we’re BEARISH: Biopharma companies with overvalued pipelines (e.g. GILD, ALXN, SHPG, REGN, CELG, NVO, BIIB); PBMs facing loss of generic dispensing margin as the AWP pricing benchmark is replaced (e.g. ESRX, CTRX); Drug Retail as dispensing margins are pressured by narrowing retail networks and replacement of AWP (e.g. WAG, CVS, RAD); and, suppliers of capital equipment to hospitals on the likelihood hospitals over-invested in capital equipment before the roll-out of the Affordable Care Act (e.g. ISRG, EKTAY, HAE, VOLC)

Premise and rationale

Share prices of drug and biotech stocks arguably can be broken down into two major components: the capital markets’ estimates of the value of assets that are well characterized (i.e. on-market, filed, and late-stage developmental projects), plus the markets’ estimates of the value of assets about which relatively little is known (i.e. pre-phase III projects). For simplicity, we refer to early, pre-phase III developmental projects collectively as the ‘hidden pipeline’

To estimate the values of hidden pipelines as a percentage of share prices, we subtract from each company’s enterprise value an estimate of the present value of everything else – marketed products, products filed for regulatory approval, products in phase III development, and all non-pharma lines of business. On average, hidden pipelines account for about 35 percent of share prices, and the percentage of share price explained by hidden pipelines ranges from a low of 8, to a high of 72 percent (Exhibit 1)

In theory, if the capital markets had equal amounts and qualities of information on these companies’ hidden and non-hidden operations, we would expect that knowing what’s in the hidden pipeline would take us roughly one-third of the way to explaining relative share price movements among these companies over time. However because the markets have substantially more information regarding non-hidden operations than they have regarding hidden pipelines, the valuations of hidden pipelines are much rougher guesses. By extension, this implies that hidden pipeline valuations are far more volatile than the valuations of non-hidden operations, and by further extension that hidden pipelines explain substantially more than one-third of relative performance among these stocks

Price versus Value …

Having solved for the market capitalization of a company’s hidden pipeline, the question becomes whether or not the assigned market value closely reflects the hidden pipeline’s true economic value. To estimate pipelines’ true (relative) economic values, we rely heavily on patent data which, unlike traditional channels of disclosure, offer a reasonably consistent and complete[1] picture of companies’ hidden pipelines

After assigning all active[2] patents to their corresponding parent companies, we take three key additional steps: 1) each patent is quality weighted[3]; 2) patents are segregated into those that correspond to either known (e.g. marketed, filed, or late stage projects) or hidden (pre-phase III) products / projects; and 3) each ‘hidden’ patent is assigned to disease, mechanism, and/or physical (chemical / biochemical characterization) categories[4]. The result is a company-by-company database that characterizes the sizes (numbers of quality-adjusted patents) and relative values (quality-adjusted patents, weighted further by the relative economic values of the disease areas in which the company conducts research) of the analyzed companies’ hidden pipelines

To determine which companies’ pipelines appear relatively over- or undervalued, we simply compare the market capitalization (price) of each company’s hidden pipeline to the apparent (relative) economic value of that company’s hidden pipeline

Exhibits 2 and 3 summarize the results. In Exhibit 2, column (b) provides our estimate of the market capitalization of each company’s hidden pipeline[5], and for reference column (c) shows hidden pipeline capitalization as a percent of total enterprise value. Column (d) gives each company’s hidden pipeline value as a percent of the total hidden pipeline value for all companies. Using BMY as an example, BMY’s $40B hidden pipeline capitalization is about 4 percent of the $993B combined capitalization for all 23 companies’ hidden pipelines. In column (e) we express the quality- and sales-weighted amount of innovation in each company’s hidden pipeline, as a percent of the total quality- and sales-weighted innovation in all 23 companies’ hidden pipelines (BMY has 11 percent of total innovation across the 23 companies, but only 4 percent of the market value). Column (g) is the ratio of columns (e) and (d); i.e. column (g) is the share of peer group innovation in a given company’s hidden pipeline, divided by that company’s share of total peer group hidden pipeline market value. Companies with larger shares of innovation than of market value (e.g. BMY) have hidden pipelines that are apparently undervalued, and vice versa[6]. Column (f) sales weights the shares of hidden pipelines depicted in column (e); and column (h) calculates the ratio of columns (f) and (d); i.e. the share of peer group sales weighted innovation in a given company’s hidden pipeline, divided by that company’s share of total peer group hidden pipeline market value. Exhibit 3 depicts the data in Exhibit 2 graphically, comparing each company’s share of the peer group’s quality-adjusted hidden pipeline (y-axis), to its share of the total peer group’s hidden pipeline capitalization (x-axis). Companies that depart significantly from the 45 degree ‘normal’ have hidden pipelines that are apparently under- (above the normal line) or overvalued (below the normal line)

BAYER, BMY, GSK, SNY, and VRTX have hidden pipelines that appear undervalued[7]; ALXN, BIIB, CELG, GILD, NVO, REGN, and SHPG have hidden pipelines that appear overvalued[8]

Implied performance, and actual performance to date

Implied performance is simply the percentage change in relative share price that would bring a given company’s hidden pipeline valuation to par. Fundamentally, the idea is that a given hidden pipeline, adjusted for the sales potential reflected in its therapeutic area mix and the quality-adjusted volume of total innovation, should have the same or nearly the same economic value[9] as an ‘average’ pipeline with the same therapeutic area mix, and quality-adjusted volume of innovation

Results are provided in Exhibit 4, both with and without sales weighting of hidden pipeline values. Again using BMY as an example, based only on the amount and quality of innovation in BMY’s hidden pipeline and assuming only that this amount and quality of innovation ultimately is valued at par with other hidden pipelines of comparable amounts and quality of innovation, we would expect BMY to outperform its peers by roughly 73 percent. If in addition, we adjust our estimate of the value of BMY’s hidden pipeline to account for the mix of therapeutic areas BMY is pursuing, we would expect BMY to outperform its peers by roughly 75 percent. Conversely if PFE’s hidden pipeline were valued at par, we would expect PFE to underperform by 6 percent (ignoring therapeutic area mix) or, adjusting for the therapeutic area mix of PFE’s pipeline we would expect underperformance v. peers of roughly 8 percent. We make no assertion that our valuation method identifies single-digit percentage mis-valuations. Instead, we simply argue that companies with apparently undervalued pipelines are more likely to outperform peers, and vice versa

Exhibit 5 provides the actual relative performance of a drug / biotech portfolio whose stock selection is based entirely on hidden pipeline valuation (all names with ≥ 20 percent implied share price gain are held long). Performance figures are provided for the entire period since we first published the method (Nov 2012) and for each interval between updates. Since inception, returns to stocks chosen based on hidden pipeline valuations are roughly 1.5x peer group[10] returns (1.6x equal-weighted; 1.4x cap weighted). The equal-weighted portfolio has beaten the eligible stocks not held during every interval between updates since initiation

Changes in pipeline values since our last update (August 2014)

Exhibit 6 tracks the changes in each company’s hidden pipeline since August 2014; and breaks those changes down into their component parts. VRTX, GILD, ABBV, JNJ, and NVO had the largest increases in hidden pipeline apparent values; AZN, LLY, MRK, and PFE had the largest decreases

We break the total hidden pipeline change into three critical drivers—positively, (1) quality improvements in the existing hidden pipeline; and (2) new additions to the hidden pipeline, whether through patent grants, purchases, new assignments or a first citation on an existing patent; and, negatively, (3) patent expirations, sales, reassignments, etc.[11]

VRTX, ABBV, NVO, and REGN led the peer group in quality improvement over the past three months. GILD, JNJ, and LLY had the most new grant / assignment activity. Conversely, BAYER, LLY, MRK, and PFE hidden pipelines lost more ground than any other company’s due to expiration, attrition and reassignment. Due to their similarly poor performance in terms of quality improvement, and middling performance on new grants/assignments, PFE and MRK saw the peer group’s largest declines hidden pipelines over the past three months

  1. The companies do not follow consistent patterns of pre-phase III pipeline disclosure through traditional information channels (e.g. SEC disclosures, investor meetings, press); some companies provide much more detail than others, and the amount of detail provided by any given company can change over time. In few if any cases do companies disclose the percentage of total pre-phase III activity that is represented by projects on which detail is given. In the case of patents, we believe the companies’ pre-phase III (but not phase III and after) behaviors are both consistent and thorough – meaning the companies all behave similarly; and also meaning that all companies disclose (i.e. patent) the great majority of pre-phase III projects that show any significant level of commercial promise
  2. We work with US patents only. At least since the Patent Cooperation Treaty (PERCENT), patent filings for larger, multi-national research-based organizations have become global – thus we believe adding ex-US patents to our datasets would add little or no useful signal
  3. Our method of quality weighting relies on several variables, including in particular citation patterns (e.g. number of citations and rate of citation accumulation) and patent ‘vintage’. Raw (unweighted) patent counts have very limited relevance
  4. Specifically, we categorize patents according to the World Health Organization’s ATC (Anatomic, Therapeutic, Chemical) classification scheme
  5. Essentially enterprise value less the present value of all products / projects / lines of business other than projects in phase II and earlier development
  6. We recognize there are two limits of our method which emerge when analyzing smaller companies. First, the most fundamental premise of the ‘hidden pipeline’ is that it is in fact hidden. In the case of very large companies, the assumption that pre-phase III pipelines are in fact hidden is robust; however the quality of this assumption deteriorates as companies become smaller. Smaller companies will have fewer pre-phase III projects, making it more practically feasible for investors to identify and assess these – making them more comparable to the assets we define as ‘tangible’. Second, our method relies on an assumption of central tendency – i.e. that a large portfolio of projects in a given set of therapeutic areas and with a given overall quality-adjusted patent count will have a similar economic value to a comparable large portfolio of the same therapeutic area mix and overall quality-adjusted count. This assumption is robust with large portfolios – especially since the larger companies have such tremendous overlap in terms of diseases (and disease mechanisms) pursued. However the assumption is weaker with small portfolios. Smaller companies A and B may be in similar therapeutic areas with similar quality-adjusted patent counts, but could be working on highly distinct approaches
  7. Based on arbitrarily defining overvalued as having a ratio of innovation : value ≥2
  8. Based on arbitrarily defining undervalued as having a ratio of innovation: value ≤ 0.50
  9. In reality we’re very aware that ‘hidden pipelines’ with similar therapeutic area mix and quality-adjusted ‘weight’ of total innovation will have different economic values. However, we argue that when these pipelines are ‘hidden’ that the market has no factual basis (other than quality-adjusted patents and therapeutic area sales weighting – which we don’t believe the market uses) on which to assign the pipelines different values – and that the market’s assignment of different economic values reflects nothing other than the market’s inability to efficiently value intangibles. Thus over time, the most reasonable guess as to the relative values of two pipelines of identical therapeutic area mix and quality-adjusted volume of innovation is that the two pipelines have identical values
  10. We define the peer group as all of the 23 companies in the screen that are not held long
  11. The fourth, “other” category captures any uncategorized mathematical remainder due largely to algorithmic calculations.
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