Probably forever, roughly every week, Barron's has an article about a few big cap stocks that they say are pretty good bargains. What's different about the articles over the past year or two though is that they seem really compelling. That's true even now, after a big market rally.

This week's article is about drug stocks. Typical of the stocks they mention is Abbott, listed with a p/e of 10 on 2011 earnings and a 3.9% yield. All of them–Bristol Myers, Lily, Medtronic, Merck, and Pfizer–have similar numbers, yields higher than the 10-year treasury and P/Es around 10 give or take. They also list some European firms, AstraZeneca, GlaxoSmithKline, Novartis, Roche, and Sanofi-Aventis, that look even cheaper. E.g. AstraZeneca is at a P/E of 7.3 and yields 5.2%.

The point of the article is that some of the firms could help shareholders if they would do some restructurings, spin-offs, break-ups, but what struck me instead is that they are look surprisingly cheap as they are. Seems to me that a lot would have to go wrong for these to do poorly compared to bonds over the next 10 years.

Vince Fulco writes: 

That has been David Einhorn's contention for some time at least on PFE. I.E. the bad stuff is already well known.

Bill Humbert writes:

I suspect this situation of high dividends will continue for some time, but the causes are not being dealt with. The system, by which I mean the internal processes used in drug discovery, is broken.

All that is being done is shuffling managers in and out. Each old set of managers floats off on their golden parachutes. The new managers talk and talk but do not make real changes to return the system back to the productive way research used to be done. The industry will slowly decline, have more M&A, and golden parachutes, until eventually the internal research organizations are disbanded.

PFE is already chopping internal research hard. The big pharmas are turning into development and marketing organizations and will shed research completely. Once they all do that, it will be fascinating to see where they will get molecules to develop.

The biotechs are hurting bad. More than a few went under, and many of the remaining ones have had their research organizations corrupted by the amazingly stupid management practices of big pharma. Lots of big pharma people went to the biotechs and wrecked them, too.

Check this out. Some data on the drug industry:

Figure A: # new drugs by year

NME = new molecular entity (new drug, although its structure could be closely related to that of an existing drug, i.e., a me-too drug)

The industry is about half as productive as it was 10-15 years ago.

Figure B:

Pfizer R&D spend

"You can see that Pfizer's R&D spending has nearly tripled since the year 2000, but that cumulative NME line doesn't seem to be bending much. And, as Munos points out, two (and now three) productive research organizations have been taken out along the way to produce these results. It is not, as they say, a pretty picture."

Alston Mabry writes: 

As long as it's the weekend and we're kicking around stock ideas…consider TEVA: They will get huge new opportunities from the blockbuster drugs coming off patent, and they've been growing revs and earnings like crazy. They play well to the "rising cost of healthcare" theme, and they are global. You're buying growth, though, not dividend.

Dan Grossman writes: 

1. The Barron's article makes no sense. If a company is about to lose half its earnings because the patent on its most profitable drug is about to expire, how does it help to sell off products or a division where earnings are not expiring?

2. Teva is in much the same position as Big Pharma. While known as a seller of generics, more than 30% of its earnings come from its non-generic multiple sclerosis drug Copaxone, which will soon face generic competition itself resulting in disappearance of these profits. Only Teva has been a lot less honest about this than Big Pharma.

John Tierney writes:

….The problem is that they have failed to deliver any important new and important blockbuster drugs for years.

Right on the money. Some blame, though, must be placed on the FDA. This story from the NYT elaborates:

Medical device industry executives and investors are complaining vociferously these days that the industry's competitive edge in the United States and overseas is being jeopardized by a heightened regulatory scrutiny.

The F.D.A., they and others say, appears to be reacting to criticism that its approvals for some products had been lax, leading to a spate of recalls of some unsafe medical devices, like implanted defibrillators and hip replacements.

Device companies have been seeking early approval in Europe for years because it is easier. In Europe, a device must be shown to be safe, while in the United States it must also be shown to be effective in treating a disease or condition. And European approvals are handled by third parties, not a powerful central agency like the F.D.A.

This article follows another that the Times published (which I can't find at the moment) last week revealing that the two drugs most commonly used for surgical anesthesia are both made only in Switzerland. The drugs are no longer being made available since Arizona, running short of the primary drug, bought some from an independent supplier, and subsequently used it in an execution– a big EU no-no. As a result, Novartis, with no control over their customer's distribution, is refusing to sell any more in the states.

The article concludes by noting that venture capital spending on the medical device industry in the US dropped 37%. Yet billions and billions are sitting on the sidelines ready to pounce on the next techno-dweeb with a social networking idea. 

John Tierney adds: 

The study, covering 2004 through 2010, found the overall success rate for drugs moving from early stage Phase I clinical trials to FDA approval is about one in 10, down from one in five to one in six seen in reports involving earlier year.

Roger Longman comments: 

Guess I sort of agree.

But issue is that while downside isn't huge, the likelihood of some price decline is possible while near-term upside unattractive since tied so closely to successful product launches. BI is only company with really great recent news (launch of Pradaxa, which will likely be a blockbuster) — but BI is private. Bayer/J&J got great news on recent competitor drug — but launch some time away and by then BI will have sewed up most of the new prescribers. Novo could do well, given extremely successful launch of Victoza — but success probably priced into the stock. NVS has Gilenya (innovative small-molecule MS drug) but reports are that it's had a troubled launch because hadn't solved the neurologists' problems with cardiac monitoring when starting the therapy.

He's right that people could buy them for the dividends but I'd wonder if the potential downsides in the stocks might not negate the effects. Stuff can and will go wrong. Merck, for example, has lost a significant chunk of the future value of SGP acquisition thanks to poor launches of Bridion and Saphris, disadvantages of boceprevir vs. Vertex's telaprevir, and — the cause of its most recent stock problem — failure of vorapaxor (most important drug in SGP pipeline).



Errors in statistics are usefully classified as type 1 and type 2. A type 1 is a false positive or undue credulity and a type 2 error is a false negative or false skepticism. The greater you try to reduce the level of error in one the greater the likelihood of error in the other.

                                          Don't reject             reject

no effect hypothesis true       correct                type 1 error              

no effect hypothesis false      type 2 error          correct

A useful way of considering the decision making is above. Consider for example the no effect hypothesis that a pill is not healthy. if it's not healthy and you say it's healthy you make a type 1. If it's healthy and you don't say it is healthy you make a type 2.

A certain agency that regulates drugs is famous for only considering the type 1 errors, making sure with endless and ruinous double blinds that type 1 errors are minimized to the excessive making of type 2 errors and keeping off magic bullets that would extend life span and health enormously.

There are many areas where these trade-offs between errors occur. For example in spam filters. You can reject good things, that's type 1. You can accept bad things– that's type 2.

Our own field often has trade-offs like this. The hypothesis that a system or set point for a trade is random is a good null hypothesis. If you accept the system, you're just incurring churning for a worthless randomness. If you don't accept the system, and it's good, why then you've lost some good money.

The decision to expand your business or trading is another area that crops up frequently. If you expand it you might get in over the head. If you dont expand it, you might miss the gold. The movement into a new field, or the engagement of an employee or employer is another frequent trade off of type 1 and type 2, gullible reaching versus excessive caution that frequently arises.

The usual way to trade off between the two types of errors is to consider the cost of both errors, and to balance your decisions based on the relative costs. Considerations relative to randomness, and variability must also be considered. Also, the myriad psychological biases that lead us to place too much reliance on avoiding the two types of errors that the cognitives have contrived with their silly experiments on college students et al.

What other trade-offs of type 1 versus type 2 do you see that mite be of use to market people or others and what better way to consider gullibility versus skepticism do you see?

Alan Millhone writes:

This weekend I will travel to Grove City, Pa for a yearly Checker Tournament. While playing I will have choices to make. Sometimes there's only one way to move. Often times there's more than one way to move or jump. Checker players and Market players need to evaluate all moves or trades before executing. In Checkers if you touch a piece you have to move that piece– often with disastrous results. If you trade on line you need to consider your trade carefully before hitting "send". Tom said, "Move in haste— repent in leisure". 

Victor Niederhoffer adds:

Sharif KhanThe trade-off in errors in games like checkers and chess would be someone offers you a seeming advantage. Your null hypothesis is that it's not worth accepting. If you take the gambit or seeming opportunity when it's really no good you're making a type 1 error.

In checkers I've found that no opportunity that looks good, no opportunity to set a trap for example, is worthwhile against a good player, as good players never make mistakes. You were too gullible. If you don't take the opportunity when it would have been good, you're making a type 2 error. You were too skeptical. I find that in checkers the type 1 errors are much more costly than the type 2 errors, but in chess I don't know enough to say. But among the good players, I think they often are too cautious or too skeptical if they wish to win a world championships. They are too likely to go for the draws. In general, I would say if you want to be the best you have to be ready to make the type 2 errors to a greater extent. But then you always risk going belly up.

The situations are not without personal applicability to myself. It's easier in squash. I played an errorless game. Never made a type 1 error of going for broke with very risky shots. Well, it wasn't that bad. i went for about 5 years without losing a game in a match or so. But it wasn't good enough to beat the infernal Sharif Khan as much as I should. I should have played a much more errorful game, being willing to accept the risky shots and confrontations and hitting it on the rise and changing my infernal errorless slice backhand to a top spin so I could belt the ball through the Khans the way the Cubans who played Jai Lai could. In other words, I didn't make as many type 2 errors as I should have. 

Anatoly Veltman comments:

I remember grandpa coaching me at 5 or so: "always believe a man." If it turns out to be a lie, you'll find ways to extricate. But if you distrusted without good reason to begin with, you risk losing a friend– and that's an ultimate loss.

Jim Sogi adds:

Why do smart people make either type 1 or 2 mistakes? Presumably, and by definition, it is not because of stupidity, so some heuristic must be at play. In type 1, the fear and result is that you look and feel stupid. In type 2, there is less risk of looking and feeling stupid, but you end up being frustrated by the loss of opportunity. The joke around here is "which is worse" –you present a bad and a ridiculously bad alternative. Weighing the cost benefit is faulty because of proven heuristics are lopsided towards avoidance rather than gain. Add in marginal utility considerations and the difficulty is even harder. 

Bill Egan comments:

I will add a twist to the type 1 error problem. I have seen certain extremely intelligent people simply be unable to conceive they might be wrong. This is a problem that gradually gets worse. They have been right 99% of the time because they are so smart, and as time passes, they make bigger and bigger bets because, 'hey, I've been right.' This isn't quite hubris or arrogance because they really are that good. Finally the odds catch up with them.

Roger Longman writes:

Roger LongmanVic,

Love this.

So seems to me the real challenge is figuring the cost of a type 1 or 2 decision.

In the case of the FDA, the costs of a type 2 error are dramatically higher than the cost of a type 1. Those costs aren't financial, or not primarily financial. There's the substantial humiliation cost, for example, of approving a drug that turns out to have some important side-effect (or a side-effect more important to a group of influential people than its benefit to a group of less influential people) and being dragged in front of a congressional committee (Charles Grassley of Iowa has been the grandmaster of this, but he's got plenty of competition). There's the bureaucratic cost of being passed over for a more visible job, or an interesting review opportunity, if your name is associated with a controversial decision.

All of which is to say: of course the FDA errs more on type 2's. And the growing pharmacopeia (much of which is generic and therefore low- cost) only encourages this bias towards type 2 errors, particularly in regard to follow-on drugs (i.e., new molecular entities in the same class as approved drugs). If — the FDA figures, albeit not publicly
– by instinct, as it were — there is already a good drug that helps a majority of people why take the chance that a second drug in the same class will provide more incremental benefit than incremental risk, which — as I note above — comes with disproportionate institutional costs?

There's also an inherent problem with drug development divorced from serious comparative effectiveness (the current system of purchasing drugs, based as much on rebates retained by payers as medical and economic value provided to patients and employers, actually discourages the kind of comparisons common in most sectors of the consumer economy).

Victor Niederhoffer comments:

Longman was editor of Windhover Information Ventures Biomed and is very knowledgeable about the FDA. I wish he had been at Prof Tabarrok 's talk at junta where the positive case for the FDA going out of business was limned with statistics and current studies on deaths caused by lack of approval.



We have often talked about the signaling effect of hubristic utterances like “We are the best”. “We are first in class, best in class, and I believe we will own this class for as long as it exists” said John La Mattina, Pfizer’s senior vice president for global research, in a November 30th, meeting with analysts. In retrospect, Mr. La Mattina must have known about the problems brewing with Torcetrapib, and this must have been troubling.One wonders about the statistical significance of 82 deaths out of 7,500 for the combined regime of Lipitor and Torcetrapib, versus 51 deaths among 7,500 people for the Lipitor regime alone. This is a statistic that should really be computed by re-sampling. One would take a 1.1% probability and a 0.7% probability and run 7,500 trials for each, noting the difference in proportions random numbers. Then repeat 10,000 times to come up with the probability of observing a difference of 31 in 2 groups of size 7,500. Not having the artful simulator around, one uses the formula below.

The standard error of difference between proportions is the square root of: p1 q1 / n1 + p2q2/n2, where p1 is close to 0.01 and q1 =0.99. Thus, the standard error is the square root of (1/100x 7500 + 99/100 x 7500), or 1/86, so the difference is some 22 standard errors away from expectations. This is a very big number, and re-sampling would not change it that much … but this is also an artifact of small standard errors for small proportions.

Despite this, this kind of exercise shows what is wrong with decision making based on the chances that something can do harm rather than based on the expected value of costs and benefits. One has seen many lines of evidence that these drugs have much life enhancing value. And is a difference of 31 deaths per 15,000 — let us say 150 years of total life expectancy — really comparable to the benefits that such a drug might have vis a vis reduced heart attacks, and enhanced life expectancy. Probably not even close.

Multiply this by the hundreds of drugs not approved, the thousands that do not get tested at all because they have risks, and the other tens of thousands that do not get invented because only the billion dollar companies can afford tests like this at all (consider the approximately 1 billion dollar cost to test a drug like this), and you see the incredible loss to life.

It would be like not using a system because it loses big 30 times out of 15,000 without taking into account how many times it makes big.

Roger Longman adds:

You heard, I suppose, that they have now completely abandoned Torcetrapib? No one knows the data, but the independent monitoring board killed it… apparently independent of the hypertension issue.

In any event, I completely concur about the predictive use of hubristic utterances, as we’ve seen with Rumsfeld, Cheney, Bush & Co. And it is particularly ridiculous with drugs.

Dan Grossman comments:

While I want to think further about the implications, my initial reaction centers on the massive misallocation to one fairly marginal drug category (anti-cholesterol) caused by the combination of dinosaur drug companies like Pfizer and overly expensive, overly time-consuming, mandated statistical testing and approval procedures, (This appears a fairly equal alliance and I am not blaming only the government.)

Anti-cholesterol (anti-lipids) is one of the only, actually the only, drug regimen where the favorable circumstance of government and medical recommendation, easy and understandable measurement through an annual blood test, and effective drugs and marketing, has been able to convince tens of millions of healthy consumers to take a drug every day at an annual cost of some $25 billion.

But this category has now succeeded. Cholesterol levels have fallen and, perhaps partially as a result although it is far from clear, so have heart attacks. And now there are generics (and Pfizer’s $12 billion Lipitor will in a couple of years also be a generic) that can bring about the same result for pennies a day. So the therapeutic problem is now taken care of at low cost, the $25 billion category can be cut to a couple of billion, with great cost saving and benefit to the public.

But Pfizer has not read Schumpeter, and with its $12 billion Lipitor grandly insists on “owning” this category “as long as it exists”, the way Proctor & Gamble or somebody might “own” the multi-billion dollar toothpaste category. It cannot give it up, even though there are probably only marginal advances in cholesterol lowering to be made in return for increasing billions of dollars chasing hoped-for slight advances, and any molecule with a slightly different mode of action that can desperately be combined with Lipitor and thus perhaps extend Lipitor’s patent life.

So Pfizer, and Merck, and Astra-Zeneca, and now Abbott with its multibillion dollar acquisition of KOS, will now spend a hundred billions dollars (literally, look at their research budgets) on this drug category that has already succeeded, instead of in a truly free, Adam Smith market where the natural incentives would be to spend the money in drug categories far more needed for the cure of disease and the advancement of human health.

John Tierney mentions:

What I would also like to know is the expected mortality rate for a study of this kind, a study involving subjects and drugs of these kinds. Clearly the Lipitor-only mortality rate of 0.68% must be well-below the threshold, otherwise there would be calls for it to come off the market as well. But there are none. And apparently the combination mortality rate of 1.09% is well over the expected mortality rate, and hence the rush to end the trials. Casino operations, I am told, make or lose money based on small differences in the percentage that the house is favored in games of chance. Pharma shouldn’t be reduced to such a state. For an interesting article on this …


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