What do I mean by “objective criteria,” and what’s the big deal about objectivity anyway? There’s so much hype generated on how to pick stocks, and so much that is not relevant that passes for what is.  This is where ignorance (of irrelevant information) is bliss. Almost every book or article on the subject repeats the same ideas: “Pick good companies with good products and good earnings and hold them through thick and thin and you’ll get rich.”  But everyone thinks they know and that they’re objective. If it really worked that way, everyone would be rich!  The only thing that varies much in this recipe for disaster is the name of the company and the details about the product.  There are several false assumptions here. Here are just a few of them:

1.      “Good” company means you can buy without regard to the price or valuation of the stock.

2.      “Good” earnings reliably predict higher stock prices.

3.      “Good” is knowable. Familiarity with a product means its stock will be profitable.

Taking these assumptions one at a time:

Fallacy #1, “Good” company means you can buy without regard to the price of the stock. The idea that the stock of a “good” company will make you money appears so obviously true, that it’s hard to imagine even challenging such a concept. Well, here goes.  As I point out elsewhere in these notes, almost all of the “good” companies that have ever existed no longer exist, and their stocks at best make great souvenirs. You would have lost almost everything if you had bought the “Nifty-Fifty” stocks in the late sixties—stocks that analysts loudly trumpeted as the ones you should buy and hold for life. Or, imagine it’s 2000 again, and you’ve just read that Warren Buffett is a major holder of Coke (KO), a “good” company.  So you buy it, and sleep like a baby.  Coming forward to the recent 2002 lows, you were down 50% from the high, and so was he. If you owned the whole company, that would come to about $100 billion.  Wait a minute, you might insist, Buffett started buying in 1988, long before those highs.  This brings me to the fallacy in the idea that a “good” company means you should buy without regard to the price of the stock. “Buying good companies at the wrong price is the equivalent of buying bad companies.”(1) And since prices fluctuate over time, “wrong price” is another way of saying “wrong time.”

Fallacy # 2: “Good” earnings predict higher stock prices. Earnings are far and away the most hyped piece of information about stocks. For example, in a recent issue of IBD (Investor’s Business Daily), the statement is made that “Profits have been the best predictor of stock performance for decades.”(2) Surely, earnings are “objective” and relevant! Unfortunately, they’re not as reliable an indicator of future prices as one might imagine.

The assumption is that if a company has good earnings, you’re going to make money—either in the form of dividends, or by having your stock appreciate. Well, if the company does pay dividends, obviously it first has to have earnings.  So, if all stocks were divided into two groups, those with earnings, and those without, and I could only make my selections from one group or the other, I’d begrudgingly have to pick stocks with earnings.  That being said, companies that pay out dividends have a long history of appreciating significantly less than other companies, and are still subject to all of the other negative forces in the market that can drive stocks lower from time to time.  You can still end up with a net loss if you buy a company with good earnings and good dividends! During the period 1953-1996, stocks with the highest earnings gains over the past year under performed the market in more years than not.  That being said, a select group of stocks with the lowest Price/Earnings ratios—meaning the price was low relative to the earnings–outperformed the market on average by 2.96% per year over the same period.  Even then, this same group of stocks under performed in 4 out of 10 years, so this was close to a coin flip in terms of reliability.(3)

Stocks do not rise and fall in synch with earnings—nor do they necessarily do so even with some sort of lag time. If earnings double, it does not go without saying that the stock will double, or ever go up at all. In the first year of each of the 1921-1929 and 1932-1937 bull markets, Dow companies (the companies, not the stocks) lost money—the only two years that they have. But the market surged in the first year of each of those two bull markets, in spite of the red ink. Dow earnings were fully 25% higher in 1929 than 1928, yet the market crashed. During the 50’s, earnings remained essentially unchanged, but prices rose. In the 70’s, earnings increased, as stocks dropped.   In the 73-74 bear market, the worst then in 40 years, earnings had their best gains in over a decade.  The year 1975 was an earnings disaster, but a bonanza for stocks. Consider the period 1964 to 1997. Dow stocks’ earnings fell in 17 of those years, and yet in 12 of the 17, the Dow rose (4).  At the bottom of bear markets, investors have only been willing to pay about $7 for every dollar of earnings (which equates to a Price/Earnings ratio of 7). At market tops, the PE has topped 20, and in the 90’s bull market, rose well into the 30’s.  The average PE of the biggest winners reached 45 before they went on to their ultimate highs, and their average then hit 85.  Bull markets have historically ended when the PE has gone over 20, and most bear markets have only ended when the PE has dropped to 10 or below.  In spite of IBD’s authoritative sounding statement about the predictive significance of earnings, the evidence is that, although earnings may not be completely irrelevant, they are not a good predictor of the future price of a stock.

As to how trustworthy those “objective” earnings numbers are, long before Enron, there have been occasional scandals involving falsification of earnings. And without quite committing a felony, companies routinely find legal creative accounting techniques to inflate earnings and conceal losses. In the 3/25/02 issue of IBD, page 1: “Through the nine months of 2001, the companies in the NASDAQ 100 told the public they earned $19.1 billion.  But they told the SEC they lost $82.3 billion.”  In the end, investors think they’re being objective when they base their trading on earnings, because these are actual numbers. How much more objective can we get?  Well, if the numbers are 1) correct, and 2) relevant, I’d agree.  But as often as not, they are neither.

Fallacy # 3, “Good” is knowable. The most important false assumption is that it’s possible to recognize a “good” company when you meet one. This is the belief that mere mortals can analyze all of the information on a company, its products, its competitors, all of the factors—past, present and future, national, international, technological, legal, etc– that may have a profound effect on its business, and from this draw a valid conclusion about the future prospects for that company’s stock. Knowing and “understanding” what Coke tastes like won’t tell you anything worth knowing about the future price of the stock. The quality of a coke surely didn’t drop by 50% in 2000-2002, but the stock did. So a quality product is probably better than no product at all, but will tell you little about whether the stock is a buy.

Yes, mere mortals can fill their heads with a vast amount of information about a company.  But regarding reaching conclusions about how this will affect the future price of the company’s stock, the evidence is shockingly conclusive: the vast army of professional analysts can’t.  Maybe an ultra rare exception like Warren Buffett can sometimes do this; then again, maybe not.  He held on while giving up half of his profit on Coke.  Before you go any further, if you’re one of those uncommonly bright individuals that got extra good grades in school, then of course you know that not everyone can do so, but that it’s possible. If there were a good correlation between all the information available on a stock—even if absolutely complete– and the future price of a given stock, then yes, you most certainly could predict its future price—except for one small problem: to the extent that such information really was predictive, the price would probably already be there. Unfortunately, there is at best only a thin correlation between all that information and future prices. Furthermore, to the extent that price moves in stocks occur as a reaction to anything knowable and analyzable, the effect of such information finds its way into the price of the stock very rapidly.  In today’s internet world, this generally happens within seconds–literally. Even if you can get the news systematically before the big money does, and systematically act more quickly than other traders on this news (some people can run a four minute mile), and systematically more accurately interpret the effect of the news on the market, you can’t know, and therefore can’t analyze, a single one of the virtually infinite number of future events that will influence the future price. Even if you’ve just bought a stock for all the right reasons, the reasons behind your decision are now history and have already had whatever effect they’re likely to have on the stock’s price.  And since the future price will depend on future events, your future profit or loss will depend on how well you manage your position, not on how much you knew on day one. But I’ll give you the benefit of the doubt: you’re a genius, a four-minute miler, and clairvoyant to boot.  You’re still up against some serious research.  In one study after another, the field of Behavioral Finance has found that human judgment is far more limited than we think (5). The brightest and most well educated specialists in any given field do significantly less well at predicting future winners and losers than the simplest statistical measures would predict (6).  For example:

College admissions administrators who’ve had a chance to interview prospective students personally are significantly less successful at picking superior students than mere high school GPA’s or SAT scores alone (without benefit of the interview) would predict.

In various studies, doctors who’ve been given a chance to examine ill people personally are significantly less successful at predicting their prognosis than statistical tables on the illness alone would predict, even when the doctors are provided with the relevant statistical tables.

Year after year, the predictions of stock market analysts and the performance of professional money managers significantly under perform the market averages. Predictions based on the simplest of statistical criteria commonly available on stocks repeatedly and significantly outperform the most in depth analysis by most knowledgeable analysts (7, 8).

None of this means that you can’t make money in the market. It means that you’re not likely to do so by just collecting a bunch–or even a ton, or even all–of the information available on a given company, its revolutionary product, its superior and innovative management, its growth rate, industry, competition, market share, current and projected earnings,  “analyzing” it, and then predicting the future.  What you can do is look up key pieces of readily available statistically significant information, and know (with 100% certainty!) whether the stock is undervalued or overvalued based on long-term statistics.  You can know right now whether AAPL, RIMM, or GOOG stocks are in principle good ideas today (the fact that they may be great companies has little to do with whether their stocks will ever make you a nickel). You can know right now, today, whether your current individual stock holdings have a positive or a negative future expectancy, based on objective criteria, not on what some “analyst” thinks, and not on how “cheap” they appear if they are now below their highs. You can know whether a hot new stock being pitched in a given article is worth the risk, or not worth considering at all.  It could be a computer chip company with revolutionary new technology that will leave Intel in the dust. Maybe it’s a new biotech company that will cure all cancer or make quadriplegics walk.  It could be a revolutionary new defense product that will make the whole world safe for more Starbucks.   It doesn’t matter how exciting the story.  You will never be able to know the future value of a stock by reading stories written to make your mouth water today. Not even if every word in the article is true.  You are only going to know the relevant truth if you look up the relevant statistics on the stock and base your buy and sell decisions on that information and on the price and volume behavior of the stock. And I have to warn you: statistics are boring, and are not likely to make your mouth water, at least not until you have lived with applying them profitably for a while.  More about these statistics and criteria, in detail, shortly.

Coming up next: a little more about risk.

Notes:

1) Olstein, Robert A, in interview in Barron’s, 11/17/03, p. 33

2) Gessel, Chris: “The Big Picture,” in Investor’s Business Daily, 1/2/04, p. 1

3) O’Shaughnessy, James P, What Works on Wall Street, Revised Edition, McGraw Hill, NY, 1998, p. 61.

4) Rothchild, John, The Bear Book: Survive and Profit in Ferocious Markets, John Wiley & Sons, NY, 1998, p. 34-36

5) Meehl, Paul E, in David Faust, The Limits of Scientific Reasoning, University of Minnesota Press, 1984, p. xii: “It is safe to say…that the mass…of investigations of the predictive efficiency of subjective human judgment…versus that of even a crude non-optimized mechanical prediction function (equation, monograph, actuarial table) is about as clearly decided in favor of the latter predictive mode as we can ever expect to get….  I am unaware of any other controversial matter in psychology for which the evidence is now so massive and almost 100% consistent in pointing in the same direction.”

6) Key works: David Faust: The Limits of Scientific Reasoning;  John R. Nofsinger: Investment Blunders of the Rich and Famous, and What You Can Learn from Them; Gary Belsky and Thomas Gilovich: Why Smart People Make Big Money Mistakes, and How to Correct Them: Lessons from the New Science of Behavioral Economics; Richard H Thaler: The Winner’s Curse: Paradoxes and Anomalies of Economic Life.

7) O’Shaughnessy, James P, What Works on Wall Street, Revised Edition, McGraw Hill, NY, 1998, pp. 12-13

Nofsinger, John R, Investment Blunders of the Rich and Famous, Prentice Hall, NY, 2002, in “Profits from the Prophets?” pp. 61-82. The author cites numerous studies on the long-term failure of expert stock analysts, Wall Street Superstars, Newsletters, Corporate Insiders, and professional financial economists, at predicting the future of the market.  Dartboards are at least as successful, and a lot cheaper.

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