A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing

by Burton G. Malkiel

Rule 2: Never pay more for a stock than its firm foundation of value. While I have argued, and I hope persuasively, that you can never judge the exact intrinsic value of a stock, many analysts feel that you can roughly gauge when a stock seems to be reasonably priced. Generally, the earnings multiple for the market as a whole is a helpful benchmark. Growth stocks selling at multiples in line with or not very much above this multiple often represent good value.

There are special risks involved in buying “growth stocks” when the market has already recognized the growth and has bid up the price-earnings multiple to a hefty premium over that accorded more run-of-the-mill stocks. The problem is that the very high multiples may already fully reflect the growth that is anticipated, and if the growth does not materialize and earnings in fact go down (or even grow more slowly than expected), you will take a very unpleasant bath.

What is proposed, then, is a strategy of buying unrecognized growth stocks whose earnings multiples are not at any substantial premium over the market. Of course, it is very hard to predict growth. But even if the growth does not materialize and earnings decline, the damage is likely to be only single if the multiple is low to begin with, whereas the benefits may double if things do turn out as you expected. This is an extra way to put the odds in your favor.

Rule 3: Look for stocks whose stories of anticipated growth are of the kind on which investors can build castles in the air. I have stressed the importance of psychological elements in stock-price determination. Individual and institutional investors are not computers that calculate warranted price-earnings multiples and print out buy and sell decisions. They are emotional human beings—driven by greed, gambling instincts, hope, and fear in their stock-market decisions. This is why successful investing demands both intellectual and psychological acuteness. Stocks that produce “good feelings” in the minds of investors can sell at premium multiples for long periods, even if the growth rate is only average.

Technical analysis is anathema to much of the academic world. We love to pick on it. We have two main reasons: (1) after paying transactions costs and taxes, the method does not do better than a buy-and-hold strategy; and (2) it’s easy to pick on. And while it may seem a bit unfair, just remember that it’s your money we’re trying to save.

Chartists believe momentum exists in the market. Supposedly, stocks that have been rising will continue to do so, and those that begin falling will go on sinking. Investors should therefore buy stocks that start rising and continue to hold their strong stocks. Should the stock begin to fall, investors are advised to sell.

The results reveal that past movements in stock prices cannot be used reliably to foretell future movements. The stock market has little, if any, memory. While the market does exhibit some momentum from time to time, it does not occur dependably, and there is not enough persistence in stock prices to make trend-following strategies consistently profitable.

an accurate statement of the “weak” form of the random-walk hypothesis goes as follows: The history of stock price movements contains no useful information that will enable an investor consistently to outperform a buy-and-hold strategy in managing a portfolio.

Human nature likes order; people find it hard to accept the notion of randomness. No matter what the laws of chance might tell us, we search for patterns among random events wherever they might occur—not only in the stock market but even in interpreting sporting phenomena.

The moral to the story is obvious. With large numbers of technicians predicting the market, there will always be some who have called the last turn or even the last few turns, but none will be consistently accurate.

new fundamental information about a company (a big mineral strike, the death of the president, etc.) is also unpredictable. It will occur randomly over time. Indeed, successive appearances of news items must be random. If an item of news were not random, that is, if it were dependent on an earlier item of news, then it wouldn’t be news at all. The weak form of the random-walk theory says only that stock prices cannot be predicted on the basis of past stock prices.

Any regularity in the stock market that can be discovered and acted upon profitably is bound to destroy itself. This is the fundamental reason why I am convinced that no one will be successful in using technical methods to get above-average returns in the stock market.

Technical theories enrich only the people preparing and marketing the technical service or the brokerage firms who hire technicians in the hope that their analyses may help encourage investors to do more in-and-out trading and thus generate commission business for the brokerage firm.

Laszlo Birinyi, in his book Master Trader, has calculated that a buy-and-hold investor would have seen one dollar invested in the Dow Jones Industrial Average in 1900 grow to $290 by the start of 2013. Had that investor missed the best five days each year, however, that dollar investment would have been worth less than a penny in 2013. The point is that market timers risk missing the infrequent large sprints that are the big contributors to performance.

If you had known the growth rates of all companies during, say, the 1980–90 period, this would not have helped you at all in predicting what growth they would achieve in the 1990–2000 period. And knowing the fast growers of the 1990s did not help analysts find the fast growers of the first decade of the twenty-first century.

I hope you remember not the current exceptions, but rather the rule: Many in Wall Street refuse to accept the fact that no reliable pattern can be discerned from past records to aid the analyst in predicting future growth. Even during the boom years of the 1990s, only one in eight large companies managed to achieve consistent yearly growth. And not even one continued to enjoy growth into the first years of the new millennium. Analysts can’t predict consistent long-run growth, because it does not exist.

Moreover, no analysts proved consistently superior to the others. Of course, in each year some analysts did much better than average, but no consistency in their pattern of performance was found. Analysts who did better than average one year were no more likely than the others to make superior forecasts in the next year.

Michael Sandretto of Harvard and Sudhir Milkrishnamurthi of MIT completed a massive study of the one-year forecasts of the 1,000 most widely followed companies. Their staggering conclusion was that the error rates each year were remarkably consistent and that the average annual error of the analysts was 31.3 percent over a five-year period. Financial forecasting appears to be a science that makes astrology look respectable.

There are, I believe, five factors that help explain why security analysts have such difficulty in predicting the future. These are (1) the influence of random events, (2) the production of dubious reported earnings through “creative” accounting procedures, (3) errors made by the analysts themselves, (4) the loss of the best analysts to the sales desk or to portfolio management, and (5) the conflicts of interest facing securities analysts at firms with large investment banking operations.

growth forecasts made in early 2000 for a wide variety of high-tech and telecom companies were egregiously wrong. U.S. government budgetary, contract, legal, and regulatory decisions can have enormous implications for the fortunes of individual companies. So can the incapacitation of key members of management, the discovery of a major new product, a major oil spill, terrorist attacks, the entry of new competitors, price wars, and natural disasters such as floods and hurricanes, among others.

A major problem that the analyst has in interpreting current and projecting future earnings is the tendency of companies to report so-called pro forma earnings as opposed to actual earnings computed in accordance with generally accepted accounting principles. In pro forma earnings, companies decide to ignore certain costs that are considered unusual; in fact, no rules or guidelines exist. Pro forma earnings are often called “earnings before all the bad stuff,” and give firms license to exclude any expenses they deem to be “special,” “extraordinary,” and “non-recurring.” Depending on what expenses are considered to be improperly ignored, companies can report a substantial overstatement of earnings.

Finally, the ability of professional fund managers to make correct decisions in moving money from cash or bonds to equities on the basis of their forecasts of economic conditions has been egregiously poor. Peaks in mutual-fund cash positions have generally coincided with market troughs. Conversely, cash positions were invariably low when the market was at its highs.

Those who can consistently flip heads will be declared winners. The contest begins and 1,000 contestants flip coins. Just as would be expected by chance, 500 of them flip heads and these winners are allowed to advance to the second stage of the contest and flip again. As might be expected, 250 flip heads. Operating under the laws of chance, there will be 125 winners in the third round, 63 in the fourth, 32 in the fifth, 16 in the sixth, and 8 in the seventh. By this time, crowds start to gather to witness the surprising ability of these expert coin-flippers. The winners are overwhelmed with adulation. They are celebrated as geniuses in the art of coin-flipping, their biographies are written, and people urgently seek their advice. After all, there were 1,000 contestants and only 8 could consistently flip heads. The game continues and some contestants eventually flip heads nine and ten times in a row.* The point of this analogy is not to indicate that investment-fund managers can or should make their decisions by flipping coins, but that the laws of chance do operate and that they can explain some amazing success stories.

The point is that it is highly unlikely you can beat the market. It is so rare that it’s like looking for a needle in a haystack. A strategy far more likely to be optimal is to buy the haystack itself: that is, buy an index fund—a fund that simply buys and holds all the stocks in a broad stock-market index.

The Nobel laureate Paul Samuelson summed up the situation as follows: If intelligent people are constantly shopping around for good value, selling those stocks they think will turn out to be overvalued and buying those they expect are now undervalued, the result of this action by intelligent investors will be to have existing stock prices already have discounted in them an allowance for their future prospects. Hence, to the passive investor, who does not himself search for under- and overvalued situations, there will be presented a pattern of stock prices that makes one stock about as good or bad a buy as another. To that passive investor, chance alone would be as good a method of selection as anything else. This is a statement of the EMH—the efficient-market hypothesis.

The market is so efficient—prices move so quickly when information arises—that no one can buy or sell fast enough to benefit. And real news develops randomly, that is, unpredictably. It cannot be predicted by studying either past technical or fundamental information.

What Markowitz discovered was that portfolios of risky (volatile) stocks might be put together in such a way that the portfolio as a whole could be less risky than the individual stocks in it.

As long as there is some lack of parallelism in the fortunes of the individual companies in the economy, diversification can reduce risk.

In general, diversification will not help much if there is a high covariance (high correlation) between the returns of the two companies.

Now comes the real kicker; negative correlation is not necessary to achieve the risk reduction benefits from diversification. Markowitz’s great contribution to investors’ wallets was his demonstration that anything less than perfect positive correlation can potentially reduce risk.

is there a point at which diversification is no longer a magic wand safeguarding returns? Numerous studies have demonstrated that the answer is yes. As shown in the following chart, the golden number for American xenophobes—those fearful of looking beyond our national borders—is at least fifty equal-sized and well-diversified U.S. stocks (clearly, fifty oil stocks or fifty electric utilities would not produce an equivalent amount of risk reduction). With such a portfolio, the total risk is reduced by over 60 percent. And that’s where the good news stops, as further increases in the number of holdings do not produce much additional risk reduction.

It turns out that about fifty is also the golden number for global-minded investors. Such investors, however, get more protection for their money, as shown in the preceding chart. Here, the stocks are drawn not simply from the U.S. stock market but from the international markets as well. As expected, the international diversified portfolio tends to be less risky than the one drawn purely from U.S. stocks.

Three academics—the former Stanford professor William Sharpe and the late finance specialists John Lintner and Fischer Black—focused their intellectual energies on determining what part of a security’s risk can be eliminated by diversification and what part cannot. The result is known as the capital-asset pricing model.

The basic logic behind the capital-asset pricing model is that there is no premium for bearing risks that can be diversified away. Thus, to get a higher average long-run rate of return, you need to increase the risk level of the portfolio that cannot be diversified away. According to this theory, savvy investors can outperform the overall market by adjusting their portfolios with a risk measure known as beta.

part of total risk or variability may be called the security’s systematic risk and that this arises from the basic variability of stock prices in general and the tendency for all stocks to go along with the general market, at least to some extent. The remaining variability in a stock’s returns is called unsystematic risk and results from factors peculiar to that particular company—for example, a strike, the discovery of a new product, and so on. Systematic risk, also called market risk, captures the reaction of individual stocks (or portfolios) to general market swings. Some stocks and portfolios tend to be very sensitive to market movements. Others are more stable. This relative volatility or sensitivity to market moves can be estimated on the basis of the past record, and is popularly known by—you guessed it—the Greek letter beta.

The calculation begins by assigning a beta of 1 to a broad market index. If a stock has a beta of 2, then on average it swings twice as far as the market. If the market goes up 10 percent, the stock tends to rise 20 percent. If a stock has a beta of 0.5, it tends to go up or down 5 percent when the market rises or declines 10 percent. Professionals call high-beta stocks aggressive investments and label low-beta stocks as defensive.

What is clear, however, is that beta, as usually measured, is not a substitute for brains and cannot be relied on as a simple predictor of long-run future returns.

To understand the logic of APT, one must remember the correct insight underlying the CAPM: The only risk that investors should be compensated for bearing is the risk that cannot be diversified away. Only systematic risk will command a risk premium. But the systematic elements of risk in particular stocks and portfolios may be too complicated to be captured by beta—the tendency of the stocks to move more or less than the market. This is especially so because any particular stock index is an imperfect representative of the general market. Hence, beta may fail to capture a number of important systematic elements of risk.

Changes in national income undoubtedly affect returns from individual stocks in a systematic way. This was shown in our illustration of a simple island economy in chapter 8. Also, changes in national income mirror changes in the personal income of individuals, and the systematic relationship between security returns and salary income can be expected to have a significant effect on individual behavior. For example, the laborer in a Ford plant will find that holding Ford common stock is particularly risky, because job layoffs and poor returns from Ford stock are likely to occur at the same time.

Changes in interest rates also systematically affect the returns from individual stocks and are important nondiversifiable risk elements. To the extent that stocks tend to suffer as interest rates go up, equities are a risky investment, and those stocks that are particularly vulnerable to increases in the general level of interest rates are especially risky.

Changes in the rate of inflation will similarly tend to have a systematic influence on the returns from common stocks. This is so for at least two reasons. First, an increase in the rate of inflation tends to increase interest rates and thus tends to lower the prices of some equities, as just discussed. Second, the increase in inflation may squeeze profit margins for certain groups of companies—public utilities, for example, which often find that rate increases lag behind increases in costs. On the other hand, inflation may benefit the prices of common stocks in the natural resource industries.

Neither technical analysis, which analyzes the past price movements of stocks, nor fundamental analysis, which analyzes more basic information about the prospects for individual companies and the economy, seems to yield consistent benefits. It appears that the only way to obtain higher long-run investment returns is to accept greater risks. Unfortunately, a perfect risk measure does not exist.

“Quant” is the Wall Street nickname for the quantitatively inclined financial analyst who devotes attention largely to the new investment technology.

Efficient-market theory, modern portfolio theory, and various asset-pricing relationships between risk and return all are built on the premise that stock-market investors are rational. As a whole, they make reasonable estimates of the present value of stocks, and their buying and selling ensures that the prices of stocks fairly represent their future prospects. By now, it should be obvious that the phrase “as a whole” represents the economists’ escape hatch. That means they can admit that some individual market participants may be less than rational. But they quickly wriggle out by declaring that the trades of irrational investors will be random and therefore cancel each other out without affecting prices.

Behavioralists believe that market prices are highly imprecise. Moreover, people deviate in systematic ways from rationality, and the irrational trades of investors tend to be correlated. Behavioral finance then takes that statement further by asserting that it is possible to quantify or classify such irrational behavior. Basically, there are four factors that create irrational market behavior: overconfidence, biased judgments, herd mentality, and loss aversion.

behavioralists believe there are substantial barriers to efficient arbitrage. We cannot count on arbitrage to bring prices in line with rational valuation. Market prices can be expected to deviate substantially from those that could be expected in an efficient market.

people deviate in systematic ways from rationality in making judgments amid uncertainty. One of the most pervasive of these biases is the tendency to be overconfident about beliefs and abilities and overoptimistic about assessments of the future.

Daniel Kahneman has argued that this tendency to overconfidence is particularly strong among investors. More than most other groups, investors tend to exaggerate their own skill and deny the role of chance. They overestimate their own knowledge, underestimate the risks involved, and exaggerate their ability to control events.

This is what psychologists mean by overconfidence. If an investor tells you he is 99 percent sure, he would be better off assuming that he was only 80 percent sure. Such precision implies that people tend to put larger stakes on their predictions than are justified. And men typically display far more overconfidence than women, especially about their prowess in money matters.

Many behavioralists believe that overconfidence in the ability to predict the future growth of companies leads to a general tendency for so-called growth stocks to be overvalued.