Hello, and welcome to Investor’s Bookshelf. Today, I’ll be explaining A Random Walk Down Wall Street. I’ll take about 21 minutes to walk you through the essence of this book: how to steadily grow your investment returns in the U.S. market by using the “random walk” investment method.
Let me begin with a story.
In 1593, a professor of botany brought tulips from Vienna to Leiden in the Netherlands. The Dutch became fascinated with these flowers and started investing in them frantically. As a result, tulip prices soared for more than 30 years. By January 1637, the price of tulip bulbs had risen twentyfold. But that was the final frenzy. Just a month later, tulip prices collapsed dramatically, falling even faster than they had risen, and soon became worthless.
Doesn’t this story sound familiar? In fact, that tulip mania of several centuries ago was the first financial crisis in world history. Since then, similar investment dramas have repeatedly unfolded: the Wall Street Crash of 1929, the U.S. housing bubble and subprime mortgage crisis in the early 21st century, and, for Chinese investors in particular, the painful stock market crashes of 2008 and 2015.
So how can we avoid investment traps and achieve steady returns? This is the question every investor cares about most. The book we are discussing today—A Random Walk Down Wall Street—provides an answer. From the title, “A Random Walk Down Wall Street” carries a double meaning. On the surface, it refers to strolling along Wall Street. But it also implies that only by adopting the “random walk” investment method can one invest with true ease. From the title we can also see that the book’s strategy is aimed primarily at American investors, though Chinese investors can certainly draw valuable lessons from it as well.
So what exactly is a random walk? Originally, it was a statistical model used to describe irregular fluctuations. But it has since been applied to many fields—ecology, economics, psychology, and more. In economics, simply put, a random walk refers to an investment method that respects the randomness of market movements. The author, Burton G. Malkiel, suggests that investors who adopt this method are more likely to achieve steady growth in their investment returns.
Speaking of A Random Walk Down Wall Street, it is a truly renowned classic. Forbes commented: “In the past 50 years, there have been no more than half a dozen really good books about investing. A Random Walk Down Wall Street ranks among these classics.” So who is the great mind behind this work? Its author, Burton G. Malkiel, is both a financial scholar and an experienced investment manager. He has served as Director of the Financial Research Center at Princeton University, Chair of the Economics Department, and a member of the U.S. President’s Council of Economic Advisers. Later, he spent eight years as Dean of the Yale School of Management. While “strolling” among top universities, Malkiel also held important positions at leading investment firms, including Vanguard Group and Prudential Financial.
Now that we’ve introduced the book and its author, let’s dive into its core content. First, I’ll explain why neither technical analysis nor fundamental analysis can predict the stock market. Then we’ll look at how to overcome “market randomness,” the greatest challenge for any investor. Finally, we’ll discuss how to choose the right random walk investment method to put into practice.
Part One
Let us first look at the first key point: why neither technical analysis nor fundamental analysis can predict the stock market.
Malkiel argues that the market is random, and short-term fluctuations in stock prices cannot be predicted. Predictions of stock prices, forecasts of company earnings, and complex price charts are of no real use in selecting stocks. In other words, a blindfolded monkey randomly choosing a portfolio of stocks would perform just as well as the so-called market experts. Naturally, investment professionals fiercely object to this claim. They argue: “But we can do technical analysis and fundamental analysis! Can a monkey do that?” Yet the real question remains: can technical and fundamental analysis truly predict the stock market?
Let’s start with once-popular technical analysis. What is technical analysis? At its core, it is the practice of plotting and interpreting stock charts in order to forecast price changes. Why do many people believe it works? Because technical analysts think that all information related to a company’s earnings, dividends, and future performance has already been reflected in past stock prices and trading volumes. Since price changes supposedly follow certain trends, technical analysis becomes necessary. Moreover, they believe that only 10% of the stock market can be understood through logical reasoning, while the remaining 90% should be analyzed from the perspective of psychology. In their view, by studying charts, one can anticipate the future behavior of all market participants. If you understand that, you can identify market trends—since the value of an asset equals the price people are willing to pay for it.
Because of this obsession with psychology, the theoretical foundation of technical analysis is also known as the “castle-in-the-air theory.” A prominent advocate of this view was the famous British economist John Maynard Keynes. He firmly believed that mass psychology was the key driver behind stock prices reaching new highs. As long as there was a “greater fool” willing to take over, one could still make money, even if a stock was worthless. It is said that Keynes himself made millions of pounds in the 1930s by applying this principle. However, Malkiel contends that technical analysis is unreliable, because its very logic is flawed.
Technical analysts study historical stock charts because they assume that past price movements can serve as a reference for the present—in other words, that history repeats itself. But scholars have rigorously tested technical analysis by examining stock price data from major U.S. exchanges going back to the early 20th century. The results showed that past price movements are not a reliable basis for predicting future ones. Whether prices rise or fall has little to do with history.
The belief that patterns repeat in the stock market is largely due to what Malkiel calls “statistical illusions.” To illustrate this, he once asked his students to conduct an experiment: they set the initial stock price at $50, then simulated price changes by flipping a coin—heads meant the closing price went up by $0.50, tails meant it went down by $0.50. They then plotted the resulting price chart. The chart looked remarkably similar to a real stock chart, even showing apparent cycles. When Malkiel showed this chart to a technical analyst, the analyst was overjoyed and asked excitedly: “Which company’s stock is this? We have to buy it immediately! This is a classic pattern! This stock will definitely rise 15 points next week!” The experiment demonstrated that stock price movements resemble a random process. Technical analysts, by ignoring this fundamental truth, end up obsessing over illusions in the charts.
Because of its strong connection with mass psychology, technical analysis has also spawned many absurd correlation theories. One famous example is the “hemline index,” which claimed that when women favored miniskirts, a bull market was imminent, while a preference for long skirts signaled a bear market. Before World War II, this theory seemed to work, but afterwards it lost its supposed predictive power. Sometimes long skirts appeared before a bear market, sometimes after, with no consistency. This rendered the theory meaningless.
In the face of market randomness, Malkiel believes that relying on technical analysis is dangerous. Past prices have no predictive value for future prices, and the market does not follow trends. Instead of trusting chart analysis and frequently trading, one would be better off simply buying and holding stocks long-term—at the very least, this approach reduces transaction fees.
If technical analysis offers little help, is fundamental analysis any more reliable? Malkiel is equally skeptical. What is fundamental analysis? In short, it is the practice of analyzing a company’s true value—such as its current operations, profitability, and other factors—in order to predict its future stock price. This logic seems more reasonable than technical analysis. Indeed, most professional Wall Street analysts are fundamental analysts, making investment decisions based on their assessment of individual companies.
Understanding a company’s situation and true value is, of course, useful. But does fundamental analysis really work? The answer is straightforward: just look at the investment performance of securities analysts. Numerous studies have found that the funds managed by professional analysts do not outperform unmanaged large stock indices. In other words, paying professionals to conduct fundamental analysis yields no better returns than randomly buying a basket of stocks yourself. Of course, some funds perform well during certain periods, but such success is never consistent. In the 1990s, The Wall Street Journal even held a dart-throwing contest, pitting stocks picked by four Wall Street experts against stocks chosen at random by tossing darts at a list. The results? Their performances were about the same.
So what’s going on? Fundamental analysis sounds reasonable. Isn’t a company’s stock price determined by its value? Malkiel explains that many factors distort analysts’ predictions. One is randomness. Many major changes that affect corporate earnings are inherently unpredictable. For example, in the early 2000s, Wall Street’s forecasts for the high-tech sector turned out to be wildly wrong. Goldman Sachs, in a mid-2000 research report, declared that the internet industry posed no long-term risks. Yet within months, hundreds of internet companies went bankrupt. Beyond that, executive mismanagement, the launch of major new products, natural disasters, and accidents—all can cause the stock prices of seemingly promising companies to collapse overnight.
Another factor is corporate fraud. Companies often produce unreliable financial statements. Malkiel famously said that a company’s profit-and-loss statement is like a bikini swimsuit: what it reveals is enticing, but what it conceals is more important. In the late 1990s bull market, many companies boldly manipulated numbers to make their financial reports look appealing. Even giants such as Kodak, Xerox, and Motorola engaged in creative accounting tricks. With such doctored reports, Wall Street analysts couldn’t even rely on basic figures, let alone make accurate stock price predictions.
And, at the end of the day, analysts are human and make mistakes. Moreover, the best analysts are often unwilling to stay in research positions, since research pays far less than sales or portfolio management. Conflicts of interest between research departments and investment banking divisions further compromise analysts’ objectivity.
Overall, Malkiel concludes that neither technical analysis nor fundamental analysis is reliable in the face of market randomness.
That concludes the first key point. Next, let’s move on to the second: how can we overcome the greatest enemy of investing—market randomness?
Part Two
From our earlier discussion, we already know that neither technical analysis nor fundamental analysis is particularly reliable. So what should investors do? Malkiel suggests taking a middle path. Although technical analysis itself is unreliable, its theoretical basis—that mass psychology influences stock prices—does contain some truth. And as for fundamental analysis, while it can hardly predict the future with accuracy, at least having some understanding of an industry and a company’s basic situation can help you form a general judgment about its stock. The problem is that both approaches are subject to randomness. Since randomness is the defining feature of the stock market, why not embrace this reality and design the most effective investment methods accordingly?
Through long-term study, academia has developed several “new investment techniques.” These approaches may help us achieve higher returns while reducing risk. So what exactly should we do? For anyone seeking to reduce risk, one effective strategy is diversification. The mathematical underpinnings of this theory are complex, but let’s look at a simple example.
Imagine living on a small island with only two companies. One is like Hilton Group, a large hotel and resort operator running beaches, tennis courts, and golf courses. The other is a manufacturer of umbrellas. Clearly, different weather conditions affect the two businesses differently. On sunny days, the hotel thrives while umbrellas sit unsold; on rainy days, the resorts do poorly while umbrella sales soar. Suppose that on sunny days the umbrella company’s return is –25% and the hotel’s is +50%. On rainy days, it is the reverse: the hotel’s return is –25% and the umbrella company’s is +50%. Assume that over the course of a year, the weather is evenly split between sunny and rainy.
If an investor buys only the hotel stock, then half the time the expected return is +50%, and the other half it is –25%. Calculating this, the annual expected return is 12.5%. The same applies if the investor only buys the umbrella stock. But note that these are expected returns. If the weather turns out to be more sunny than rainy—or vice versa—the results will differ, creating uncertainty, or risk. However, if the investor splits their funds evenly between the two stocks, no matter what the weather is, the return stabilizes at 12.5%. The risk of fluctuation disappears. While this investor may not earn more from favorable weather, at least they will not lose the 12.5% return.
This example illustrates that diversification effectively reduces risk. Malkiel notes that diversification has a critical threshold: about 50 stocks. Spreading investments across 50 different stocks reduces risk by more than 60% compared with holding just one stock. But beyond that, the difference diminishes—holding 50 stocks isn’t much riskier than holding 500. In practice, the least risky portfolio consists of about 17% foreign stocks and 83% U.S. stocks, which not only minimizes risk but also tends to improve returns.
Diversification helps reduce market risk and, at times, even enhances returns. This principle provides valuable guidance in designing personal investment plans. Beyond diversification, we must also guard against irrational behaviors that often trap investors.
Before investing, it is important to understand how people actually behave. Traditional economics assumes that investors are rational when building models. But more and more financial scholars have found that many investors act irrationally. These irrational emotions and behaviors include overconfidence, judgment biases, herd behavior, loss aversion, regret, and others—all of which can affect stock prices.
Take herd behavior, for example. In the 1950s, social psychologist Solomon Asch conducted an experiment in which seven participants were asked a very simple question that even a child could answer correctly. But six of them, acting as confederates, deliberately gave the wrong answer first. The result? The seventh participant often followed suit and also gave the wrong answer. Asch concluded that even when people know the correct answer, social pressure can push them to conform.
In the stock market, such herd behavior can cause disaster. The tulip mania we mentioned earlier, and the dot-com bubble in the early 21st century, both bore the marks of herd behavior. What makes this effect especially frightening is that it afflicts not just ordinary investors, but professionals as well. Sometimes, professionals do not follow their own research but simply go along with the crowd. In 2000, for instance, large inflows poured into high-tech “growth funds.” The result was heavy losses in the following two years.
Behavioral finance scholars, reviewing history, have drawn lessons from these irrational behaviors and offered practical advice. Among the most important lessons are: avoid following the herd; avoid overtrading—if you must trade, sell your losing stocks rather than your winning ones; and never believe in a foolproof strategy.
That, then, is the second key point of the book: build diversified portfolios, avoid irrational behaviors, and understand how the market efficiently incorporates information. The “new investment techniques” Malkiel advocates help us confront randomness—the greatest enemy of investors.
Part Three
Finally, let’s turn to the most crucial question: how exactly should we invest using the random walk method?
The simplest approach is to regularly invest in index funds through a systematic investment plan. As we’ve already noted, neither professional funds nor professional managers consistently outperform index funds, which offer both lower risk and higher returns. So why not just buy index funds directly? That’s correct in principle, but it doesn’t mean you should buy a large lump sum all at once. If you happen to buy at a market peak, even index funds won’t save you. That’s why the key is to invest regularly and systematically over the long term. Systematic investing is one of the most effective ways to reduce risk. Generally, the longer an investor holds stocks or funds, the smaller the volatility of returns becomes.
Of course, as we discussed earlier, portfolio diversification is also a critical way to reduce risk. Malkiel provides an example portfolio for people in their fifties. He suggests allocating about 5% of assets to money market funds (similar to products like Yu’e Bao in China), 27.5% to bond index funds, 12.5% to real estate investment trust (REIT) index funds, and 55% to stock index funds. Of that stock portion, 27% should be in U.S. domestic index funds, 14% in developed-country index funds, and 14% in emerging-market index funds.
But what if you don’t trust index funds and prefer to buy stocks directly? You can adopt a hands-on “random walk” approach—picking and managing your own stocks. Still, there are some rules worth following. Although you shouldn’t rely solely on fundamental or technical analysis, you can combine them effectively.
For instance, when choosing stocks, it’s best to look for companies that seem capable of achieving above-average earnings growth for at least five consecutive years. Companies with solid operations are naturally more reliable—that’s fundamental analysis. At the same time, if a company’s chairman is especially good at storytelling and inspiring investors’ imagination, that too may support the stock’s prospects—this reflects the role of market psychology, or technical analysis.
The book also reminds us that no matter how good a company’s operations may be, you should never pay more than its true value. Take Alibaba as an example. Suppose its stock currently trades a little above $100, and that represents its fair value. If tomorrow its price suddenly soared to $1,000, you would need to pause and ask whether that exceeds its reasonable worth. And once you’ve done your research and decided to buy, you should minimize trading. This reduces fees and transaction taxes, and long-term holding lowers risk. Another popular Wall Street maxim is worth remembering: “Keep the winners, sell the losers.”
If you can combine fundamental and technical approaches wisely, you can stroll through the stock market with confidence. Of course, if you happen to find a particularly capable “random walker”—someone like Malkiel himself—whom you fully trust in both character and skill, you could hand your portfolio over to them. But as we discussed earlier, professional fund managers tend to underperform index funds, so this strategy is only for reference.
In short, whichever random walk method you choose, the most important thing is that it suits you.
Summary
Alright, that brings us to the end of today’s discussion. Let’s briefly summarize the key points we’ve covered.
First, Malkiel argues that the market is inherently random. Purely relying on technical analysis or fundamental analysis cannot effectively solve this problem, nor can it enable accurate predictions of the investment market. Instead of exhausting ourselves trying to find patterns in a fundamentally random market, it is better to adopt a “random walk” approach and design investment strategies that embrace this reality.
Second, decades of research show that diversified portfolios, avoidance of irrational behaviors, and understanding the way markets efficiently process information can help us overcome market randomness.
Finally, Malkiel outlines three approaches to random-walk investing: investing in index funds, building your own diversified portfolio, or hiring someone else to manage your investments. Among these, he most strongly recommends index fund investing.
My personal takeaway from this book is that the world of investing is full of uncertainty and volatility. Rather than trusting the endless commentary of hindsight “experts” in the market, it is wiser—like Malkiel—to carefully consider the true nature of the market we face, and then craft a suitable strategy. With his knowledge and experience, Malkiel offers us thoughtful and practical investment advice. The next step, of course, depends on our ability to put it into practice.
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