Historically tested long-term strategies that always outperform the market
“O’Shaughnessy’s conclusion that some strategies do produce consistently strong results while others underperform could shake up the investment business.”
“What Works on Wall Street is indisputably a major contribution to empirical research on the behavior of common stocks in the United States. . . . Conceivably, the influence of What Works on Wall Street will prove immense.”
—The Financial Analysts’ Journal
“O’Shaughnessy’s latest, What Works on Wall Street, is a serious inquiry into the investment strategies that stand up under long-term scrutiny and is refreshing research for every investor.”
—Stocks and Commodities
“A bible for investment strategies. . .”
About the Book:
Recent history has witnessed one of the worst stock market beatings ever. As a result, abysmal returns are being called “the new normal,” financial “experts” are ringing the death knell of buy-and-hold, and investors’ faith in equities has hit an all-time low. You have two choices. You can abandon the stock market based on what is happening today. Or you can invest today based on what will happen in the future.
Containing all new data, What Works on Wall Street, Fourth Edition, is the only investing guide that lets you see today’s market in its proper context— as part of the historical ebb and flow of the stock market. And when you see the data, you’ll see there is no argument: Stocks work.
Now in its second decade of helping investors succeed with stocks, What Works on Wall Street continues to provide the most effective investing strategies, presenting incontrovertible data on what works and what doesn’t. Updated with current statistics and brand-new features, What Works on Wall Street offers data on almost 90 years of market performance, including:
- Stocks ranked by market capitalization
- Price-to-earnings ratios
- EBITDA to enterprise value
- Price-to-cash flow, -sales, and -book ratios
- Dividend, buyback, and shareholder yields
- One-year earnings-per-share percentage changes
Providing you with unparalleled insights into stock performance going back to 1926, What Works on Wall Street is a refreshingly calming, objective view of a subject that is usually wrapped in drama, hyperbole, and opinions that are plain wrong.
This comprehensive guide provides the objective facts and winning strategies you need; all you have to do is make the decision to ignore the so-called market experts and rely on the long-proven approach that has made What Works on Wall Street an investing classic.
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About the Author
James O'Shaughnessy is Chairman and CEO of O'Shaughnessy Asset Management. In his 1996 bestseller, What Works on Wall Street, he revolutionized investment strategy with groundbreaking research. Since then, he has written three more books: How to Retire Rich, Invest Like the Best, and, most recently, Predicting the Markets of Tomorrow: A Contrarian Investment Strategy for the Next Twenty Years.
Read an Excerpt
WHAT WORKS ON WALL STREETThe Classic Guide to the Best-Performing Investment Strategies of All Time
By JAMES P. O'SHAUGHNESSY
McGraw-HillCopyright © 2012 James P. O'Shaughnessy
All right reserved.
Chapter OneSTOCK INVESTMENT STRATEGIES: DIFFERENT METHODS, SIMILAR GOALS
Good intelligence is nine-tenths of any battle.
There are two main approaches to equity investing: active and passive. The active approach is most common. With this approach managers attempt to maximize their returns at various levels of risk by buying stocks they believe are superior to others. Usually, the managers assess stocks in a traditional manner. They analyze the company, interview management, talk to customers and competitors, review historical trends and current forecasts, and then decide if the stock is worth buying.
Active investors are guided by styles, broadly called growth and value. The type of stocks they buy depends largely on their underlying philosophy. Growth investors buy stocks that have higher than average growth in sales and earnings with expectations for more of the same. Growth investors believe in the company's potential and think a stock's price will follow its earnings higher.
Value investors seek stocks with current market values substantially below liquidating value. They use factors like price-to-earnings (PE) ratio or price-to-sales ratios (PSR) to identify when a stock is selling below its intrinsic value. They bargain hunt, looking for stocks in which they can buy a dollar's worth of assets for less than a dollar. Value investors believe in the company's balance sheet, thinking that a stock's price will eventually rise to meet its intrinsic value.
Actively managed funds often use a hodgepodge of techniques from both schools of investing, but the most successful have strongly articulated strategies. The majority of mutual funds, professionally managed pension funds, and separately managed individual accounts are managed with an active approach.
TRADITIONAL ACTIVE MANAGEMENT DOESN'T WORK
Active management seems to make perfect sense until you review the record of traditional, actively managed funds. The majority does not beat the S&P 500. This is true over both short and long periods. Figure 1.1 shows the percentage of those actively managed diversified mutual funds in Morningstar's database that beat the S&P 500 over a 10-year period. While the record looks quite strong since 2007, the average—over all of the periods covered back to 1991—is just 30 percent. Thus, for all 10-year periods since 1991, 70 percent of actively managed funds failed to beat the S&P 500 over previous 10-year periods. What's more, this record overstates traditionally managed active fund performance, because it doesn't include all the funds that failed to survive for a 10-year period.
Passive indexing has exploded in the past two decades as a result. Investors buy an index that they think broadly represents the market, such as the S&P 500, and let it go at that. Their objective is to match the market, not outperform it. They're willing to give up their shot at outperforming the market for the security of not underperforming it. Since the publication of the first edition of this book in 1996, index managers have continued to see their assets under management soar. According to the September 21, 2009, issue of Pensions & Investments, an industry publication, "Worldwide index assets under management were $4.6 trillion as of June 30, 2009." What's more, since 1996 the popularity of exchange traded funds (ETFs)—index funds that are listed and traded on exchanges like stocks—has exploded, furthering what amounts to a revolution in investment management characterized by investors continuing to flock to more structured, disciplined investment strategies. The Economist magazine reported in its January 23, 2010, issue that, "At the end of 2009 ETF assets under management topped the $1 trillion mark for the first time, according to Blackrock, a fund-management firm. The industry's assets were just $40 billion at the end of 1999."
What's more, since the disappointing results of the last decade for equity returns, many institutions have planned to reduce their investments in equities, and those investments they do make will likely be made in low-cost index funds. According to a July 7, 2009, article in the industry publication Fundfire, "Institutional investors are expected to reduce their equity allocations to as low as 35 percent to 45 percent by 2015, down from the 55 percent they placed in equities in 2007, according to a survey released yesterday by The Boston Consulting Group."
WHAT'S THE PROBLEM?
Conventional academics aren't surprised that traditionally-managed funds fail to beat the market. Most have long held that markets are efficient and that current security prices reflect all available information; they argue that prices follow a random walk and move without rhyme or reason. According to their theories, you might as well have a monkey throw darts at a stock page as attempt security analysis because stock prices are random and cannot be predicted.
Yet the long-term evidence in this book contradicts the random walk theory. Far from following a random walk, the evidence continues to reveal a purposeful stride. The 46 years of data found in this edition proves strong return predictability. Indeed, the CRSP data included in this edition extends this to 83 years of historical data. What's more, this return predictability continues to persist even since the first edition of this book was published in 1996. The market clearly and consistently rewards certain attributes (e.g., stocks with low price-to-earnings, price-to-cash flow, and price-to-sales ratios) and clearly and consistently punishes others (e.g., stocks with high price-to-earnings, price-to-cash flow, and price-to-sales ratios) over long periods of time and over many market cycles. Indeed, the crash of October 2007 through March 2009, where the S&P 500 fell a dizzying 51 percent from peak to trough, has led many academics and portfolio managers to challenge the notion that markets are in any way efficient. According to the New York Times, "Market strategist Jeremy Grantham has stated flatly that the Efficient Market Theory is responsible for the current financial crisis (in 2008 and 2009), claiming that belief in the hypothesis caused financial leaders to have a 'chronic underestimation of the dangers of asset bubbles breaking.'" A careful review of the data suggests that the stock market is a complex, adaptive system with feedback loops that allows for bubbles and crashes and always follows similar patterns during both trends. We'll learn more about this in the coming chapters on behavioral finance and neurofinance.
Yet the paradox remains: if past historical tests—as well as the real-time results that were generated with the strategies featured in this book since its initial publication—show such high return predictability, why do 70 percent of traditionally managed mutual funds continually fail to beat the S&P 500 over long periods of time? Finding exploitable investment opportunities does not mean that it is easy to make money, however. To do so requires the ability to consistently, patiently, and slavishly stick with the strategy, even when it is performing poorly relative to other methods. One of the central themes of this book is that all strategies have performance cycles in which they over- and underperform their relevant benchmarks. The key to outstanding long-term performance is to find strategies that have the highest base rate, or batting average (more on that later), and then stick with that strategy, even when it's underperforming other strategies and benchmarks. Few people are capable of such action. Successful investors do not comply with nature; they defy it. Most investors react very emotionally to the short-term gyrations of the market, and I've seen many who follow my strategies and portfolios in real time say, "Well, the strategies used to work, but they don't anymore," after just a few months of underperformance. Indeed, history documents several periods in which factors have inverted, with the long-term factor that normally produces outstanding results being walloped by the factor that has historically led to disastrous results. What appears dull and boring in a long-term compilation of factor returns becomes emotional and frightening when investors live through it in real time, leading to doubt as to whether the long-term results will somehow change. Our very human nature leads us to believe that we live in unique times in which the lessons from the past no longer apply. Generation after generation has shared this outlook, and by ignoring the lessons of the past, each generation has repeated the same mistakes of its predecessors. In the next chapter, I argue that the reason traditional management doesn't work well is that decision making is systematically flawed and unreliable. This provides an opportunity to those of us who use rational, disciplined methods to buy and sell stocks using time-tested methods to do much better than a simple index like the S&P 500, essentially allowing the disciplined investor to arbitrage human nature.
Since this book was first published in 1996, a school of academic thought called Behavioral Economics has grown in popularity to explain why these performance anomalies continue to exist even after being documented and written about extensively. This work has received a great deal of public attention and centers around a new paradigm for evaluating how people actually make investment choices. In his book Behavioral Finance: Insights into Your Irrational Mind and the Market, James Montier writes:
This is the world of behavioral finance, a world in which human emotions rule, logic has its place, but markets are moved as much by psychological factors as by information from corporate balance sheets ... [T]he models of classical finance are fatally flawed. They fail to produce predictions that are even vaguely close to the outcomes we observe in real financial markets ... of course, now we need some understanding of what causes markets to deviate from their fundamental value. The answer quite simply is human behavior.
While I examine some of the tenets of behavioral finance in Chapters 2 and 3, I think one of the principal reasons classically trained economists were getting the wrong answers was that they were asking the wrong questions.
STUDYING THE WRONG THINGS
It is no surprise that academics find traditionally managed stock portfolios following a random walk. The past records of most traditional managers cannot be predictive of future returns because their behavior is inconsistent. You cannot make forecasts based on inconsistent behavior, because when you behave inconsistently, you are unpredictable. Even if the manager is a perfectly consistent investor—a hallmark of the best money managers—if that manager leaves the fund, all predictive ability from past performance of the fund is lost. Moreover, if a manager changes his or her style, all predictive ability from past performance is also lost. Traditional academics, therefore, have been measuring the wrong things. They assumed perfect, rational behavior in a capricious environment ruled by greed, hope, and fear. They have been contrasting the returns of a passively held portfolio—the S&P 500—with the returns of portfolios managed in an inconsistent, shoot from the hip style. Track records are worthless unless you know what strategy the manager uses and if it is still being used. When you study traditionally managed funds, you're really looking at two things: first, the strategy used, and second, the ability of the manager to implement it successfully. It makes much more sense to contrast the one factor (in this case, market capitalization) S&P 500 portfolio with other one or multifactor portfolios.
WHY INDEXING WORKS
Indexing to the S&P 500 works because it sidesteps flawed decision making and automates the simple strategy of buying the big stocks that make up the S&P 500. The mighty S&P 500 consistently beat 70 percent of traditionally managed funds over the long term by doing nothing more than making a disciplined bet on large capitalization stocks. Figure 1.2 compares the returns of the S&P 500 with those of our Large Stocks universe, which consists of all the stocks in the Compustat database that have market capitalizations greater than the database mean in any given year. This effectively limits us to the top 16 percent of the companies in the database by market capitalization. Stocks are then bought in equal dollar amounts. The returns are virtually identical. $10,000 invested in the S&P 500 of December 31, 1926, was worth $23,171,851 on December 31, 2009, a compound return of 9.78 percent. The same $10,000 invested in our Large Stocks universe was worth $21,617,372, a compound return of 9.69 percent. (Both include the reinvestment of all dividends.) And it's not just the absolute returns that are so similar. Risk, as measured by the standard deviation of return, is also virtually identical for the two strategies. The S&P 500 had an annual standard deviation of return of 19.27 percent, whereas the Large Stocks universe had 19.35 percent. And remember that our Large Stock universe is equally weighted whereas the S&P 500 is cap weighted—the results would be even closer if we ran our Large Stocks universe on a cap-weighted basis.
Thus, far from being "the market," the S&P 500's returns are the result of a simple strategy that says, "Buy big stocks." The reason this works so well is that the S&P 500 never varies from this strategy. It doesn't wake up in the morning and say, "You know, small-cap stocks have been doing well recently. I think I will change and become a small-cap index," nor does it watch as Ben Bernanke gives testimony to Congress and say, "Yikes! Today I'm going to become the S&P cash and bond index!" It just continues to passively implement the strategy of buying big stocks, and that's why it's so effective. The S&P 500 beats 70 percent of conventionally managed funds because it never varies from its underlying strategy of buying large-capitalization stocks. It never panics, has second thoughts, or is envious when other indexes outperform it. The key to its long-term success is its unwavering disciplined implementation of an investment strategy.
Yet, indexing to the S&P 500 is just one form of a passive implementation of a strategy, in this case consistently buying big stocks. As you will see in later chapters, the S&P 500's long-term results are fairly mediocre, coming in the bottom third of all strategies we tested. There are numerous strategies that do vastly better than the S&P 500 over long periods of time. One example is called "Dogs of the Dow," which simply buys the 10 highest-yielding stocks in the Dow Jones Industrial Average each year. This is a great example of another strategy that works consistently over the long term. From 1928—when the Dow Jones Industrial was expanded to 30 stocks—through 2009, the strategy consistently beat the S&P 500. Indeed, it beat the S&P 500 in almost all rolling 10-year periods, with only five 10-year rolling periods out of 72 underperforming the S&P 500. So $10,000 invested in the Dogs of the Dow on December 31, 1928 (when the Dow Jones Industrial Average was expanded to 30 stocks), was worth $55 million on December 31, 2009, an average annual compound return of 11.22 percent, compared to a $10,000 investment in the S&P 500 which would have grown to just $11.7 million or an average annual return of 9.12 percent. You'll find a number of other such winning strategies in this book.
It took the combination of fast computers and huge databases like Compustat and CRSP to prove that a portfolio's returns are essentially determined by the factors that define the portfolio. Before computers, it was virtually impossible to determine what strategy guided the development of a portfolio. The number of underlying factors an investor could consider (characteristics that define a portfolio like PE ratio, dividend yield, etc.) seemed endless. The best you could do was look at portfolios in the most general ways. Sometimes even a professional manager didn't know what particular factors characterized the stocks in his or her portfolio, relying more often on general descriptions and other qualitative measures. Traditional approaches to investing relied heavily on the portfolio manager's insights about prospects for the business and/or industry and often specifically what the manager thought about the management team of the corporation. As we will learn in coming chapters, intuitive-based management is almost always outperformed by the consistent application of time-tested strategies.
Excerpted from WHAT WORKS ON WALL STREET by JAMES P. O'SHAUGHNESSY Copyright © 2012 by James P. O'Shaughnessy. Excerpted by permission of McGraw-Hill. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
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Most Helpful Customer Reviews
Fantastic read! I really enjoyed the chapter on the financial downsides of the furry lifestyle.
An excellent book detailing the issue of Gems within Wall Street and how to combat them. Sadly I did not give it 5/5 due to the lack of talk about the JQ,