This article is the second in a series about screens designed by famous investors. The first, on Benjamin Graham, can be found here; for an overview of the subject, see my article Can Screening for Stocks Still Generate Alpha?

In 1988, William O’Neil published How to Make Money in Stocks, which has apparently sold over two million copies since then. In this article, I’m going to take a close look at O’Neil’s techniques, philosophy, and system.

O’Neil calls his system CAN SLIM after the first letters of each of its principal seven components. Unfortunately, it sounds more like a diet than an investing system—and indeed, O’Neil’s writing has a lot in common with diet books.

In my opinion, some of O’Neil’s guidelines make no financial sense and could be very harmful to investors, while others are tremendously valuable. At the end of the article I’m going to create a screen that follows all of his rules and demonstrate why it simply doesn’t work.

O’Neil versus O’Shaughnessy

William O’Neil and James O’Shaughnessy are from very different generations: O’Neil was born in 1933 and O’Shaughnessy in 1960. Both of them have devoted a considerable portion of their lives to examining stocks that outperform the market. But they took very different approaches.

O’Neil made a special study of “superstar stocks” whose prices doubled, tripled, or went even higher. He was interested in finding out what these stocks had in common with each other. O’Shaughnessy, on the other hand, studied the market as a whole, and tried to find factors that effectively classified stocks into potential winners and losers.

These very different approaches had very different results, predictably.

Let’s take betting on horses as an analogy. The O’Neil approach would be to look at winning horses and see what they had in common. The O’Shaughnessy approach would be to classify all possible bets not just according to the characteristics of the horses but according to the betting odds.

The odds in betting are similar to the prices in the stock market. In parimutuel betting, odds are determined purely on the basis of how interested the aggregate bettors are in each horse, just as in the stock market the price is determined purely on the basis of how interested the aggregate investors and traders are in each stock. This, as both O’Neil and O’Shaughnessy recognize, is valuable information indeed.

In looking at horses (and what I write here is purely hypothetical, based on no data at all, and given purely for illustration), O’Neil might have noticed that horses who are likely to win often have odds that have gotten lower in the last few weeks, like a horse whose odds have gone from 4:1 to 5:2. O’Shaughnessy might have noticed that the highest payoffs are on safe, reliable horses with little glamour whose odds are uncharacteristically high—20:1 or higher. Thus O’Shaughnessy favors unglamourous stocks that are underpriced while O’Neil favors stocks that are making new highs; O’Shaughnessy finds that high-growth stocks underperform while O’Neil finds that they outperform.

But there’s another major difference in their approaches. Before O’Neil even approaches the historical study of a stock, he knows whether it was a winner or loser. He comes to the investigation of stocks armed with foreknowledge. O’Shaughnessy, on the other hand, sets up backtests to test his theories and does his utmost to avoid look-ahead bias. He wants to find factors that will predict whether a stock’s price will rise or fall without knowing the answer in advance.

It’s pretty obvious which approach is more “scientific” and which is more calculated to appeal to a broad audience. The promise of stocks that double or triple in value is easy to sell; the promise of avoiding look-ahead bias will only appeal to the statistically minded investor. That’s why O’Neil’s newsletter, Investors Business Daily, has over 100,000 subscribers who each pay over $400 a year, and why his book has sold over two million copies. O’Shaughnessy’s success, while considerable, pales in comparison.

Chart Patterns

How to Make Money in Stocks (I read the fourth edition, published in 2009) starts with dozens of pages devoted to charts illustrating cup-and-handle patterns; more of these recur throughout the book (the head-and-shoulders pattern also plays a large part). I’m afraid that I personally have no patience with these charts. Each of them shows a pattern and what happened in the days immediately after the pattern. Most of them show a sharp rise in the stock’s price immediately after the pattern comes to an end. This, to me, is all anecdotal evidence, or twenty-twenty hindsight. What about all the stocks that experienced a sharp rise without ever having a cup and handle in their charts? What about all the cups and handles whose price subsequently fell? Could you recognize a cup and handle if it were on the very right side of the chart, before the subsequent rise? Take a hundred random stocks and chart them, stopping on some random date in 2019. How many have cup-and-handle patterns on the right edge of the chart? I’m sure there must be at least four or five. What happened to those stocks after that random 2019 date? Did they all zoom up? Or did some of them take a plunge? Now take ten random stocks. In each one, go back in time and find a six-month period of great price appreciation. What patterns do you see immediately before that six-month period? Now go back and time and find a six-month period of great price depreciation. What patterns do you see immediately before that plunge? Is there any real difference?

A large number of academic papers have been written on using chart patterns to predict stock prices, and the near-unanimous conclusion is that they simply don’t work very well. There is practically no statistically significant evidence for them, and it’s not for lack of trying. A typical study, dating from 2017, is Empirical Evaluation of Price-Based Technical Patterns Using Probabilistic Neural Networks, which also includes an overview of previous studies. (The overview, unfortunately, mixes up studies of momentum factors with studies of chart patterns; the former have been shown to work, over and over again.) This particular study “reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed.” And that’s after the author, Samit Ahlawat, who is very well versed in technical analysis, has carefully identified and studied dozens of different patterns, all of whose results he carefully tabulates in his article.


“C = Current Big or Accelerating Quarterly Earnings and Sales per Share.”

O’Neil found that explosive quarterly earnings were the most predictive of all the factors he looked at when he examined what his “superstar stocks” had in common before their huge increases. In doing so he focused primarily on the growth in quarterly earnings per share over the same quarter the previous year.

In my own research, I have found that this factor—whether you use net income as your basis, or operating income, or EBITDA, or the current quarter’s earnings estimate, or all of them—is indeed a very powerful predictive force when it comes to short-term price gains. Nothing in the CAN SLIM system is, to my mind, more important than this one factor. In fact, of the stocks I currently own, 80% of them sport earnings growth of over 25% by this measure, which is O’Neil’s threshold, and I’ll probably sell most of those that don’t quite soon.

O’Neil also emphasizes the importance of some sales growth and introduces the idea of accelerating growth. Again, he’s right on the money here. If I had to choose one chapter of How to Make Money in Stocks that all short-term investors (those who typically hold stocks for less than a year) should read, I would choose this one.


A stands for Annual Earnings Increases. Here O’Neil argues that we should invest in companies with high annual earnings growth and high return on equity. He also devotes considerable space to attacking the use of the P/E ratio and claiming that stocks with high P/E ratios can be superb bargains.

Below are six bar charts. For each of them, I took one factor and ranked, using Portfolio123, all US stocks with a price greater than $15 (O’Neil recommends limiting your choice to such stocks) on one factor against other stocks in the same sector, with monthly rebalancing, excluding N/As from consideration. (The leftmost bar is the S&P 500.) The left three charts reflect the compounded returns over the last ten years, the right three over the last twenty.


The top row is EPS growth, the most recent quarter compared to the same quarter last year (the C in CAN SLIM). Clearly the top half of stocks on this measure perform better than the bottom half.

The second row is EPS growth, the most recent trailing twelve months compared to the twelve months before that (the A in CAN SLIM). Clearly, stocks with middling annual EPS growth outperform stocks with high or low annual EPS growth.

The third row is trailing-twelve-month P/E, with lower numbers on the right. While P/E makes absolutely no difference over the last ten years, over the last twenty low P/E stocks have outperformed high P/E stocks more forcefully than high EPS growth stocks have outperformed low EPS growth stocks.

In short, I don’t believe this chapter offers any useful advice. In fact, limiting yourself to stocks with very high annual growth will exclude many of the stocks most likely to outperform. And I strongly believe that low P/E compared to stocks in the same industry is a valuable factor in deciding what stocks to buy.


N stands for new, as in “Newer Companies, New Products, New Management, New Highs off Properly Formed Bases.” As far as new products go, quite a few industries (energy, mining, utilities) are not going to be offering many of those. But O’Neil doesn’t spend much time on such companies, devoting half the chapter to promoting stocks that are making new highs. The following bar charts rank stocks on how close they are to their twelve-month high price.

Merge_from_ofoct 2

As you can see, companies that are very close to their twelve-month high price are not primed to outperform.


S stands for supply and demand. Here O’Neil advocates buying companies with low floats—in other words, small companies—because they have much more room to grow than big companies. He also advocates investing in companies that buy back shares. Both of these, in my opinion, are excellent ideas.


L stands for Leader or Laggard. One way to tell whether a stock is a leader or a laggard is to use a multifactor ranking system to do so, something that O’Neil hints at when he writes, “By number one, I don’t mean the largest company or the one with the most recognized brand name. I mean the one with the best quarterly and annual earnings growth, the highest return on equity, the widest profit margins, the strongest sales growth, and the most dynamic stock-price action.” I’m a firm believer in ranking stocks, and it looks like O’Neil likes to do so as well.


I stands for Institutional Sponsorship. O’Neil suggests that winning stocks already have a significant number of institutional investors, and that investors look for stocks that are followed by quality portfolio managers and sport an increasing number of buyers. But he also warns against stocks that are oversubscribed. This is excellent advice: stocks with an increase in institutional holders do better than stocks with a decrease; it’s not a bad idea to avoid stocks with fewer than twenty institutional holders; and it’s very wise to avoid the fifty stocks with the largest percentage of their shares held by institutions.


O’Neil argues that if you try hard enough, you can predict the direction of the market as a whole, at least in the short term, so that you know when to buy stocks and when to go to cash. “In your analytical tool kit,” he writes, “you absolutely must have a proven, reliable method to accurately determine whether you’re in a bull (uptrending) market or a bear (downtrending) market.”

This strikes me as a fool’s errand. I’ve written at length on market timing, so I won’t spend time on it here. Suffice it to say that O’Neil offers no sure tips on market timing, instead advising readers to check certain ratings and columns in IBD. Characteristically, IBD published a column on March 31st entitled “Corona Virus Stock Market Crash: Here’s How to Spot a Market Bottom.” This was eight days after the market bottomed, and IBD was advising investors what to look for. In fact, the biggest one-day percentage gain of the entire year so far was on March 24, the day after the market hit bottom. If you missed that gain because you’d gone to cash, you were probably a victim of the belief that you could time the market. Because I know I can’t time the market, I remained fully invested, lost a third of my money, and then proceeded to more than double what I had left.

In sum, when it comes to CAN SLIM, I like four out of O’Neil’s seven rules: C, S, L, and I.

The Most Dangerous Part of the Book

Right at the beginning of Part II is the most dangerous part of O’Neil’s book: his advocacy of selling any stock you own that goes down in price more than a certain percentage. “Always,” O’Neil writes in boldface,“without Exception, Limit Losses to 7% or 8% of Your Cost.” This is a classic example of anchoring bias and one of the biggest mistakes novice investors make (yes, I made it too when I was a novice investor). Remember: the future price of a stock is completely unrelated to the price you paid for it. The price you paid for it is irrelevant. It’s absolutely worthless information. Never look at the price you paid for a stock when you’re trying to decide whether to sell, hold, or buy more.

“Now hold on,” O’Neil might say. “What I’m talking about is simply smart money management. Cut your losses before they become big.”

But every time you sell a stock, you forego the chance that it’ll rise in price. And if a stock has just fallen in price over the last week or two, it has a better than 50% chance of rising in price over the next week or two (stock prices tend to mean revert over the short term). Not only that, by selling you are locking in your loss. Is that smart money management?

When to Sell

O’Neil follows this chapter with another on when to sell, which is based on the principle of looking for what he calls “climax tops.” Once again, the pages are full of charts and patterns. The chapter is long and complicated, full of advice like this: “Sell if a stock closes at the end of the week below a major long-term uptrend line or breaks a key price support area on overwhelming volume. An uptrend line should connect at least three intraday or intraweek price lows occurring over a number of months. Trend lines drawn over too short a time period aren’t valid.” For the same reasons I dismissed chart patterns earlier, I can’t put my faith in this.

Money Management

O’Neil’s chapter on money management has some good advice in it, but it’s also full of “told-you-so” charts illustrating the falls of Enron and AIG. It’s here that O’Neil finally lays out his rules for what stocks to consider: a minimum price of $15 per share and no OTC or foreign stocks. I can’t understand why he buries these most basic rules at this point in the book.

The 21 Mistakes to Make

O’Neil has a delightful chapter, appropriately the 13th, called “Twenty-One Common Costly Mistakes Most Investors Make.” I’m quite proud to say that in my investing I deliberately make more than half of these 21 “mistakes” every day. Clearly, O’Neil and I are from different investing universes. Here are the “mistakes” I make:

  • I stubbornly hold on to my losses (when I have good reason to believe the stock will increase in price).
  • I buy on the way down in price. Let’s say I buy a diamond bracelet for $500 and plan to resell it for more. Then I see an identical bracelet for $400. Why wouldn’t I buy that one too?
  • I average down in price. Same as above.
  • I don’t learn to use charts.
  • I don’t have “specific general market rules to tell when a correction in the market is beginning or when a market decline is over.”
  • I fail to “understand the importance of . . . learning how to use charts to significantly improve selection and timing.”
  • I select “second-rate stocks” because of low price/earnings ratios. A lot of my biggest investing successes have been in totally “second-rate stocks.”
  • I want “to make a quick and easy buck.” Every day, every week, every month. It’s by making lots of quick and easy bucks that you can compound your winnings.
  • I cash in “small, easy-to-take profits” when a stock I hold no longer ranks highly and I “hold the losers” when those losers rank highly.
  • I worry “too much” about taxes and commissions (and transaction costs).
  • I never transact at the market, placing only limit orders, and spend hours trying to get good fills.

Investing Like a Professional?

When Part III, called “Investing Like a Professional,” rolled around, I was wondering if the book would ever end. The hucksterism begins to get truly wearisome. “Regardless of your current position in life or your financial standing, it’s clearly possible for you to make your dreams come true using the CAN SLIM system. You may have heard or read about the thousands of individuals who have changed their lives using this book and Investor’s Business Daily. It really happens, and it can happen to you if you are determined and have an overpowering desire, no matter how large or small your account . . . as long as you make up your mind, work at it, and don’t ever let yourself get discouraged.” It’s almost like a parody of a self-help book, and a far cry from “investing like a professional.” The professionals I know have no patience with language like this—which is followed by dozens more cup-and-handle stock charts illustrating successful stocks.

Thankfully, there is some more material of value after this. O’Neil wisely advocates investing in industry groups that are leading the market. I think this is such valuable advice that it should have been added to the CAN SLIM system. There’s a lot more in this part; but I think I’m on the verge of exhausting the reader. So let’s move forward to chapter 20 . . .

The CAN SLIM screen

O’Neil concludes his book with a list of “important rules and guidelines to remember” that summarizes the rest of the book. I have used this list, along with material from the rest of the book, to create a stock screener on Portfolio123, which I used as a basis to simulate O’Neil’s investing system.

Here are the screening rules:

  1. price greater than $15
  2. no foreign or OTC stocks
  3. annual EPS growth greater than 25% each of the last three years
  4. projected EPS growth greater than 25%
  5. most recent quarter’s EPS growth (over same quarter last year) greater than 25%
  6. previous quarter’s EPS growth (ditto) greater than 25%
  7. most recent quarter’s sales growth (ditto) greater than 25% OR greater than trailing twelve-month (TTM) sales growth (acceleration)
  8. ROE greater than 17%
  9. current quarter’s profit margin higher than TTM profit margin
  10. price closer to the 52-week high than the 52-week low
  11. relative price strength (as measured by the 52-week price change) in the top 15%
  12. the number of institutional holders has increased over the last year
  13. a minimum of twenty institutional holders
  14. avoid the top fifty stocks most held by institutions
  15. avoid the 20% of industries with the weakest six-month price gains

But there’s a major problem with this screen: no stocks pass it. If I use FactSet data, only 58 stocks have passed this screen over the last 15 years. (The most recent are Enphase Energy (ENPH), Netflix (NFLX), Hamilton Lane (HLNE), and ServiceNow (NOW), all of which passed the screen at one point or another since April.) During the entirety of 2019, only one stock passed the screen—Amazon (AMZN) for a couple of weeks in January.

And how do the stocks which do pass the screen fare?

Terribly. If you had bought each of the stocks that passed the screen over the past 15 years and held each for six months, you would lose an average of 6% per stock. If you changed your sell rules so that you sold if the stock went 7.5% below its purchase price or if the most recent TTM EPS change was worse than it was a year ago (both rules that O’Neil strongly advocates), your average return rises to -1.4%, which is still terrible.

CAN SLIM in the Real World

IBD has put a modified version of the CAN SLIM system into practice in an ETF whose ticker is FFTY; it holds the top fifty stocks according to IBD’s own criteria and rebalances weekly, with a higher portfolio weight assigned to the top-ranked tickers. The ETF’s performance is nothing to write home about: although it’s up over 6% YTD, it has lagged the S&P 500 over the last year, the last three years, the last five years, and since its inception in April 2015.

AAII has three CAN SLIM screens with different criteria for each. The first has a ten-year annualized performance of 6.3%, the second 10.7%, and the third, which is based on the third edition of How to Make Money in Stocks (I’ve been using the fourth), 23.5%. All of these numbers are out-of-sample. If you had put an equal amount of money into each of them ten years ago, you would have outperformed the S&P 500 by 2.8% per annum. These screens are very well-designed, but necessarily leave out many of the requirements of CAN SLIM.

The CAN SLIM Antidote

At the same time as I read How to Make Money in Stocks, I also read Mohnish Pabrai’s The Dhando Investor. Pabrai is a follower of Warren Buffet, and in this book he expounds on the mantra of value investing by using interesting examples of Indian immigrants who have bought businesses and made fortunes on them. It’s a short and quite repetitive book, and his maxims I found too simple to be really useful. But there’s one concept that I think is well expressed and likely to lead to investing success: concentrate on low-risk but high-uncertainty assets.

Many assets are underpriced because of high uncertainty about their prospects. The prospects of a few of these assets can be broken down into, say, a half-dozen possibilities. If each of the possibilities is low-risk (a high margin of safety) and a few of them are high-return, you’ve got yourself a relatively risk-free bargain with massive potential.

I don’t think you could find two immensely successful investors and authors with more opposing viewpoints. Both of their books are easy to read and full of anecdotal evidence, though O’Neil’s book is about four times as long. While it’s not easy to backtest O’Neil, it’s impossible to backtest Pabrai. But my gut tells me he’s right.

My takeaway? There are lots of ways to invest. Learn from the masters, but keep your inner b.s. meter on high alert.

My CAGR since 1/1/2016: 36%.

My top ten holdings right now: LMB, SGC, CYD, DIIBF, ISDR, KRRGF, LFVN, NLS, RMNI, DPMLF.