This is certainly not the last word on strategy development but it's a great, and possibly the best, starting point. The book is every bit as "little" as the title suggests and its explanations are clear even for novices. Best of all, though, is what Greenblatt preaches: buy shares of GOOD COMPANIES that can be obtained at GOOD PRICES. That, ultimately, is what stock-market investing is all about. All else amounts to variations on this core theme. Moreover, Greenblatt worked with screening and ranking, meaning his ideas can be implemented directly on Portfolio123. In fact, we've done just that by offering a Greenblatt "All-Star" screen
which follows his approach and also makes (and explains) our own changes - as an example of what you can do if, as we hope, you copy models like this into your account and adapt to your preferences.
These four works offer much that can help you learn to think about stocks. Adapting to Portfolio123 can be a challenge. Two of the works do not seem to result from any quantitative efforts and one other speaks in quantitative terms but preserves intellectual property by withholding enough detail to frustrate those who want to precisely copy.
It's been quite a while since Lynch managed the Fidelity Magellan mutual fund and his genius isn't always easy to translate to screening/ranking. But the successes he achieved were so great his ideas should be studied even today. What you're looking for here is a mindset, not necessarily a formula, but a very important mindset.
It's Warren Buffett. Need we say more? Well, actually, we do. The only way to directly get Warren Buffett's words in writing is to read the Chairman's Letters from the Berkshire Hathaway annual filings. These are available in public documents so you certainly can do that, and you should read at least some of them. But this book presents what is probably as good an overall one-source summary as you'll see. Buffett never endorsed it, but he directly told me that he saw it (the first edition) before publication and did not communicate any objections to the author. As with the Lynch book, it's really tempting to adapt formulas to express this approach, and we've tried with one of our All-Star models. But Buffett didn't do formulas, so again, guidance regarding a mindset is what you're really after.
This classic, by the publisher of Investors' Business Daily, looks like it will be easy to use to create screens and ranks, but it can ultimately be frustrating when one notices that O'Neil withholds just enough information - a very small amount but just enough - to prevent us from precisely implementing on our own without subscribing to IBD. Even so, he offers much to help one develop a mindset and a different one from that you'd find in Lynch or Buffett. The latter are closer to buy-low/sell-high; O'Neil is more along the lines of buy-high/sell-higher.
Fundamental merit, valuation; ideas about how a stock should behave are vital. But so, too, are ideas about how stocks actually do behave, whether or not the purist would say the market is doing the wrong thing. Jim Cramer is a master at understanding and explaining this mindset. Don't be fooled by his CNBC antics. He was a very successful hedge-fund manager over a prolonged period of time. He knows what's what. And he can educate in an engaging and entertaining manner.
These books are more advanced, but still quite readable. The really heavy-duty advanced work comes with adapting the ideas to Portfolio123. But if you are willing to roll up your sleeves, you can use these ideas to help you uncover some opportunities others, even others who screen and rank, may be missing.
Earnings quality is a hot topic and one that very often can't be addressed by screening and ranking with the sort of data we use. This book is an exception. Not everything discussed here readily lends itself to application on Portfoli123, but there is a lot that can be used - assuming you're willing to work slowly and diligently to build heavy-duty screens (more so screens than ranking systems).
These authors use the Greenblatt model (his "magic formula") as a starting point, critique it, and develop their own enhanced version. The weakness of the book is that their new super formula can't be implemented on Portfolio123 (you'd need to license a database and hire your own developers). The strength of the book is the transparency of the thought process. You can learn a lot following the authors as they move from Point A to Point B.
There's been much written about the topic of valuation and it can be difficult to collect and keep up with all of it. Damodaran does a great job in summarizing the accumulated theoretical wisdom and discussing issues that come up in terms of practical application. Not everything can be translated to a Portfolio123 system, but for a primer on the kinds of things you should or shouldn't try to do, this is a great resource.
This is it; the big enchilada, THE classic. (Surely you didn't think I'd leave it out!) It's not an easy read. In fact, I'm not sure even Graham or Dodd's friends or relatives sat down and read cover to cover. The publishing house editors had no choice; they were getting paid to do that. Their students probably had to do it, or at least come close. But how can a serious investor not have it in his or her library? And actually, if you zero in on parts that impact issues with which you're wrestling, you can still find great wisdom in it. I continue to refer to it and quote it often.
Part of me cringes as I recommend these books. On the surface, it looks like they endorse and even flaunt one of the worst things a Portfolio123 user can do on the platform; engage in curve fitting or data mining. In fact the title to one of these books, the better known classic as it turns out, trumpets and may even be responsible for coining the curve-fitter's mantra: "what works." That may be the single most unfortunate choice of a title in the realm of stock-market literature. If you get either or both of these books, please, please, please make sure you go beyond the surface. These authors study factor performance and draw conclusions from historical data testing. But they don't engage in and even argue against data mining. The difference is mainly in what they choose to look at. If the idea makes no sense, they don't even bother studying it. If you recognize what they're doing, you can learn a lot here.
The classic in factor study. Remember, though, if you think he's just data mining, it means you haven't read closely enough.
This is similar to O'Shaughnessy's work although not quite as well known. The two together make for a fascinating comparison. See what comes out the same data items and see where they differ. It's important to recognize that even when you engage in something so seemingly black-and-white as factor study, different conclusions can be reached; differences in detail that is. The underlying principles that make certain factors work are common to both works and transcend formula specifics.
The book that planted a seed that eventually became Portfolio123. "The Inefficient Stock Market" is a nice slap in the face to Modern Finance. Mr. Haugen challenges that notion by creating and testing a non-optimized 20-factor ranking system. Besides confirming the value of what we do on Portfolio123 (test results showed a 32% annual return for the highest-rated stocks versus 18% for the S&P 500 over a 20-year period), the book offers an over-the-shoulder look, so to speak, at the process of developing a ranking system.
Did you ever think of yourself and your Portfolio123 peers as pioneers? You should because that's what we are. As classic as some of the investment theories we profitably apply may be, the way we implement them, through use of objective rules, formulas, rank factors, etc. is, in the grand scheme of things, quite new. It's not the same as looking up numbers and computing ratios. What we do goes way beyond that. We use data to find ideas and develop comprehensive investment stories. Ben Graham didn't do it. David Dodd didn't do it. Peter Lynch didn't do it. Warren Buffett didn't and doesn't do it. O'Neil does it but isn't giving away all the ingredients to his secret sauce. I've seen clues that suggest Jim Cramer may do more of it than many realize but for the most part, he's don't-ask-don't-tell when it comes to this sort of thing. So generally, as we learn to navigate the world of investment data (something that will probably be old hat by Y2.1K), we often find that established learning resources come up short when it comes to addressing our unique needs. Here are two early entries that can hopefully help you navigate our still under-charted waters.
Yeah, I know; self-plug. Sorry about that. But seriously, explaining to people why they should screen to find stocks is not such an easy sell, and especially so in the early days of the Internet, when there were loads of free screeners out there that couldn't help a bathing-suit-clad adventurer in the Arctic find a sniffle. They were just toys that let web surfers utter ooh and ah for a couple of minutes before they were encouraged to surf off to other banner-ad-bearing pages. And looking at data presentations scattered among still-more ad-bearing web pages left surfers with a sense of confusion, not just over differences between on-line broker-advertisers like Datek (Remember them?) and E*Trade but also more substantive things, like return on equity versus 26-day price return. A lot of the early-days web sites went away or "reoriented" their focuses toward other things. So the Portfolio123 community, that uses and actually profits from this stuff, can still use some guidance. I try to balance between data as a finding tool and as a research tool with, perhaps, more TLC devoted to finding.
He was doing his thing (on his first edition) about the same time I worked on Screening the Market and we talked occasionally. As with my book, Harry covered finding and analyzing but his was much more oriented toward analyzing, and he was more inclined to build company stories around guru guidance. Again, as with O'Shaughnessy/ Tortoriello, it can be valuable to see how two different sources approach topics that those who don't really look closely might erroneously believe to be cut-and-dried.