Online Investing: How to Find the Right Stocks at the Right Time, Second Edition

Online Investing: How to Find the Right Stocks at the Right Time, Second Edition

by Jon D. Markman


9 New & Used Starting at $1.99


Using one of author Jon Markman's momentum-stock models, a single $10,000 investment in 1986 would be worth $5.4 million today. That's ten times what the S&P 500 did! In Online Investing, Second Edition, Markman builds on his innovative "year-trading" model and shares new wealth-building strategies — once again demonstrating how to use the power of the Internet and a home PC to do in minutes what Wall Street professionals do full time. The book provides updated information on growth year-trading and delivers new content on month-trading, seasonal trading patterns, where momentum comes from, and how to pick the stocks with the potential to gain 10,000% in 10 years! In the book's highly regarded first edition, this award-winning author made complex investment terminology and trading strategies accessible to even the lay investor. Online Investing, Second Edition delivers more of Markman's expert and easy-to-read advice, complete with powerful new techniques for finding — and buying — the right stocks at the right time.

Product Details

ISBN-13: 9780735611238
Publisher: Microsoft Press
Publication date: 02/17/2001
Series: EU-Independent Series
Pages: 370
Product dimensions: 7.28(w) x 9.05(h) x 1.07(d)

Read an Excerpt

Chapter 5.

  • Specialized Stats
  • HiMARQ Analysis
    • Identifying Patterns
    • Slugging Averages
    • Buddy System
    • The Nicoski Momentum Oscillator

Chapter 5 Counting

At a party aboard a friend’s boat on Lake Washington, my five-year-old daughter fell into a lively conversation with an émigré from the small African nation of Togo. The chat quickly turned to the subject of kindergarten, and the woman told my daughter a curious thing.

When she was little, the woman said, no one kept track of the date on which children were born. To determine whether a boy or a girl was old enough for school, the village schoolteacher would ask them to stretch and bend their right arm directly over their head and try to touch their left ear. If a child’s arm was long enough, she said, they were ready to start.

My daughter, Janie, immediately attempted the task and was disappointed to learn she didn’t measure up. She happily went on to kindergarten anyway, but the discussion got me thinking. The Togo technique for gauging the passage of years with the natural ratio of arm length to head size, not a calendar, luminously reflected my quest to discover alternative ways to measure stocks against the passage of days, months, and years.

Over the 18 months since the first edition of this book, I interviewed dozens of Wall Street strategists to gather fresh insights into hidden relationships between time and money, and I invented a few of my own techniques as well. In every case, the motivation was the same: to determine which quantitative factors characterize the best stocks to own at any given moment from the perspective of earnings growth, fundamental value, calendar or fiscal seasonality, group psychology, price momentum, and volatility. It’s a quest that quantitative analysts blandly call "the search for alpha"—that is, the search for price gain in excess of a benchmark, such as the Standard & Poor’s (S&P) 500 Index.

I shared most of the ideas as they emerged with readers of my weekly column at MSN MoneyCentral and will use this chapter to highlight and synthesize some of the better ones. They all require some proficiency in the use of spreadsheet software such as Microsoft Excel. I won’t explain how to exploit this software in detail; the point of this chapter is to encourage you to consider learning to combine the power of the Web and spreadsheets in ways that were not previously possible. In Appendix C of this book, found online at, I have provided homemade spreadsheets with macros that will enable anyone with modestly advanced Excel skills to begin exploring these ideas.

Specialized Stats

Why bother examining these statistical relationships? My interest in new stock stats stems partly from my love for baseball. This dusty, sweaty sport appears on the surface to be leagues removed from the genteel game of investing—but partisans of both are strangely more analytical about understanding their favorite teams than their favorite stocks. Visit any major sports Web site, for instance, and you will typically find a Stats link right on the home page, offering access to tables full of data about all the hitters in each league easily sorted by year-to-date batting average, on-base percentage, and slugging percentage. Sometimes you’ll find more obscure metrics, such as hitters’ stats against individual teams and pitchers, or in particular ballparks, or in day games vs. night games. And every major newspaper in America offers at least one detailed page of baseball stats.

Visit any major financial news Web site, in contrast, and you’ll have to hunt for a list of even the current day’s top 10 performers, much less a quickly sorted list of the top 100 or 500 most heavily traded stocks. Moreover, the metrics are largely limited to the current day’s percentage gain or loss, plus the raw number of shares traded that day. Click a little deeper and you might find one-year, six-month, and three-month percentage returns for individual stocks. But it’s pretty hard to find and sort through year-to-date return, volatility measures, alpha, or any more-sophisticated measurements of performance that might help you get beneath the yadda-yadda-yadda of the market players and learn about the tectonic changes that are truly going on beneath the surface. Try to do the same in a mainstream metropolitan newspaper, and you’ll find a much shorter list of actively traded stocks, and nothing more exotic than a New Highs / New Lows list.

What accounts for this disparity between the quality of baseball stats and financial stats? Despite the fact that stocks appear on the surface to be all about numbers—growth rates, price/earnings ratios, returns on equity, and so forth—the high priests at brokerages and in the press typically profess wariness about statistics, preferring to talk mainly about ethereal factors like management, new products, and global economic conditions. Very few technical analysts or statisticians have managed to rise above the investment media’s aversion to math to offer just plain facts about the condition and direction of the only thing that ultimately matters about a stock—its price. The Web sites (—published by the newspaper Investor’s Business Daily) and ( are a couple of exceptions that prove the rule.

The fact that baseball has exalted stats to higher and higher levels is thanks in significant part to sportswriter Bill James, and his philosophy could also serve as a standard for statistics-loving investors. From 1977 to 1988, James published a series of books called "Baseball Abstracts" that extolled the advantages of understanding baseball by analyzing an aggregation of data about players and teams rather than just observing them on a daily basis and listening to their timeworn refrains. "Clichés are the soldiers of ignorance," he wrote, while "statistics look at games by the hundreds….Without them, it is impossible to have any concept of the game, save for meaningless details floating in space."

My favorite Jamesian conceit is the invention of new statistics from time to time to uncover trends that aren’t well explained by conventional means. So in that vein, in this chapter I will present HiMARQ analysis, which reveals stocks’ historical monthly and quarterly performance; slugging percentages, which reveal stocks’ daily and weekly volatility; the buddy system, which attempts to combine stocks with negatively correlated performance and volatility; and the Nicoski M26/M52 oscillators, which reveal whether stocks are overbought or oversold compared with the average historic ratio of their current price to their price 26 to 52 weeks ago. An important caveat before starting: unlike the screens presented in the previous three chapters, most of the ideas presented here cannot easily be historically tested to determine their usefulness as predictors of future stock prices. Yet that should not prevent you from examining each and thinking about how they might be installed in your arsenal of investment weapons. (No matter whether they all work all the time—and I guarantee that they won’t—you will still be allowed to progress to kindergarten.)

HiMARQ Analysis

In December 1996, shareholders of can’t-miss computer networking company 3Com were riding an unbelievable high. In the prior five and a half years, their stock had risen a stunning 3650%—outdistancing such market luminaries as America Online, up 1750% during the same period; Dell Computer, up 1235%; Microsoft, up 540%; and even mighty Cisco Systems, up 3090%.

That means 3Com scored an average annual gain of 93% for half a decade, thanks to enthusiasm for its network-interface card, a high-margin industry standard that was practically an emblem for the rapid, worldwide growth of business productivity in the mid-1990s. Yet as Christmas lights were coming down that month, 3Com’s stunning run was about to end. An earnings shortfall in February 1997, precipitated by Intel Corporation’s price-slashing entry into its market, led to one of the most prolonged slides in high-tech stock history up to that time. Over the next three years, 3Com’s shares fell 65%. It didn’t happen all at once. After an initial sharp drop, the rest came in heartbreaking dribs and drabs. Many longtime shareholders cursed a troublesome and ill-timed merger with modem maker U.S. Robotics and waited patiently for the turnaround.

But by August 2000 they were still waiting—despite the firm’s successful spin-off of a division that designs and manufactures the wildly popular Palm family of personal digital assistant devices (acquired, incidentally, in the U.S. Robotics deal). So if they were smart they were wondering two things: would there ever be a decent bounce again that would let them get out whole, and what they should do the next time that "buy and hold" turned into "buy and die"?

One way to solve this question is to determine whether—all fundamental factors aside—there is one time of year that tends to be more favorable to an individual stock than other times. And second, is that time of year so favorable that it’s worth waiting for?

The answer is hard to come by with pen and paper, but it turns out to be fairly simple if you’re handy at downloading data from the Web and manipulating it in a spreadsheet program like Microsoft Excel. I’ve provided detailed instructions in Appendix C (found at, but essentially you just need to use MSN MoneyCentral or Yahoo! Finance features that permit the export of historical monthly stock price data from charts, then create a column for monthly percentage change, and finally use Excel’s PivotTable and Group functions to combine and group that data into months and quarters. The result is something I call "Historical Monthly Average Return Quotients," or HiMARQ analysis for short.

HiMARQ analysis helps you figure out the best month to sell or buy both individual stocks like 3Com and whole industrial sectors, such as networking equipment makers, oil drillers, or airlines. It works best for growth companies as well as for industries characterized by seasonal sales patterns. First I’ll offer a broad overview of the concept, and then I’ll suggest a theory as to why it works—and along the way I’ll propose ways to take advantage. If it sounds like too much work to do yourself, in Appendix C you will find the HiMARQ stats for the 100 largest companies that have traded at least five years through November 2000. Also in Appendix C, you’ll find the 25 large-cap stocks with the best HiMARQ stats for each month.

Identifying Patterns

3Com is an interesting test case for monthly price-change analysis because its historical pattern is so clear. The stock has gained 2.6% on average each month over its 12-year history, but these advances have not been evenly distributed throughout each year. Instead, 3Com has consistently experienced terrific rallies from September 1 through December 31, as shown in Table 5-1, and has then declined or shifted into neutral for the next eight months. Its best months by far are September and December, when it has gained 12.6% and 11.8% on average, respectively. Its worst are almost all the other winter, spring, and summer months, as shown in the table.

Table 5-1. 3Com HiMARQ Analysis, 1989–2000

Throughout its 12-year history, 3Com stock has consistently rallied sharply from September to December and declined from January through April.

1st Quarter 
2nd Quarter 
3rd Quarter 
4th Quarter 

Skeptics of this sort of analysis might immediately imagine that a few stray months’ returns are skewing the data. But a closer look at the returns of individual months suggests that’s not the case. 3Com has experienced only two negative Septembers in its history, as shown in Table 5-2, and they were way back in 1989 and 1990. Even during the recent downtrend, the stock has managed nice advances in the month. In 1999, for example, the stock was up 16.2% in September even as the Nasdaq Composite Index and Dow Jones Industrial Average were down 0.7% and 6.6%, respectively, and industry stalwarts Cisco Systems and Intel were down 3.1% and 9.9%, respectively. In 2000, the stock was up 13.3% while the Nasdaq Composite Index plunged 13.3%.

Table 5-2. 3Com Septembers, 1989–2000

Since 1991, 3Com has never experienced a negative September.

SeptembersPrice Chg.

3Com likewise has experienced only two negative Decembers in its history (-6.5% in 1997 and -4.0% in 1996) and three negative Novembers (1997, 1995, and 1991). But its Januaries are a different matter: 8 out of 11 have been negative. And it has suffered two of its greatest one-month declines in Februaries: -46.6% in 1997 and -28.8% in 1999. You also seldom want to own this stock in April, it appears: 8 out of 11 have been negative. So the message seems clear: buy 3Com just before Labor Day, sell it after Christmas, and avoid it the rest of the year.

These seasonal price-change patterns play out for most growth and cyclical companies—particularly ones in industries, like technology and retail, that are characterized by strong seasonal buying patterns for their products. Semiconductor and semiconductor-equipment companies such as Intel, Micron Technology, and Applied Materials, for instance, regularly experience fabulous rallies in January that have little to do with the so-called "January effect" (which is a market theory that suggests small-cap stocks tend to outperform the market in January). And consistently the best months for America Online on average since 1992 are September (+11.5%), November (+15.6%), December (+19.9%), February (+11.3%), and March (+15.1%). The April–August period is regularly just barely better than neutral for the stock.

Indeed, for some stocks there have been can’t-miss months in the past—months in which they have recorded a gain every year that they’ve been public. In Table 5-3, I’ve listed 50 stocks with market caps greater than $1 billion that have perfect records of 6-0 or greater and a 6% return in a single month.

Table 5-3. Perfect Records

Some stocks have never recorded a negative result in certain months. These 50 have market caps greater than $1 billion, and they have perfect records of at least 6-0 and a minimum average gain of 6% in at least one month through October 2000.

MonthAverage GainPositiveNegative
Dana Corp.Dec6.1%130
Kinder MorganSep6.4%120
Dollar GeneralMar15.2%110
St. Jude MedicalMay8.7%110
Cisco SystemsNov14.0%100
SunGard Data SystemsFeb10.9%90
Foundation Health SystemsMay10.6%90
C&D TechnologiesMay10.3%90
Kimco RealtyDec7.5%90
ImClone SystemsNov32.1%80
Valassis CommunicationsDec16.0%80
Express ScriptsDec15.1%80
Boston ScientificDec7.3%80
Marine Drilling Mar19.7%70
Dura PharmaceuticalsDec13.6%70
Barnes & NobleMar12.8%70
Panamerica BeveragesNov11.7%70
Wind River SystemsAug10.7%70
North Fork BancorpSep9.0%70
Martin Marietta MaterialsApr6.9%70
Incyte GenomicsDec34.9%60
American Eagle OutfittersMar23.4%60
Flextronics InternationalNov20.8%60
Inhale Therapeutic SystemsJan20.6%60
JDS UniphaseNov19.8%60
Legato SystemsSep19.3%60
VERITAS SoftwareDec15.7%60
Affiliated Computer ServicesDec13.7%60
Darden RestaurantsSep13.5%60
Northwest AirlinesMar12.6%60
Inhale Therapeutic SystemsNov11.5%60
Orthodontic Centers of AmericaSep10.9%60
Adecco SAFeb10.4%60
PMI GroupOct9.5%60
Nippon Telegraph & TelephoneJun9.2%60
Catalina MarketingDec9.1%60
First Industrial Realty TrustDec7.3%60
Bank of MontrealOct6.4%60

Why do these patterns occur so consistently? I think at least five factors are at work, in no particular order:

  • Industrial and retail product-buying cycles. Smart institutional investors are always looking ahead at sales and earnings growth for stocks over the next three to six months. HiMARQ analysis lets you pick up on their investment pattern without knowing exactly why it’s happening.
  • Product announcement cycles. Companies get into the habit of making their major product announcements at the same time every year. These announcements often energize investors. HiMARQ analysis picks up on that pattern as well.
  • Annual shareholder and analyst meetings and quarterly earnings reports. Companies hold their stock-moving shareholder or analyst meetings at the same time every year. And, of course, they announce earnings reports at the same time each year. HiMARQ analysis allows you to pick up on that pattern without knowing the details.
  • Insider selling habits. The fate of many great growth stocks, like Microsoft and data-storage products maker EMC, is still largely controlled by their companies’ founders. They appear to sell their very large blocks of stock regularly at certain times of year that coincide with federal securities rules and their own habits.
  • Analyst upgrade cycles. Veteran market-moving brokerage analysts who have researched and commented on individual stocks over a long period probably pick up on the four cycles above and regularly get in the habit of upgrading or downgrading stock at the same times each year.

HiMARQ analysis should be used only on stocks with at least four years’ price history, a ratio of positive to negative results in the target month of at least 2:1, and a standard deviation for the month’s returns that is lower than the average return. Ken Moss, chief software architect at MSN MoneyCentral, devised a formula for the best possible combination of HiMARQ factors: (Average monthly return times number of positive months) minus (standard deviation for the month’s returns times number of negative months), the result of which is multiplied by 100. In his honor, I call this figure the KenMARQ Index—and the higher the figure, the better.

For example, payroll-processing powerhouse Paychex has scored a 14.1% gain in Septembers over the past 11 years, its standard deviation for those returns is 8.6%, and it has racked up 11 positive Septembers vs. no negative Septembers. Thus, ((0.141 * 11) - (0.86*0)) * 100 equals a KenMARQ Index score of 154. In general, a KenMARQ score of 25 or better is preferred. Scores greater than 75 are terrific; stocks with negative scores should be avoided. As you'll see in Appendix C, the scores vary widely by month: September has two dozen $1-billion market cap stocks with KenMARQ scores greater than 75, while August has fewer than 10. In September 2000, while the S&P 500 Index sank 5.5%, Paychex was again up 20.0% in the month.

Table 5-4 lists stocks with market capitalizations greater than $10 billion and trading history greater than five years that have the best KenMARQ rankings for each month. As mentioned, in Appendix C you will find a table of the top 25 stocks over $1 billion for each month sorted by KenMARQ Index, as well as a full list of the HiMARQ stats for the top 100 stocks by market capitalization with at least five years of trading history.

Table 5-4. Most Reliable Months for Large-Cap Stocks

Yahoo! Finance and MSN MoneyCentral both allow users to download daily, weekly, and monthly stock prices in a spreadsheet. The data can then be manipulated with tools in Excel like PivotTables and Grouping. You can thus uncover interesting trends in stock prices that are invisible on the surface. That’s how I determined that the following large-cap stocks with 5-year trading histories tend to perform best in certain months of the year. (They are ranked by month and average gain through October 2000.)

CompanyMonthAvg. GainStd DevPos. Mos.Neg. Mos.KenMARQ
Cisco SystemsJan12.02%8.9%9199.29
Linear TechnologyJan12.00%11.0%8185.04
UnitedHealth GroupJan7.47%6.5%8246.82
Adecco SAJan6.56%4.7%4121.54
Adecco SAFeb10.39%5.2%6062.35
Intimate BrandsFeb7.19%4.7%4124.02
El Paso EnergyFeb5.79%4.7%7135.81
Intimate BrandsMar12.31%11.9%3213.16
Estee Lauder CompaniesMar8.39%7.0%4126.58
Australia and New Zealand Banking GroupApr6.17%3.6%6036.99
First DataApr4.32%3.5%7126.75
Bank of MontrealApr3.39%1.3%6020.37
Human Genome SciencesMay10.72%10.1%6154.24
Washington MutualMay7.14%6.5%10164.92
First DataMay6.00%6.0%8141.99
Quaker OatsMay5.95%5.2%13266.89
JDS UniphaseJun14.85%12.8%6176.28
PE Biosystems GroupJun10.72%8.0%2021.44
Sanyo Electric CompanyJun8.88%8.5%5044.40
Estee Lauder CompaniesJun7.37%5.5%4123.95
AstraZeneca PLCJun5.77%5.5%6223.73
Adecco SAJun5.64%2.7%6033.85
Flextronics InternationalSep16.72%16.2%5251.22
PE Biosystems GroupSep11.71%6.7%2023.42
Capital One FinancialSep11.30%7.4%5149.11
Human Genome SciencesSep8.34%7.6%5134.09
Australia and New Zealand Sep5.99%5.3%5124.65
British Sky Broadcasting GroupSep5.96%5.6%5124.23
Bank of MontrealSep5.36%4.9%4211.58
Royal Bank of CanadaOct8.48%6.1%4127.79
Bank of MontrealOct6.42%5.4%6038.51
Flextronics InternationalNov20.77%15.8%60124.59
JDS UniphaseNov19.77%8.0%60118.62
America OnlineNov15.61%12.5%7196.70
Cisco SystemsNov14.04%9.6%100140.38
Australia and New Zealand Banking GroupNov7.57%6.1%4124.14
Donaldson, Lufkin & JenretteNov7.21%5.9%4122.94
Forest LaboratoriesNov6.16%5.6%10250.27
Royal Bank of CanadaNov5.29%4.5%4116.69
Endesa SANov4.83%4.7%9329.30
Clear Channel CommunicationsDec10.91%6.2%7170.13
Sun MicrosystemsDec9.95%8.6%121110.77
Maxim Integrated ProductsDec9.38%7.6%8259.86
Interpublic Group of CompaniesDec8.98%7.6%121100.12
Akzo Nobel NVDec7.08%5.7%10165.03
Aegon NVDec6.71%5.6%13275.93
Carlton CommunicationsDec6.68%5.6%8242.28
Tyco InternationalDec6.40%6.0%10251.90
Northern TrustDec5.56%4.0%8236.42

Sector HiMARQs

To enhance your potential for positive results using HiMARQ analysis, keep in mind that stocks will be buoyed by favorable historical results for their industrial group. This concept was brought to my attention by Gitanshu Buch, who trades derivatives in the highly cyclical oil and gas complex for a living. Buch ran HiMARQ analysis for sector indexes and color-coded them by the degree of their positive and negative results; the result looked like a weather map showing bands of hot and cold across the country, so he cleverly called it a "heat map" of the market.

Here are the highlights: Depending on your time frame, the heat map suggests that the best sector each month typically outperforms the S&P 500 Index by anywhere from a 6:1 to a 10:1 margin. Even the second-best and third-best sectors outperform the benchmark index by around 3:1. While the concept of sector rotation by professional investors is well known by now, until now no one has published a table that systematically reveals the best sectors historically for each month of the year. If the patterns described in the heat map hold even half as well in the future as they have in the past, we can forget about market anomalies as simplistic and vague as the January effect and start talking about the February effect, the March effect, and so on.

I’ll leave the reasons that these effects may exist to the finance professors. But since some are counterintuitive (for example, the airline sector appears to decline regularly in August, just when vacation travel is at its peak), it would seem that the seasonality has something to do with the way institutional traders regularly forecast future sectorwide earnings growth. The three best sectors historically for each month of the year are listed in Table 5-4. (See Appendix C for the full list.)

Table 5-4. HiMARQ Analysis by Sector

Certain months have in the past proven auspicious for particular sectors.

MonthTop Sectors  
MarchAirlineNatural gasOil
MayNetwork equipmentNasdaq 100 
JulySemiconductorNetwork equipmentHigh-tech
SeptemberBiotechnolo gySoftware 
NovemberNetwork equipmentBrokerage 
DecemberBiotechnologyTelecommunicationsAll high-tech

In practice during 2000, I learned that HiMARQ analysis is useful as a powerful second or third screen for stocks that had first been collected and sorted via other fundamental or technical methodologies. HiMARQ analysis isn’t the elusive Holy Grail for investors, but it is a reasonably solid predictor of future prices in certain circumstances.

Slugging Averages

Now let’s move on to find consistent big hitters.

The most common way to judge baseball players is to compare their batting averages, which is simply at-bats divided by hits. If a player has 3 hits in 10 at-bats, his batting average is .300. But a more sophisticated and popular way to judge players is by overweighting the hits that count the most—the doubles, triples, and home runs. To do this, you total up a player’s total bases and divide by at-bats. That’s called the "slugging percentage." In this measure, players that get the most extra-base hits come out on top.

Translating the concept to stocks, I decided I wanted to know how many times over a one-year and three-year period an individual stock experienced a day or a week in which it either rose or fell by 10%, 15%, or more. After all, most of the time our stocks wander aimlessly, dribbling down 1% to 3%, only to advance 1% to 3% the next day. It's really those big days-when a stock gets crushed or soars-that end up characterizing our feeling about volatility and risk. To quantify this, you must first download the daily or weekly closing prices of any stock into Excel by using the File/Export Data function of MoneyCentral's Chart control. You must next calculate the daily change of each stock and then create a separate table with formulas that group all that activity into eight buckets, as shown in Figure 5-1. For daily changes, the buckets should be 1%-5%, +/- 5-10%, +/- 10-15%, and greater than 15%. For weekly changes, use buckets of 1-10%, 10-15%, 15-20%, and greater than 20%.

Figure 5-1. To create slugging and whiffing percentages for stocks, download daily or weekly data from MSN MoneyCentral or Yahoo! Finance into spreadsheet software, and then create tables that turn the raw numbers into meaningful information. (See Appendix C for an example.) (Image unavailable)

Next establish a simple linear expression for quantifying all these moves—giving a stock one point for landing in the first bucket, two for the second bucket, three for the third, and four for the fourth. Then add these and divide by the total number of days (or weeks), and voila, you have a figure that approximates baseball’s slugging percentage on the upside and a "whiffing" percentage on the downside. You can also create another table on the same spreadsheet that pulls out each stock’s five best and five worst days (or weeks) in the period.

I find that these figures help me understand the differences in volatility between two stocks that look very similar on the surface in a clearer way than standard deviation and beta. Take the two big semiconductor makers, Micron and Texas Instruments. Through the end of June, the key figures, in my view, were these: Texas Instruments never had a gut-wrenching day of -15% performance, but it also had only a single day in which it was up more than 10%. In contrast, Micron had three days in which it fell 10% to 15%, but it also recorded eight days of 10-15% moves up and three days of 15%+ moves up.

Overall, Micron recorded a slugging percentage of .782 vs. a whiffing percentage of .544, a slug-to-whiff ratio (SWR) of 1.44. Texas Instruments recorded a slugging ratio of .547 and a whiffing percentage of .421 for an SWR of 1.29. That’s pretty close, but the advantage goes to Micron for being more volatile in a positive way. (The figures correlate well with the two stocks’ beta in contrast with the S&P 500 Index—a more standard measure. They were 1.54 and 1.41, respectively.) Now, when you calculate the average gain of the two stocks over that time—0.67% per day for Micron vs. 0.45% for Texas Instruments—you have to conclude that Micron was reasonably easy to own over its period of outperformance vs. Texas Instruments, even though it was a bit more volatile. In general, an SWR greater than 1.4 is desirable, and the higher the better.

You may find the SWR useful or useless—after all, Micron went on to fall 60% from July to November 2000—but the point is to learn to use spreadsheets and the historical quote download function at the major Web finance portals to find interesting statistical relationships.

Buddy System

Back in the good old days of Hollywood, "buddy movies" were a staple. Frank Sinatra and Dean Martin. Bob Hope and Bing Crosby. And more recently, Woody and Buzz. When cares of the world weighed down one buddy, the other would belt out a song or tell a joke to lift his spirits. They rounded each other out, showing that even celebrities work better in teams.

Wouldn’t it be great if our star stocks had buddies too? Just when one is getting kicked in the face with a downgrade by some bully analyst who just doesn’t understand how profitable a company’s genomic business is going to be in 2005, the other one would leap 15% on the wings of a new deal with a Brazilian beverage conglomerate.

The best stock buddies would be ones that ended up in the same place—let’s say, up 30% over a six-month period but via diametrically opposite routes. Plotted on the same chart, their lines would look sort of like bow ties or gap-toothed smiles—mirror images that start and end in the same place but seldom travel together. Whenever one zigs, the other zags, but ultimately their prices end up in the upper right quadrant of the chart.

The idea of finding stock buddies isn’t new—value-oriented money manager Nancy Tengler in Walnut Creek, California, says it’s "what we all try to figure out: how to find stocks that dampen each other’s volatility without damaging results."

But finding suitable twisted pairs is remarkably hard. Hedge-fund managers for decades have tried to do it with sectors rather than individual stocks. For instance, in a period of rising inflation, they simultaneously initiate long positions in commodity-related industries and take short positions in interest-sensitive industries. The theory is that higher prices will help producers of things like aluminum and oil but harm outfits like insurers that hold bonds. If well executed, the strategy would render the fund manager profitably market-neutral.

Most individual investors, though, don’t try to make big macroeconomic calls with their positions and probably shouldn’t be shorting. But we can still try to create orthogonal, or market-neutral, positions in our portfolios by teaming up stocks that benefit independently from sector rotation.

To take a crack at this concept quantitatively, I've combined the Stock Matcher feature of Stock Screener at MSN MoneyCentral with the slug-to-whiff ratio spreadsheets. The ideal pair of stocks would be expected to record a greater number of positive vs. negative days together than either would record alone, as well as more terrific days (change greater than 10% to 15%) and fewer bad days (change worse than -10% to -15%). While their combined average daily return might ultimately be lower than that of one of them individually, the diminished volatility should make each easier to own in times of extreme volatility.

To begin the hunt for dynamic duos, start by determining the target stock—the one you really want to own. To find its buddy, visit the Stock Screener section of MSN MoneyCentral Investor, click Stock Matcher in the left navigation pane, and then enter your primary stock’s ticker symbol in the quote box on the subsequent page and click Go. Stock Matcher shows securities with statistical characteristics similar to the target stock. But because you want to reduce volatility, you must add one criterion that is not in the Stock Matcher default: demand a beta that is one-half that of the target stock. If the stock were PeopleSoft, for instance, here are the steps:

  • On the Stock Matcher page, type PSFT in the Name Or Symbol box. Then click Go.
  • Use the scroll bar in the Stock Screener control to reach the first empty criteria field. Click in that field, and choose Trading & Volume / Beta <= PSFT/2. (That’s literally the stock symbol divided by 2. Type PSFT in the box, then type the divisor sign and the numeral 2, and then click Run Search.)

The resulting list may be long or short, but the ones to pick are stocks in completely different industries from your target. The best buddies historically for techs are drugs. For instance, in October, potential Stock Matcher buddies for PeopleSoft would have included Dura Pharmaceuticals and Teva Pharmaceutical Industries.

In late June 2000, my top choice as a buddy for Advanced Micro Devices was Quest Diagnostics, the world's leading clinical laboratory firm, with $3 billion in annual revenues. (See Figure 5-2.) Quest Diagnostics has the advantage of being in a business that's utterly uncorrelated with semiconductors; its workers perform more than 50 million routine medical tests a year for doctors, hospitals, HMOs, corporations, and government agencies. Over the first six months of 2000, the two stocks would have matched each other nicely: AMD recorded seven days with better than 10% gains and only one day with a 10% loss, for a slug-to-whiff ratio of 1.55. Quest recorded four days of better than 10% gains and no days with a 10% loss, for an SWR of 1.56. Put them together, though, and you get an excellent SWR of 1.71, with just two days of +10% and no -10% days. Considered together, the two recorded 65 positive days and just 54 negative days, for an excellent daily average return of 0.89%. In the year's ultimate test of suitability for buddyhood, AMD suffered a 7% decline on April 14-Black Friday-but had its spirits lifted by Quest Diagnostic's 4% gain that day, so the net loss for the pair was just 1.4%. On Rebound Monday, meanwhile, AMD rose 10% while Quest Diagnostic rose 13%. Good friends, indeed.

Figure 5-2. The relationship between the trading of Advanced Micro Devices and Quest Diagnostics during the first half of 2000 shows two stocks that are negatively correlated. One goes up when the other goes down—but they both generally trend up. (Image unavailable)

As it turned out, the next three months were deadly for AMD—it fell 49% from June 21 through Sept. 30. But Quest Diagnostics was up 56% in the same period. Another pair that worked out well for the summer and early fall were drug/financial buddies Merck (+3%) and American Express (+16%).

To take advantage of this idea, if you think it makes sense, you would split each prospective purchase into two parts and put 50% of your funds into each stock. For instance, if you decided to put $10,000 into American Express but wanted to pair it up with a buddy, instead put $5,000 into American Express and $5,000 into Merck.

Shortly before this edition went to press, former Goldman Sachs executives launched a new Web site that handles the Buddy Stock issue in an elegant, almost effortless way. Visit Finportfolio (, register for free, click the Asset Analysis link on the left, and enter up to 15 ticker symbols in the text box that appears. Click the Analyze button, and you’ll get two tables: One showing the average return, volatility and Sharpe ratios of the selected tickers, and the other showing their "correlations" to each other from 1.0 to 0.01(with 1.0 being an exact match in terms of the price and magnitude of price movement, and 0.01 being the least matched). I ran the PeopleSoft matches mentioned previously through this analysis in November and discovered that Teva indeed had a slightly higher return but a 0.10 correlation—making it potentially a perfect buddy. Also check out the site’s Portfolio Optimization feature, which teaches you how to change the weighting of stocks in a portfolio to achieve an ideal balance between risk and reward.

The Nicoski Momentum Oscillator

Ed Nicoski, chief technical analyst at US Bancorp Piper Jaffray, has developed a wide range of momentum measures to help him find value in stocks over the past 20 years. He likes to focus on outperforming stocks in outperforming groups by using the deceptively simple technique of sifting through stocks that have just begun to emerge from yearlong consolidations and are exhibiting the technically powerful characteristic of seeing their short-term (50-day) moving averages move above their long term (200-day) moving averages. He then adds one more trick before narrowing down his buy list: he determines which of these names are trading at the low end, or at least the median, of their historic 6-month or 12-month price momentum. Most large companies’ shares, it seems, trade at some steady ratio of their current prices to their prices six months ago. When the ratio expands to two or three times its long-term average, he believes the stock price has become "overbought," or extended; and when the ratio shrinks more than normal, the stock has become "oversold."

You can plot this 26-week and 52-week price-momentum oscillator yourself, once you get a little bit handy with a spreadsheet. I call them the Nicoski M26 and M52 ratios, respectively. Here’s a basic lesson on how to create them. For more, see Appendix C.

  • Visit the charting area of MSN MoneyCentral Investor, run a five-year or ten-year chart on a stock like Intel, and then use the File/Export Data feature to send daily or weekly prices to an Excel spreadsheet on your personal computer.
  • If you’re using daily prices, rows for market holidays will be blank and must be deleted. To do so easily, highlight the top row of the spreadsheet, click Data on the top Excel menu, and then choose Filter/Autofilter. On any column, click the arrow that appears, slide to the bottom of the list of numbers, and choose Blanks. Then delete all the blank rows. Next, highlight the spreadsheet’s top row again, choose Filter/Autofilter from the Data menu again, and the filtering feature will be turned off. (You do not need to do this for weekly or monthly data.)
  • The Excel table contains columns for Date, High, Low, Close, and Volume. Delete the High, Low, and Volume columns, and insert two new columns for the M26 and M52 ratios. For M26, create a formula in the row six months above the last price in your table that divides that price by the price six months earlier. (For instance, if the last price in the table is $10 on 5/6/96 and the price six months later on 11/6/96 is $11, the ratio is 11/10 or 1.10.) Then copy that formula all the way up the M26 column. Do the same for M52, only starting one year above the last price in your table.
  • Create a separate table on the same spreadsheet that computes the average M26 and M52 ratios for the stock. In that same table, create a formula that compares the stock’s current M26 and M52 ratios to the averages, as shown in Figure 5-3. (If you’re really a whiz with Excel, you can also plot the relationship between the daily or weekly price and one of the ratios using the software’s Chart/CustomTypes/Lines on 2 Axes feature.)

You’ll see that the table neatly reveals the oscillation between times when the stock became very extended from its average historic 26-week and 52-week momentum to the upside or downside and what happened next. You’ll see that some stocks trade at a 3.0 or greater ratio for months at a time, but occasionally fall to a 1.5 ratio, a 1.0 ratio, or even sink to a 0.75 ratio or less.

This oscillator shows that very often a stock is in its normal 6-month and 12-month momentum ratio zone even near new highs. Ed Nicoski cautions that the method is mostly used for forecasting where stocks will be in a year, not tomorrow, and that the best conditions arise when historically strong stocks’ short-term and long-term price ratios both dip below 1.0. "My clients are mostly large institutions, and they are looking for inflection points that are meaningful—not where they can get a few points in a month," he said in an interview.

Figure 5-3. In early November 2000, the 26-week and 52-week momentum ratios of Intel were perched at levels well below their historic averages. The M26 ratio had fallen to as low as 0.5 in mid-October when its price fell to $35 after an unexpected earnings warning; that was a historically low ratio for the stock, and signaled a buying opportunity. Sure enough, the stock had surged to $45 a month later. Creating formulas like these in Excel is easy once you advance beyond a basic understanding of the program. (Image unavailable)

I used this technique very profitably in late 2000 to determine the price at which Intel would reach its historical momentum nadir after its catastrophic fall from grace in the early autumn. According to the oscillator, that price was $35. And sure enough, that’s exactly where it stopped falling and turned around.

Now let’s move on to learn about how to find great stocks to analyze from online newspaper, magazine, and newsletter sources.

Table of Contents

Acknowledgements  Page ix
Preface to the Second Edition   Page xi
CHAPTER 1  Introduction to Model Portfolios  Page 3
    Improving Your Odds  Page 4
    Determining Success Factors  Page 4
    The Yeartrader  Page 5
    Building Yeartrading Models  Page 8
CHAPTER 2  Momentum Models  Page 17
    Understanding Momentum Stocks  Page 19
    Finding the Best Momentum Stocks  Page 20
        The Original Flare-Out Growth Model  Page 21
        1999 Flare-Out Growth Screen: Quality Momentum  Page 26
        2000 Flare-Out Growth Portfolio: Flame-Out  Page 29
        Creating the Screen in Stock Screener  Page 30
CHAPTER 3  Growth Investing Models  Page 39
    How to Build Large-Cap Growth Models  Page 41
        Redwood Growth: Bigger Is Better  Page 44
    How to Build Defensive-Growth Models  Page 49
        MVP Growth—Most Valuable Players  Page 49
CHAPTER 4  Value Investing Models  Page 55
    Growth and Value: What’s the Difference?  Page 55
        How to Build Value Stock Models  Page 58
CHAPTER 5  Counting  Page 69
    Specialized Stats  Page 70
    HiMARQ Analysis  Page 71
        Identifying Patterns  Page 72
        Sector HiMARQs  Page 80
        Slugging Averages  Page 81
        Buddy System  Page 83
        The Nicoski Momentum Oscillator  Page 86
CHAPTER 6  Web News Organizations  Page 91
    The Nightly Round  Page 92
    Touring Yahoo! Finance  Page 92
        The Yahoo! Finance Home Page  Page 93
        Today’s Markets  Page 94
        International  Page 98
        Research & Education  Page 100
        News & Editorial  Page 103
    Touring CBS MarketWatch  Page 105
    Touring MSN MoneyCentral Investor  Page 107
        SuperModels  Page 107
        Jubak’s Journal  Page 109
        Strategy Lab  Page 109
    Touring  Page 111
        Stock Analysis  Page 111
    Touring TheStreet.Com  Page 114
        Market Updates  Page 116
        Commentary  Page 116
        Conference Coverage  Page 117
        Mutual Funds  Page 117
    Touring Other Sites  Page 118
CHAPTER 7  Newspapers and Magazines Online  Page 121
    Wall Street Journal (and Family)  Page 122
        Barron’s Online  Page 123  Page 125
    New York Times  Page 127
    Washington Post  Page 128
    Dallas Morning News  Page 129
    Los Angeles Times  Page 131
    Forbes  Page 131
    Fortune  Page 133
    Other Notable Publications  Page 134
CHAPTER 8  Newsletters  Page 137
    INVESTools  Page 139
        Getting Started  Page 140
        Take a Sample  Page 140
    Trading Markets  Page 141
    The Financial Center  Page 142
        One Favorite: Bedford & Associates  Page 143
    Other Notable Newsletters  Page 145
CHAPTER 9  Fundamental Analysis  Page 149
    What Does the Company Do?  Page 150  Page 150
        Hoover’s Online  Page 152
        SEC Filings  Page 152
        Press Releases  Page 154
    How Much Does the Company Sell?  Page 154
        Revenue  Page 154
    How Much Does the Company Earn?  Page 158
        Types of Earnings  Page 158
    How Profitable Is the Company?  Page 163
    How Is the Company’s Financial Health?  Page 166
        Debt/Equity Ratio  Page 167
        Current Ratio  Page 168
        Quick Ratio  Page 170
        Interest Coverage  Page 170
        Leverage Ratio  Page 170
        Book Value  Page 170
    What’s the Company’s Return from Investments?  Page 171
        Return on Equity  Page 172
        Return on Assets  Page 173
    How Efficient Is the Company’s Management?  Page 174
        Income and Revenue per Employee  Page 174
        Inventory Turnover  Page 175
    How Does a Company’s Stock Price Compare with Its Earnings and Sales?  Page 176
        Price/Earnings Ratio  Page 176
        Price/Sales Ratio  Page 184
        Price/Book Ratio  Page 184
        Price–to–Cash Flow Ratio  Page 185
        Conclusion  Page 186
CHAPTER 10  SEC Documents  Page 187
    Retrieving the Forms Online  Page 188
        The Original EDGAR  Page 189
        FreeEDGAR  Page 189
        EDGAR Online  Page 194  Page 197
        EdgarScan  Page 201
        EDGAR on the Financial Portals  Page 204
    Primer on the Forms  Page 207
        Forms 10-Q and 10-K—Quarterly and Annual Reports  Page 207
        Proxies  Page 215
        Form S-1—IPO Registration  Page 216
        Forms 4 and 144—Insider Trading  Page 219
CHAPTER 11  Analyst Recommendations  Page 225
    What Analysts Do  Page 226
        MSN MoneyCentral  Page 227
        Yahoo! Finance  Page 234
        Zacks Investment Research, First Call, and I/B/E/S  Page 235
    Where to Find Analysts’ Reports  Page 237
        Robertson Stephens  Page 237
        Gruntal & Co.  Page 238
        Bear Stearns  Page 239
        SG Cowen  Page 240
        US Bancorp Piper Jaffray  Page 241
        Merrill Lynch  Page 241
        Salomon Smith Barney  Page 242
        Goldman Sachs  Page 242  Page 243
        Discount Brokerages  Page 246
CHAPTER 12  Technical Analysis: Following and Forecasting Trends  Page 247
    Charting Engines  Page 249
        MSN MoneyCentral  Page 249  Page 253
        BigCharts  Page 258  Page 266
        ClearStation  Page 270
        Other Sites  Page 274
    Volume and Money Flow  Page 275
        The Source of Volume  Page 277
        Why Volume Works  Page 279
CHAPTER 13  Community  Page 281
    Bulletin Boards  Page 283
        Silicon Investor  Page 283
        Raging Bull  Page 286
        MSN MoneyCentral  Page 288
        ClearStation  Page 291
        The Motley Fool  Page 293
        Yahoo! Finance  Page 295
        Other Message Boards  Page 296
    Chat Rooms  Page 299
        Yahoo! Chat  Page 299
        MSN MoneyCentral Chat  Page 300
CHAPTER 14  Options  Page 301
    Option Basics  Page 302
        What Is an Option?  Page 302
        The Basic Strategies  Page 310
        Advanced Strategies  Page 321
CHAPTER 15  Buying and Trading Stocks  Page 327
    Choosing an Internet Broker  Page 329
        Rating Services  Page 329
        Narrowing Down  Page 332
        Getting Started  Page 339
        Advanced Online Trading—ECNs  Page 340
    Online Portfolio Managers  Page 342
        MSN MoneyCentral  Page 342
        ClearStation  Page 347
CHAPTER 16  The 30-Minute Investor  Page 349
    Creating a Portfolio  Page 350
        Ideal Portfolio: 7 to 20 Names  Page 350
        Setting Expectations  Page 351
        Investing Online in 30 to 60 Minutes a Year  Page 352
        Investing Online in 30 to 60 Minutes a Month  Page 354
        Investing Online in 30 to 60 Minutes a Week  Page 354
        Investing Online in 30 to 60 Minutes a Day  Page 355
INDEX   Page 357

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