2001. This is 13 times as much asthe hundred dollars invested in Group 5 would grow and about 2.4 timesas much as Group 3. Results are reported every three months in Value Line’sSelection & Opinion.As for the statistical significance of the results, the average variability ofthe mean quarterly return for each group comes to between 1 percent and 1.5percent. Thus, the differences between the performance of Groups 1 and 2,and the other three groups, are three variabilities away from the other, orabout 1 in 500 to have occurred by chance variations alone.
5.04.5
4.31%
4.0
3.64%
Percentage
3.53.0
2.81%
2.5
2.08%
2.01.51.01%
1.00.50.0
1
2
3
4
5
Figure 10.1 Value Line Timeliness Rankings: Quarterly Rebalance,September 30, 1983–June 30, 2002
224 Practical SpeculationTable 10.1 Performance of Value Line Timeliness GroupsLong/Short Strategy(Long Group 1 with Group 5 Hedge)
Annual returnAnnual standard deviationBeta
Group 1
Group 5
Long/Short
17.48%27.53%1.323
3.08%23.19%1.225
12.86%21.51%0.097
Note, however, that the variability of the returns in Group 1 and Group 5appears to be higher than for the three middle groups.An interesting practical test of Value Line’s timeliness system is to consider what would happen if we bought the Group 1 stocks and sold theGroup 5 stocks on a quarterly basis. We found that the long-short systemmakes money in 75 percent of the quarters. The average performance appears in Table 10.1.Looking at the record as a whole, the prospective record of Value Line’stimeliness rankings is the greatest practical demonstration of forecasting accuracy in the history of prediction, dating back at least 4,000 years to the Oracleof Delphi. The predictions have been disseminated to millions of people, billions of dollars have been invested in following the predictions, and themethodology has not changed much over time.Others support our analysis. Writing in 1973, the Nobel Prize winner Fischer Black reviewed Value Line’s performance and concluded, “Yes, Virginia,there is hope. . . . Most investment management organizations would improve their performance if they fired all but one of their securities analystsand then provided the remaining analyst with the Value Line service.”3Mark Hulbert, dean of accreditation for stock-picking newsletters, put theValue Line Investment Survey record in perspective as of April 30, 2002. ValueLine was among the top five performers on a risk-adjusted basis in all four ofHulbert’s evaluation time frames—5 years, 10 years, 15 years, and 20 years.Hulbert’s calculations are shown in Table 10.2.Hulbert concluded that an investor need not follow all the weekly ValueLine recommendations to achieve the reported returns. Rebalancing a portfolio once a year with Group 1 stocks would result in about the same performance as updating weekly because of savings on brokerage commission andspread, Hulbert reported in the January 2001 issue of the Hulbert FinancialDigest.4
Chapter 10 • The Periodic Table of InvestingTable 10.2
225
Value Line Investment Survey Ranking
Value LineInvestment SurveyAnnual Return (%)
Ranking amongNewsletters(Risk-Adjusted)
Measured overHow Many Years
Wilshire 5000Annual Return(%)
15.812.313.513.7
2354
2015105
14.411.411.87.7
Source: Mark Hulbert. Used with permission.
James J. Choi, in an academic review of Value Line’s performance published in the September 2000 Journal of Finance and Quantitative Analysis,concluded:Value Line’s raw record is impressive. Over the entire sample period, the averagerank portfolio returns are ordered exactly as Value Line predicts. In addition,Ranks 1 and 2 almost double the market’s annualized return, while the inverse istrue for Rank 5. I find (after extensive statistical attempts to explain the anomaliesaway) that Value Line continues to outperform relative to current models of expected return before transaction costs, with mean abnormal portfolio returnsreaching magnitudes of up to 45 basis points a month, even after controlling formarket momentum and earnings surprise effects.5
Value Line’s Other SystemNot as well known as the timeliness effects, and strangely reminiscent of theperiodic table’s bifurcation into groups as well as periods, Value Line has developed a further classification of stocks based on their technical performance. In the spirit of pure technical analysis, these rankings are based onprice moves alone, with particular reference to the changes up to the currentmonth from each of the 11 previous months.These rankings are developed by such elementary statistical means that italmost hurts. The technique is a cross-sectional regression of the price appreciation of each stock on the left side of the equation versus 10 measures of pastshort-term and long-term price appreciation on the right side. Eisenstadtlikens the results of the procedure to the movements of a snake slithering upand down based on the terrain it has just passed and sees ahead but with eachpart of the snake’s body dependent on the other. Results on a real-time basis
226
Geometric annual returnExcess returnAnnual standard deviationSharpeAnnual AlphaT (a = 0)BetaCorrelation with benchmark% of times portfol > bmkT (% outperf = .5)T (port.ret = mkt.ret )Wilcox RSACF(1) for excess returnsACF(2) for excess returns
15.41%4.05%25.90%0.602.55%0.711.230.8359.7%1.651.541.620.03−0.18
113.13%1.77%18.67%0.701.80%1.161.000.9462.5%2.121.251.92−0.280.01
2
Table 10.3
9.85%−1.52%16.04%0.61−0.33%−0.410.890.9840.3%−1.65−1.90−2.14−0.260.11
3
Timeliness Groups
6.48%−4.89%16.13%0.40−3.24%−2.570.870.9527.8%−3.77−3.75−3.700.030.01
4
Summary Statistics
0.08%−11.29%21.63%0.00−11.43%−4.761.100.9020.8%−4.95−4.43−4.730.00−0.02
5
Long-Short = 15.34%Sigma = 19.4%T = 3.394L > S 70.8% of timeT(% > .5) = 3.536n = 723.400.08−0.22
17.60%0.65
11.37%
VL Arithmetic
227
Source: Value Line.
Geometric annual returnExcess returnAnnual standard deviationSharpeAnnual AlphaT (a = 0)BetaCorrelation with benchmark% of times portfol > bmkT (% outperf = .5)T (port.ret = mkt.ret )Wilcox RSACF(1) for excess returnsACF(2) for excess returns
Value Line Variability
16.21%4.84%27.91%0.582.88%0.721.300.8256.9%1.181.671.400.28−0.08
112.08%0.72%18.45%0.661.23%0.680.970.9256.9%1.180.480.95−0.08−0.01
29.51%−1.86%15.72%0.60−0.40%−0.430.870.9738.9%−1.89−1.99−2.26−0.160.19
3
Technical Groups
6.69%−4.68%18.00%0.37−3.71%−2.200.950.9330.6%−3.30−2.73−3.180.18−0.21
411.37%
−0.90%−12.26%24.16%−0.04−12.41%−3.721.150.8425.0%−4.24−3.32−3.63−0.040.01
Long-Short = 17.11%Sigma = 22.6%T = 3.228L > S 69.4% of timeT(% > .5) = 3.3n = 723.520.21−0.09
17.60%0.65
VL Arithmetic
5
228 Practical Speculationare available since 1983. Amazingly, stocks in the Technical Analysis Group 1have performed even better than the Timeliness Group 1 (see Figure 10.2).The 1983–2002 performance difference between the top and worstranks for the technical groups (4.7 percent versus 0.7 percent) is slightlygreater than for the timeliness groups in the same period. As in the timeliness groups, the variabilities of Group 1 and Group 5 are highest, and all theresults are highly unlikely to have occurred by chance variations alone (seeTable 10.3).What would happen if the timeliness and technical groups were combined? What reactions—chemical, electrical, or profitable—might arise?The returns, when classified by both timeliness and technical analysis,seem to carry what the academics would call incremental information and whatthe average user would call opportunities for nice extra profits. Within each technical “period,” the timeliness groups tend to further segment the returns.For example, the Timeliness 1 and Timeliness 2 categories give an averageof 6 percent a quarter when ranked with Technical 1 versus about −1 percentfor the three other timeliness categories with Technical 1. Similarly, within theTimeliness 1 category, the Technical 1 and Technical 2 periods give returns of5.5 percent versus about zero for the other three Timeliness categories withinTimeliness 1.As good as the Value Line rankings are by group and period, we can improve them by noting that they seem to work much better in the first andfourth quarters than in the second and third (see Table 10.4).
5.54.7%
Percentage
4.53.4%
3.5
2.7%2.5
2.1%
1.50.7%0.5–0.5
1
2
3
4
5
Figure 10.2 Value Line Technical Groups: Quarterly Balance, June 30,1983–June 30, 2002
Chapter 10 • The Periodic Table of Investing
229
Table 10.4 Performance of Group 1 CompaniesClassified by Calendar QuarterQuarter
Mean PriceAppreciation
PercentUp
StandardDeviation
1234
8535
72646572
13111312
The CriticsThe periodic table has gone through many highways and byways, critiquesand improvements since it was first established. New groups of elements,such as the noble gases, have been discovered. The ordering by atomicweight has been replaced by a classification by atomic number. Similarly, theValue Line rankings have not been without problems and naysayers. Themost obvious problem is that they do not work in many quarters. TimelinessGroup 1 makes money in about two-thirds of the quarters. The actual returnfrom following the Group 1 timeliness rankings in the four quarters endedSeptember 30, 2001, would have been −30 percent, and following the Group 1technical rankings would have led to a loss of 40 percent. Such results can tryusers’ souls.Even in better times, the critics had a field day negating the significance ofthe Value Line results. “Our findings show that the results of the Value Lineenigma may reflect an association between Value Line rank and beta (i.e.,risk),” wrote Robert S. Kaplan and Roman L. Weil in a 1973 study.6 GurHuberman and Shmuel Kandel, in a 1990 study, concluded: “The purportedabnormal returns of positions based on Value Line’s rankings are seen ascompensation for the systematic risk associated with these positions.”7William A. Sherden, in his 1998 book The Fortune Sellers, asserted: “The ValueLine enigma is a myth. The investor service is not invincible, just enormouslyinfluential. This also suggests that the market is not nearly as rational as financial economists would have us believe.”8We will not deign to consider whether we should fault a service thatnearly everyone can use in the form disseminated and explain away the results because of its wide use. Let the reader decide. A much more disturbingproblem is the performance of the Value Line mutual fund. From 1987through 2001, the fund’s average quarterly performance was 0.96 percent,
230 Practical Speculationversus 4.6 percent for the Value Line timeliness rankings over 1983–2001(see Figure 10.3).“We’ll always lag the Group 1’s,” said Alan Hoffman, the Value Line mutualfund’s manager, in an interview. “The Group 1 is a theoretical number that ignores transaction costs and dividends.” Moreover, he noted, the Value Linefund’s assets range between $300 million and $350 million. Many of the Group1 stocks trade only a couple of thousand shares a day. “You could never buyenough of the 1’s to actually make a portfolio of them.”
Mr. Hill Strikes It RichWe would contrast these results with those of Henry Hill, a long-time ValueLine subscriber from Florida. Starting in 1977, Hill religiously rebalanced hisportfolio every week based on the Group 1 rankings. We spoke to Hill, who is85, in March 2002. He told us that he turned an initial investment of“$80,000, something like that,” into $48 million as of 2000, or $26 millionafter taxes and living expenses.Hill began studying the stock market as a civil engineering student atPurdue University in Lafayette, Indiana, in the 1930s. “My math professorand I were both interested in the market. We tried to determine what factorsinfluenced value. We decided consistent growth in earnings was the principalfeature.” After graduation in 1938, he went to work in the petroleum business
Timeliness Group 14.6%
5.04.54.0Percentage
3.53.02.52.01.51.0
Value Line Fund0.79%
0.50.0
1
2
Figure 10.3 Value Line Fund versus Timeliness Group 1, 1987–2001.Source: Mark Hulbert. Used with permission.
Chapter 10 • The Periodic Table of Investing
231
in Venezuela, but continued to study the stock market. He heard about ValueLine in 1944. “Sam [Eisenstadt] had almost the same approach as I did. But Iwas having a hell of a time following 20 stocks by hand, and he had a computer. That was the last time I did any work” on picking stocks.Hill says he has a solid wall of account transaction records that impressedthe CPA sent down by Money magazine to audit him for an article three or fouryears ago. Until recently, he bought all 100 of the Timeliness Group 1 stocks,rebalancing his portfolio every week. “Five or six years ago, when Sam [Eisenstadt] checked on the technical rankings, it looked like there would be some advantage to confining ourselves to stocks ranked Group 1 from both theTimeliness and Technical standpoints.” This reduced the stocks in his portfolioto between 18 and 46. “It made it easier to manage the account.”In 1999, facing a serious illness and concerned that his wife, Ollie, wouldfind managing the account burdensome, Hill liquidated his stocks andbought Treasury bills with the proceeds. As it happened, the market wasabout to begin its worst downturn since the 1970s. The account now providesa tax-free income of $1 million a year.Thus, the practical implementation of Value Line ratings has madeMr. Hill rich.
A Mind Still Open after 58 YearsAs Will and Ariel Durant wrote in The Lessons of History after a lifetime ofstudying the panoply of world history, it is appropriate to ask at the end of aseminal study what use the study has served. Have you found in your workonly amusement, only a method of scratching out a living amid a stormy market, a way of finding some good and bad stocks? Have you learned anythingabout the nature of markets, of companies, of science?9Sam Eisenstadt is alive and kicking after 58 years of continuous service atValue Line, and well able to reflect on his record. In 2001, in fact, he attendeda swing dance night at the Niederhoffer house and danced circles aroundmost of the younger attendees—including Victor, who still fancies himselfsomething of an athlete. In October 2001, Eisenstadt graciously consented toan interview to look back over his career. We started by asking him why hefeels the timeliness classification has worked and whether he was discouraged by recent results.“The rationale behind using earnings momentum and earnings surpriseis their tendency to persist, which does not get fully priced into the stock,” hesaid. “At least, that is what academic studies seem to show. The fact that they
232 Practical Speculationhave not worked well recently is more a reflection of a ‘value’ bent to the market than the argument offered. I daresay when the market reverts (as it alwayshas) to a growth orientation, you’ll see a revival in both earnings momentumand surprise.”We asked him if he is pleased by what he has accomplished. “To some extent, I am pleased—primarily by questioning the efficiency argument. In thelate sixties, Wall Street was on the defensive with an onslaught from the University of Chicago claiming that we were all in effect wasting our time. Therewas no response forthcoming from the financial community. We had a record,albeit a short one at the time, that indicated we were on to something. Sincethen, the efficient market adherents have softened their arguments, and Idaresay Value Line had something to do with it.”Value Line initially placed much importance on time series analysis, butchanged its approach in 1964–1965. We asked Eisenstadt why.“We were trying to separate stocks from one another. Time series separates years from one another—finding years of over- and undervaluation. Ourproblem was to differentiate stocks. In order to do so, we had to measure allstocks with a common yardstick, rather than a different formula for each company, which resulted from time series analysis. Since we assumed we couldnot predict the market, it made sense to reduce everything to a relative basisand bypass the market forecast. There were always relatively underpriced oroverpriced stocks—regardless of the level of the market.”Then we asked the hard question. What would he have done differently?“In the early years, we spent almost all the time working with time seriesanalysis—a leftover from the visual fittings that Arnold Bernhard had devisedin the early thirties. Early on we started to use multiple regression analysis toincrease the number of factors and years to get better fits—which, of coursethe process produced. Despite the better fits, we did not get the kind of resultswe were striving for. We were attempting to predict absolute prices for individual stocks using their own histories of prices, earnings, dividends, andbook values, and so on. Because we were dealing with absolutes, we had periods of time when most stocks were undervalued or overvalued, without sayingmuch about individual stocks.“We spent many years [until 1965] working in this environment. It’s difficult for me to take myself back, since at this point I know the future, but Ishould have gone to cross-sectional analysis at a much earlier time andchanged the system to a relative [ranking] basis.“As for the future—50 years from now—I think stock selection will become increasingly difficult. We will become more and more dependent on accurate earnings forecasts, which I suspect have not improved much over the
Chapter 10 • The Periodic Table of Investing
233
years. With computers humming and quants concentrating on the field, I canonly see the problem becoming increasingly difficult. We’ll have to keep upwith the development of new statistical techniques and high-powered computers to provide us with an edge.”He concluded with some enduring guidance:I have learned that stock selection is not a random process. The application of statistical methods can be helpful, but it is very difficult to outperform the market.We are working with very low information coefficients that become statisticallysignificant when the universe is large. Nor does the system workequally well in all phases of the market. There are times when a“value” approach produces better results. The system is adaptive,however, and adjusts to a changing environment. If the environmentchanges rapidly and frequently, it creates difficulties for the system. Nevertheless,over the 36-year history of the system, it has had a respectable batting average andattracted the attention of the academic community.
His advice for future investigators: “Don’t give up and become complacent. We’re only explaining a tiny amount of the unknown. Be curious, keepan open mind, and persist.”
11
WHEN THEY SWINGFOR THE FENCES, WERUN FOR THE EXITS
What Should I Trade?Lyrics by Victor Niederhoffer and Laurel Kenner. To the tune of“What Is a Man?” from Pal Joey by Richard Rodgers andLorenz Hart.There are so many, so many ways to tradeBut baseball is the model that’s for meIt tells the Average JoeHow he can make some doughThe times change far too much to trade without it1. What’s a good trade?Should I buy stocks now?Am I a bull? Am I a bear?Am I the value or the growth kind?Am I the old or the tech kind?What’s a good trade?Trading’s like baseball235
236 Practical SpeculationWin with a bat, or win with a glove.Value has lagged since the world beganWhat should I buy? What should I sell?What makes a winning trade?2. First, runs expandStocks set to fall thenNext, runs contract—bull’s horns are sharpIt’s all so easy after the factVol first goes down, Bulls are set to frownWhen balls are dead, Time to steal basesWhen balls are live, Swing from the heelsCycles have changed since the world beganOffense at times, Defense at times.That makes a winning trade.
O
ne afternoon in March 2002, we were sitting on a bench on 81st Streetwith Larry Ritter, dean of baseball history, talking about players andseeing how far Vic could hit a tennis ball down Broadway. We both knew wewere in the presence of one of the greats, and requested Larry’s advice on howto begin this chapter. He told us to always begin with Babe Ruth. There reallyis no better way to begin, because the Babe epitomizes an important predictive relation between baseball and the stock market. We call it the Home RunIndicator. Consider:• In 1927, two years before the Crash, Ruth hit a record 60 home runs.Similarly, in 2000, two years after Mark McGwire broke Roger Maris’record of 61 home runs in 1998, the market began to decline drastically.• In the 1930s, Ruth’s slugging dropped to .500 and lower; and Americaslid into the Great Depression. (We’ll return to this pattern of homeruns and market moves.)
Chapter 11 • When They Swing for the Fences, We Run for the Exits
237
Unlike the well-known Super Bowl Indicator (mentioned as aspurious correlation in Chapter 8), the Home Run Indicator doesnot rest on the outcome of a single game. Rather, it relies on thetendency of long-term trends in the market and society to show upfirst on the baseball field. Two examples:1. The internationalization of baseball preceded that of the stock marketby more than 100 years. Baseball teams toured the world starting in1888, but the New York Stock Exchange listed its first foreign companyonly in 1990.2. Baseball was integrated in 1944, with Jackie Robinson breaking the barrier. The NYSE didn’t admit its first black member until 1967. (Thefirst female member was admitted in 1970.)The foreshadowing quality of baseball is detailed in the time line at theend of this chapter. But for the investor, it is the game itself—home runs, inparticular—that bears the most watching.
Win with a Bat or Win with a Glove (Artist: Milton Bond, © 1993).
238 Practical SpeculationTo explain, we need to go back to the first two decades of the twentiethcentury—“the dead ball era”—before the Babe gave up pitching and took upbatting in earnest. The balls were called “dead” because they did not carry longdistances no matter how hard they were hit. Spitballs and doctored balls werepermitted, favoring the pitchers. Earned run averages of 3 were the norm. Theoffense was fought with bunts, slides, sacrifice flies, place hitting, and basestealing. “Brains were as important as brawn,” wrote Ritter, “maybe more so.”Home runs were rare. From 1901 to 1918, total home runs declined from 455 ayear to 235. In 1908, Sam Crawford led the league in home runs—with seven.Outside the ballpark fences, the market languished. In 1918, the Dow closed at82.20, little more than double the 40.94 at which it began life in 1896.After the hard days of World War I, authorities eliminated spitballs andother doctored balls, put cork in the ball to liven it up, changed the balls morefrequently, and outlawed trick pitches. The Babe started swinging from theheels. His slugging percentage went from the .400-to-.500 level to .847 in1920, and stayed at .600 or higher for the next 10 years. Ruth’s transformationfrom pitcher to batting star serves as the starting point of the no-holds-barredspirit of the 1920s. “I swing big, with everything I’ve got,” said Ruth. “I hit bigor I miss big.” From 1918 to 1930, home runs increased from 235 to 1,565 ayear. In the world beyond the fences, business was expanding. In 1924, aftertreading water for the prior decade, the Dow had five years of unbroken gains,rising 260 percent through the end of September 1929.As the century progressed, home runs continued to predict and reflectthe stock market and society:1935 to 1943: The number of home runs diminished. Bunting and sacrificing again came into fashion—appropriate to the Depression-erapsychology that gripped the market.1943 to 1952: Home runs expanded. Stars like Ted Williams came to thefore. From 1948 to 1965, the Dow soared from 119 to 969.1963 to 1991: Home runs diminished. A major star of the period was LouBrock, who stole a record 118 bases in 1974. “Brock,” wrote Ritter,“represented a return to the old-fashioned style of baseball: bunting,stealing, trying to out-think the opposition.” In the world outside theballpark, recessions and market crashes became the rule.After major setbacks in 1966, 1969, 1973–1974, and 1977, the Dow finished 1981 at 875, or 9 percent below its 1965 close.After a players’ strike in 1994 threatened interest in the game, a generalmove toward immediate gratification came about in the area of sports and, darewe say it, in the offices of the nation’s capital. Home runs once more began to
Chapter 11 • When They Swing for the Fences, We Run for the ExitsTable 11.1
239
Home Runs by Year
Year
BaseballLeagues
DowLeague
1990199119921993199419951996199719981999200020012002
3,3173,3833,0384,0303,3054,0814,9624,6405,0645,5285,6935,4585,059
2633.663168.833301.113754.093834.445117.126448.277908.259181.4311497.1210786.8510021.508341.63
climb. The market, too, slowed down a bit in 1994 when the Federal Reservedoubled the federal funds rate; but after that it was up and away for the averages.Table 11.1 shows the combined home run totals for the American andNational Leagues during the 1990s, alongside the Dow league’s year-endclosing levels.
Playing to the CrowdTwo questions arise: What causes the cyclical shifts in home runs? Whyshould home runs foreshadow market moves?We hypothesize that the owners of Major League baseball teams act toanticipate the mood of the fans by adjusting the balance between offense anddefense. If hard times are ahead, they tone down the game, making the batters work harder for home runs so that the audience can feel catharsis. If thespirit of the times is expansive, they liven up the action and letthe home run records roll. The owners can make thesechanges by changing the rules in myriad ways: Balls, bats, andgloves change; the surface and size of the fields change; thestrike zone changes; pitching rules change. These things can occur gradually,over several years. In the meantime, the market is likely to catch up.We see another element at work. The scientific play that dominated in1900–1918, 1932–1946, and 1970–1977 represented the kind of attention todetail and hard work that is apparently a precondition to success. A period of
240 Practical Speculationsmall gains leads, both in baseball and markets, to a desire for home runs. Inbaseball, it led to Babe Ruth in the 1920s, and to Mark McGwire and BarryBonds in the 1990