.  At  the  end  of  the  time  it  wouldhave been worth $613,441 for an annual return of 10.3 percent. So muchfor long-term rate of earnings increases.
METHOD TO THE MADNESS OF WALL STREET
What we see here is that there is rhyme and reason to the way Wall Streetworks. It takes no rocket scientist to ﬁgure out that if you buy value stocks,at any given point in time, and hold onto them you will do better than sim-ply buying the averages themselves. Since our goal as long-range investorsis to outperform the averages we can now attain that with a high degree ofconﬁdence. Our goal is easily within our reach.
If we decide to try to signiﬁcantly outperform the market by selectingone  or  two  hot  stocks,  we  now  know  we  can  statistically  increase  ourchances  of  being  correct  if  we  stay  with  stocks  that  have  positive  funda-mental  readings.  As  brokers  and  investment  advisers  sing  their  songs  tosway us to part with our hard-earned cash we can simply compare notes tosee whether those stocks ﬁt our criteria.
The  most  signiﬁcant  negative,  as  O’Shaughnessy  sees  it,  is  deﬁnitelythat of an extreme price-to-sales reading. Work I have done, as well as workdone by others, indicates it is far safer to buy a stock that has a 1.0 or lowerprice-to-sales reading if you want to ensure your chances against failure.
If  you  were  to  couple  that  with  requiring  your  stock  to  have  a  lowprice-to-book ratio or cash ﬂow ratio, I think you are virtually assured ofoutperforming the market averages using the last two readings. If you wantto  look  for  the  “discount  to  earnings”  selection  technique  I’ve  presentedhere you could assure yourself of a better opportunity by again referring tothis fundamental knowledge or wisdom.
The main thing I want to steer you away from is buying rumor or con-cept stocks. We know stocks go up for only one of two reasons: hype andhope or value and value. The hype and hope stocks may well have expo-nential rises. They can show spectacular performance, but the problem isthat  they  crash  in  an  equally  spectacular  fashion.  Indeed,  the  stocks  aresubject  to  spectacular  declines.  That  is  part  and  parcel  of  investing  instocks  that  do  not  have  underlying  value.  Why  place  ourselves  and  ourmoney at risk in such a scenario? It is beyond me that reasonably intelli-gent people make such unintelligent investment decisions.
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But  greed  . . .  what  an  emotion  . . .  a  brief  phone  call,  a  few  wordsabout how great things will be in the future and how much the stock hasalready rallied, and the human mind simply blows out all logical circuits.That voice inside your head is saying, “There’s quick and easy money to bemade here, and that money is mine,” while what you should be doing ischecking  the  underlying  conditions.  It  amazes  me  that  people  will  spendmonths looking for the best car in order to buy one that ﬁts their needs andrepresents the best value, but will make an investment decision on 10 timesthat amount of money in just a few seconds!
There is always plenty of time to invest. Anytime anyone, about anystock, says you have to get in today, right now, warning ﬂags should go up.There’s always a place to get into the market. Time is our friend, not ourenemy. Let’s make no rush to judgments when it comes to investing.
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11
PUTTING IT ALL TOGETHERFOR LONG-TERM INVESTMENT SUCCESS
It’s not who you know, but what you know . . .and now you will know!
By now you realize that it does make a difference when you buy and sellstocks and also which ones you buy and sell. By now you understand thereis  reality  to  the  marketplace;  that  certain  things  matter.  Those  things  areprimarily  earnings  but  they  also  include  the  buying  and  selling  your  ofstock by mutual funds or insiders.
As described in the preceding chapter, James P. O’Shaughnessy turnedhis  computers  on  for  a  study  of  things  such  as  price-to-earnings  ratios,price-to-book ratios, price-to-cash ﬂow ratios, the value of dividend yields,and  relative  strength  or  momentum.  Those  of  you  who  have  studied  hiswork know he places the most importance on price-to-sales ratios and mo-mentum along with companies with smaller capitalizations.
While I think the world of the work this gentleman has done, I differwith him on two of these supposed indicators. The ﬁrst is that of marketcapitalization. O’Shaughnessy looked at the returns of stocks with marketcapitalization by breaking them down into zones of those companies withless than $25 million, $25 million to $100 million, $100 million to $250million,  then  stepping  up  to  $500  million,  up  to  $1  billion,  and  ﬁnallythose companies with over $1 billion of capitalization.
O’Shaughnessy  concluded  that  “stocks  with  market  capitalizations
157
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between $25,000 and $100 million, as well as those with market capital-izations between $100,000 and $250 million, do outperform large stockson an absolute basis, but fail when risk is taken into account.”
This suggests to me that there may not be a distinct advantage to thisparticular  group  of  stocks.  Most  prognosticators,  though,  have  seen  thisthe other way around and have focused their attention on small-cap stocks.I believe that to be in error, and here’s why.
There  further  appears  to  be  a  distinct  advantage  in  this  game  of selecting stocks with capitalization under $25 million. No doubt aboutit; this group of stocks substantially outperformed the other cap groups.O’Shaughnessy’s  work  shows  that  $10,000  invested  in  these  types  ofstocks  in  1951  would  have  grown  to  $800,000  by  1966.  Many  advi-sory  services  and  funds  grabbed  onto  this  little  tidbit  of  informationfrom  O’Shaughnessy  and  became  very  big  advocates  of  buying  low-cap companies.
There is a distinct problem with this approach, however. It is simplythis: The small companies in this study, and to the best of my knowledgein all studies of this kind, have a distinct bias to them. The bias is thatthere were many, many small-cap companies that never made it throughall  the  years.  They  went  bankrupt,  or  bust,  someplace  along  the  way.Thus the losers are not in the database. All one ﬁnds in the database arethe winners! That simple little fact of life, I believe, severely skews any re-ports on the value of investing in small issues. So, the next time you getthe song and dance about investing in small caps you might want to thinkof this bias.
Additionally, as O’Shaughnessy points out, it is extremely difﬁcult tobe invested in the stocks, simply because there’s such little trading volumein them.
Figure  11.1  reﬂects  the  results  of  small-cap  funds  and  the  S&P  500from 1991 into 2001. As the graph shows, small caps substantially under-performed  the  market  from  1998  into  2001,  when  both  small  caps  andthe S&P came into synch with each other. The actual performance of theseissues indicates there is little if any advantage to small-cap stocks. True,they certainly did outperform the market averages from 1991 into 1998,but  following  that  point  the  broad-based  S&P  500  outperformed  thesmall caps.
So  while  many  analysts  have  indicated  small-cap  funds  should  andwill outperform the market averages, that has not been the case in reality.What’s  more  important,  I  believe,  is  the  inherent  bias  of  their  research
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Figure 11.1 S&P 500 versus Small-Cap Funds
with  the  dropout  effect  of  so  many  small-cap  companies  that  are  nolonger in existence.
Another point of contention I have, not so much with O’Shaughnessybut with followers of his work, is their love of his data that shows stockswith  the  best  relative  strength  tend  to  have  the  best  future  performance.This is the old adage of what goes up should continue going up. But the re-ality is that may not last for long. His best portfolio strategy is that of buy-ing  stocks  with  high  relative  strength.  The  problem,  however,  is  that  therisk one is exposed to with these stocks is substantially greater than withstocks  selected  on  fundamental  criteria  alone.  It  is  for  this  reason  that  Iwould prefer to focus strictly on the underlying conditions that cause price,rather than price itself.
It’s just like Logic 101 in college, when you were told you cannot pre-dict A with A—which is exactly what technicians and market mathemati-cians  have  done  for  years.  They  simply  looked  at  the  trend  of  price  topredict the future trend of price. Such an approach is destined to have you
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buying stocks at a major high at some point. It’s like my Russian rouletteanalogy; there’s a bullet in there for you.
My  goal  as  an  investor  is  consistent  rates  of  return  greater  than  themarket averages. That brings me contentment. To achieve it I need to con-trol risk.
Thus, I think it is wise that we let someone else take the risk. I reallydon’t care if others make a killing in the market or outperform me at anygiven time. I say that because I absolutely know that on a longer-term basisthey  won’t  beat  my  consistent  program.  No  way!  They  may  well  havesome  big  up  years,  but  they  will  also  have  some  horrible  down  years.Those down years wipe away all the champagne bubbles of the past.
If I want to speculate on catching a big winner, all I need to do is referto the technique of buying stocks at a “discount” in relation to earningsgrowth.  There  are  plenty  of  opportunities  using  this  technique  to  ﬁndstocks destined for strong up moves while at the same time being investedin quality.
WHEN A MARKET LOW COMES
At some point in the not too distant future, stocks will be under pressure,declining. Most investors will be on the sidelines or licking their woundsfrom the beating they have been taking. That’s exactly the scenario whereyou want to begin looking for investment opportunities. You might refer tothe  investment  advisory  or  any  of  the  other  sentiment  data  to  suggest  amarket low is close at hand.
At  that  time  you  can  begin  poring  through  chart  books,  data  fromyour broker, or advisory services such as Value Line or Investor’s BusinessDaily to ferret out stocks that have continued to have increases in earningsyet  have  declined  in  price  for  the  current  quarter.  Almost  any  brokerageﬁrm can give you the earnings estimate so you’ll know coming into the endof that quarter whether the relationship or a diversion does exist.
The key ingredient to the O’Shaughnessy study, I believe, is his studyof future price activity in relation to what he has identiﬁed as the price-to-sales ratio. This is similar to the price-to-earnings ratio. However, it is nota  reﬂection  of  earnings,  but  rather  that  of  sales  to  the  number  of  sharesoutstanding times price. This is arrived at by taking the total capitalizationof the company and dividing this by annual sales. All we’re looking at hereis revenues to shares as opposed to earnings to shares.
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There is an apparent problem here; the company may have large rev-enues  but  no  proﬁts.  That’s  a  distinct  possibility.  O’Shaughnessy  has  de-scribed this ratio as the “King of the Value Factors.” In one of his studieshe  took  the  50  lowest  price-to-sales  ratio  stocks  (the  bullish  ones)  andcompared those to the 50 highest price-to-sales ratio issues. He calculatedthat $10,000 invested in this group of stocks in 1951 would have turnedinto  $8.2  million  dollars  by  1996.  That’s  a  16  percent  per  year  com-pounded rate of return.
Had an investor chosen to invest that same $10,000 in the 50 stockswith the highest price-to-sales ratios in 1951, the $10,000 would have beenworth $91,000 in 1996. What a huge and gargantuan difference this price-to-sales ratio can make.
If  you  want  to  control  the  risk  of  your  investments  then  you’d  deﬁ-nitely want to pay attention to this price-to-sales ratio. I performed an in-teresting study of my own, thanks to the folks at Qualitative Analytics inChicago (312-322-4690). In my study we looked at buying the Dow JonesIndustrial Average too each year at the end of October. Our selection crite-ria as to which stocks to purchase was broken down in four ways.
In  one  test  we  bought  the  ﬁve  Dow  stocks  with  the  highest  price-to-sales ratios, in the next the ﬁve stocks with the lowest price-to-sales ratiosin the Dow. We then turned our attention to the ﬁve stocks with the highestP/E  ratios,  and  completed  the  test  by  looking  at  the  ﬁve  stocks  with  thelowest P/E ratios.
The results are fascinating:
Selection Technique
Annual Return 1980–2000
High price-to-sales
Low price-to-sales
High price-to-earning
Low price-to-earnings
Low yield
High yield
Low price and high yield
10.74%
15.74%
14.69%
15.98%
11.13%
15.95%
20.08%
This is staggering! An investor who had the wisdom to place money inthe  ﬁve  Dow  stocks  with  the  lowest  price-to-sales  or  lowest  P/E  ratiostocks made almost 16 percent per year. An investor purchasing the high
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P/E stocks didn’t do quite as well, and the value buyer did better but onlyby a percentage point.
However, the investor in the high price-to-sales stocks blew an oppor-tunity  for  proﬁts.  The  value-seeking  investors  earned  5  percent  more  peryear,  on  average.  The  5  percent  a  year  is  staggering  when  you  begin  tocompound this performance.
The  lesson  should  be  perfectly  clear—to  avoid  risk.  Avoid  stockswith  high  price-to-sales  ratios.  An  operating  rule  based  on  my  studywould be that you do not want to buy stocks with a price-to-sales ratioin  excess  of  1.0.  The  lower  the  better.  Given  a  choice  between  twostocks, both of which ﬁt our fundamental criteria and one with a price-to-sales ratio of .95 and the other .65, it’s an easy choice. Take the oneat .65.
Interestingly enough, we did this same study on the Dow Jones UtilityAverage  to  see  what  effect,  if  any,  the  same  ﬁlters  might  have  on  perfor-mance in this interest rate sensitive average. My idea here was to purchasethe utilities starting the ﬁrst week of April and exiting the ﬁrst week of Oc-tober. There has been a distinct seasonal tendency for the Utility Average torally at this particular time, which is the reason the study focused in on justthis zone of opportunity.
Let’s  take  a  look  now  at  the  performance  breakdown  based  on  thesame fundamental considerations we just used in the Dow Jones IndustrialAverage.
Selection Technique
Annual Return 1980–2000
High price-to-sales
Low price-to-sales
High price-to-earnings
Low price-to-earnings
Low yield
High yield
Low price and high yield
8.89%
9.77%
6.90%
10.43%
10.36%
10.33%
11.82%
Again  we  see  the  importance  of  these  ratios.  While  the  utilities  werenot quite as responsive to the high price-to-sales ratio, there still is a sub-stantial  difference,  almost  three  percentage  points  per  year,  between  theworst approach and the best approach. What is consistent is that high P/E
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and high price-to-share studies show this is not as productive as doing itthe other way around.
Notice  that  in  both  the  Dow  Jones  Industrial  and  Dow  Jones  Utilitystudies the most proﬁtable (highest-yielding,) return came from purchasingthe lowest priced stocks with the highest yield. This, then, is the ultimatecombination for investment success. There are two reasons why adding thelow price criterion improves performance. The ﬁrst is simply one of mathe-matics.  It  is  easier  for  a  $10  stock  to  have  a  10  percent  gain  than  for  a$100 stock to have 10 percent gain.
That may explain the second reason going on here, which is that in-vestors, funds, and such are more attracted to the lower-price stocks. Per-haps  they,  too,  note  it  easier  for  them  to  have  a  larger  percentagegain—that it should come quicker and easier than for a high-priced stock.What do bargain hunters look for? Not high-priced stocks. They look forlow-priced stocks. That’s why this combination of low price and high yieldcomes out ahead.
I’d now like to open up my private ﬁles to you on research I’ve donealong this line on a whole host of fundamental measures. Much of this youmay  have  read  about,  or  will,  and  will  hear  many  voices  claiming  manythings about the importance of these ratios. Well, here, I think for the ﬁrsttime ever, are the answers to these ratios. Here is ratio reality.
The following studies are focused on just the Dow 30 stocks. As youwill see I have tested a variety of ratios and sometimes used these ratios onthe ﬁve highest-priced or ﬁve lowest-priced stocks in the Dow 30. Each testrepresents a hypothetical buy in mid-October along with an exit on April15 or the last trading day in the following August. (Apr15Pct = % changefrom October to April; AugLTDPct = % change from October to August.)This gives you the opportunity to see what effect time has on a value mea-sure. The last two columns are the most relevant as they reﬂect the percentof gain or loss in the time period.
The  ﬁrst  test  is  our  standard  to  measure  valuation  performancesagainst. Table 11.1 reﬂects buying all 30 of the Dow Jones Industrial Aver-age stocks with the entry and exit dates per the preceding paragraph. Theaverage  October-to-April  gain  has  been  5.5  percent  from  1975  through2001. Had you bought all 30 of the Dow stocks and exited in April, that’swhat  you  would  have  earned.  Now,  you  and  I  would  like  to  do  better.Maybe we can, but we need to compare other selection techniques againstthis performance to see whether we can select stocks more apt to advance.If we beat this performance yardstick, we have beaten the market.
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164 PUTTING IT ALL TOGETHER FOR LONG-TERM INVESTMENT SUCCESS
Table 11.1 All Dow Jones Industrial Average Portfolio
Date
Port
Apr15Amnt AugLTDAmnt Apr15Pct AugLTDPct
10/27/1975 DJ_ALL_PORT10/29/1976 DJ_ALL_PORT10/31/1977 DJ_ALL_PORT10/30/1978 DJ_ALL_PORT10/29/1979 DJ_ALL_PORT10/27/1980 DJ_ALL_PORT10/26/1981 DJ_ALL_PORT10/29/1982 DJ_ALL_PORT10/31/1983 DJ_ALL_PORT10/29/1984 DJ_ALL_PORT10/28/1985 DJ_ALL_PORT10/27/1986 DJ_ALL_PORT10/26/1987 DJ_ALL_PORT10/31/1988 DJ_ALL_PORT10/30/1989 DJ_ALL_PORT10/29/1990 DJ_ALL_PORT10/28/1991 DJ_ALL_PORT10/26/1992 DJ_ALL_PORT10/29/1993 DJ_ALL_PORT10/31/1994 DJ_ALL_PORT10/30/1995 DJ_ALL_PORT10/28/1996 DJ_ALL_PORT10/27/1997 DJ_ALL_PORT10/26/1998 DJ_ALL_PORT10/29/1999 DJ_ALL_PORT10/30/2000 DJ_ALL_PORT10/29/2001 DJ_ALL_PORT
0.9860.019–0.0260.805–0.1790.9030.3302.450–0.7971.4764.5664.8633.3782.4151.7567.6815.3463.760–0.0116.41613.89710.65935.17131.389–7.319–6.70317.838
18.4870.301–0.46013.482–2.85412.2394.79927.138–6.26411.54231.96925.53317.6889.9285.85128.01913.9328.816–0.02111.66819.73511.78332.39123.436–4.401–4.09012.187
1.066–0.4780.8390.9921.279–0.0341.2823.3300.3402.0095.0969.5073.8907.248–0.30410.8543.9077.5614.05112.98515.13527.03211.20531.7681.028–5.513–7.893
19.977–7.47415.02816.61620.401–0.46518.61636.8952.67315.70835.68249.91320.36829.799–1.01439.59510.18217.7307.99423.61321.49329.88110.31923.7190.618–3.364–5.392
5.5% Average Gain
Largest Increase in Proﬁt Margin
Let’s start with a test of what takes the October buy in the ﬁve Dow stocksthat had the largest increase in proﬁt margins over the past 12 months (seeTable 11.2).
Numerous authors contend this is the best possible value measure forselecting a stock. What we see here is that since 1975 such a selection tech-
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Table 11.2 Five Largest 12–Month Increases in Proﬁt Margin in Dow JonesIndustrial Average
Date
Port
Apr15Amnt AugLTDAmnt Apr15Pct AugLTDPct
2.16410/27/1975 DJProﬁt_PORT1.06110/29/1976 DJProﬁt_PORT–0.39210/31/1977 DJProﬁt_PORT1.29710/30/1978 DJProﬁt_PORT–0.77710/29/1979 DJProﬁt_PORT–0.76410/27/1980 DJProﬁt_PORT0.41910/26/1981 DJProﬁt_PORT2.21210/29/1982 DJProﬁt_PORT–2.64710/31/1983 DJProﬁt_PORT1.33510/29/1984 DJProﬁt_PORT4.59710/28/1985 DJProﬁt_PORT1.74510/27/1986 DJProﬁt_PORT2.82110/26/1987 DJProﬁt_PORT0.71810/31/1988 DJProﬁt_PORT0.53910/30/1989 DJProﬁt_PORT3.95310/29/1990 DJProﬁt_PORT0.89110/28/1991 DJProﬁt_PORT4.61010/26/1992 DJProﬁt_PORT–4.03010/29/1993 DJProﬁt_PORT2.11510/31/1994 DJProﬁt_PORT8.41210/30/1995 DJProﬁt_PORT1.52710/28/1996 DJProﬁt_PORT13.17610/27/1997 DJProﬁt_PORT10/26/1998 DJProﬁt_PORT13.22710/29/1999 DJProﬁt_PORT –12.760–0.62410/30/2000 DJProﬁt_PORT5.60110/29/2001 DJProﬁt_PORT
1.5380.7520.3842.5532.276–2.5931.5003.308–0.4941.6870.2244.2743.4435.014–1.3466.3530.8458.405–3.2157.0625.25911.0151.29017.730–9.845–2.528–1.870
24.99810.659–3.84112.611–6.893–5.8604.61519.444–18.1489.88933.