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Recent earnings reports have raised the possibility of a changed outlook for some of these Internet stocks.

The following table contains much of the fundamental analysis measures considered by well-schooled CFA investment analysts. Lots of data there suggest what these stocks might gain for investors.

Symbol

AAPL

GOOG

FB

AMZN

EBAY

MSFT

IBM

XOM

EPS_past 12 months

$41.04

$33.73

$0.40

$1.21

$2.84

$2.00

$13.77

$10.26

EPS_next year Dec'13

$52.60

$49.37

$0.65

$2.60

$2.74

$3.34

$16.72

$8.01

Forward P/E ratio

10.9

12.3

43.4

84.3

15.7

8.8

11.4

10.6

% Gain to next year

28%

46%

63%

115%

-4%

67%

21%

-22%

5yr Growth Rate Est.

22%

16%

27%

34%

13%

9%

11%

9%

PEG ratio

0.58

0.91

2.07

5.37

1.39

1.05

1.14

1.23

PriceTarget

$739.00

$745.00

$39.50

$253.00

$47.00

$36.00

$215.00

$92.50

% Upside, now to Target

29%

23%

35%

17%

9%

25%

13%

9%

Market.Capital ($bn)

536.0

197.8

60.4

98.8

55.6

245.4

218.0

397.1

beta

0.91

1.13

NA

0.77

1.1

1.06

0.65

0.76

Price Now

$574.97

$607.99

$29.34

$217.05

$43.19

$28.83

$191.08

$85.24

52wk High

$644.00

$670.25

$45.00

$246.71

$45.48

$32.95

$210.69

$87.94

52wk Low

$353.02

$480.60

$25.52

$166.97

$26.86

$23.79

$157.13

$67.03

52wk Range Index

76

67

20

63

88

55

63

87

But it only does half (or less) of the decision-support job. The two most important investment considerations are risk and return. The return potentials may be in the table above, but where is the risk appraisal?

The past-year low price only tells what has been, and offers no indication of how likely it may be to return, or if there might be any limit to the downside. The Range Index shows where the price now is, in a low (zero) to high (100) range.

Don't try to claim that beta provides any definition of risk. It only measures the past price volatility of each stock, relative to the S&P 500 index. Volatility is uncertainty, not risk.

Future volatility can contain risk, but it is not risk. Risk has to do with getting hurt, not encountering pleasant surprises -- and volatility contains both. And since risk lives only in the present and the future, descriptions of history are only useful to the extent that they are expected to be repeated in the future.

Until a useful separation of uncertainty into its beneficial and detrimental parts can be presented, virtually all of the elaborate hokum of the capital asset pricing model charade is just fantasy to snow the gullible investor client with (hopefully) impressive algebraic detail. Quantitative investment analysts have spent decades with these deceptions, attempting to show some benefit from elaborate modeling with inadequate data and fatally flawed concepts. I have managed teams of quant researchers consisting of PhD mathematicians, trying to produce useful investment advantages this way, but virtually all published results are of trivial dimensions.

Many investment organizations pursuing fundamental analysis spend the bulk of their energies in determining what the competitive circumstances and industry interplay are likely to allow companies to report as earnings in the future. That is highly necessary, but not sufficient to tell which issues are likely to see the related values recognized by the markets, and when.

That deficiency fails to recognize that stock investing is mankind's second most serious game, next to war. The game's scorecard is kept in terms of the cumulative growth rate of one's invested capital. That is largely dependent on what some other player in the game is willing to pay to own what you have, and in turn, depends on what he or she thinks some future investor will ultimately be willing to pay.

The human element is what provides the game's challenge, and its continuing attraction. The quant analysts are hoped-for help agents in this process, but have been largely ineffective. Technical analysts are the inverse of the fundamental process, with virtually all their efforts spent on price changes, shunning economic valuations.

Unfortunately, while there is much mythology underlying the offered rationales in technical analysis, the demonstrations tend to be long on anecdotal illustrations and short on hard data in support of the notions for expected subsequent price behaviors. Furthermore, much of the analysis only looks backward in time at past events and simply speculates assertively about the future.

Also unfortunately, the behavioral analysis clique has largely entertained itself with human errors and illusions, rather than recognizing and studying what the intelligent and highly motivated players do correctly. The behaviorists' hope appears to be to take advantage of the errors, or just avoid them. Instead, our firm looks to the best-informed and most experienced players to see how they defend against the risks they must take with their capital to earn enormous returns, in their roles as market-makers.

When called upon to provide market liquidity these pros hedge their exposures, typically with price insurance on a specific issue-by-issue basis, to the extent required in each particular deal. The cost of protection comes out of their profit spread, so they only buy what is regarded as essential. Those judgments are under constant review, based on their instantaneous world-wide information gathering and communications systems.

What they will pay for protection, and how it is structured, tells just how far they believe prices are likely to go, both up and down. The other side of the protection trade is provided by the prop desks of other market-making firms who will take on the risk if paid adequately; again, in their judgment. Knowledgeable buyers and sellers keep the insurance market current and quite efficient.

Translating those forward-looking activities into specific price range forecasts is a rational and systematic -- but unshared -- process that we have done daily for more than a decade on over 2,000 widely held and actively traded stocks. In the process, we have developed detailed actuarial tables on their subsequent market price changes following some seven million forecasts, stock by stock, day by day.

Here is how the forecasts now look for the above Internet-centric stocks, after recent earnings reports. (Exxon Mobil is included simply as a big-cap contrast.)

Market-Maker Forecasts:

Symbol

AAPL

GOOG

FB

AMZN

EBAY

MSFT

IBM

XOM

MM Forecast High

$656.84

$670.76

$34.34

$244.69

$45.79

$30.88

$202.53

$88.03

MM Forecast Low

$568.80

$594.91

$27.07

$209.17

$39.74

$27.38

$186.76

$82.40

Range Index

15

17

31

22

57

41

27

50

MM Forecast Upside

14.2%

10.3%

17.0%

12.7%

6.0%

7.1%

6.0%

3.3%

MM Forecast Downside

-1.1%

-2.2%

-7.7%

-3.6%

-8.0%

-5.0%

-2.3%

-3.3%

3month Max Drawdown

-2.3%

-9.7%

-5.3%

-4.9%

-9.1%

-9.7%

-7.0%

-5.0%

Total Days of Forecasts

1504

1986

41

577

557

3163

3163

3162

# Days as Extreme as at present

69

407

NA

25

524

1266

527

2333

% Profitable Outcomes

68%

49%

NA

80%

79%

51%

57%

57%

Average Gain per position

5.5%

1.2%

NA

7.4%

6.0%

0.1%

4.0%

0.5%

Average Days Held

31

36

NA

27

26

35

34

35

Annual Rate of Gain

55%

9%

NA

93%

74%

1%

34%

4%

Population Rank Now

98

47

NA

92

29

55

36

50

IF @ AAPL Range Index

% Profitable Outcomes

68%

47%

NA

NA

100%

62%

71%

64%

Average Gain per position

5.5%

0.8%

NA

NA

12.8%

8.3%

12.2%

3.4%

Annual Rate of Gain

55%

6%

NA

NA

277%

76%

163%

27%

The principal advantage of the market-maker forecasts is their explicit, asymmetric, downside prospects.

Following each forecast a record of the next three months' end-of-day prices is kept. The sixth data row of the table indicates the average worst-case experience for each of the forecasts counted in data row eight of the table. Range Index is the metric extreme being referred to. Obviously, the small sample size here for Amazon puts several of its results into question.

Beside the three-month record, each extreme forecast is put to a time-disciplined test, using the top of the forecast range as a sell target, with a forced closeout at the end of two months if that target is not yet reached. The results of that test are what are referred to in the next four table rows.

The last three rows of the table emphasize the importance of current price in the comparative evaluation process. They show what each stock's prior experiences have been following Range Indexes at least as low as Apple's present 15. Stocks like eBay, Microsoft, and IBM have had performances at least as good as Apple when their forecast reward-to-risk balances (the Range Index) have been as attractive. This is why active investing to take advantage of price irregularities has such substantial performance gain potentials over the passive buy-and-forget practices continually urged on the investing public.

Given present market-maker expectations, at present the most desirable buy among these alternatives (for future wealth-building purposes) is Apple.

Source: Is It Time To Buy Apple, Google, Or Other Internet-Centric Stocks?