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31

96 The Expert Way to Lose Your Savings . Table 5-1

Analysis Forecast Errors by Industry 1973-1996

62iSubindustry Groupings: Average Error: 50% Median Error: 43%

Industry

% Error

Iruiustry

% Error

Capital Goods

Insurance

Chemicals

Metals/Mining

Communications

Consumer Goods

Publishing

Entertainment

Textiles

Financial

Tobacco

Foods

Transportation

Healthcare

62 subindustries represented in 15 above industties for simplification.

Source of data: A-N Research Corp. (Formerly the research department of Abel Noser Corp.) and I/B/E/S, 1973-1996.

rors continued to be high across almost all industries, with growth industries often showing larger errors than industries considered to be more volatile. This result is so consistent we should call it a rule:

RULE 5

There are no highly predictable industries in which you can count on analysts forecasts. Relying on these estimates will lead to trouble.

The high-visibility, high-growth industries have as many errors as the others. That analysts miss the mark consistendy in supposedly high-visibility industries-and give them much higher valuations-suggests that these industries are often ove riced.

Finally, lets settle one remaining item of business with analysts forecasts. Are they less accurate in periods of boom or recession, when eamings presumably are more difficult to calculate? Is this a possible reason why their forecasts are not any better?



Table 5-2

Analysts Forecast Errors in Expansions and Recessions 1973-1996

Surprises (Absolute Value)

Positive Surprises

Negative Surprises

Expansions

44.9%

23.2%

-79.1%

Recessions

47.4%

25.5%

-77.4%

Full Sample

44.3%

23.7%

-76.5%

(1973-1996)

All figures are average surprises.

Surprise = (Actual - Forecast)/IActualI, in percent

Source of data; A-N Research Corp. (Formerly the research department of Abel Noser CoRR) andI/B/E/S, 1973-1996.

Analyst Forecasts in Booms and Busts

Our 1973-1996 study covered five periods of business expansion and four periods of recession. If you think about it, you might expect to see analysts forecasts too high in periods of recession, because eamings are dropping sharply for economic reasons that are impossible for the analyst to predict. Conversely, in periods of expansion, estimates might be too low, as business is actually much better than economists and company managements anticipate. It certainly seems plausible, and at first provides a partial explanation for the battered analysts record. Unfortunately, it just aint so, as Table 5-2 indicates.

The table is broken into three columns: All Su rises, which is the average of all positive and negative 8 8 8 through the study, Positive 5 8 8, and Negative 5 8 8." The su rises are shown for each period of business expansion or recession. The bottom row shows the average of all consensus forecasts for periods of both expansion and recession. The average 8 8 for all expansions (44.9%) is little different from the average 8 8 through the entire period (44.3%), or the average su rise in recessions (47.4%). Moreover, the average of positive 8 8 8 in expansions and recessions are also very similar (23.2% versus 25.5%), as are negative su rises (-79.1% versus -77.4%).

The statistical analysis demonstrates that economic conditions do not seem to magnify analyst errors. They are about the same in periods of expansion or recession as they are at other times. What did come out clearly is that analysts are always optimistic. Their forecasts are too optimistic in periods of recession, and this optimism doesnt decrease in periods of economic recovery, or in more normal times. This last find-



ing is not new. A number of research! papers liave been devoted to tlie subject of analyst optimism, and, with the exception of one that used far too short a period of time, all have come up with the same conclusion. This is an important finding for the investor: if analysts are generally optimistic, there will be a large number of disappointments created not by events, but by initially seeing the company or industry through rose-colored glasses.

How optimistic are analysts estimates? Jennifer Francis and Donna Philbriclc studied analyst estimates from the Value Line Investment Survey, some 918 stocks for the 1987-1989 period. Value Line is well known on the Street for having near-consensus forecasts. The researchers found that analysts were optimistic in their forecasts by 9% annually, on average. Again, remembering the devastating effect of even a small miss on high-octane stocks, these are very large odds to be stacked against the investor looking for ultra-precise eamings estimates.

The overoptimism of analysts is brought out even more clearly by I/B/E/S, the largest eamings forecasting service, which monitors quarterly consensus forecasts on more than 7,000 domestic companies. In a report to its subscribers, I/B/E/S stated that the average revision for stocks in the S&P 500, which make up approximately 75% of the market value of stocks traded on the New York Stock Exchange, is 12.9% from the beginning to the end of the year in which the forecast is made. Analysts revise their estimates 6.3% in the first half and 19.5% in the second half of the year. Despite these estimate changes, according to I/B/E/S, analysts tend to be optimistic. What seems apparent is that analysts do not sufficiendy revise their optimistically biased forecasts in the first half, and then almost triple the size of the revisions, usually downward, in the second half of the year. Even so, their forecasts of eamings are still too high at year-end.

In a recent study, Eric Luflcin"* and I provided further evidence of analysts overoptimism. Between 1982 and 1997, analysts overestimated the growth of eamings of companies in the S&P 500 by a startling 188%. The actual growth was 7.8% annually, while the original projected growth at the beginning of each year was 21.9%.

What makes analysts so optimistic? The subject is anything but academic, Isecause it is precisely this undue optimism that induces many people, including large numbers of pros, to buy these stocks. As we have seen in the recent examples, and will see more thoroughly in the chapters ahead, unwarranted optimism exacts a fearful price.

A mle is in order here.



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