In punditry, if you are going to be wrong, it pays to be spectacularly wrong.

[Connie WorksHere]

click link for the tables to show up:
http://blog.pundittracker.com/the-bermuda-triangle-of-punditry/

JULY 24, 2013 | FINANCE, POLITICS

Apple’s stock is expected to rise today after the company surpassed
consensus expectations in yesterday’s earnings report. Apple’s reports
are quite the spectacle, driving the already frenzied CNBC network
into a hyperbolic state. Each financial metric is dissected in
real-time, usually with the assistance of financial analysts who
presumably are also speed-readers, capable of offering deep insights
only five minutes after the report’s release.

A comparable spectacle is the pundit hysteria going into each Apple
release, with droves of analysts making predictions on how each
product line fared over the past three months. Fortune catalogued all
of this quarter’s analyst estimates in a post earlier this week.

What’s most striking about these estimates is just how clustered they
were. Take the iPhone, which is Apple’s biggest value driver and
arguably the most anticipated metric in the release. The consensus
among the 30 Wall Street analysts was that Apple would sell 26.6
million iPhones, which was well shy of the 31.2 million that Apple
actually reported. More interestingly, 17 of the 30 estimates were
between 26-27 million, while 80% (24/30) were between 24-28 million.
(Note: the full range was 23-30 million, which too failed to capture
the actual result).

These ranges seem shockingly narrow for a global consumer technology
product, particularly one in the volatile smartphone industry. After
all, how can all of the analysts’ ‘proprietary channel checks’ yield
the same (wrong) answer?

We don’t mean to single out Apple analysts, as most members of the
financial pundit class seem to adhere to clustering as well. Consider
the S&P 500 Forecasts issued by each investment bank at the beginning
of each year. We catalogued three years worth of estimates made by six
bulge bracket firms:

Annual S&P 500 Forecasts

Bank201120122013
Average (Banks)137813381543
UBS135013251425
Barclays142013301525
Credit Suisse125013401550
Goldman Sachs145012501575
JP Morgan140014301580
Bank of America Merrill Lynch140013501600
Actual125814261692*

201120122013
Average Projected Rise9.9%6.4%8.2%
Actual Rise0.3%13.4%18.8%

The mean S&P estimate was considerably off-target each year: by +960
basis points in 2011, -700 basis points in 2012, and -960 basis points
so far this year. As with Apple, the clustering effect was very
pronounced, with five of the six analyst predictions each year falling
within a 100 point range (1350-1450 in 2010, 1250-1350 in 2011, and
1500-1600 in 2012) – ranges which failed to capture the actual result
in every instance.

So what is behind the errant clustering? The biases of anchoring and
recency are likely culprits, with analysts anchoring to a baseline
(e.g. Apple management’s guidance, S&P’s current level) and
extrapolating from recent trends. We believe career risk is also at
play: as investor Joel Greenblatt put it, “It’s much safer to be wrong
in a crowd than to risk being the only one to misread a situation that
everyone else had pegged correctly.”

But how do we reconcile the incentive for pundits to not stray from
the consensus – and thus minimize career risk – with the bombastic
pundits that we all love to rail on? (See: this guy). Why aren’t they
concerned about career risk? Well, here’s the catch:

In punditry, if you are going to be wrong, it pays to be spectacularly wrong.

We explain using the following matrix:

Reaction to outcomes by prediction type

Prediction TypeOutcome: CorrectOutcome: Incorrect
ConsensusExpectedForgivable
ContrarianSubdued PraisePink Slip
Wildly ContrarianHeroCelebrity

The first prediction type (“Consensus”) is greeted with minimal credit
when correct and minimal blame when incorrect. As we discussed with
the Apple analysts and S&P 500 forecasters, pundits focused on career
preservation adhere to this ‘safety in numbers’ approach. The last
prediction type (“Wildly Contrarian”) is typically made by pundits who
crave media attention. Regardless of outcome, they are able to parlay
their provocative predictions and media prowess into cash by writing
books, hitting the speaking circuit, and developing a cult-like
following. This is howOne-Hit Wonders and Broken Clocks are born.

That leaves the middle prediction type, which we refer to as the
‘Bermuda Triangle’ of punditry. These pundits are contrarian enough to
create career risk for themselves but not contrarian enough to garner
mainstream attention. Correct predictions are greeted with modest
praise — say, a pat on the back from a few colleagues — while
incorrect predictions draw intense scrutiny. Low reward, high risk.

Our hunch is that the best pundits are stuck in this Bermuda Triangle,
quietly amassing first-rate track records but lacking a platform to
reach a wider audience. Instead, our professional ranks and airwaves
are cluttered with pundits who make Consensus and Wildly Contrarian
predictions. Nate Silver is a rare exception, having made the leap
from quant to superstar. We would argue that Silver was aided by the
criticism leveled at him by conservatives, which created a false
perception that his election predictions were wildly contrarian when
they were in fact only moderately so.