Perhaps the problem starts with the idea that intelligence is related to the ability to correctly and consistently predict the movements of markets and individual securities.
A number of factors explain why this is so much harder than many people assume.
The first is that a forecast for, say, an individual stock usually depends on a multitude of other assumptions. If just one of those assumptions proves incorrect, the whole edifice comes crashing down. Some examples:
In July 2010, a major brokerage in Australia placed a 'buy' rating on the steel producer Bluescope Steel, saying the company was cheap relative to its peer group and stood to benefit from a recovery in the Australian housing market.1
Unfortunately, Bluescope got much cheaper. In fact, it was the worst performing stock on the Australian market over the subsequent year. From July 2010 to the end of April 2012, Bluescope delivered a negative total return of just under 80 percent, compared with the broad market's 6 percent gain over the same period.
The company was hit by a combination of forces over this period, including falling domestic steel prices, a surging Australian dollar that made it uncompetitive with cheaper imports and a slowdown in the home building market.
Clearly, the brokers who tipped the stock as a buy incorrectly assumed housing was on an upswing. Their assumptions about the currency and conditions in global steel markets may also have been awry. Many things can bring a forecast undone.
Another wildcard is technology and consumer preferences. In June 2009, a brokerage2 raised its price target on Research in Motion, maker of the Blackberry mobile device, citing increased shipments in the smartphone market and stable margins.
"Our extensive retail checks suggest the unit trajectory for RIM will likely exceed Street estimates," the analyst said in a note to clients. Rating the stock as a "conviction buy", he raised his six–month price target to $C96 from $C85.
That was a shame, because from that moment on, it was virtually all downhill for RIM and by May 2012, its shares were trading at a little under $C12.
What the market didn't see was the phenomenal take–up of the Apple iPhone and devices running Google's Android operating system. For whatever reason, consumers decided the Blackberry phone was clunky and uninteresting in comparison.
Alongside incorrect assumptions, changes in technology and shifting consumer preferences, forecasts often can fall down because of external events totally unrelated to the company under consideration.
In February 2011, a major Japanese brokerage3 raised its rating on nuclear power station operator Tokyo Electric Power ('Tepco') to 'outperform' from 'neutral', while lifting its price target for the stock to Y2,450 from Y2,050.
Just weeks after that forecast, a devastating earthquake and tidal wave crippled the company's Fukushima Dai–Ichi nuclear power station, left thousands dead and forced 160,000 people from their homes.
Of course, nothing can compare to the human losses of that disaster. But over the 16–month period to the end of April this year, Tepco was the worst performing stock on the Japanese equity market, falling nearly 90 percent in value.
By March this year, Tepco was facing billions in compensation claims and was pleading for $US12 billion in public funds to avert an outright collapse.
Who could have predicted the scale of that disaster? And who knew that Tepco would be so poorly prepared?
So you can see forecasting in financial markets is hard. It is hard because any one of the many assumptions underpinning your outlook can come undone. It is hard because technology and consumer preferences can change in unpredictable ways.
But, most of all, it is hard because correctly forecasting markets requires an ability to predict news before it happens. It doesn't matter how smart an analyst might be. It doesn't matter how careful they are in their assumptions. Things can still happen out of left field that can wreck their careful analysis.
The possibility of industry–specific or stock–specific factors damaging our investments is the reason we should diversify – across stocks, across asset classes, across industries and across countries. We need to lessen the impact of the unexpected.
It all recalls a telling quote from the 1950s from the then British Prime Minister Harold Macmillan. A journalist asked the politician what could cause his government to run off course. Macmillan's reply was typically dry and succinct:
"Events, dear boy, events."
