What we see here is the finicky nature of how year-end targets can unravel based solely on the independent displacement of major risk events, relative to the end of the calendar year. A recent example of this is the suppression of a correction in the equity markets at the end of 2013, creating a gap in the actual 2013 stock market return vs. Wall Street forecasters who themselves were caught shooting in the dark as they erratically changed their full-year forecasts throughout 2013. And alas, the correction initiated precisely in January 2014. Of course these large forecasting gaps can equally go in either direction.

The weights and the distributions of the risk events shown above yield a 6% standard deviation (AA). Or an interquartile range (75th percentile to 25th percentile) that is usually mathematically higher than 12%.

So the timing of risk we just showed creates just as much independent uncertainty as does the inherent growth model uncertainty between the higher price target forecasters vs. the lower price target forecasters!

In the illustration below, we'll see a scaled version of what a 2014 forecast for a 5% rise in equity markets really looks like based just on risk timing.

A few forecasts are shown, and the along vertical axis we see the typical deviation we have on that annual forecast from just the timing of risk. Sure, something near a 5% return in 2014 can happen, though there is a very strong likelihood that you'll see nothing close to this. You may easily see a negative return. Conversely you could see a double-digit return.

Even if the return were in the single digits, we know it is within a one I range of other true return patterns, such as that for a 0% return or a 10% return. Or if we include the uncertainty beyond risk mis-timing, then this is only within a half I range, or within a one I range of either -5% and 15% returns.

So, not much is truly known about the equity returns when considering the multiple sources of error uncertainty. When Wall Street strategists start the year by providing a lower return forecast, they've added very little helpful information for long-term asset allocators.

This is due to the current lack of Wall Street discussion of risk modeling into calendar-year investment projections. But similar to the trend in providing a broader distribution of rankings and binomial paths, perhaps this knowledge will one day enter the mainstream.

For now, those that celebrate and subscribe to these quirky, full year targets for important long-term investment decision-making could heed the quote of the 19th century social reformer, Robert Owens:

All things I thought I knew; but now confess
The more I know, I know, I know the less.

At the time of publication, the author held no positions in any of the stocks mentioned.

This article represents the opinion of a contributor and not necessarily that of TheStreet or its editorial staff.

Salil Mehta is a statistician and risk strategist, who has developed an engaging method to teach quantitative techniques. 

Salil has 17 years of experience, of which a dozen years were on Wall Street, performing proprietary trading and economic research for firms such as Salomon/Citigroup, and Morgan Stanley.  He also served for two years in a leadership role, as the Director of Analytics, in the U.S. Department of the Treasury for the Administration's $700 billion TARP program.  Salil is also the former Director of the Policy, Research, and Analysis Department in the Pension Benefit Guaranty Corporation.  He completed a graduate degree in mathematical statistics from Harvard and also completed the Chartered Financial Analyst exams, as well as being a current dual candidate member of the Society of Actuaries.  In addition to having lectured on probability and economics at a number of leading universities, Salil has authored academic articles.  He currently provides advisory to the heads of several organizations, and he teaches graduate statistics at Rutgers on the weekends this year, in addition to current Georgetown teachings. 

Salil has also been acknowledged or on air interviewed and by a number of leading publications, such as the National Bureau of Economic Research, American Statistical Association, New York Times, CNBC, Wall Street Journal, Financial Times, Barron's, CFA Institute, Tom Keene, Bloomberg, and Businessweek.  He has also completed a statistics and analytics topics book, a working draft of which is available for Georgetown library users.  His blog, Statistical Ideas, provides an interesting discussion of various statistical applications, and the bottom of this link offers downloadable, refresher presentations on the basics of probability and statistics.  Also the site has been added to the syllabus of several leading universities. 

This site is also added to RePEc's selective blog aggregator.  You directly contact Salil and follow him on Facebook and Google+ and Skype (name: saliltreasury).  And you can subscribe to the site.

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