Guru Report Card – A Must Read

Feb 26, 2014   //   by Profitly   //   Profitly  //  Comments Off on Guru Report Card – A Must Read

A lot of people forget to look at the track records of many of the trading “gurus” on the internet. This is a huge mistake, and it’s one of the reasons people should use sites like more often.

As you can see on their pages listing all of their trades, Tim, Nathan (InvestorsLive), and Paul (super_trades) all have success rates FAR above the average and above any of the individuals that CXO Advisory Group tracked.

Tim has a success rate of 73% (

Nathan has a success rate of 75% (

Paul has a success rate of 75% ( and published a post just last month that gave several gurus and market analysts a disturbingly low success rate of just 47%!

“The results are in and they are bad. After tracking 68 experts and 6,582 market forecasts, CXO Advisory Group has concluded that the average market prediction offered by experts has been below 50% accuracy. Flip a coin and your odds for predicting the market are better.”

guru first


This took place from 2005 through 2012, where CXO collected 6,582 forecasts for the U.S. stock market offered publicly by 68 experts. There were both bulls and bears employing technical, fundamental and sentiment indicators. It also has some forecasts included those in CXO’s archives, with the oldest coming from the end of 1998.


The following chart tracks the growing number of gurus tracked over time based on initial forecast dates. The number of gurus actively issuing forecasts is usually less than the cumulative total tracked due to some of the gurus ending their forecasts or media pulling the plug on them.

guru cumul


Here is how they graded the gurus:

The vital grading methodology was to compare forecasts for the U.S. stock market to the S&P 500 index returns over the future interval most relevant to the forecast horizon. In general, the people that conducted the survey said they:

  • Exclude forecasts that are too vague to grade and forecasts that include conditions requiring consideration of data other than stock market returns.
  • Match the frequency of a guru’s commentaries (such as weekly or monthly) to the forecast horizon, unless the forecast specifies some other timing.
  • Detrend forecasts by considering the long-run empirical behavior of the S&P 500 Index, which indicates that future returns over the next week, month, three months, six months and year are “normally” about 0.1%, 0.6%, 2%, 4% and 8%, respectively. For example, if a guru says investors should be bullish on U.S. stocks over the next six months, and the S&P 500 Index is up by only 1% over that interval, we would judge the call incorrect.
  • Grade complex forecasts with elements proving both correct and incorrect as both right and wrong (not half right and half wrong).

Weaknesses in the methodology include:

  • Some forecasts may be more important than others, but all are comparably weighted. In other words, measuring forecast accuracy is unlike measuring portfolio returns.
  • Consecutive forecasts by a given guru often are not independent, in that the forecast publishing interval is shorter than the forecast horizon (suggesting that the guru repetitively uses similar information to generate forecasts). This serial correlation of forecasts effectively reduces sample size.
  • In a few cases, for gurus with small samples, we include forecasts not explicitly tied to future stock market returns. There are not enough of these exceptions to affect aggregate findings.
  • Grading vague forecasts requires judgment. Random judgment errors tend to cancel over time, but judgment biases could affect findings. Detailed grades are available via links below to individual guru records. Within those records are further links to source commentaries and articles (some links are defunct). Readers can therefore inspect forecast grades and (in many cases) forecast selection/context.
  • S&P 500 Index return measurements for grading commence at the close on forecast publication dates, resulting in some looseness in grading because forecast publication may be before the open or after the close. Very few forecast grades are sensitive to a one-day return, and we try to take looseness into account in grading any forecasts that focus on the very short term.

Neither CXO Advisory Group LLC, nor any of its members personally, received any payments from the gurus graded.

The following chart tracks the inception-to-date accuracy of all 6,582 graded forecasts in the sample.

guru accur


The next chart shows the distribution of the individual gurus accuracies for the entire sample.

guru histo



The table below is a list of the gurus graded, along with associated number of forecasts graded and accuracy. Their names link to individual guru descriptions and forecast records. It appears that a forecasting accuracy as high as 70% is a rarity.

Guru Forecasts Accuracy
David Nassar 44 68.20%
Ken Fisher 120 66.40%
Jack Schannep 63 65.60%
David Dreman 45 64.40%
James Oberweis 35 62.90%
Steve Sjuggerud 54 62.10%
Cabot Market Letter 50 60.40%
Louis Navellier 152 60.00%
Jason Kelly 126 59.70%
Dan Sullivan 115 59.10%
John Buckingham 17 58.80%
Richard Moroney 56 57.10%
Aden Sisters 40 55.80%
Jon Markman 36 55.30%
Carl Swenlin 128 54.90%
Bob Doll 161 54.70%
Paul Tracy 52 53.80%
Bob Brinker 44 53.30%
Mark Arbeter 230 53.20%
Gary Kaltbaum 144 53.10%
Robert Drach 19 52.60%
Don Luskin 201 52.00%
Laszlo Birinyi 27 51.90%
Tobin Smith 281 50.20%
James Dines 39 50.00%
Ben Zacks 32 50.00%
Doug Kass 186 49.20%
Richard Rhodes 42 48.80%
Bernie Schaeffer 81 48.80%
Clif Droke 100 48.60%
Stephen Leeb 27 48.30%
S&P Outlook 145 48.30%
Carl Futia 98 48.20%
Charles Biderman 48 47.90%
Trading Wire 69 47.80%
Don Hays 85 47.10%
James Stewart 115 47.00%
Richard Band 31 46.90%
Jim Cramer 62 46.80%
Gary D. Halbert 93 46.40%
Dennis Slothower 145 45.60%
Bill Cara 208 45.60%
Gary Savage 134 45.00%
Marc Faber 164 44.60%
Jeremy Grantham 40 44.20%
Tim Wood 182 43.80%
Jim Jubak 144 43.40%
Martin Goldberg 109 43.10%
Price Headley 352 42.00%
Linda Schurman 57 41.40%
Donald Rowe 69 40.60%
Igor Greenwald 37 40.50%
Nadeem Walayat 67 40.50%
Bob Hoye 57 40.00%
John Mauldin 211 39.90%
Jim Puplava 43 39.50%
Comstock Partners 224 37.90%
Bill Fleckenstein 148 37.30%
Gary Shilling 41 36.60%
Richard Russell 168 36.50%
Mike Paulenoff 12 35.70%
Abby Joseph Cohen 56 35.10%
Peter Eliades 29 34.50%
Steven Jon Kaplan 104 32.10%
Curt Hesler 97 32.10%
Robert McHugh 132 28.60%
Steve Saville 35 23.70%
Robert Prechter 24 20.80%