Wednesday, 30 January 2013

% of S&P 500 Stocks Above 50 DMA

One indicator I sometimes like to look at to see if the market is getting a bit frothy is the % of S&P stocks that are above their 50 day moving average.

As you can see from the chart below, its at a very big high, almost 90% though in the last few days, its starting to drop.

Looking at the same chart for 200 day moving average, you see another high of 87%...

Tuesday, 29 January 2013

Market Overbought?

My feeling (note: FEELING) is that the market's starting to get overbought...  There's been too much rallying of the general market while several individual companies are starting to drop.  I usually see this sign before markets reverse.  Then again, usually this happens a month or more before the drop actually happens.

Will the trend continue?  Who knows but some of my indicators support overbought positions if not yet reversals.

Just a thought.

Friday, 25 January 2013

Is Market Getting Overbought?

The S&P 500 is hitting more and more new highs, just barely in range of 1500 now.  After an extremely strong second half 2012 and a strong YTD in 2013...are we getting a bit too far?  A good indicator I've always used is the % of stocks that are above their moving day average.  As you can see below, we are getting close to the highs that usually indicate a medium term drop/consolidation.

Interesting thing to consider when you look to buy into the market some more...

Thursday, 24 January 2013

Lite posting lately but big news next week

Unfortunately, been a bit busy lately so haven't had time to do much posting.  On the bright side, after some delay due to corporate compliance issues, expect some big news next week!

Sunday, 6 January 2013

Declining Trend in GDP Growth Post WWII Recessions came out with a post recently calling the current economy "Obamalocks" since its featuring subpar growth with accomodative fed policies.  I don't quite agree with their explanation but its interesting to look at the data they accumulated.  

As you can see from their chart, the current 2009 recession recovery has been relatively weak.  They have some inaccuracies/inconsistency in their number, one example is that they counted median without 2009 numbers but counted average with 2009 numbers.  I didn't check if their GDP numbers are accurate but one thing that struck to me is if you look at the increasing red trend.  If you average out the GDP numbers for each recession and plot it by date, you'll find something interesting.

Notice the very obvious trend where the GDP growth trend after every recession post WWII is progressively getting worse.  The only big outlier is the 1958 recession which was much lower but this may be more of a definition.  If you look at the tablet above from Bespoke, they included several negative GDP values about 3 years after the recession.  So in essence 1958 had a slight double dip recession effect in its average.  If you take that double dip value out, you'll find a much better fit.  While thats questionable from a methodology standpoint, its still interesting to see the long term trend.

Data excluding "double dip" effect from 1958 recession

So what's responsible for this slowdown in GDP recovery trend?  I have some theories but not sure.  Will have to look into it some more.

Thursday, 3 January 2013

How accurate are stock pickers?

Ran into this article from the CXO Advisory group today and thought it was interesting.  I'm sure everyone who reads about the market comes across various newsletters are experts that proclaim they know what the market will do in the future (hey I do this too but I don't get paid for it) and wondered how accurate it is.  CXO looks at their predictions and determines how accurate their advice is.  Turns around, most predictions are about a coin flip at ~45-50% (note that this is different than predicting what one company out of hundreds will do well, it merely says if the stock they chose did well).  I'm interested to see if they have a figure for what % of companies do well.

This info is not intended for you to only follow the accurate callers as past performance is not indicative of future results.

See full link

Grading Methodology:

The essential grading methodology is to compare forecasts for the U.S. stock market (whether quantified or qualitative) to S&P 500 index returns over the future interval(s) most relevant to the forecast horizon. However, many forecasts contain ambiguities about degree and timing, equivocations and/or conditions. In general, we:
  • 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.

David Nassar4468.2%Doug Kass17847.6%
Jack Schannep6166.7%Jeremy Grantham3747.5%
Ken Fisher11565.0%Don Hays8547.1%
David Dreman4463.6%James Stewart11547.0%
James Oberweis3562.9%Marc Faber15447.0%
Louis Navellier14959.9%Richard Band3146.9%
Jason Kelly12559.4%Jim Cramer6246.8%
Dan Sullivan11559.1%Gary D. Halbert9346.4%
John Buckingham1758.8%Dennis Slothower14445.3%
Cabot Market Letter4858.8%Jim Jubak14144.2%
Steve Sjuggerud4958.5%Bill Cara19844.2%
Richard Moroney5556.4%Tim Wood18243.8%
Carl Swenlin12555.4%Martin Goldberg10943.1%
Jon Markman3655.3%Price Headley34842.0%
Aden Sisters3954.8%Gary Savage11941.9%
Bob Doll16154.7%Linda Schurman5741.4%
Paul Tracy5253.8%Donald Rowe6940.6%
Bob Brinker4453.3%Igor Greenwald3740.5%
Mark Arbeter23053.2%Bob Hoye5440.4%
Gary Kaltbaum14453.1%John Mauldin21139.9%
Robert Drach1952.6%Nadeem Walayat6639.7%
Don Luskin20152.0%Jim Puplava4339.5%
Laszlo Birinyi2751.9%Comstock Partners20339.3%
Tobin Smith28150.2%Gary Shilling4037.5%
James Dines3950.0%Bill Fleckenstein14837.3%
Ben Zacks3250.0%Richard Russell16636.9%
Richard Rhodes4248.8%Mike Paulenoff1235.7%
Bernie Schaeffer8148.8%Abby Joseph Cohen5635.1%
Stephen Leeb2748.3%Peter Eliades2934.5%
S&P Outlook14548.3%Curt Hesler9233.0%
Carl Futia9648.1%Steven Jon Kaplan9931.4%
Clif Droke9748.1%Robert McHugh12828.9%
Charles Biderman4847.9%Steve Saville3523.7%
Trading Wire6947.8%Robert Prechter2321.7%

Wednesday, 2 January 2013

Graphic Chart - Five Years Later

Interesting chart from the NYTimes:


Five Years Later, Some Countries Still Lag

Five years ago, in the fourth quarter of 2007, the United States economy peaked before what became known as the Great Recession set in. Some countries, China and Australia among them, never saw their economies decline, but most did, and some of the largest economies remain smaller than they were in 2007. The world economy — measured by total gross domestic product in 56 countries for which data are available — is now about 6 percent larger. But share prices have yet to fully recover in most countries. Related Article »

Tuesday, 1 January 2013

S&P 500 Returns by Sector

The year is finally over.  Overall, 2012 was great for the stock market after a dismal 2011.  Below is the performance data from Standards & Poor for the S&P 2012 Year performance, broken out by sector:

Index NameAdjusted Market Cap ($Million)Index LevelPerformance
S&P 500 (TR)N/A2,504.441.69%.91%-.38%16%
S&P 50012,742,436.251,426.191.69%.71%-1.01%13.41%
Cons Disc1,464,901.61376.062%.24%1.57%21.87%
Cons Staples1,352,391.20360.781.11%-2.54%-2.48%7.52%
Health Care1,530,449.35462.951.22%-.38%-.5%15.19%
Info Tech2,426,353.68463.822.16%-.12%-6.21%13.15%
Telecom Svc389,865.37146.041.14%-1.09%-7.06%12.5%

As you can see, total return including dividends was a sweet 16% while non-div return was still a nice 13.41%.  The two big winning sectors for the year: Financials at 26.26% and Consumer Discretionary (really? in such a depressed economy?) at 21.87%.

There was only one loser: Utilities at -2.91%.  This can be seen as more of a correction as 2011 was great for the Utilities and dividend sector.  Energy was also a big loser at 2.33%.  If the trend plays out that the poor performer becomes better next year, energy may be an interesting play for 2013.

Sector Breakdown

 (as of 31-Dec-2012)

Sector Breakdown