Sunday, April 22, 2007
The Big Picture Watching Trading Volume Fade . . .
In this case, insteaed of just looking at the S&P 500, Barry Ritholtz points us to a Michael Kahn Barron's article where he takes a look at the volume as well and discovers some interesting patterns.
What other factors might be worth adding into the mix and giving us even more depth of understanding of the basic workings of this important headline financial indicator?
For example, it would be interesting to see what percentage of the S & P volume was NEW short sales and what percentage of the volulme was Covering Purchases for previous short sales.
It might also be worthwhile to look at the average number of shares per trade which might give a clue into what percentage of the volulme was due to large financial instituions and what part was coming from the "little guy."
Friday, April 20, 2007
St. Louis Fed: March 2007 Tip of the Month
It's now possible for registered users of their FRED database to create, name and save customized graphs that can then automatically be updated on later visits to the site. Looks like a great feature.
Saturday, April 14, 2007
Friday, April 13, 2007
On the plus side we have the complete time range and the use of the derived value of the inflation adjusted DOW as the variable to watch rather than the Headline raw DJIA value. In my view, the inflation adjusted number really should be the Dow value that we watch for the same reasons that we use inflation adjusted numbers everywhere else.
Thursday, April 12, 2007
The Big Picture Historical Bear Market Contractions
I liked the creative way that the width of the bars in this chart shows the second dimension of length of the contraction period.
Wednesday, April 11, 2007
Using just the ordinary blog posting mechanism and the ability to embed JPG pictures as the Trend Visualization Appliance, the resulting article has a high degree of explanatory power and immediacy. You can literally see what Calculated Risk is talking about as you read as a result of the great blend of clarifying text with the easy to read trend graphics in close proximity.
Not relying on the normal conventions of providing only one or two graphics for headline numbers, Calculated Risks walks us through a series of 6 or 7 less well known but still key factors. This leaves the reader/viewer which a much more complete view of the dynamics that are at work in this one complex area.
Combine this with good references back to data sources and consistently good judgment on what would constitute a reasonable time frame, leaves with a very useful end result that provides the reader with the maximum amount of infomation in relatively short period of time.
The only difficulty with this is the same one I face with this TimelineView blog -- namely that as a limitation of BLOGGER.COM the largest allowed size of the embedded images is just too small to be fully readable and understandable in most instances. To really understand the charts, at least at first, usually requires clicking for the larger image and then using the browser navigation controls to go back to the main page. This really slows down the flow of reading and understanding.
I think we can all learn a lot by watching how Calculated Risk handles these kinds of presentations. Take a look at the post or other similar posts that appear regularly at Calculated Risk and you will see what I mean.
Another hat tip to Barry Ritholtz in his post on The Big Picture The Capital Commerce Debate this time highlighting some eye opening and vital data about MEW (Mortgage Equity Withdrawal) using charts from courtesy of CalculatedRisk.
You can find a very recent Calculated Risk article here with comments on the debate between Barry Ritholtz and Don Luskin and a clarification of the meaning of the second chart here.
It sure does look like capital spending has turned a big corner.
The chart is courtesy of Bob Bronson. You can find some more of Bob's posts here.
For reference, here's is a similar chart from about 6 months ago.I found it interesting how well Bob's analysis back in October, before the raw data turned the corner, was able to predict the recent changes in Capex.
I also like the way Bob has integrated textual explanations and graphical pointers right in with the charts themselves.
The only negative point I see with this presentation is that the dates are hard too read clearly.
We've recently come across another great new data sharing and visualization web site called Swivel and have uploaded the same data set there for comparison purposes at: http://www.swivel.com/data_sets/show/1004780.
Both Many Eyes and Swivel fall into a category I have started calling Trend Visualization Appliances. The amazing Gapminder work also fits into this category as do all the various stock market visualization tools currently available.
The old fashioned form of trend visualization appliances (e.g. a standard report including graphical output) can still be pretty useful when managed carefully. For further comparison, here is the link to a PDF report that graphically presents the same data - vizualizing-trends-ohanlon-testimony.pdf.
Similarly, for data sharing, the old fashioned URL links to csv files offer an alternative to the WEB 2.0 mechanisms such as Many Eyes and Swivel. Here's the Iraq trend data the old fashioned way: ohanlon-key-factors.csv
So many choices, so little time. How can we decide which is best for our purposes?
I'll be revisiting this topic, but for now, my own criteria for deciding which tools I will use at a given moment are:
1) ease of use,
2) shortness of learning curve, and
3) personal productivity and time saving -- the speed at which I can navigate through complex trend data sets to discover previously hidden patterns.
What do you think?
Tuesday, April 10, 2007
You can set the time period for the trend graphs created to be the entire time since the year 2000 that JOLTS data has been collected. Here are a few examples. It's somewhat puzzling to me why the Openings rate seems to be going steadily up from 12/2003 onward while the Hiring rate stays steady beginning around 12/2004.
There's a ton more data at the BLS web site. The biggest difficulty for me and I bet for others is just how time consuming and inflexible the BLS' trend visualization application (TVA) is to work with.
As has been typical in the BLS formal reports like this, the time span is too short to be able to place recent behavior in context and the number of factors shown in visual form is far too few to fully grasp what is going on in the world of employment.
In the full monthly report itself which runs to 15 pages, there are numerous tables with literally hundreds of important factors. Unfortunately, these covering an even shorter two year time period with even fewer sample trend sample points per factor. This again matches the current BLS standard not-particularly-reader-friendly approach for their "printed" reports.
In my view, this standard BLS approach is far from the best way to present important trend data. All the time consuming work of searching for and extracting meaning is left to reader (which means in most cases it will never happen) or it is left to the expert pundits who will typically comment on a few of the sub factors that they find most interesting and perhaps present one or two charts.
Of course, back at the BLS web site, just about all the trend data for all the factors for all the time periods is available for those who have the time and skill to track it down.
voiceofsandiego.org: Toscano... Motivated Sellers Abound
It would be nice to be able to flip through a series of charts like this for a sampling of strategic areas or state by state to be able to compare how the San Diego trends match up against other parts of the country.
Hat tip to Kirk for this post at Reasons Unbeknownst
Monday, April 9, 2007
Violence in Iraq - The Battle for Baghdad - New York Times
If you click on the Sunni Area, Shiite Area, and Green Zone labels in the upper right corner, the map will be painted with some subtle coloring that I found helpful.
The forward and back controls that that show you week by week changes did not work well for me, often not responding to my mouse clicks. The pull down menu worked better but of course is a lot more clumsy. A PLAY button with some speed control would have been really helpful.
The time span runs backward to February 11th so this chart doesn't allow comparisons of how post "surge" events relate to the pre "surge" situation.
Here's a before and after the 'surge' graphic from this morning's New York Time article on: Patterns of War Shift in Iraq Amid Buildup of U.S. Force that sheds some light on the degree to which the "surge" is working.
Disaggregating the figures by area is helpful at clarifying better what is happening on the ground. For example, the increase in Baghdad deaths and Diyala deaths due to IED both look substantial while the drop off in total deaths in Anbar Province is also striking.
By selecting a 7 week Before Period and a 7 week After Period, this graphic sets up the possiblity of direct and straightforward Apples to Apples comparisons .
I would also have liked to see all the same key factors represented on this chart (such as Baghdad deaths by IED, Baghdad deaths by other causes, Anbar deaths by IED) also available as a week by week trend table and charts with the time frame stretching back to the beginning of the war so that the trends showing up in the current casualty levels could be put put into a broader context.
Of course, adding in other factors such as US troops wounded, Iraqi forces deaths and wounded, and Iraqi civilian deaths and wounded would give a much fuller picture of the degree to which the "surge" has so far changed things in Iraq.
Saturday, April 7, 2007
His comments on selective perception, cognitive bias, the "recency effect" and the soft prejudice of low expectations apply well beyond the NFP example.
Let's begin with a quick word on cognitive bias. Humans are guilty of this -- selectively perceiving and recalling what agrees with their world view. We are all guilty of this, and while we cannot escape it, being aware of it at least allows some measure of recognition, and perhaps, adaptation to the phenomena.Unfortunately, the two trend chart he shows only go back three years (suffering themselves from the recency effect) so we can't compare the relatively weak current numbers to the NFP increases of the 1990s. The two charts shown in the Bureau of Labor Statistics Employment Situation Report also only go back 3 years.
Let's use the NFP data as an example: Consider another bias, the Human tendency to overemphasize more recent data versus the totality of information and the overall trend. This is a cognitive bias known as the recency effect. Despite the overwhelming evidence showing this to be a generally weak jobs recovery (the worst since WWII), our primate brains interpret a single good data point as proof of something better. ...Regardless, we also see some of the soft prejudice of low expectations in yesterday's data: 180k is hardly a rockin' strong number, relative to population growth,. Put that into the context of recent expansions such as the 1990s. Oh, and in that more recent period, there was no BLS Birth/Death adjustment, responsible for nearly a million fictitious jobs in 2006.
So, below I have posted a 15 year view showing year over year percentage change for the seasonally adjusted value of the growth in NonFarm Payroll. It's pretty clear from this longer term chart that our growth in non-farm employment during this latest expansion never got back to the steady, multi-year 2.5% growth rate of the late 1990's and appears to be declining again.
For example, the recent drop off shown above does not seem to square with the reported drop in the headline unemployment rate. Maybe some other factor of the thousands of factors recorded by Bureau of Labor Statistics (BLS) can help explain it.
Too often, really important factors aren't mentioned at all, and the ones that are mentioned are not accompanied by their corresponding trend charts.
Here's a link to a short list of the most popular BLS statistics which might be a useful starting point for a deeper analysis.