The Lawrence Chan Blog

We are getting close to release the custom market breadth data in the Historical Data Bank.

The delay is more than what we bargain for as hard disk problems and other messy issues strike. Just like what Murphy’s Law says, anything that can go wrong will go wrong. Anyway, the problems are resolved. We are moving towards our goal as quickly as possible.

I am also setting up daily charts of the major indices with their custom market breadth data posted so that members can utilize the information much more easily.

Will keep you all posted.

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I was asked if chart reading works with Bitcoin. My answer is yes. From 5-minute resolution to its daily charts, chart patterns work very well with the virtual currency ever since the various exchanges provided the means to buy and sell Bitcoin easily.

I was also questioned by many if STOPD would work with Bitcoin because it is such a new market and not having that many players. What many people do not understand is that the dynamics of a market, any market, is governed by the goal driven nature of the participants. The moment you have at least 20 to 30 participants active any moment trading a market, STOPD will give you useful information.

Here are some interesting charts of Bitcoin.

1. 1st quarter of Bitcoin range bracketed in blue lines, its 150% expansion highlighted in orange, 200% expansion highlighted in red and 300% expansion target highlighted in pink.

Bitcoin reacted to the target levels and then pullback to 50% of 1st quarter range before finding support.

Some people call that coincidence. I call these expected outcomes.

2. The year range highlighted in blue.

Correction back down to 50% of the year’s range and then one more push down to the Fibonacci fans’ favourite at 38%. Since Bitcoin participants are not in general engaging in market making activities, it has a higher tendency to hit the Fib ratios as I explained in STOPD.

3. Double top break

Pure classic double top play with 50% target (orange line) tagged first (left green arrow) and then 100% target (red line) tagged later (right green arrow).

4. Measured move

Measured move highlighted in dark red.

5. Trend line break

This one is in progress. From what I see, $1300 is in the card.

For reference to the chart patterns, see Art of Chart Reading

STOPD refers to my ebook Special Theory of Price Discovery

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Unlike Nasdaq 100 and other indices managed by various exchanges, information on the S&P family of stock market indices are proprietary. That means all kinds of information about the S&P indices are not public information and that such information can only be obtained through various paid services that licensed the information directly from the owner of the S&P indices. The data you get from these paid services in turn bound you to certain limitations in terms of the scope of usage. Hence no one can make public the data they obtained directly from the source or its licensed data providers.

A Matter Of Public Records

This creates a special problem for those traders who are interested in the historical components of S&P 500 and its related indices but do not have access to services like Compustat or Bloomberg. Luckily, various fragments of information about these indices have to be made public due to the regulatory requirements of the financial products created around these indices. Hence, there exists public records of the public announcements of the changes to the constituents of these indices and snapshots of the lists of index components over time.

As I mentioned in my other article on Custom Market Breadth Components Resources, to track the latest set of components in S&P 500 can be done easily with the latest updates at the SPY ETF main site, or, if you track many indices, using a low cost paid service like TC2000 will save you time and effort.

On Going Tracking Is Easy

As long as you keep up with the changes once a year, your data set should be reasonably good for constructing your own custom market breadth data. The reason why it is possible to keep track of the changes once a year is that the number of companies switched every year is around 20 to 30. Majority of these changes are made towards the second half of the year. Statistically it means the overall usefulness of your custom breadth data are still good as long as many of the switched out companies are still trading.

If you are serious about the quality of your breadth data, you can go as frequent as a change is made. Personally, I found the data set on most breadth data are good if you keep your component list updated either monthly or quarterly.

Tracking Down Historical Components Is Difficult Detective Work

For historical components, however, it is a lot more complicated if you have not collected the information at the time they were made public. There is no readily available lists of historical components for download anywhere on the Net. You will have to go to the source which is very expensive or reconstruct the list yourself by back tracking all the component changes from the announcements.

I found the press releases from S&P (www.standardandpoors.com) itself provides the cleanest series of events to follow. If you plan to reconstruct the historical component lists yourself, this is the best way to go.

Other free sources are often incomplete and lack updates over time. For example, the list of components and the changes from wikipedia.org are incomplete at this point in time. The various online records of historical changes are incomplete because retail traders in general do not understand that certain corporate actions can still lead to component changes while the events themselves are not announced specifically as a planned change of components in the S&P 500.

A good example of such is the change of ownership of certain listed companies. These companies can be bought or sold by their parent companies any time. If the new owner of the listed company causes a change of industrial classification according to S&P, a replacement will be elected but it will not be a regular component change event.

Another source of confusion is symbol change of existing components. Many long lasting companies like to own the more iconic symbols for their stocks. When such symbols were freed up due to various reasons like bankruptcy or merger, many companies will rush in to apply for takeover of these rare symbols.

It may sound silly but who are we supposed to judge when people actually fight for special license plates on their cars.

If you find the description above confusing, be assured that you are not alone.

It has always been messy with corporate actions.

Mix Up Of Historical Data On Individual Components

This issue is actually an even bigger challenge than the maintenance of correct components. I guess I should mention it here because it is caused by the same messy issues with the way symbols are being reused.

Due to the reuse of symbols among the listed companies, it is very easy to make the mistake of using the wrong historical data from your data library for analysis and custom market breadth data generation.

Here is a good example. Washington Mutual was listed under the symbol WM before its collapse back in 2008/2009 financial crisis. Since its change of symbol at the time of its bankruptcy proceedings, Waste Management Inc. assumed the symbol WM from Washington Mutual even though it was listed under WMI happily for many years. Now that when you look at the historical components of S&P 500 by symbols only, many people will mistaken that the WM back in 2008 is the same WM showing up in 2009.

Casual researcher on the S&P 500 components will likely mix up the two even if they are aware of the potential issue. Many data vendors would stitch the Waste Management Inc. stock data under the two separate symbols into the new one that is now in use. It is standard practice and is a big convenience for majority of the traders. There is no problem with that until you actually need access to the data of Washington Mutual. The data services have no way in telling that the symbol WM you are looking for is in fact Washington Mutual. In another words, your access to the historical data for Washington Mutual is forever lost.

As you can see, there is no easy solution to this problem unless the data service you are using provides access to historical data by CUSIP numbers or other unique identification code. As far as I know, only high-end data services catering to institutional clients provide this level of historical data access.

My Snapshots Of Historical Components

I have been constructing my own custom breadth data since 1990s. Hence I have a large collection of snapshots of historical components of many indices. They may not be the most complete set and likely to contain errors here and there. I believe, however, they are useful to anyone interested in the historical changes in the S&P 500 components over the years. They are also critical for the accurate construction of custom market breadth data.

I am making the yearly snapshots of the S&P 500 component list available in the Historical Data Bank.

The data set starting from year 2008 is available for free to all members.

If you choose not to join us, you can purchase the data individually.

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Bennie arranged a not so important speech 1 hour before market close.

Result?

S&P managed to hold its ground by close.

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This video is an interesting take on branded currencies.

What 6 months can do to Bitcoin? This video was filmed back in June 2013 and now Bitcoin is standing at USD $800.

I like to add to the video that an item having the store of value property need people to commit other existing assets into the item first. For example, a bottle of Tide (you have to watch the video to understand) being used as a medium of exchange requires existing money (or any other store of value items) being committed first in exchange for the bottle of Tide. This point has been missed by both supporters and haters of Bitcoin.

The value of a virtual currency comes from the value of the other accepted assets already committed in exchange for the virtual currency. Since we know a lot of money and efforts (which takes money too) have been committed into Bitcoin and other virtual currencies. This alone gives you a valuation of Bitcoin above nothing. This is how we evaluate companies not making money. This is how we evaluate countries. You cannot all of a sudden telling yourself the evaluation method human has used for eons does not apply to virtual currencies.

In other words, many people think that Bitcoin does not worth anything missed the point.

A famous antique painting being committed with $40 million dollars from its latest buyer worth every penny of the $40 million dollars as long as it is real (i.e. what the painting is claimed to be in terms of the painter and picture in question). The operative keyword here is that it worth more than zero. The same logic applies to virtual currencies because existing value has been transferred into these virtual currencies already.

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