The Lawrence Chan Blog

S&P 100 (OEX) is a subset of the S&P 500 Index containing the top 100 most valuable companies within the S&P 500. This index has long been used for the construction of financial derivatives like index options for decades. The most well known symbol for S&P 100, OEX, is in fact the ticker symbol for the index options listed at Chicago Board Options Exchange.

Special Challenge With Component Changes

Due to the nature of the selection process of the S&P 100 components, it is obviously designed to have as few component changes as possible. Yet, the dotcom bubble happened. And then the financial crisis happened. These mega swings in the stock market created several interesting issues with S&P 100.

Within the past decade, the component changes varies significantly every year.

In year 2008, more than 10% of components are changed.

In year 2010, there was only one single switch of symbol. In this particular case, it was Berkshire Hathaway took over Burlington and assumed its position in the index.

From 2006 to end of 2013, only 60% of the components stayed within the index all the way. Hence, S&P 100 is not really a stable set of companies as many people perceived.

Another odd issue with S&P 100 is that its component changes often happen during the first 2 quarters of the year, rendering a yearly updated component list mismatched by several issues just a few months going into the year.

Using a yearly component list approach still works but the custom market breadth data generated will often contain 2% to 3% error relative to the ones generated from an exact component list.

Not The Same As Nasdaq 100

The market capitalization concentration issue with Nasdaq-100 does not impact S&P 100 as much due to the fact that S&P 100 is designed to represent a diversified group of industries. Thus it is not possible to just pick the top ranking components in terms of market cap to construct useful custom market breadth data. At times, certain industries within the index would lead while the stocks from other sectors will lag behind. Choosing blindly a subset of the components based on market cap will lead to completely ineffective custom breadth data when those selected components are lagging.

On the other hand, S&P 100 Index exhibits more than 85% correlation with the S&P 500 Index movements. That makes the whole basket of S&P 100 components a great choice for constructing more responsive market breadth data for use with S&P 500. Thus, for any reason, if you cannot track all of S&P 500 components in real-time, you can always use just the S&P 100 components for the construction of proxy breadth data. The result will still work very well.

Snapshots of Historical Components

The yearly snapshots of the component list is available at the Historical Data Bank.

Similar to the other data files, the archive is available for free to all members.

You can purchase the data files individually from the data bank as well.

Resources

S&P 100 at Wikipedia.org

iShares S&P 100 ETF main page

CBOE OEX microsite

CBOE OEX Index Components

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Nasdaq-100 (NDX) is one of the most followed index worldwide. Many derivative products are created around the index itself to allow participations in its rise and fall all around the world. Unlike S&P 500, Nasdaq-100 does not require a company to be listed within United States only. This makes Nasdaq-100 a truly international index.

Due to the design of Nasdaq-100, its components are switched quite often, making it one of the indices requiring more maintenance afford than the others. In general, if you are tracking custom market breadth on NDX, keeping the index component list updated every month is good enough. If you cannot afford to do so, at least once every quarter will keep your breadth data relevant for analysis purpose.

Change of Business Model

Nasdaq used to offer its historical data for free at its main website. Since 2009, however, things started to change. We have seen the creation of a specialized website by Nasdaq that monetizes the data from Nasdaq. Many useful and time-sensitive information are now part of the paid services where you can get directly from the nasdaqomx.com website or from one of its redistributors. This makes it more difficult to obtain historical data on Nasdaq-100 for analysis.

Similar to the S&P 500 historical constituents, it is possible to reconstruct the snapshots of historical components of NDX manually if you are interested in doing so yourself. What you can do is track down the historical press releases of the component changes (which are a matter of public record) and then reverse engineer the information from the latest list of components. It is a tedious task and quite time-consuming.

For the latest component lists, it is easiest to get it from either the Powershare QQQ main page or the Nasdaq-100 home page. (Links in the resource area below)

Issues With Component Changes

I have already discussed about the various issues related to component changes in the S&P 500 article. I will not repeat that here. Feel free to check out the article using the link below in the resource section to learn about potential problems related to working with sets of historical components.

In a stable market environment, Nasdaq-100 seldom has many component changes. In fact, it can be months between changes happen. Many changes are caused by merger and acquisitions, not because of market cap ranking change. Nasdaq adopted the strategy to batch replace the components in the bottom of the top 100 market cap companies in December. Thus the last 2 weeks of past few years will cause your monthly updated component list off by 5 to 6 (or even more) issues.

The difference between your list and the actual components would be 5% or more after the change. Luckily, during that time of the year, market activities are slowest and the new components are just the bottom ones in the top 100, which you can tell from the next section, are not that important. Hence you can relax at the end of the year and do the switch after new year.

The Peculiar Behaviour Of Market Capitalization Weighted Index

Nasdaq-100 is particularly sensitive to changes of the top 30 components. The main reason is that the top 30 components already contribute to more than 70% of the total market cap of the index. Thus the fluctuations of these components play a much bigger role than the rest of the components combined.

This special behaviour is caused by the fact that Nasdaq-100 is a pure market cap weighted index. Unlike S&P 500 that enforce extra rules with diversification into multiple industries, Nasdaq-100 focuses on one and only one thing, the market cap. Hence stronger a component performs relative to the other components in the index, the higher its weighting will become. The opposite is also true, meaning that the weaker a stock performs relative to the other components, the lower its weighting (and importance) will become.

In another words, it is possible to construct custom market breadth data on Nasdaq-100 using just the top 30 components only. Custom market breadth created this way requires special attention because the size of the basket is so small that the basket of components has to be updated whenever there is a change of top 30 components by weighting. This can happen anytime and can happen every day during extreme market environment.

The custom market breadth data constructed from the top components are often leading in nature so it is a trade-off of effort and usefulness.

Snapshots of Historical Components

I have a set of Nasdaq-100 historical component lists going all the way back to 1990s.

I am making the quarterly and month snapshots of the Nasdaq-100 component list available in the Historical Data Bank.

The quarterly set starting from year 2008 is available for free to all member.

You can purchase the data individually from the data bank.

Resources

Nasdaq-100 Home Page at NasdaqOMX.com

Powershares QQQ main page

Nasdaq-100 at Wikipedia.org

Custom Market Breadth Components Resources

S&P 500 Historical Constituents Change History

DaytradingBias.com Historical Data Bank

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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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