Capital Weight Vs Equal Weight

It has bothered me for quite some time, that indexes and ETFs are generally calculated in a manner different than composite data.  The inescapable question was, are we comparing apples and oranges?  There was only one way to find out and that was to recalculate each index and ETF using an equal weight methodology.

Although this data has been available on the site for some time now, I was not satisfied with the way it was calculated.  Most methods experience some sort of “drift” over the long run.  The chart looked the same, more or less, but values exhibit an upward bias (usually) over the long run winding up with values that don’t really relate to anything in the real world.  Here is the example that woke me up to this problem.  Buy a stock at 50.  It goes up to 100, a 100% gain.  It goes back down to 50, a 50% loss.  Cumulative gain by this simple method is 50%.  Obviously, you see the problem.  Most methods seem to incorporate some degree of this “drift”.  The charts look OK, but the numbers don’t relate to anything.

After much experimenting and brain searching, it occurred to me that every issue simply needed to start at a value set at a specific point in time.  That makes sense, but component stocks often have different starting trade dates and I want to use their entire price history.  Others were trading before our historical datafiles starting date.  The answer was to set each component’s start value (the value for comparing all other price history of that component) to the stock’s price at the beginning of the current trading year.  If that value became 100%, how did all other prices compare (percentage-wise) to that value?  Needless to say, I wouldn’t be writing this if it did not work.

To make the values more readable and prevent them from going negative, when compiling each component’s value it starts at 100 (beginning of this year’s value).  So when the equal weight chart below shows a current close value of 101.58, it means that combined equal weighted component performance is up 1.58% since the first of this year.  The S&P 500 itself is down 1.32% for the year (actual values).  The difference is accounted for by the differences between capital weighting and equal weighting.  The components with heavier weighting are currently pulling the index down more than they would if they had an equal weight to every other component.  In my opinion, that is very significant.

This calculation is performed on the price history of about 3400 stocks and then accumulated into the various recalculated indexes and ETFs using their current component lists.  The method exhibits absolutely no “drift” and values remain true to real price performance.


In addition to actual market values, MasterDATA now additionally provides historical data for each index and ETF followed recalculated utilizing an equal weight methodology.  The two charts immediately below, display an example of each (click on their link).

 

Capital Weighted S&P 500 Index (displays “actual” market values)

 

Equal Weighted S&P 500 Index (displays MasterDATA’s recalculated equal weighted market values)

 
Index and ETF Methods of Component Weighting
Three primary methods exist for the weighting of components within indexes and ETFs:

 
Capital Weighting
Price Weighting
Equal Weighting
 
The S&P 500 Index is capital weighted (see top chart example).  Component weighting utilizing this method is based upon market capitalization, so one component basically counts more or less (carries more or less weight) than the next when figuring the current value of the total composite.  A one point price move in a component with a larger market capitalization affects the parent index or ETF total value more than a component with a smaller market capitalization.

The Dow Jones Industrial Average is price weighted.  In this method, component weighting is based upon the price of the component issue.  In other words, if you started a new 30 component index today, this method would add the current price of the 30 components and divide by 30.  Similar to capital weighting, one component thus carries more or less weight than the next, but in this method the higher priced components carry more weight than lower priced components (as simple as this method sounds, it gets very complicated, very quickly when the divisor is changed – in other words, in our example, you would no longer divide by 30, but instead, another number calculated to compensate for various component changes such as spin-offs, etc. – the Dow’s divisor is currently less than one).

Composite / breadth data is inherently equal weighted.  Each component of an index or ETF carries exactly the same weight as the next.  If 316 components of the S&P 500 Index increase in price for the day, the composite / breadth statistic, advancing issues (or simply “advances”), is 316.  Because most indexes and ETFs are not similarly equal weighted, a possible conflict presents itself.  For example, using a capital weighted index like the S&P 500 Index with equal weighted composite / breadth data could potentially be akin to comparing “apples and oranges”.

To address this issue, in addition to providing historical market values, MasterDATA recalculates all followed indexes and ETFs as equal weight (see bottom chart above example).  Both sets of historical data are included in downloads from this site, both actual values and recalculated values. 

The process of recalculating each index and ETF as equal weight developed into a much larger project than one might anticipate from the idea’s initial conception.  For one thing, each of as many as 3400 component issues had to be filtered and manually corrected for historic price errors (our data vendor is one of the biggest and “best”, but an error here and there can quickly result in a major impact on total values).  Additionally, numerous methods were implemented before arriving at one that displayed absolutely no “drift”, but instead provides meaningful values comparable to the indexes’ and ETFs’ actual market valuation.

The result of this work is very intriguing.  While the overall chart patterns remain basically the same, price moves are generally smoother.  A large price move in a heavily weighted component does not overly impact the index or ETF value unless other components experience similar movement.  From a technical analysis point of view, equal weighting might be considered the ideal methodology for composites.  In any event, the data is provided at no additional charge.  It is your decision and your decision alone to use it or not.

 

 

Comments, criticisms and suggestions are always appreciated.  I thank you for your past support and hope you continue with us.  In any event, good trading.

Best,

Larry Carhartt

MasterDATA
The Only Source for Index & ETF Composite/Breadth Reports, Charts & Data
lc@masterdata.com
www.masterdata.com

Recalculated Equal Weight Index and ETF Datafiles Reformulated

A final method of recalculating all followed indexes and ETFs as equal weight is now in place. The results incur no “drift” and values relate directly to real market values. Additionally, since, as a part of the process, all values relate to a percentage scale, indexes and ETFs may be readily compared without further computation. In a chart, this means that any number of indexes and ETFs can appear in the same chart using the same scale. In an indicator or trading signal, numerous indexes and ETFs can be used in conjunction without raising comparability issues.

Although this data has been available on the site for some time now, I was not satisfied with the way it was calculated. Most methods experience some sort of “drift” over the long run. The chart looks the same, more or less, but values exhibit an upward bias (usually) over the long run winding up with values that don’t really relate to anything in the real world. Here is the example that woke me up to this problem. Buy a stock at 50. It goes up to 100, a 100% gain. It goes back down to 50, a 50% loss. Cumulative gain by this simple method is 50%. Obviously, you see the problem. Most methods seem to incorporate some degree of this “drift”. The charts look OK, but the numbers don’t relate to anything. This issue is now resolved and data posted on the web site for download. Results significantly exceed initial expectations.