Making sense of stock charts
Traditional Stock Charts can Mislead

Almost every stock chart presents incomplete data for a security’s total return.  Simply put, stock charts don’t reflect dividends and distributions.  Stock charts simply show price data.  A handful of charts superimpose dividends over the price data.  Such charts are an improvement, but require mental gymnastics to correctly interpret total return.

At the end of the year, I suspect the vast majority of investors are much more interested in how much money they made than whether their profits come from asset appreciation, dividends, interest or other distributions.  In the case of tax-differed or tax-exempt accounts (such as IRA, Roth IRAs, 401k, etc. accounts) the source of returns is unimportant.  Naturally, for other portfolios, some types of return are more tax-advantaged than others.  In one case I tried to persuade a relative that MUB (iShares S&P National AMT-Free Muni Bd) was a good investment for them in spite of it’s chart, because the chart did not show the positive tax impact of tax-exempt income.

Our minds see what they want to see.  When we compare two stocks (or ETFs) we often have a slight bias towards one.  If we see what we want in a stock’s chart, we may look past the dividend annotations and make a incorrect decision.

This 1-year chart comparing two ETFs illustrates this point.  These two ETFs track each other reasonably well until Dec 16th, where there is a sharp drop in PBP.  This large dip reflects the effect of a large distribution of roughly 10%.  Judging strictly by the price data, it at first appears that SPY beats PBP by 7%.  When factoring the yield of PBP, about 10.1%, and SPY, roughly 1.9%, shows a 1.2% 1-year out-performance by PBP.  First appearances show SPY outperforming;  a little math shows PBP outperforming.

Yahoo! Finance provides raw data adjusted for dividends and distributions.  Using the 1-year start and end data shows SPY returning a net 3.77%, and PBP returning a net 4.96%.  The delta shows a 1.19% out performance by PBP.  Yahoo! Finance’s table have all the right data;  I would love to see Yahoo! add an option to display this adjusted-price data graphically.

Total return is not a new concept.  Bill Gross was very insightful in naming PIMCO’s “Total Return” lineup of funds over 25 years ago.  Many mutual funds provide total return charts.  For instance, Vanguard provides total return charts for investments such as Vanguard Total Stock Market Index Fund Admiral Shares.  I am pleased to see Fidelity offering similar charts for ETFs in research “performance” reports for its customers.  Unfortunately, I have not found a convenient way to superimpose two total-return charts.

While traditional stock and ETF charts do not play a large roll in my investment decisions, I do look at them when evaluating potential additions to my investment portfolio.  When I do look at charts, I’d prefer to have the option of looking at total return charts rather than “old fashioned” price charts.

That said, I prefer to use quantitative portfolio analysis as my primary asset allocation technology.  For such analysis I compute total return data for each asset from price data and distribution data, assuming reinvestment.  Reformatting asset data in this way allows HAL0 portfolio-optimization software to directly compare different asset classes (gold, commodities, stock ETFs, bond ETFs, leveraged ETFs, etc).  Moreover, such pre-formatting allows faster computation of risk for various asset allocations within a portfolio.

A large part of my vision for Sigma1 is revolutionizing how investors and money managers visualize and conceptualize portfolio construction.  The key pieces of that conceptual revolution are:

  1. Rethinking return to always mean total return.
  2. Rethinking risk to mean something other than variance or standard deviation.

Many already think of total return as the key measure of raw portfolio performance.  It is odd, then, that so many charts display something other than total return.  And some would like to measure, manage, and model risk in more robust ways.  A major obstacle to alternate risk measures is a dearth of financial portfolio optimization tools that work with PMPT models such as semi-variance.

HAL0 is designed from the ground up to address the goals of optimizing portfolios based on total return and a wide variety of advanced, more-robust risk models.  (And, yes, total return can be defined in terms of after-tax total return, if desired.)

Disclosure:  I have long positions in SPY, the Vanguard Total Stock Market Index, and PBP.





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