Click to learn more about author Steve Miller.
Last week wasn’t a good one economically for the POTUS. February jobs numbers were much lower than anticipated; the balance of trade deficit ballooned to its highest level in a generation even as the president doubled down on purported deficit-busting tariffs; the budget deficit skied to record levels as supply side assurances of the tax cut again disappointed; and the stock market gave back a noticeable slice of its so-far spendid 2019 gains.
The POTUS took public solace in that 12% market return even after weekly the hiccup, I’m sure much to the chagrin of his economic advisors. Alas, when last he boasted of market performance under his watch in the fall, stock indexes promptly fell over 15%. Buyer beware.
Be that as it may, a 12% uptick so far in 2019 is significant. Yet while many investors are happy, my son is not among them. He bought shares in a Wilshire 5000 index etf in September, and remains in the red now. What gives?
The timing of when an investor enters the market is obviously important, but is generally not reflected in portfolio returns visuals, which focus on the growth of a $1 investment starting at a chosen point in time — often at the beginning of a year. What, though, if an investor’s starting point is different than the chosen one — how can she see how her portfolios performed over her time horizon?
In addition to tracing the growth of $1 from a given start date to the current, I like to look at the growth from all starting points to the present. With that info, an investor can readily assess their position by locating performance from their start date to now.
Producing such data using market index returns is the topic of this blog. In the analysis that follows, I focus on performance of the Russell 3000 index, a Wilshire 5000-like portfolio for “measuring the market”. In a script that’s run prior to this one, I download two files — a year-to-date and a history, that provide final 3000 daily index levels starting in 2005. Attributes include index name, date, level without dividends reinvested, and level with dividends reinvested. I then wrangle the data using Pandas to get to the desired end state daily percent change figures.
The technology used includes JupyterLab 0.32.1, Anaconda Python 3.6.5, Pandas 0.23.0, and Seaborn 0.9.0.
Import packages and append the personal library directory to the system path.
Load the personal library and document function signatures. The prarr and blanks functions are used in this article.
Save the current working directory. Set and migrate to the new working directory.
Load the most current Russell 3000 performance data previously assembled/wrangled.
The stock market performance of the current POTUS is +27% overall, +20% for 2017, -5% for 2018, and +12% so far in 2019. Very good, but substantially lagging his predecessor’s.
Build the alternative performance dataframe. The start date is 2017-01-20 — inauguration day. The returns of every market day from 2017-01-20 to 2019-03-11 are computed.
Perform procedural control breaks processing to find runs of in-the-red index performance.
Determine the dates surrounding the start and finish of the 49 day performance drought.
Prep for seaborn graphics.
Plot the journey of the Russell 3000 index performance under the current POTUS. A 27% increase over 26 months is very good — though those caught getting started during the big 2018 decline suffered.
Graph the forward-looking performance of the Russell 3000 index. Read the visual as follows: if you had invested $1 on a given x-axis date, what would your account look like effective 3/11/2019?
Overall, time is a return friend. Those who bought at the beginning of 2017 have seen the rewards. Yet there are 8 in-the-red zones, the largest of which surrounds the dates 8/1/2018 thru 10/10/2018. Those unlucky enough to have purchased an R3000 etf or mutual fund during that period are under water at this point, some by almost 5%. My son, who purchased in 9/2018, is unfortunately in that group. On the other side, the lucky ones who invested just 3 months ago have seen up to 15% appreciation. Timing is everything!
That’s it for now. More data analysis next month.