It might be an interesting experiment to match stock price histories againt the profiles of fractal mountain ranges.
… thinking that in its beginning the value of a company is zero – sea level. And, in the end … 🙂
This image doesn’t help you much if you’re the person travelling across the continent, though. You still don’t know what’s over the next hill. And, unlike real terrain, you can’t look ahead over valleys (low prices) to see the hills – or ocean – beyond.
But, it’s a nice image.
Just talked with Eric about stocks and random walks and thinking machines and split wing politics.
And, about a little script, portfolio_track.py, I whipped up to expose how a bunch of stocks were doing against “the market”. With the the quantities removed, we have:
Symbol 1yrGain Market-relative ------------------------------------ AEOS 20.9% 5.0% ~ ^GSPC ALDA 74.3% 6.5% ~ ^GSPC ATYT 101.0% 97.3% ~ ^GSPC AV -32.0% -41.7% ~ ^GSPC BBBY -41.5% -98.1% ~ ^GSPC BC -22.3% -30.7% ~ ^GSPC BSX -23.2% -34.1% ~ ^GSPC DOW -14.5% -24.2% ~ ^GSPC EEM 78.4% 61.9% ~ ^GSPC EGY -45.4% -124.1% ~ ^GSPC EWY 15.7% -0.8% ~ ^GSPC FLEX -34.6% -45.5% ~ ^GSPC FORD -59.2% -107.1% ~ ^GSPC GM 96.3% 70.3% ~ ^GSPC GTW 0.0% -47.9% ~ ^GSPC HD 2.5% -7.2% ~ ^GSPC JAKK 468.1% 420.2% ~ ^GSPC KSWS -193.0% -240.9% ~ ^GSPC LXK -33.5% -47.8% ~ ^GSPC MMM 4.1% -4.9% ~ ^GSPC MRK 13.1% 6.0% ~ ^GSPC MTEX -77.2% -125.1% ~ ^GSPC NTGR 98.7% 99.3% ~ ^GSPC - OPTN 89.8% 42.0% ~ ^GSPC OVTI 86.2% 76.3% ~ ^GSPC PCAR -4.0% -12.9% ~ ^GSPC PCL -9.7% -19.4% ~ ^GSPC PLMD 58.3% 42.5% ~ ^GSPC PLT 76.9% 61.1% ~ ^GSPC PXR 52.7% 36.9% ~ ^GSPC SWK 10.1% 0.3% ~ ^GSPC TBL 53.9% 44.1% ~ ^GSPC TDW 127.1% 108.3% ~ ^GSPC USG 173.3% 166.7% ~ ^GSPC VBR 42.9% 26.3% ~ ^GSPC WDC 401.3% 385.7% ~ ^GSPC WFR 149.8% 139.0% ~ ^GSPC WINS -32.4% -99.5% ~ ^GSPC X 91.0% 80.1% ~ ^GSPC ------------------------------------ ^GSPC 12.0% ------------------------------------ Absolute 52.4% Relative 40.4% ~ ^GSPC
which, bottom line, means that, as of this moment, I’m a stock market genius. Check back in a week or two when the most recently bought stocks, all but one of which are doing badly, really start to clock in.
There are two possibilities here:
1) My luck is good.
2) I don’t know how to keep doing well. Picking these stocks is not a reproducable process.
BTW, I don’t have some of the stocks listed above any more. Sold a few for various – arrgghh – short term capital gains ’cause they really seemed to be ready to go away. But, checking just now shows that I’m not a genius after all. All but one are up now from their sell price. Oh well.
To explain the script output columns:
Symbol – Add it to http://finance.yahoo.com/q?s= and you’re good to go.
1yrGain – Gain or loss, as a percentage of the money, normalized to 1 year. E.g. If a stock were held for a half year and has gained 20% during that time, then the number in this column would be 40%. Eric points out that this number can be really, really misleading. A stock that is held for 1 week for a 1% gain would have a 52% value in this column. Really looks good. But isn’t a big money maker.
Market-relative – Gain above the market in 1-year-ness. In other words, this is the gain above what the same amount invested in some market average – in this case S&P 500 – would have been.
Absolute: Raw, 1-year-ness gain overall. This is what most people look at. I don’t consider it interesting, as it’s gotta be considered relative to just sitting back and holding some market tracking “stock” or “fund” or whatever.
Relative: Overall portfolio gain relative to the “market” index in 1-year-ness terms.
Interestingly, the only stock held during a period of market loss in this list is NetGear, NTGR. The S&P 500 hasn’t been all that hot this past year. Up 12% for the weighted times that I’ve had stocks in this portfolio isn’t bad, but it’s surprising that it hasn’t dropped a bit during more than one of the periods reflected by each of these holdings.
These numbers above don’t include dividends and trading costs. If trading costs were included, the bottom line numbers would probably go down a couple percent each. Dividends might bring the numbers right back up, though, as several of these stocks pay out well.
WARNING to self: The script is pretty untested. I’ve reason to believe that it’s not correct. Too, that is uses end-of-previous-day prices for the “market” is inherently inaccurate, given that most of my trades come at the beginning of the day. Anyway, one thing I’ve learned doing this stock market stuff is that single-digit changes one way or the other are noise. One could argue that larger changes can be noise, too. Consider Google’s run-up. That might be called “crowd noise.”