discounting in the age of algorithms

what discounting is

In traditional Wall Street parlance, discounting is factoring into today’s prices the anticipated effect of expected future events.  Put another way, in the best possible case, it’s buying a stock for, say $.25 extra today, thinking that in a week, or a month or a year, news will come out that makes the stock worth $1, or $10, or $100 more than it is today.

two components

They are:

—having/developing superior information, and

–correctly gauging what effect dissemination of the news will have on the stock.

In my experience, the first of these is the easier task.  Also, the answer to the second problem will likely be imprecise.  In most cases, “The stock will go up a lot when people understand x” is good enough.

examples

In the early days of the Apple turnaround, the company launched the iPod, which ended up doubling the company’s size.  So the key to earnings growth for AAPL was the rate of increase in iPod sales.  The heart of the iPod back then was a small form factor hard disk drive.  There were only two suppliers of this component, Hitachi and Seagate (?), so publicly available information on production of the small HDDs had some use.  Much more important, however, was that there was only one supplier of the tiny spindles the disks rotated around.  And, unknown to most on Wall Street, that small Japanese firm published monthly spindle production figures, which basically revealed AAPL’s anticipated sales.

Same thing in the early 1980s.  Intel chips ran so hot that they had to be encased in ceramic packaging–for which there was only one, again Japanese, source, Kyocera.  Again, monthly production figures, in Japanese, were publicly available.

In both cases, the production figures were accurate predictors of AAPL (INTC) unit sales a few months down the road.  Production ramp-up/cutback information, again public–though not easily accessible–data, was especially useful.

Third:  Back in the days before credit card data were widely available, retail analysts used to look at cash in circulation figures that the Federal Reserve published to gauge the temper of yearend holiday spending intentions.  The fourth-quarter rally in retail stocks sometimes ended in early December if the cash figures ticked down.

In all three cases, clever analysts found leading indicators of future earnings.  As the indicators became more widely known, Wall Street would begin to trade more on the course of the indicators rather than on the actual company results.

today’s world

Withdrawal of brokerage firms from the equity research business + downward pressure on fees + investor reallocation toward index investing have made traditional active management considerably less lucrative than it was during my working career.

A common response by investment firms has been to substitute one or two economists and/or data scientists for a room full of 10k-reading securities analysts who developed especially deep knowledge of a small number of market sectors.  As far as I can see, the approach of the algorithms the economists/programmers employ isn’t much more than to react quickly to news as it’s being disseminated.  (They may also be looking for leading indicators, but, if so, I don’t see any notable success.  Having seen several failed attempts–and having worked at the one big 1950s -1970s  success in this field, Value Line–I’m not that surprised at this failure.)

My thoughts: 

–there’s never been a better time to be a contrarian.  Know a few things well and use bouts of algorithmic craziness to trade around a core position

–For anyone who is willing to spend the time watching trading during days like Wednesday there’s also lots of information to be had from how individual stocks move.  In particular, which stocks fall the most but barely rebound?   which fall a little but rise a lot when the market turns?  which are just crazy volatile?

One response

  1. Pingback: What stocks to invest in = discounting in the age of algorithms « PRACTICAL STOCK INVESTING | Stock Investing

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