liquidity and stock price changes

daily liquidity and price movements

Liquidity has a lot of different meanings.  Right now, though, I just want to write about what I think is making stocks yo-yo to and fro on any given day.

 

The default response by market makers–human or machine–to a large wave of selling of the kind algorithms seem to trigger is to move the market down as fast as trading regulations allow.  This serves a number of purposes:  it minimizes the unexpected inventory a market maker is forced to take on at a given price; it allows the market maker to gauge the urgency of the seller; the decline itself eventually discourages sellers with any price sensitivity, so the selling dries up; and it reduces the price the market maker pays for the inventory he accumulates.

A large wave of buying works in the opposite direction, but with the same general result: market makers sell less, but at higher prices and end up with less net short exposure.

 

From my present seat high in the bleachers, it seems to me the overall stock market game–to make more/lose less than the other guy–hasn’t changed.  But we’ve gone from the old, human-driven strategy of slow anticipation of likely news not yet released to violently fast computer reaction to news as it’s announced.

Today’s game isn’t simply algorithmic noise, though.  Apple (AAPL), for example, pretty steadily lost relative performance for weeks in November, after it announced it would no longer disclose unit sales of its products.  Two points:  the market had no problem in immediately understanding that this was a bad thing (implying humans were likely involved)   …and the negative price reaction continued for the better part of a month (suggesting that something/someone constrained the race to the bottom).  As it turns out, decision #1 was good and decision #2 was bad.  Presumably short-term traders will make adjustments.

my take

On the premise that dramatic daily shifts in the prices of individual stocks will continue for a while:

–if investors care about the high level of daily volatility, its persistence should imply an eventual contraction in the market PE multiple.  Ten years of rising market probably implies that this won’t happen overnight, if it occurs at all.

–individual investors like you and me may have more time to research new companies and establish positions, if the importance of discounting diminishes

–professional analysts may only retain their relevance if they actively publicize their conclusions, trying to trigger algorithmic action, rather than keeping them closely held and waiting for the rest of the world to eventually figure things out

–the old (and typically unsuccessfully executed) British strategy of maintaining core positions while dedicating, say, 20% of the portfolio to trading around them, may come back into vogue.  Even long-term investors may want to establish buy/sell targets for their holdings and become more trading-oriented as well

–algorithms will presumably begin to react to the heightened level of daily volatility they are creating.  Whether volatility increases or declines as a result isn’t clear

 

 

 

 

machines vs. humans

…a financial Industrial Revolution?

I remember reading, years and years ago, an analysis of changes in the nature of work that happened during the Industrial Revolution.  The general idea is that, say, candlesticks had been made as one-of-a-kind items, out of precious materials and ornate decoration, worked for months by an artisan who had spent years learning how to do this.  Yes, the end product was useful, but it was also very expensive, meant for a niche audience, and acted as a sign of the owners’ superior wealth, taste and privilege.  In contrast, the “new” candlestick was made, fast and cheap, out of ordinary stuff, by a guy who knew how to operate a machine.

Today we find it hard to imagine the possible appeal of most pre-IR objects.  Yet they were once the norm.

 

The macro/microeconomic research-based stock market investment reports of the kind I used to create were made by people, like me, who served long apprenticeships under masters of the craft.  The work tended to only start to approach minimum standards after the author had, say, five years of practical experience in an investment management firm.  Buy-side portfolio managers like me also used the voluminous output of internal or brokerage house analysts who spent their careers studying a specific industry group.

By 2019, most of the experienced buy- and sell-siders have either retired or been laid off,  and have been replaced in many cases either by computer-controlled index-tracking products or by algorithms.  The main forces in today’s daily stock market trading have become machines, some programmed to carry out the wacky theories of the academic world, others to react to signals from the patterns of trading itself (i.e., technical analysis) or to news stories (typically written by reporters trained mostly as writers) or to extrapolate from the patterns of past business cycles.

progress or free-riding?

Are the research reports of a decade or two ago analogous to the candlesticks of the Pre-IR era?  Are algorithms like early industrial machines?  Are they a better and cheaper, although different, way of dealing with financial markets than having a very expensive group of human craftsmen?  Does this mean those who decry algorithms are simply Upper East Side-dwelling Luddites?

I don’t know about “simply.”  My feeling is that algorithms are here to stay.  And my experience as an investor is that it’s very dangerous to think that just because you don’t like or understand something that it serves no purpose.

Still, my suspicion is that as it stands now, there’s a healthy dose of free-riding to algorithmic trading.  In other words,  it looks to me as if some algorithms rely on reading the signals of human professional investors as they move in and out of stocks in response to their research findings.  As those humans are displaced by machines, however, those signals will disappear–implying algorithms will have to evolve if their raw material is to be something other than random noise.

 

 

 

 

 

 

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?

oil inventories: rising or falling?

The most commonly used industry statistics say “rising.”

However, an article in last Thursday’s Financial Times says the opposite.

The difference?

The FT’s assertion is that official statistics emphasize what’s happening in the US, because data there are plentiful.  And in the US, thanks to the resurgence of shale oil production, inventories are indeed rising.  On the other hand, the FT reports that it has data from a startup that tracks by satellite oil tanker movements around the world, which seem to demonstrate that the international flow of oil by tanker is down by at least 16% year on year during 1Q17.

Tankers move about 40% of the 90+million barrels of crude brought to the surface globally each day.  So the startup’s data implies that worldwide shipments are down by about 6 million daily barrels.  In other words, supply is now running about 4 million daily barrels below demand–but we can’t see that because the shortfall is mostly occurring in Asia, where publicly available data are poor.

If the startup information is correct, I see two investment implications (neither of which I’m ready to bet the farm on, though developments will be interesting to watch):

–the global crude oil supply/demand situation is slowly tightening, contrary to consensus beliefs, and

–in a world where few, if any, experienced oil industry securities analysts are working for brokers, and where instead algorithms parsing public data are becoming the norm, it may take a long time for the market to realize that tightening is going on.

It will be potentially important to monitor:  (1) whether what the FT is reporting proves to be correct; (2) if so, how long a lag there will be from FT publication last week to market awareness; and (3) whether the market reaction will be ho-hum or a powerful upward movement in oil stocks.  If this is indeed a non-consensus view, and I think it is< then the latter is more likely, I think, than the former.

This situation may shed some light not only on the oil market but also on how the discounting mechanism may be changing on Wall Street.