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.

 

 

 

 

 

 

looking at today’s market

In an ideal world, portfolio investing is all about comparing the returns available among the three liquid asset classes–stocks, bonds and cash–and choosing the mix that best suits one’s needs and risk preferences.

In the real world, the markets are sometimes gripped instead by almost overwhelming waves of greed or fear that blot out rational thought about potential future returns.  Once in a while, these strong emotions presage (where did that word come from?) a significant change in market direction.  Most often, however, they’re more like white noise.

In the white noise case, which I think this is an instance of, my experience is that people can sustain a feeling of utter panic for only a short time.  Three weeks?  …a month?  The best way I’ve found to gauge how far along we are in the process of exhausting this emotion is to look at charts (that is, sinking pretty low).  What I want to see is previous levels where previously selloffs have ended, where significant new buying has emerged.

I typically use the S&P 500.  Because this selloff has, to my mind, been mostly about the NASDAQ, I’ve looked at that, too.  Two observations:  as I’m writing this late Tuesday morning both indices are right at the level where selling stopped in June;  both are about 5% above the February lows.

My conclusion:  if this is a “normal” correction, it may have a little further to go, but it’s mostly over.  Personally, I own a lot of what has suffered the most damage, so I’m not doing anything.  Otherwise, I’d be selling stocks that have held up relatively well and buying interesting names that have been sold off a lot.

 

What’s the argument for this being a downturn of the second sort–a marker of a substantial change in market direction?  As far as the stock market goes, there are two, as I see it:

–Wall Street loves to see accelerating earnings.  A yearly pattern of +10%, +12%, +15% is better than +15%, +30%, +15%.  That’s despite the fact that the earnings level in the second case will be much higher in year three than in the first.

Why is this?  I really don’t know.  Maybe it’s that in the first case I can dream that future years will be even better.  In the second case, it looks like the stock in question has run into a brick wall that will stop/limit earnings advance.

What’s in question here is how Wall Street will react to the fact that 2018 earnings are receiving a large one-time boost from the reduction in the Federal corporate tax rate.  So next year almost every stock’s pattern in will look like case #2.

A human being will presumably look at pre-tax earnings to remove the one-time distortion.  But will an algorithm?

 

–Washington is going deeply into debt to reduce taxes for wealthy individuals and corporations, thereby revving the economy up.  It also sounds like it wants the Fed to maintain an emergency room-low level of interest rates, which will intensify the effect.  At the same time, it is acting to raise the price of petroleum and industrial metals, as well as everything imported from China–which will slow the economy down (at least for ordinary people).  It’s possible that Washington figures that the two impulses will cancel each other out.  On the other hand, it’s at least as likely, in my view, that both impulses create inflation fears that trigger a substantial decline in the dollar.  The resulting inflation could get 1970s-style ugly.

 

My sense is that the algorithm worry is too simple to be what’s behind the market decline, the economic worry too complicated.  If this is the seasonal selling I believe it to be, time is a factor as well as stock market levels.  To get the books to close in an orderly way, accountants would like portfolio managers not to trade next week.

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?

discount brokers and technical analysis

The stock market can be considered as the place where the financial characteristics of publicly traded companies meet the hopes and fears of potential equity investors.  Fundamental analysis addresses the former issue, technical analysis the latter.

In the US of the Roaring Twenties, technical analysis served both functions.  Depression era reforms that forced companies to release accurate and readily understandable financial statements had not yet been enacted.  So the trading activity of “insiders,” detected by carefully watching price and volume movements, was the best gauge one could get of how firms were actually doing.

Since that’s no longer the case, why do online brokers (Merrill Edge is the only exception I know of) provide such lame information on company fundamentals?

Several reasons, I think:

–brokers earn their revenue from trading, not from investment results.  (For what it’s worth, there’s a strong belief in the professional investment community that there’s an inverse correlation between the amount of trading a portfolio manager does and investment performance.)  So it makes some business sense that they should provide tools that make trades easy to do, with an old-style video game-like interface that makes it seem important and fun.

–a fundamental research effort is a headache.  It’s difficult to create and sustain.   It’s expensive, as well.  Arguably, offering proprietary research also exposes the broker to liability if the recommendations don’t pan out.  The user needs some accounting/economic background to understand what’s being said.  The broker who provides fundamental research has an obligation to consider whether a recommendation is suitable for a given client–opening another can of worms.  And, of course, an emphasis on fundamentals runs the risk of refocusing clients away from frequent trading, to the detriment of profits.

–discount brokers do offer a kind of fundamentals-based product through actively-managed mutual funds and ETFs, as well as through their sponsorship of networks of financial planners for whom they provide back office services.  Offering fundamental research might put brokers into competition with those planners.  And the fundamentally-based fund offerings carry a much higher price tag than DIY trading.

 

stop orders

The idea behind stop orders is to try to minimize losses in times of stock market turbulence.  In almost forty years of stock market investing, however, I’ve never used one.  In fact, I don’t know any professional trader or portfolio manager–or any amateur, for that matter–who has.

The details of what each kind of order does may differ a bit from broker to broker, so it’s important to read an exact definition on your brokerage website before you transact.  Generally speaking, here’s what they are:

what they are

Stop orders come in several flavors.

stop loss order.  The user selects a stop price below the current quote of a stock he owns.  If the stock declines to the stop price, a market sell order is entered.  (The order entered is a market order to ensure a sell transaction happens.  That might not be the case with a limit order.)

One important aspect of the stop loss is that trading after the stop has been triggered can be significantly below the stop level.

stop limit order.  Here the limit is typically placed above the current price, because the user wants to buy (to, for example, limit losses on a stock that has been sold short).  Once the stop is reached, a limit order for the stock at the stop price is placed.  That order will be filled before the stock can go higher.

One can also place a stop below the current price, combined with a limit sell order at the stop.  But this gives no guarantee the stock will be sold.

trailing stops.  I don’t get these at all, although I understand they’re popular with trading-oriented individuals.

Two characteristics:

—-the stop price and the limit price can be different.  Once the stock reaches the stop price, the limit order is placed.

For example. the stock is trading at $50 when the trailing stop is initiated.  The stop is at $45, triggering a limit order at $40. This protects the seller against a “flash crash”-like temporary dive, which he’s vulnerable to with a stop loss order.  The analogue on the buy side would be a stop at $55, triggering a limit order at $53.

—-the “trailing” part is that in the case of a sell order, the stop and limit are adjusted upward if the stock begins to rise (trailing behind along the same trajectory as the stock).  In the case of a buy order, the stop and limit trail along with the the stock if it begins to fall.  In either case, the stop and limit remain unchanged if the stock starts to move in an unfavorable direction.

An example (simplified a bit):  the stock is trading at $50.  You place a trailing stop sell order with the stop at $49 and the limit at $48.  The stock rises to $55.  The stop rises to $54 and the limit to $53. The stock then declines to $54.  This triggers the stop, which activates the sell order at a limit of $53 or better.  You sell at, say, $53.50.  If the stock’s initial move is to fall to $49, the limit order at $48 (or better) is placed.

 

my problem with stops

It isn’t that they take a bit of getting accustomed to.  It’s that the user makes relatively complex trading plans in anticipation of a market environment that may develop in a much different way than the plans have envisioned.  Their virtue, which is that they automate a trading plan in advance, is also their vice–that they can prevent you from applying human judgment based on the most current information at the time this may matter the most.

Maybe it’s just that I haven’t used these tools, but it seems to me that the door to unintended consequences is opened pretty wide by their employment.

order types: market vs. limit

market order vs. limit order

The two basic types of stock transaction orders are market and limit.

market order tells your broker to execute the transaction immediately at the best available price.  Your mindset should be that being sure the trade is done is more important than the price it is accomplished at.  You might, for example, be using the proceeds from a sale to pay a bill or to buy another stock the same day.

For highly liquid stocks, an individual’s market order should have little or no impact on trading.  So a market order should get you a price at or near the quote you see on your computer screen.  Microsoft, for instance, trades over 60,000 shares a minute.  So a 100-share, or even a 1,000-share order, is just a drop in the bucket.

In my experience, the only time a market order might be a worry is in the case of an illiquid stock where your order would be a significant portion of the day’s trading volume.  If so, a market order could get ugly.  When I ran a small institutional trading operation for a number of years, I thought that I could be no more than a quarter of daily trading volume.  Big institutions figure they can be no more than 10% without making a visible impact on prices.

 

limit order specifies a price that is the maximum you will pay to purchase or the minimum you will accept for a sale.  The broker is required to transact at the limit price if the opportunity presents itself.  He is permitted to transact for you at a more favorable price than the limit, but is not allowed to transact at a less favorable one.  So the trade may not get done on a given day.  Depending on your instructions, your unfilled order will either be cancelled at the end of the day or carried over to the next trading day.

Typically a limit is set at a better price than the current market.  I may enter a limit order, for example, to buy a stock at $68 when it’s trading at $70.

But a limit can also be set at a worse price for me than the current market and used as a quasi-market order.   If I want to buy a less-liquid stock, for example, I can enter a limit order at $70.50 when it’s now trading at $70.

 

Personally, I use limit orders a lot.  When I’m buying I will typically buy a third of my intended position at the market and set a limit at, say, 5% below the market for the second third.  If the stock hits that limit, I’ll set a lower limit for the final third.

I’ll scale up in a similar fashion when I’m selling.

 

Stops tomorrow.