At least for the time being, please see my Medium page for posts I’ve published on financial markets since I stopped writing daily articles in this space. I hope to see you there, and maybe one day back here again if the need to blabber every morning should again be felt. You can never know, the future is unpredictable, but I assign a low probability.
Since taking a break from my daily notes in this space about a year ago, I’ve published a handful of longer pieces on Medium; and for some of you who used to follow me here, I thought you might be interested in these. The most recent one is copied below and the link for all the others is this. Maybe one day I will return to the daily exercise, and until then I hope you will stay well and check in on occasion.
Networks are having a moment, or rather, they’ve been having it for quite some time, long before the big contagion and the flattening of curves, but the moment is expanding. Stephen Wolfram is working on a new physics model that would explain the universe in terms of network evolution and lead to the long-sought Unified Theory in the field. Niall Ferguson published a book that presents modern history as a series of network events, governed by network behavior and attributes. It is generally recognized in financial markets (themselves dense network structures) that the modern economy was borne of the InterNET (emphasis added). Most currently, cryptocurrencies — a revolutionary but natural extension from all that — are inherently network structures.
And yet there has been very little in the areas of finance and economics — in the mainstream at least, really nothing — to seek to bridge the analysis of value and strategy with what would seem an important counterpart in network science. If science is in this case too strong a word, network theory would be a good enough place to start. While the footprint of commercial networks has been growing to the point where regulators are even taking note, the resulting arguments don’t seem rooted in any network concepts or their context, which renders the debate — at a minimum — incomplete.
Perhaps the financial conventions that we still resort to, predicated on old-economy metrics and trends, are adequate enough. It is nevertheless non-trivial and now also opportune to try to understand the network asset on its terms. What follows are high-level notes, almost a preface, in support of an investment thesis that may be backtested with promising results. This is only a summary, purposefully light on supporting detail and examples that could one day turn it into someone’s massive book. But the interested reader should be able to consider cases, circumstances and criteria from one’s own observations that will fit the mold.
1. In an economy dominated by technology advancement and the digitization of virtually all things, there is a diminishing distinction between companies in the so-called “technology” segment and all the others. On the supposition that a time will soon arrive when the distinction will have altogether vanished, the more important difference is between companies that offer product or service solutions with and those without network effects.
2. The latter are in a state of constant need to upgrade, update, reinvent, ride cycles up and down, and reduce pricing on their offering, or else become disrupted or commoditized in a competitive environment that is moving ever faster. The former — businesses with network value — are better able to withstand attack and well positioned to drive down the costs of growth, because the network mesh is both a base and self-perpetuating driver.
3. But not all networks are the same, in fact they’re mostly very different from each other. At the highest level, these include marketplaces, exchanges, communication systems, platforms, artificial intelligence and other connected data tools — to name some of the principal categories — which may also combine several of the listed fields. And by the same token, network effects — defined as the improvement of network experience or value with the addition of new sources of engagement — are also different among the varied types. These can be broadly classified as user or data network effects — the first a matter of popularity and the second a matter of depth — which may also for some networks work in combination.
4. At levels below the very high, there are more nuanced distinctions that define the nature of the asset, often relative along a continuum, rather than binary or absolute. Examples of such qualities include the centralized, decentralized, and distributed topologies; the single-, bi-, and multi-directional data flows; and the single and manyfold layers of the network, which often co-exist and may in fact be linked, internally or externally to the individual business unit. There are also differences of strength between the ties that link the nodes together, and there are differences in size and numbers of the clusters that ensue.
5. These things and others shape the network profile, which in turn shapes its value and potential. Distributed networks, for instance, all other things being equal, may be more valuable than the centralized variety at the opposite extreme, because the single center-point of the former is a vulnerability to its whole. But all other things are not equal, and thus the network whole is best to understand without such oversimplification. Perhaps the one true constant in the general assessment of all sorts is the value of engagement, an attribute that’s always worthy, regardless of the other qualities described.
6. The so-called FAANG contingent — a less than ideal grouping as each of the five constituents is a different type of network from the others — is a highly visible sampling of the digital network asset class. Because they’re big and public and have evolved in more or less transparent ways, they make good subjects for more general analysis that can be carried over to the less developed cases. The fastest growing (if not already dominant) forces in many if not all the major industry segments — transport, finance, commerce, education, health, security, most recently biotech and manufacture — are additional examples.
7. In all the branches and the realms, it is difficult to the extreme to build a network from ground up. Unlike a product or a service that is designed, produced, and introduced into the market — successfully or not — a network is a complicated being, almost biological in nature, and subject to improbable conditions to take root. And when it does — a miracle in ways, which follows an inflection point that’s hard to manufacture — the network must be nurtured like an organism. The influences and the outcomes (which is to say, the behavior of nodes and clusters) aren’t always easy to anticipate, and there further comes a time at which the growth will asymptotically stall. When this occurs, new use cases or network offerings may provide a lift, but it isn’t always known during such times if the desired effect will materialize. Conversely, a robust and growing network can create great optionality, which may among other things enable an expansion into contiguous network areas.
8. Despite the frequent overlaps of categories — for instance, in finance and commerce, in information and entertainment, messaging and transactions — it has been observed that where user network effects are a core network driver, the resultant entities tend to be unique. Think, Instagram, LinkedIn, Twitter, YouTube, in the social realm, despite the basic similarity ingrained in messaging. It’s also been observed that as new categories form, the landscape tends to winner-take-most or even winner-take-all (power law) competitive effects. The same is true of the behavior within the network, as big clusters tend to become bigger and even dominant at times. (The influencer economy is a result of such network patterns.) What all of this describes is a tendency to centralization, even among the decentralized, that the subject networks may seek to mitigate.
9. Financially, the network’s most attractive features are profitability (after the inflection point) and predictability (as network effects take form). In combination with the winner-take-most advantage previously described and the high-gross-margin nature of the software business in the digital economy, the cash flow of the operation can be superior to almost any other business type. In cases where this is not so, it is advisable to wonder why. Perhaps the subject isn’t quite the network that we might assume it is, while, on the other hand, it’s also true that there are companies that unsuspectedly reveal themselves as such.
10. As certain network platforms have grown and reinvested cash over the years, the concern has recently arisen that these have taken on monopolistic forms which have to be controlled. Should there be a breakup of some kind (which wouldn’t be a first, e.g., Ma Bell), it may be worth remembering the nature of the living organism. Plants can continue to grow after a branch or two have been sliced off, and even branches may evolve into a tree or two once they’re replanted. After a while, we may be back to where we started, or someplace very different and strange, because the consequence of change in complex systems is often unintended.
One way perhaps to summarize all the above and draw a financial conclusion in terms that match traditions and accepted standards, is this:
In the digitized economy, networks and network effects are capital, and seeking to develop these is a capital investment; technology advancements and disruptions are important, but on their own are an expense.
In the long term, however, network effects may also turn negative and spiral down, while the technology expense can in the short term create value.
Reinterpreting the networks (2020)
Interpreting the networks (2017)
I started to jot down my morning notes 300 days ago. It was right after Labor Day, when the world is energized and set to blossom. I didn’t know what I would write about, although I set myself some ground rules in the masthead, to limit the endlessly potential subject matter with enough diversity to keep it interesting. The interest was mainly for my own part, to be honest, which is to say, I used the vehicle to take notes, to learn, to force myself to think about the meaning(s) of events and patterns that I saw or lived or read about.
It started brief and barely richer than a tweet in the initial weeks. Like this one here, for instance, The miracle of blocks, from a Boulder hotel room; and this other the next day, The bridge, hanging at the Denver airport. As time went on, and somewhat to my own chagrin, the posts got longer and more verbose; in part, I guess, because I felt encouraged by a growing readership, in part because I got more comfortable with my posting voice, but also, to be fair, because the world became more complicated and amazing into the new year and after.
So, lately, what started as a series of brief morning notes has turned into a daily blog of standard form and length (e.g., Convergence, platforms and new market color, The consequence and its intentions, New verticals and horizontals, The truth, which isn’t linear, The market standard-bearers), examples from just the past ten days or so. And I don’t think I can keep this up.
The time it takes each morning is far longer now than it once was, and, what is much more problematic, I’m falling behind as a result in reading that I used to do during these same early hours. The writing itself, I think, suffers, as the subject of these notes and of my learning starts to get repetitive and stagnant.
In other words, I think, the time has come. 300 posts, including this, is a handy milestone number on which to end. I’ll probably return (I say this to myself) to speculate and write about some favorite subjects (if I had to guess), but perhaps it won’t be daily and it may not be for a while. On the other hand, when it does happen, I will likely have some new material, having caught up by that time with so much reading I would like to do. By then as well, perhaps, the world will also settle into a new normal, which hopefully will be a good thing and provide a new and interesting area of study.
Until then, the posts are all here for the browsing, categorized by subject and pull-down menu reference, each with links to others at the bottom, suggested by the platform based on some tagging reference, I imagine, which makes the bounce-around even more of an adventure. As well, there is a drop-down menu organized by month, which may at some point be another reference, a document of sorts as we progressively evolved from one world to another.
I guess, in ways, this may have been a book. Maybe sometime in the future, and maybe we’re already there, such interactive and dynamic books will be a whole new category. Like fiction, history, biography, business, mystery and etc., but converging all of these and more, like markets do, and other networks.
There is much self-inflicted pain in markets caused by stubborn, antiquated measures, definitions, guideposts, dating back to who-knows-when and still the basis of the segmentation and the analytics, the theories and allocations, which have not much to do with what in actuality goes on out there, in the world where it most matters.
The More Markets Change, the More They Stay the Same, according to The Wall Street Journal, in a profile article that includes the following chart in support of the observation.
I’m sure there are clear demarcation lines somewhere that separate and organize the items in each of the listed pairs, but if the average investor were to take a minute and really think through what if anything these designations mean and what would cause a given stock to be in one and not another, it might take more than a minute to arrive at a dubious non-answer.
To keep things simple by focusing on the biggest of the subjects for this type of exercise, we can look at the grand five public companies that we sort of know as FAA[M]G, or Big Tech, or the Big 5: Apple, Amazon, Facebook, Google, Microsoft, listed alphabetically to avoid subjective bias. Eyeballing the categories again, I think these five could comfortably fit in every single one of every listed pair, except for small – which is itself a clue, I think, that we should dig a little further.
These were at one time small but they no longer are. And the trajectory has come about in record time, and hasn’t budged since the five’s grand arrival.
And now they keep on growing and expanding the area of their shadows, where all the others in this new market move around. In the chart below, the dark blue line down at the bottom stands for the 505 companies in the S&P 500 Index, which includes the five of the superior lines above. Were these to be stripped out, the index obviously would look worse, much worse…
How much worse exactly would depend on the percentage composition of the total that the Big 5 represent. As of June 30, according to Slick Charts, that figure stood at roughly 22%. So, of 505 stocks in the widely accepted benchmark index, five constitute more than one fifth.
It pays, I think, given this circumstance, to look to these Big 5 (which straddle all the standard categories, as mentioned) for particular analysis and profiling. What makes them special? What drives their largeness, value, growth, tech and non-tech, U.S. and all the world, and all of that?
A few years back I shared my views on what I believed (and still do) was the answer, and even though there’s been a lot that’s changed since then, the principle remains: Networks 3.0 – defined by digital dimensions. As I extrapolate from there to have a look at all the others in the referenced big list (i.e., look to the networks and their deep effects), here are some results:
All five of the top 5 comprise 22%, as has been said. Of the remaining 15 in the top 20, eight are similarly characterized (including two global finance institutions) and in the aggregate make up another 8% of the S&P 500. And because it pains me to leave out #21 and #22 on the list, as they’re such obvious examples, this adds another 2% to the total.
Thus, roughly 32% (rounding error excused) or almost one-third of the S&P 500 Index – that which is the standard of all performance measurement in markets – is supported by 15 companies (out of the top 22).
Each of these is different from the others in the grouping, judged by product, service, customer base, technology solution, location, and so on, but each of these is a very large and growing network. It may be about time to recognize this as a new market fact and standard, and draw some new conclusions, research some new metrics, publish new reports, maybe even build a whole new index. It helps, as a start, to be merely cognizant, as apparently the investors and traders implicitly already are.
Related reading: Interpreting the networks (2017).
My friend Dave just published a new book, and it’s become my favorite entrepreneurship reading in a while. The one that previously held the distinction had been Ben Horowitz’s Hard Thing About Hard Things, which told of the experiences of the “wartime CEO”, drawing contrast to the “peacetime” variety.
Dave Balter’s book, resembling the other in its honesty, transparency, and attention to the human factor, may be understood as the perspective of the CEO in hybrid state – which is perhaps to say, peacetime on the surface while war is raging down below. And maybe that is after all the truth behind all startups. Nowadays, perhaps, this relevance extends much farther out than that.
The title grabs you right away – it grabbed me in any case – with its refreshing candor. Both the title and the book could only be the product of one who’s been there, many times.
Between the front page and final sentences that relate the author’s encounter with a prospective hire, “[w]ith so much thankfulness for [a near-death] experience [that he described] – and so much humility – that he was hired on the spot”, the essay collection brings together a series of reflections from the trenches, from the road, from the office and the home, alone and in the company of assorted others: teammates, investors, board members, competitors, customers, distantly back or in a recent moment, always right there in the thick of it.
In a post ahead of the book’s publication – Why My New Book is Destined to Fail – the author explains his intention and guards against an anticipated reaction from some readers. Many will wonder about the scope of an essay collection that doesn’t run a conventional narrative thread. “Often times that thread is thin,” he says. “Only upon reflection does the silk shed its translucence, becoming visible in the light of day. Sometimes it takes years for that to happen. Sometimes it never does. But, taken as a whole,” he says, “[such] books convey countless meanings.”
Taken as a whole, the seemingly unthreaded mix is truer, by far, to the entrepreneurial experience than any linearly standard version can convey. With a beginning, a middle, and an end, a book about entrepreneurship might in fact miss the point entirely; or, if not the point, then certainly the truth.
As is by now well known, the economic age we’re in isn’t defined by its tech/mobility/cloud/data/AI/robots/connectivity, at least not in terms of isolated pieces, but by the aggregation of these things and others to form a simpler and more comprehensive designation: the digitally networked age. For proof, one only has to look to market leadership for guidance, those which have deepened, widened, grown – consistently and dominant – for all of the past decade.
Accepting this to be the case, and going with the age’s stated definition, the lens through which we may assess strategic actions, splits or combinations is similarly one of network rules, parameters and vision. We can look at forms of product distribution, the processing of information, fund flows, and other basic elements of modern economics, as network forms that all evolve and dynamically flow to change the living body of the network’s interlinked topology. It is a continuous, self-energizing phenomenon – always on its way, never arrived – the way that any living body is characterized by the same.
In the case of enterprise expansions or retreats, defenses of attacks, friendships and enmities, the underlying network fundamentals map out a vertical and horizontal structure that is much like the verticals and horizontals of any other time: the former is a build or takedown of the integrated stack, the latter is a widening or narrowing of scope.
An offering that supplements the core is in this illustration vertical, and one that enters a new field is horizontal. What makes these movements more complex and interesting now than in the past (now in the digitally networked age), is the positioning of the competing networks relative to one another: a dance of sorts, a play that tests the limits and the balances of power on the stage.
With the above as a translator application – there are others – to make sense of enterprise directions, here are selected headlines from the recent news…
The line that separates intended and unintended consequences is only as clear as the intentions and the consequences are. Both forms of clarity ought to ideally be satisfied for the cause-effect of actions and reactions to flow as though in controlled lab conditions. But there is no lab per se in economics, it’s all an open field of influence and noise that makes the separating line in question rigorously delicate.
There are plenty of intentions, goodness knows – some clear, many approximately so, and some just vague and loose enough to be distorted – the lot of them could probably be plotted on a chart that would describe a bell-curve, where the horizontal axis runs the clarity continuum. The same type of statistical representation could probably be drawn for consequence, and in both cases the little subsets that are close enough to clarity may or may not overlap with one another in the underlying data set.
Which is to say, the ideal case of perfect clarity – in both intention and desired outcome – is theoretically, and very likely, minuscule. The vast majority, the dominant activity by far, is thus a matter of degree, a question only of how unclear or how far from perfection is the particular case, as measured by the combined consequence and its original intention.
The market is a voting mechanism, it is said, that bases its decisions on perceptions shaped by much of the above. Whether the market is right or wrong – and it is strictly speaking almost always wrong, as evidenced by perpetual price movement – is additionally complicated by the interconnection between its individual subjects: companies, industries, financial instruments, trends, themes and etc..
The degree of uncertainty, the magnitude of imperfection as contemplated in the (un)intended consequence environment described, is the degree of risk, on one hand, and optionality, on the other. Many refer to the latter as opportunity, but the choice of words can be deceptive – diminishing, as it does, the element of chance in the equation.
At the levels down below the market and the big economy – the micro levels of the enterprise, technology, product, customer base, social group, and individual – which are all influenced by and also shape the macro picture, the described elements are more or less the same. There is an important difference though, I believe, in that the macro set tends to be more aware of these things than the micro set tends to be. This is an added element of risk (and all of its assorted flip-sides) because the macro is almost always a diversified portfolio, while the micro almost never is.
Sometimes – even if this is not one such – there are few things (but there are some) that can do to lift your spirits like our favorite ‘70s dance band can. It doesn’t any harm, however, to look.
Even if this isn’t a time like that.
As if it isn’t enough that Economics as a discipline is all tangled up and circular, always as if deeper in the hole it has itself created. As if that isn’t enough to bring out the mystery and excitement of this sort-of-art and sort-of-science but really neither one. In addition, there is the shaky ground of the very information that is underlying, on which models are constructed and which adds to the adventure in unbounded ways.
I’ve wondered about the data behind employment trends before, and I keep wondering about it. While the updates keep updating, the gap that puzzles me remains. Maybe the puzzle is my own, I’m sure the specialists have it all figured out and reconciled. I’m sure it’s as precise as algebra, and only subject to interpretation. Which is where I stumble.
Here goes my mental block again…
If you add up the big bars in Exhibit A (initial weekly claims) and compare the ballpark figure to the latest total in Exhibit B (continuing claims), you may conclude that more than half of those who filed initial claims in the past months have returned to work. All while the noted new-claim bars each week stay elevated and stubborn, unprecedentedly almost.
First off, the two results seem somehow contradictory, and secondly, they don’t quite reconcile to common sense. If the shutting down of a giant economy can lead to the loss of more than 40 million jobs at once, and then with upkeep, how (when?) has that shock reversed concurrently to the tune of half?
No doubt, there is an economic explanation. Perhaps it’s rooted in the “seasonal adjustment” noted in the fine print of the graphs, perhaps it’s in the difference between the data sources, some based on agency statistics and others on surveys of some kind? (Do people still respond to those? Like Nielsen television ratings back when there wasn’t a digital connection?) Perhaps. Or maybe it’s all really as accurate as a clock. That seems like the most likely possibility, in light of market scrutiny at every single moment.
But either way, perhaps it doesn’t matter. If the vocabulary and the grammar are accepted, the language is the language that is and should be used. It’s important for there to be a way to tell the time and be in sync for our appointments, as much as we may secretly question the schedule.
In anticipation of what might be coming up if certain trends continue…
… it is now suggested that the banks should save…
… which may offset the signal of low rates on market calculations…
… because no rate is low enough when funds stop flowing, or high enough when savers need to save.