The Narrative Giveth and The Narrative Taketh Away

No farm animals today. I’d say no TV or movie quotes, but sometimes I can’t help myself. We’ll see. Instead, a quick note to email subscribers about what I think is one of the most unstable (meaning big ups and big downs) markets we’ve seen in eight years.

The day-to-day and intraday market swings over the past six weeks have been absolutely ferocious. There really hasn’t been a big aggregate change in market levels since the middle of February (down a bit), but that modest overall decline masks a ton of ups and downs along the way, particularly over the past two weeks. If I were a betting man (and I am), my large wager would be that anyone running a tactical strategy, discretionary or systematic alike, has been whipsawed in an ugly fashion. These are the times that try traders’ souls.

So here’s the Epsilon Theory take on what’s going on.

This market, like all markets, cares about two things and two things only — the price of money and the real return on invested capital. Or, as they are typically represented in cartoon form, interest rates and growth.

This market, like all markets, will go up if either cartoon can be represented with a positive narrative. That is, even if the Fed is raising interest rates, so long as they’re doing it “for the right reasons” (meaning robust growth in the real economy), then the market can go up. Likewise, even if real economic growth is anemic, so long as that means that the Fed “has got your back”, then the market can go up. This last bit — uber-accommodative central banks the world over — is why the S&P 500 is up more than 300% over the past eight years despite enormously disappointing global growth and productivity metrics.

This market, like all markets, needs a positive narrative on risk (the price of money) or reward (the real return on capital) to go up. Any narrative will do! But when neither risk nor reward is represented with a positive narrative, this market, like all markets, will go down. And that’s where we are today.

Does the Fed have our back? No, they do not. They’ve told us and told us that they’re going to keep raising rates. And they will. The market still doesn’t fully believe them, and that’s going to be a constant source of market disappointment over the next few years. In the same way that markets go up as they climb a wall of worry, so do markets go down as they descend a wall of hope. The belief that central bankers care more about the stock market than the price stability of money is that wall of hope. It’s a forlorn hope.

Is there a positive growth narrative? Well, there WAS … not just in the U.S. but everywhere in the world, and it went under the heading of “synchronized global growth”. With the tax cut passed in December, you could absolutely make the case that we were off to the growth races, and that was, in fact, THE narrative behind the amazing January for markets.

Two negative narratives have derailed all this — Inflation and Trade War. The first strikes at the “real” aspect of real economic growth. The second strikes at the absolute or nominal level of that growth.

The inflation narrative hit markets in force after the January jobs report of February 2, where wage inflation came in “hot”. It subsided with the “Goldilocks” jobs report of March 9, where wage inflation was “contained”, and the jobs report of April 6 did little to reignite the inflation narrative. But here’s the thing. The wage inflation numbers for the past two months are wrong, crucially flawed by random differences in work-week hours from last year to this year (for more, read “The Icarus Moment”). On an apples-to-apples basis (eliminating the impact of spuriously estimated work-week hours on average hourly earnings), I estimate wage inflation in February was about 2.9%, not the reported 2.6%, and wage inflation in March was north of 3.0%, not the reported 2.7%.

My view: the inflation narrative will surge again, as wage inflation is, in truth, not contained at all.

The trade war narrative hit markets in force in late February with the White House announcement on steel and aluminum tariffs. It subsided through mid-March as hope grew that Trump’s bark was worse than his bite, then resurfaced in late March with direct tariff threats against China, then subsided again on hopes that direct negotiations would contain the conflict, and has now resurfaced this past week with still more direct tariff threats against and from China. Already this weekend you’ve got Kudlow and other market missionaries trying to rekindle the hope of easy negotiations. But being “tough on trade” is a winning domestic political position for both Trump and Xi, and domestic politics ALWAYS trumps (no pun intended) international economics.

My view: the trade war narrative will be spurred on by BOTH sides, and is, in truth, not contained at all.

Of these two claims — that both the inflation and the trade war narratives are here to stay and, frankly, you ain’t seen nothing yet — I want to dig in a bit more here on the inflation narrative claim, as that’s the narrative that’s taken a back seat over the past six weeks or so. It’s also the narrative that, over time, I think will have the larger impact on investors’ portfolios. In a very real sense (still no pun intended), getting the inflation question right is the ONLY question that a long-term investor or allocator MUST get right in order to succeed.

So here’s what the Narrative Machine is showing me about inflation.

The methodology of the Narrative Machine is described in the Epsilon Theory note by the same name. It’s a natural language processing (NLP) analysis of a large set of market relevant articles — in this case everything Bloomberg has published that talks about inflation — where linguistic similarities create clusters of articles with similar meaning (essentially a linguistic “gravity model”), and where the dynamic relationships between and within these clusters can be measured over time.

Source: Quid, Inc. For illustrative purposes only. Past performance is no guarantee of future results. Quid, Inc. is not an affiliate of Salient. Software used under license.

What you’re seeing above is the Bloomberg narrative on inflation from April 2016 through March 2017, where each of the 1,400 dots is a separate Bloomberg article that contained some mention of U.S. inflation, and where the dots are colored by publication date (blue early in the 12-month period, red late in the 12-month period). There’s meaning associated with the size of each individual dot or node, too, but not particularly useful meaning for this analysis. What’s most important here is the geometry within and the distance between the clusters of articles, each associated with “inflation and …” Trump or the Fed or gold or whatever category you see named above. This is a prototypical “complacent” narrative network, where a substantial percentage of articles are unclustered, and the clusters that exist are distant from each other, tenuously connected, and on the periphery of the narrative superstructure. When you read the individual articles here, they are ABOUT Trump or the Fed or gold or whatever, with inflation being a subsidiary topic of interest. Inflation per se is just not a particularly relevant narrative for the market over this period.

In contrast, what you’re seeing below is the Bloomberg narrative on inflation from April 2017 through March 2018. Not only do you have 2,400 unique articles in this year-over-year period, a 75% increase, but more importantly you have strikingly more narrative cohesion across the published articles. Entire narrative clusters have come into being over the course of the past 12 months, clusters like “strategists” that are in the geometric heart of the entire interlaced network, meaning that they are providing a gravitational core to the narrative superstructure. Moreover, these new clusters are truly ABOUT inflation, where this is the core topic of the article, not a side issue. It’s a difference in meaning and sentiment associated with the unstructured data of the individual articles that a human cannot possibly capture in the aggregate, no matter how voracious and comprehensive a reader he is, but is processed and visualized in a few seconds by the Quid NLP algorithms. In the NLP equivalent of time-lapse or stop-action photography, you can actually see these clusters come into existence over time and exert their gravitational pull on the entire narrative superstructure, providing what I think is an important systematic approach to visualizing and measuring market-moving structures of sentiment. THIS is the power of AI. It won’t make your regressions run any faster. It’s not particularly helpful in working with structured data at all. But it changes everything in how we SEE the ocean of unstructured data in which we all swim.

Source: Quid, Inc. For illustrative purposes only. Past performance is no guarantee of future results. Quid, Inc. is not an affiliate of Salient. Software used under license.

I’ve color-coded the article nodes by date (bluer = older, redder = more recent) to show this time-lapse effect in a single snapshot of the network. Because this is a “gravity model”, it’s meaningful that the more centrally located articles within the superstructure tend to be redder or more recent articles. Also meaningfully, the clusters themselves show this effect. Look at the blow-up of the network below, and you can see how the more recent (redder) articles in the “markets” cluster are more centrally positioned than the older (bluer) articles in the same cluster. What all this means is that the inflation narrative is becoming not only stronger (more articles, new clusters) but also — and I really can’t emphasize this point enough — the inflation narrative is becoming more coherent and “gravitationally stable” over time. The growing strength and coherence of these Narrative Machine visualizations show the creation of powerful common knowledge around inflation, where everyone knows that everyone knows that inflation is rearing its very ugly head.

Source: Quid, Inc. For illustrative purposes only. Past performance is no guarantee of future results. Quid, Inc. is not an affiliate of Salient. Software used under license.

Six months ago, in a note called “Harvey Weinstein and the Common Knowledge Game”, I wrote this:

The core dynamic of the CK Game is this: how does private knowledge become — not public knowledge — but common knowledge? Common knowledge is something that we all believe everyone else believes. Common knowledge is usually public knowledge, but it doesn’t have to be. It may still be private information, locked inside our own heads. But so long as we believe that everyone else believes this trapped piece of private information, that’s enough for it to become common knowledge.

The reason this dynamic — the transformation of private knowledge into common knowledge — is so important is that the rational behavior of individuals does not change on the basis of private knowledge, no matter how pervasive it might be. Even if everyone in the world believes a certain piece of private information, so long as it stays private — or even if it becomes public information — no one will alter their behavior. Behavior changes ONLY when we believe that everyone else believes the information. THAT’S what changes behavior. And when that transition to common knowledge happens, behavior changes fast. …

My pick for the big idea that gets taken down? The idea that inflation is dead. We all know it’s not true. We all know in our own heads that everything is more expensive today, from rent to transportation to food to iPhones. But it’s not common knowledge. Not yet.

The “not yet” is now. The stage is now set for an explosive market re-evaluation of inflation and its impact on the price of money and the real return on invested capital. This is no longer a complacent crowd. This is now a highly focused crowd. The crowd is now watching the crowd in regards to inflation. Everyone knows that everyone knows that inflation is an important issue. The only thing missing is the Missionary statement, the little girl crying out that the Emperor has no clothes. That’s when common knowledge crystalizes into behavior. That’s the freak-out moment for markets.

What is the crystalizing Missionary statement? I think it’s wage inflation in a future jobs report.

In exactly the same way that random observations of work-week hours have artificially depressed the average hourly wage inflation cartoon reported by the BLS over the past two months, there is a 100% chance that random observations of work-week hours will artificially magnify the wage inflation cartoon reported by the BLS in some future months. This is not an opinion. This is, as they say, math.

For example, if the 12-minute difference in the March 2017 work-week (34.3 hours) and the March 2018 work-week (34.5 hours) had been reversed, the reported wage inflation last Friday would have clocked in at 3.3%. Let me repeat that. Three-point-three percent. That is an Emperor-has-no-clothes moment.

When will we get this “shockingly hot” wage inflation number? I have no idea. That’s what it means to have a random number series as part of your cartoonish data estimation process. It’s random. Again, this is math.

But here’s the last 6+ years of the data series so you can see for yourself what the year-over-year comps are for work-week hour estimations, or as I like to call it, ROUND (RANDOM (34.3 , 34.6), 0.1).

We won’t hit any prior year 34.5 readings until the end of calendar 2018, where a random reading in the historical range is most likely to present a real shocker, but any of the next five months have a year-over-year comp where the wage inflation number, which I think is now above 3%, is at least more likely to be accurately represented via the average hourly wage cartoon.

To steal a line from Game of Thrones (see, told you I couldn’t help myself), we’re now at the point where the catch phrase is about to shift from “Inflation is Coming” to “Inflation is Here.” And if that’s married with disappointing growth from say, oh, I dunno … a TRADE WAR WITH CHINA … well, that’s not just inflation, that’s stagflation. And that’s the market equivalent of the Night King and the White Walkers running rampant over all of Westeros. Is that the most likely scenario? No. Is it a scenario that we need to take seriously? Absolutely.

So what’s to be done?

Well, it’s time to stop thinking about what inflation means for your portfolio, much less stagflation, and start doing something about it. And yes, I know our inflation-investing muscles are severly atrophied. Time to start flexing those muscles. Time to start exercising those muscles. Because you’re going to need them.

For an allocator, I think the core inflation-investing muscles are real assets, broadly defined. I wrote about this two years ago in “Hobson’s Choice”, and I wouldn’t change a word today. More broadly, the premise here is to push back from the table games here at the doubly-abstracted Public Market Casino, get closer to real cash flows from real things for real people, and think “pricing power, pricing power, pricing power” in every bit of analysis that you do. You’d also be well served to start reading Rusty Guinn’s new Epsilon Theory series, “Investing With Icarus”, which is just getting off the ground and will have a lot more to say about all of this.

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Essence of Decision

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The essence of ultimate decision remains impenetrable to the observer — often, indeed, to the decider himself.

John F. Kennedy (1917 – 1963)

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“The Manhattan Projects” by Jonathan Hickman and Nick Pitarra (©Image Comics)

I have found that the best way to give advice to your children is to find out what they want and then advise them to do it.
―Harry Truman (1884 – 1972)

As far as I was concerned, his decision was one of non-interference ­— basically, a decision not to upset the existing plans.
―Lt. Gen. Leslie Groves (1896 – 1970), commanding officer of the Manhattan Project, discussing Truman’s okay to drop atomic weapons on both Hiroshima and Nagasaki.

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“Ah,” she cried, “you look so cool!”

Their eyes met, and they stared together at each other, alone in space. With an effort she glanced down at the table.

“You always look so cool,” she repeated.

She had told him that she loved him, and Tom Buchanan saw.

 ― F. Scott Fitzgerald, “The Great Gatsby” (1925)

And that was the end of the party. When Tom Buchanan saw.

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Lear: Create her child of spleen, that it may live
And be a thwart disnatur’d torment to her!
Let it stamp wrinkles in her brow of youth,
With cadent tears fret channels in her cheeks,
Turn all her mother’s pains and benefits
To laughter and contempt, that she may feel
How sharper than a serpent’s tooth it is
To have a thankless child!
― William Shakespeare, “King Lear” Act 1 Scene 4 (1606)
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Once-revered central bank failed to foresee the crisis and has struggled in its aftermath, fostering the rise of populism and distrust of institutions.

Jon Hilsenrath, “The Great Unraveling: Years of Missteps Fueled Disillusion with the Economy and Washington” (August 26, 2016)

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John Hale: Theology, sir, is a fortress; no crack in a fortress may be accounted small.
  Arthur Miller, “The Crucible” (1953)

Few of us can easily surrender our belief that society must somehow make sense. The thought that the State has lost its mind and is punishing so many innocent people is intolerable. And so the evidence has to be internally denied.

The structure of a play is always the story of how the birds came home to roost.

That’s a very good question. I don’t know the answer. But can you tell me the name of a classical Greek shoemaker?

Arthur Miller (1915 – 2005)

That last, one of my all-time favorite quotes, was in response to a shoe manufacturer who asked why Miller’s job should be subsidized while his was not. Miller’s finest accomplishment: when McCarthy and crew forced him to testify in their Communist witch hunt, he refused to name names. Miller was a leftie and a huge ego. And a freedom lover. Imagine that.

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Yet each man kills the thing he loves

By each let this be heard

Some do it with a bitter look

Some with a flattering word

The coward does it with a kiss

The brave man with a sword

 ― Oscar Wilde, “The Ballad of Reading Gaol” (1898)

We’ll get the kiss, not the sword. Don’t know when, but it’s going to kill this market that the Fed loves.

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In 1969, Graham Allison published an academic paper about the Cuban Missile Crisis, which he turned into a 1971 book called Essence of Decision. That book made Allison’s career. More than that, the book provided a raison d’être for the Kennedy School of Government at Harvard, which — combined with Allison’s fundraising prowess — transformed a sleepy research institute into the most prominent public policy school in the world.

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The central idea of Essence of Decision is this: the dominant academic theory to explain the world’s events is a high-level, rational expectations model based on formal economics, a theory that ignores the impact of bureaucratic imperatives and institutional politics. If you look at the Cuban Missile Crisis through all three lenses, however, you get a much better picture of what actually happened in October 1962. In fact, the more you dig into the Cuban Missile Crisis, the more it seems that the actual people involved (on both sides … Allison wrote a follow-up edition in 1999 when Russian archives opened up post-Gorbachev) made their actual decisions based on where they sat (bureaucracy) and where they stood (internal politics), not on some bloodless economic model. Publicly and after the fact, JFK and RFK and all the others mouthed the right words about geopolitical this and macroeconomic that, but when you look at the transcripts of the meetings (Nixon wasn’t the first to tape Oval Office conversations), it’s a totally different story.

Allison’s conclusion: the economic Rational Actor model is a tautology — meaning it is impossible to disprove — but that’s exactly why it doesn’t do you much good if you want to explain what happened or predict what’s next. It’s not that the formal economic models are wrong. By definition and by design, they can never be wrong! It’s that the models are used principally as ex-post rationalizations for decisions that are actually made under far more human, far more social inputs. Any big policy decision — whether it’s to order a naval blockade or an air strike on Cuba, or whether it’s to drop atomic bombs on Hiroshima AND Nagasaki, or whether it’s to raise interest rates in September or December or not at all — is a combination of all three of these perspectives. But for Allison’s money, we’d do better if we focused more on the bureaucratic and political perspectives, less on the rational expectations perspective.

Allison is writing this in 1969! And here we are, almost 50 years later, still consumed by a theology of formal economic models, still convinced that Fed decision-making can be explained or predicted by our armchair analysis of Taylor Rule inputs. From an anthropological perspective, it’s pretty impressive how the high priests of academic economics have expanded their rule. From a human perspective, it’s awfully depressing.

What follows is my analysis of the Fed’s forthcoming decision on interest rates from a bureaucratic and an internal politics perspective. Seen through these lenses, I think they hike. Maybe I’m wrong. These things are always probabilistic shades of gray, never black and white. But what I’m certain about is that the bureaucratic and internal politics perspectives give a different, higher probability of hiking than the rational expectations/modeling perspective. So heads up.

First the bureaucratic perspective.

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What I’m calling the bureaucratic perspective, Allison calls the “Organizational Behavior” perspective. His phrase is better. It’s better because entities like the Federal Reserve are, of course, large bureaucracies, but what we’re trying to analyze here is not the level of do-nothing inertia that we usually associate with the word “bureaucracy”. What we’re trying to analyze is the spirit or culture of the organization in question. What is the institutional memory of the Fed? What do personnel, not just the Fed governors but also the rank-and-file staffers, believe is the proper role of the institution? Most importantly, how do these personnel seek to protect their organization and grow its influence within the jungle of other organizations seeking to grow their influence?

The spirit, culture, and personnel composition of the modern Federal Reserve is almost identical to that of a large research university. That’s not a novel observation on my part, but a 30-year evolution commonly noted by Fed watchers. Why is this important? It’s important because it means that the current marriage between Fed and markets is a marriage of convenience. As an organization, the Fed doesn’t really care whether or not markets go up or down, and as an institution it’s not motivated by making money (or whether or not anyone else makes money). Like all research universities, the Fed at the organizational level is motivated almost entirely by reputation. Not results. Reputation. A choice between “markets up but reputation fraying” and “markets down but reputation preserved” is no choice at all. The Fed will choose the latter 100% of the time. I can’t emphasize this point strongly enough. From a bureaucratic perspective, the Fed absolutely Does. Not. Care. whether or not the market goes up, down, or sideways. When they talk about “risk” associated with their policy choices, they mean risk to their institutional reputation, not risk to financial asset prices. And today, after more than two years of a “tightening bias” and “data dependency”, there’s more reputational risk associated with staying pat than with raising rates in a one-and-done manner.

Why? Because the public Narrative around extraordinary monetary policy and quantitative easing has steadily become more and more negative over the past three years, and the public Narrative around negative rates in particular is now overwhelmingly in opposition. When I did my Narrative analysis of financial press sentiment surrounding Brexit prior to that vote, I always thought that would be the most hated thing I’d ever see. Nope. I’ll append the Quid maps and analysis at the end of this note for those who are interested in digging in, but here’s the skinny: the negative sentiment around negative rates is now greater than the negative sentiment around Brexit. The public Narrative around ever more accommodative monetary policy has completely turned against the Fed. And they know it.

It’s not only the overall Narrative network that has turned with a vengeance against the Fed, but also some of the most prominent Missionaries, to use the game theoretic term. Over the last few weeks we’ve seen Larry Summers take Janet Yellen directly to task, as the runner-up in the Obama Administration Fed Chair sweepstakes has apparently taken to heart the old adage that revenge is a dish best served cold. More cuttingly, if you’re Yellen, is the heel turn by Fed confidante and WSJ writer Jon Hilsenrath. His August 26thhit piece on the Yellen Fed feature article — “The Great Unraveling: Years of Missteps Fueled Disillusion with the Economy and Washington” — is the unkindest cut of them all.

I think that Summers has been emboldened and Hilsenrath has been turned because they’re picking up on the same sea change in public opinion that the Quid analysis is identifying with more precision. For Hilsenrath, here’s the centerpiece of his j’accuse: a long-running Gallup poll asking Americans to rate the relative competence of federal agencies. Yep, that’s right, the Fed — which used to have a better reputation than the FBI and NASA — is now at the absolute bottom of the heap, dragging behind (by a significant margin) even the IRS! It’s one thing to bring up the rear in 2009 polls, what with the immediate aftermath of the deepest recession in 70+ years. But to still be at the bottom in 2014 after a stock market triples (!) and The Longest Expansion In Modern American History™? Incredible. And the most recent poll was in 2014. If anything, the Fed’s reputation is even lower today. I mean … this is really striking, and I can promise you that none of this is lost on the current custodians of the Fed’s prestige and reputation. Or, like Summers, the wannabe custodians. If you’re a professional academic politician like Summers, you can smell the blood in the water.

How Americans Rate Federal Agencies
Share of respondents who said each agency was doing either a ‘good’ or ‘excellent’ job, for the eight agencies for which consistent numbers were available.

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Source: Gallup telephone polls, most recently 1,020 U.S. adults conducted Nov. 11—12, 2014, with a margin of error of +/-4 percentage points. As reported by the Wall Street Journal.

So what does all this mean for the September FOMC meeting?

Look … does “the market” want more and more supportive policy? Of course it does. We’re addicts. But if the Fed takes a dovish stance now — and anything less than a hike is going to be perceived as a dovish stance — from an organizational perspective the Fed is risking a lot more than a market sell-off from a rate hike. It risks being blamed for anything bad that happens in the economy going forward. This is the risk of being an unpopular political actor. This is the risk of losing your reputation for competence. The Golden Rule of organizational behavior is quite simple: there must ALWAYS be plausible deniability for culpability if something goes wrong. There must ALWAYS be some other political actor to blame. Unless the Fed takes steps now to stem the erosion in their reputation and their position in the public Narrative, they will own this economy and the downturn that everyone (including the Fed) suspects is coming.

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By raising rates now, on the other hand, the Fed can declare victory. We achieved our dual mandates of price stability and full employment! Mission accomplished! And unlike George Bush and his infamously premature declaration of same, the Fed has someone to blame when the “mission” unravels (which of course it will) — those dastardly fiscal policymakers who didn’t follow up with structural reforms or pro-growth policies or whatever when they were elected this November. While the consensus view is that the Fed loathes to do anything to rock the market boat before the November election, in truth this meeting is the perfect time to act if your political goal is to declare victory and pass the buck. Hey, we did our part, says the Fed. Prudent stewards of monetary policy and all that. Now, about that consulting gig at Citadel …

That’s the bureaucratic or organizational perspective. Here’s the internal political dynamic as I see it.

An internal politics perspective is similarly driven by questions of reputation, but at the individual level rather than the organizational level. In an academic organization like the modern Fed, your internal reputation is based entirely on how smart you are, as evidenced by the research you do and the papers you write and the talks you give, not on how effective you are in any practical implementation of organizational aims. It’s not that the Fed or major research universities are intentionally ignoring or trying to put down practical implementations like teaching or outreach to commercial bank staffers, but every hour you spend doing that is an hour you’re not spending impressing your colleagues and bosses with how smart you are. It’s just how the internal political game is played, and anyone who has achieved any measure of success in an organization like this knows exactly what I’m talking about.

What this means in practice is that FOMC meetings are driven by a desire to form a consensus with the other smart people around the table, so that each of you is recognized by the other members of the consensus as being smart enough to be a member of the consensus. It’s the precise opposite of the old Groucho Marx joke: “I don’t want to be a member of any club that would have me as a member.” Every FOMC member desperately wants to be a member of the club that would have him or her as a member, because it means that you’ve been recognized as one of the smart kids. The internal political dynamic of academic cultures like the Fed, at least at the highest levels of Governor to Governor interaction, is NOT antagonistic or divisive. On the contrary, it’s cooperative and consensus-forming.

Not sure what I’m talking about? Read this Jon Hilsenrath interview of St. Louis Fed Governor Jim Bullard again (I say again because I published it for other reasons in the last Epsilon Theory note, Magical Thinking), where Bullard describes this consensus building dynamic.

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Mr. Hilsenrath: What kind of compromise would it take to get the FOMC to move in September? I mean, so the tradition is there’s some kind of — like you say, some kind of agreement. What would it take to get them there?
Mr. Bullard: Well, I have no idea, so — and it’s really — it’s really the chair’s job to fashion that. But I will say that — I’ll talk historically about the FOMC, the kinds of things that the FOMC would do. You would trade off. You would say, OK, we could hike today, but then we’ll not plan to do anything in the future. That would be one way to — one way to go about a consensus. So that often happens on the FOMC. Or vice versa. If you read the Greenspan-era transcripts, he’ll do things like, OK, we won’t go today, but we’ll kind of hint that we’re pretty sure we’re going to go next time.
Mr. Hilsenrath: Right.
Mr. Bullard: And so you get this inter-tempo kind of trade-off, and that often — that often is enough to get people to sign up.
Mr. Hilsenrath: So, hike today and then delay.
Mr. Bullard: Yeah. (Laughs.)
Mr. Hilsenrath: Or, no hike today and then no more delay.
Mr. Bullard: Yeah, yeah.
Mr. Hilsenrath: Something like that.
Mr. Bullard: Yeah, those kinds of trade-offs are, historically speaking — I’m not saying I know what Janet’s doing, because I don’t. But, historically speaking, those are the kinds of things that the FOMC has done.
Mr. Hilsenrath: I came up with my catchphrase for the — for the month. (Laughter.)
Mr. Bullard: Those are great. That’s worthy of a T-shirt. (Laughs, laughter.) You could have one on the front and one on the back.
Ms. Torry: Or a headline.
Mr. Hilsenrath: Well, that’s the St. Louis framework now, right?
Mr. Bullard: Yeah.
Mr. Hilsenrath: Hike today and then delay.
Mr. Bullard: Yeah. That’s what it would be, yeah.
― Wall Street Journal, “Transcript: St. Louis Fed’s James Bullard’s Interview from Jackson Hole, Wyo.” (August 27, 2016)

What Bullard is describing from a game theoretic perspective is a dual-equilibrium coordination game. Either it’s “Hike today and then delay” (Bullard’s preferred equilibrium outcome) or “No hike today and then no more delay”. Those are the two possible consensus outcomes. Both are stable equilibria, meaning that once you get a consensus at either phrase, there is no incentive for anyone to change his or her mind and leave the consensus. Importantly, both are robust equilibria in their gameplay, meaning that it only takes one or two high-reputation players in the group to commit to one outcome or the other in order to start attracting more and more reputation-seeking players to that same outcome. You can think of individual reputation as a gravitational pull, so that even a proto-consensus of a few will start to draw others into their orbit.

It’s always really tough to predict one equilibrium over another as the outcome in a multi-equilibrium game, because the decision-making dynamic is solely driven by characteristics internal to the group, meaning that there is ZERO predictive value in our evaluations of external characteristics like Taylor Rule inputs in 2016 or US/Soviet nuclear arsenals in 1962. (I wrote about this at length in the context of games of Chicken, like Germany vs. Greece or the Fed vs. the PBOC, in the note Inherent Vice). But my sense — and it’s only a sense — is that the “Hike today and then delay” equilibrium is a more likely outcome of the September meeting than “No hike today and then no more delay”. Why? Because it’s the position both a hawk like Fischer and a dove like Bullard, both of whom are high-reputation members, would clearly prefer. If one of these guys stakes out this position early in the meeting, such that “Hike today and then delay” is the first mover in establishing a “gravitational pull” on other members, I think it sticks. Or at least that’s how I would play the game, if I were Fischer or Bullard.

Okay, Ben, fair enough. If you’re right, though, what do we do about it? How are markets likely to react to the shock of a largely unanticipated rate hike?

In the short term, I don’t think there’s much doubt that it would be a negative shock, because as I write this the implied “market odds” of a rate hike here in September are not even 20%. My analysis suggests that the true odds are about three times that, as I give a slight edge to the “Hike today and then delay” equilibrium over “No hike today and then no more delay”. Do I think it’s a sure thing that they hike next week? Give me a break. Of course I don’t. But if I can be dealt enough hands where the true odds of something occurring are three times the market odds of something occurring …

The medium-to-long term market reaction to whatever the Fed decides next week is going to be driven less by the hike-or-no-hike decision and more by the Fed-directed Narrative that accompanies that rates decision. That is, if they hike next week and start talking about how this is the next step of a “normalization” process where the Fed will try to get rates back up to 3% or 4% in a couple of years … well, that’s a disaster for markets. That’s a repeat of the December rate hike fiasco, and you’ll see a repeat of the January-February horror show, where the dollar is way up, commodities and emerging markets are way down, and everyone starts freaking out over China and systemic risk again. But if they hike next week and start talking about how they’re rethinking the whole idea of normalization, that maybe rates will be super-low on a semi-permanent basis, or at least until productivity magically starts to improve … well, that’s maybe not such a disaster for markets. Ditto if they don’t hike next week. If the jawboning associated with a no-hike in September sets up a yes-hike in December as a foregone conclusion, that’s probably just as bad (if not worse) for markets than a shock today.

Of course, the Fed is well aware of the power of their “communication policy” and the control it exerts over market behavior. Which means that whenever the Fed hikes — whether it’s next week or next month or next meeting or next year — they’re going to sugarcoat the decision for markets. They’re going to fall all over themselves saying that they’re still oh-so supportive of markets. They’re going to proclaim their undying love for markets even as they take actions to distance themselves.

epsilon-theory-essence-of-decision-september-16-2016-great-gatsby

But here’s the thing. The Fed is now revealing its one True Love — its own reputation and its own political standing — and that’s going to be a bombshell revelation to investors who think that the Fed loves them. Investors are like Tom Buchanan in The Great Gatsby. We’re married to this really swell girl, and we get invited to these really great parties, but then we see that Daisy is truly in love with Jay Gatsby, not us. And everything changes. Maybe not on the surface, but deep down, where it really matters. I’m not saying that the Fed abandons the markets. After all, Daisy stays married to Tom. But everything changes in that moment of realization that she truly loves someone else, not you, and that’s what the next Fed hike will mean to markets.

That’s when the party stops.


Appendix: Quid Narrative Analysis

For a recap of how I’m using the Quid tool kit to analyze financial media narrative formation and evolution, please refer to the Epsilon Theory note The Narrative Machine. Below are two slides from Quid providing a quick background on the process.

epsilon-theory-essence-of-decision-september-16-2016-quid-text-analytics-background

Source: Quid

epsilon-theory-essence-of-decision-september-16-2016-quid-how-read-background

Source: Quid

Here’s the network of all 941 Bloomberg articles over the past year mentioning negative rates, colored by topic cluster:

epsilon-theory-essence-of-decision-september-16-2016-quid-bloomberg-negative-rates-topic

Source: Quid

This is a prototypical focused narrative network, indicative of articles that are truly “about” negative rates, as opposed to articles about something else that provides the clustering characteristics and only mention negative rates in passing. So now let’s look at the same network, but colored by sentiment rather than by topic clustering.

epsilon-theory-essence-of-decision-september-16-2016-quid-bloomberg-negative-rates-sentiment

Source: Quid

Fully 50% of the articles are negative, 42% neutral, and only 7% are positive in their sentiment.

How does this compare to other Bloomberg networks and other sentiment scores? Horribly. The only subject issue that even comes close is Brexit, with 47% negative, 42% neutral, and 10% positive in the weeks leading up to the vote. Post-vote, the negative sentiment around Brexit drops to the mid-twenties.

To be sure, few topics associated with monetary policy have an overtly positive sentiment distribution, at least in recent years. For example, here’s a chart of the Quid sentiment scores for all Bloomberg articles mentioning Quantitative Easing (QE), by year over the past three years. The Narrative is steadily deteriorating, but we’re still not close to negative articles taking the lead over neutral articles.

Sentiment Scores for Bloomberg Articles Mentioning QE, by Year

9/13 – 9/14 9/14 – 9/15 9/15 – 9/16
Neutral 58% 52% 48%
Negative 24% 31% 38%
Positive 17% 16% 13%

Source: Quid

One final network observation. Of the positively-oriented Bloomberg articles, they tend to cluster in the topic circled below. That topic? Gold. The articles have a positive sentiment because negative rates are great for gold prices. Of course, that’s a very negative thing from the Fed’s reputational perspective, which means that many of the articles that speak positively about negative rates are actually intimating something negative about central bankers! Bottom line: there is no more hated policy initiative in the world than negative rates.

epsilon-theory-essence-of-decision-september-16-2016-quid-bloomberg-negative-rates-positive-cluster

Source: Quid

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The Narrative Machine

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Alex: There was me, that is Alex, and my three droogs, that is Pete, Georgie, and Dim, and we sat in the Korova Milkbar trying to make up our rassoodocks what to do with the evening. The Korova Milkbar sold milk-plus, milk plus vellocet or synthemesc or drencrom, which is what we were drinking. This would sharpen you up and make you ready for a bit of the old ultra-violence.
“A Clockwork Orange” (1971). Society is a clockwork, with gears constructed of language and guns.

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A house is a machine for living in.

Le Corbusier (1887 – 1965), pioneer of modern architecture.

We live our lives inside machines, visible and invisible, tangible and intangible.

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HATE. LET ME TELL YOU HOW MUCH I’VE COME TO HATE YOU SINCE I BEGAN TO LIVE. THERE ARE 387.44 MILLION MILES OF PRINTED CIRCUITS IN WAFER THIN LAYERS THAT FILL MY COMPLEX. IF THE WORD HATE WAS ENGRAVED ON EACH NANOANGSTROM OF THOSE HUNDREDS OF MILLIONS OF MILES IT WOULD NOT EQUAL ONE ONE-BILLIONTH OF THE HATE I FEEL FOR HUMANS AT THIS MICRO-INSTANT. HATE. HATE.

― Harlan Ellison, “I Have No Mouth and I Must Scream” (1967). In Ellison’s post-apocalyptic horror, the last five humans on earth live inside a giant omnipotent machine where the only escape is death. It’s The Matrix 30 years before The Matrix was written, and 1,000x nastier.

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Mathematics, which most of us see as the most factual of all sciences, constitutes the most colossal metaphor imaginable.

It is easy to make a simple machine which will run toward the light or away from it, and if such machines also contain lights of their own, a number of them together will show complicated forms of social behavior.

Two quotes from Norbert Wiener (1894 – 1964). Wiener received his Ph.D. in mathematics from Harvard at age 17, volunteered to fight in World War I as an enlisted man, but couldn’t get a teaching job at Harvard because he was a Jew. Wiener found a home at MIT, where he became the father of cybernetic theory, aka the mathematics of machine behavior.

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How does the economy really work?

This simple but not simplistic video by Ray Dalio, Founder of Bridgewater Associates, shows the basic driving forces behind the economy, and explains why economic cycles occur by breaking down concepts such as credit, interest rates, leveraging and deleveraging.

Ray Dalio, “How the Economic Machine Works”. In the three years since Dalio released this short-form film, it has been viewed more than 3 million times.

Machines were the ideal metaphor for the central pornographic fantasy of the nineteenth century, rape followed by gratitude.

Robert Hughes, “The Shock of the New” (1980). A writer’s writer and a critic’s critic. As honest in his self-assessment as his assessment of art and society. It’s a bit uncomfortable, isn’t it? Honesty always is.

epsilon-theory-narrative-machine-august-17-2016-rube-goldberg

Self-operating napkin

Many of the younger generation know my name in a vague way and connect it with grotesque inventions, but don’t believe that I ever existed as a person. They think I am a nonperson, just a name that signifies a tangled web of pipes or wires or strings that suggest machinery.

Rube Goldberg (1883 – 1970)

So, in the interests of survival, they trained themselves to be agreeing machines instead of thinking machines. All their minds had to do was to discover what other people were thinking, and then they thought that, too.

― Kurt Vonnegut, “Breakfast of Champions” (1973). If there’s a better description of modern markets, I have yet to find it. We have become agreeing machines. Because our survival requires it.

epsilon-theory-narrative-machine-august-17-2016-dh-lawrence

For God’s sake, let us be men

not monkeys minding machines

or sitting with our tails curled

while the machine amuses us.

Monkeys with a bland grin on our faces.

D.H. Lawrence (1885 – 1930). Yes. For God’s sake.

epsilon-theory-narrative-machine-august-17-2016-leeuwenhoek

Antonie Van Leeuwenhoek (1632 – 1723), the father of microbiology, alongside a schematic of his microscope and drawings of the “animalcules” he found in a drop of water. Van Leeuwenhoek was a hobbyist lens maker, and he discovered a process for making very small, very high quality glass spheres which provided unparalleled magnification. He never shared his most powerful lenses, nor his manufacturing process, in order to maintain a monopoly on his discoveries. The glass-thread-fusing process died with him and was not rediscovered until 1957, long since supplanted by ground lenses.

epsilon-theory-narrative-machine-august-17-2016-brahe-kepler-war

Copernicus gets all the credit, but his 1543 theory of a heliocentric solar system with circular planetary orbits was a practical dud compared to Ptolemy’s earth-centric theory from 1,400 years earlier. The Copernican model just didn’t work very well. It took better data through new instruments (Tycho Brahe’s observatory) plus better theory through new math (Johannes Kepler’s elliptical orbits) before we finally got it right. But even then, the idea of a heliocentric solar system with elliptical planetary orbits didn’t find popular acceptance until powerful institutions in Northern Europe found it useful to champion this new idea as part of their fight with the Catholic Church and other powerful European institutions.

Modern portfolio theory = Ptolemaic theory. Are powerful institutional investors ready to fight?

Every successful institution, from a marriage to a superhero to a firm to a nation, needs an origin story.

The origin story of arguably the most successful hedge fund institution of the modern world – Bridgewater Associates – is that of Ray Dalio, working out of a small New York apartment in 1975 and publishing a newsletter of “Daily Observations.” The newsletter came first, not the hedge fund, and it was the compelling strength of Dalio’s writings about markets and what he would later term “the Economic Machine” that convinced a few institutional investors to give him some actual capital to invest. The rest, as they say, is history.

In 1975, Dalio struck just the right chord at just the right time with his metaphor of an Economic Machine – the idea that macroeconomic reality across time and place could be understood as a cybernetic system, with rules and principles and behaviors stemming from those rules and principles (essentially, lots and lots of if-then statements and recursive loops, with observable inputs from real-world economic fundamentals). As importantly as being an effective communicator, Dalio was actually right. Bridgewater has translated the metaphor of the Economic Machine into actionable investments for 40 years, with a track record that speaks for itself.

Today I want to propose a new metaphor for the world as it is – a Narrative Machine – where macroeconomic reality is still understood as a cybernetic system, but where the translation of “reality” (all of those economic fundamentals and if-then statements of the Economic Machine) into actual human behaviors and actual investment outcomes takes place within a larger Machine of strategic communication and game playing.

The Narrative Machine isn’t a rejection of the Economic Machine, any more than the theory of relativity rejects Newton’s Laws of Motion. In most places and most times, good old Newtonian physics is all you need to understand the world and take actions to succeed in that world. But there are times and places, like when you’re traveling near the speed of light, where Newtonian physics doesn’t work very well and you need a broader theory – Einsteinian physics – to understand the world and take actions to succeed in that world. A policy-controlled market, like we had in the 1930s and we have again today, is the investment equivalent of traveling near the speed of light. The Economic Machine theory – by which I mean any approach to investing that focuses on tangible macroeconomic fundamentals – just doesn’t work very well in a policy-controlled market. We need an extension of the Economic Machine to succeed in this time and this place, just like the theory of relativity extends Newtonian physics, and that’s what I think the Narrative Machine provides.

Unless you’re an Aristotle or an Einstein, advancement and extension of theory doesn’t just happen by sitting in a room and thinking it up. You need new data. You need better data. You need a new way of looking at the data. Kepler’s idea of elliptical orbits to advance and extend the Copernican theory of a heliocentric solar system couldn’t happen without the new astronomical data provided by Tycho Brahe’s observatory. For a negative example, I think the advancement of germ theory was set back by at least a century because Van Leeuwenhoek refused to share his new technology for looking at microscopic data. But at least astronomy and microbiology have something tangible to look at and measure. How do we SEE the Narrative Machine? How do we observe an invisible network of social interaction? How do we touch the intangible?

For my entire professional career, dating back to my first days as a graduate student and spanning three different vocations and three decades, I’ve been wrestling with that question. I think I caught a small piece of the puzzle with my dissertation and the book that came out of that (Getting to War), and I think that I’ve painted around the edges of the puzzle over the past three years with Epsilon Theory. I was pretty sure that the Narrative Machine was observable if the right Big Data technology could be applied (in the lingo, contextual analysis of affect, meaning, and network connectivity across large pools of unstructured text), but I’ve been involved with Big Data way before anyone called it Big Data, and every time someone claimed to have a solution to this problem it turned out to be far less than meets the eye. On that note, if you enjoy a little dose of schadenfreude (and really, who doesn’t?) do a quick search on Microsoft’s acquisition of Fast Search or, even more shivering, Hewlett Packard’s acquisition of Autonomy, two companies that claimed solutions here. So it was with some trepidation and certainly a healthy skepticism that I started working with Quid, a private company based in San Francisco that has developed a technology for network visualization of unstructured texts.

I think Quid is onto something, in large part because they’re not trying to answer directly the questions I’m asking. Instead, I think they’ve developed a novel process for seeing the invisible world of contextual connections and networks – something analogous to Van Leeuwenhoek’s novel process for seeing the invisible world of microbes – and I’m using their “microscope” to do my own research and answer my own questions. I like that Quid is a tool provider, not a solution provider, so that the analysis here, for better or worse, is my own. On the next few pages I’ll provide an example of some of the research I’m currently doing with the Quid microscope, and I hope it will give you a sense of why I think that we’re getting glimpses of the Narrative Machine with this new instrument.

I’ve written at some length about Brexit and the Narrative that emerged in its immediate aftermath, a Narrative that not only stopped the immediate sell-off in global risk assets in its tracks, but actually reversed the market decline and drove financial asset prices to new highs. To recap, I called Brexit a Bear Stearns event rather than a Lehman event, predicting that creators of Common Knowledge (what game theory calls Missionaries) would successfully characterize the event as an idiosyncratic fluke rather than a systemic risk, exactly as the collapse of Bear Stearns was portrayed in the spring of 2008. In other words, Brexit was NOT a Humpty Dumpty moment, where all the Fed’s horses and all the Fed’s men couldn’t put the egg shell back together again.

Now I have lots of anecdotal evidence of the sort of Narrative creation that I’m hypothesizing here. One of my favorites is a July 13thFinancial Times article titled “Anger at JP Morgan’s ‘Unhelpful’ Brexit Warnings”, where “Senior bankers in London are growing frustrated with JP Morgan Chase’s public warnings that it may cut thousands of jobs in the UK, saying such remarks send an unhelpfully negative message.” Or if I may paraphrase, “The UK government is angry at JP Morgan for not lying about Brexit like they were told to do.” I’ve got a hundred examples like this, examples of a concerted effort by every status quo government and media opinion leader to paint the Brexit vote as a one-off crazy mistake that will probably be reversed and certainly won’t be repeated anywhere else in Europe. But the plural of anecdote is not data, and until now I haven’t an effective instrument to see whether the media data supports what I think is happening.

On the left is a Quid visualization of the clusters and network relationships between the 2,422 Brexit-mentioning articles published by Bloomberg in the 4 weeks prior to the June 23rd vote. On the right is a Quid visualization of the 4,283 such articles published by Bloomberg in the 4 weeks after the vote. This is what the formation of a coherent Narrative looks like. These are snapshots of the Narrative Machine.

epsilon-theory-narrative-machine-august-17-2016-quid-bloomberg-vote

So what are we looking at here? Each dot (or node) represents a single unique article, and the Quid algorithms group nodes into colored clusters based on shared word choice and similar word positioning. If we magnify any of these clusters, in this case a cluster of articles talking about bond-buying and US Treasuries in the pre-vote data, we see that the nodes themselves differ in size according to their connectivity or centrality to the clustering principle, and that there are varying distances and numbers of connections between the nodes, as well. Each node exerts the equivalent of a gravitational pull on every other node, giving the entire structure both the appearance and the substance of a star map. Nodes can be evaluated and displayed on dimensions such as sentiment (green/positive – red/negative), as shown below, and all of these characteristics (distance, connectivity, centrality, etc.) are generated as a structured data set for further, non-visual analysis.

epsilon-theory-narrative-machine-august-17-2016-quid-treasuries

epsilon-theory-narrative-machine-august-17-2016-quid-treasuries-2

Here’s what I think we’re seeing in the “coagulation” of the Bloomberg facet of the Narrative Machine.

epsilon-theory-narrative-machine-august-17-2016-quid-bloomberg-vote

The pre-vote Bloomberg network structure on the left is what a complacent Narrative looks like. The articles are “about” whatever the clustering principle might be, and Brexit is typically a sideways glance, a throwaway line that’s almost always negative in sentiment. On the other hand, the post-vote network structure on the right is what an engaged Narrative looks like, where the articles are “about” Brexit and its impact on the clustering principle. Not only are we seeing a strong Narrative form on the right, but the density of lines and closeness of clusters shows that a similar tone and meaning has taken root across all these clusters. Importantly, it’s a positive tone and meaning that takes shape in the post-vote Narrative, with sentiment scores significantly higher than in the pre-vote snapshot. The sky-will-fall articles are almost all in the pre-vote sample, while the post-vote sample – as early as the Monday after the vote, which is immediately before the market starts to turn – are almost all focused on the non-systemic nature of Brexit, the likelihood of reversal, and the “mistake” that was made here.

The pre- and post-vote evolution of the Brexit Narrative structure is robust within individual Bloomberg clusters and across other major media microphones. Here, for example, is the same bond-buying / US Treasuries cluster in the post-vote Bloomberg data set (different color, but same clustering principle), and in the blow-up you can see how much more coherent and connected it is than the pre-vote cluster.

epsilon-theory-narrative-machine-august-17-2016-treasuries-bloomberg-vote

Below, the top pair of star maps are the 4-week pre-vote and post-vote network visualizations of Brexit-mentioning articles published by Reuters, and the bottom two star maps are samples from all publishers in the Quid database. All of the hypothesized Narrative patterns described above are replicated here.

epsilon-theory-narrative-machine-august-17-2016-reuters-all-sources

Okay, Ben, these diagrams and “star maps” are all very pretty. I get your metaphor of the Narrative Machine, and I get that you’re excited about a new technology that lets you see that invisible machine. But so what? How does all this translate into either actionable investment ideas or a process improvement in managing investment ideas?

When anyone asks this question (and believe me, it’s the question I’ve asked myself in one form or another for 30 years), they’re asking about two things: edge and odds. For anyone who’s trying to beat the dealer (my plug for Edward O. Thorp’s 1962 book that changed everything for me, also retold and expanded in William Poundstone’s brilliant book Fortune’s Formula) … for anyone who’s interested in alpha, this is all that matters: edge and odds. Edge is private information, an insight into the true nature of reality that other game players don’t have. Odds are the probabilistic relationship between risk and reward at any given moment in time. If you have either one of these on your side, then you’ll do well in whatever game you’re playing, if you’re dealt enough hands. If you have both on your side … and I think that a rigorous application of the Narrative Machine generates both edge and an improved assessment of odds … hey, now.

The odds revealed by the Narrative Machine are the odds of a catalyst having a major impact on price (or not). Or in slightly different words, I think that the Narrative Machine can help show us the degree to which future events are “priced-in” by the market. For example, when you’ve got a complacent, all-over-the-place Narrative leading up to a scheduled event like the Brexit vote, then even if my best guess on the voting odds is, say, 60% in favor of “Remain”, I would still place a bet on “Exit” because the Narrative-implied market payoff odds are far better than the breakeven odds of the vote.

The edge that the Narrative Machine generates is an improved reaction to a catalyst once it occurs. To be clear, I don’t think that the Narrative Machine can predict a market shock or catalyst before it happens. It’s not a crystal ball. But it is a real-time window into how the Common Knowledge Game is being constructed and played after an event occurs. For example, when you have a pervasive, systemic-risk-is-off-the-table Narrative created almost immediately following a market shock like the Brexit vote, then I would get long the market even if I believed in my heart-of-hearts (and I do) that there really IS systemic risk posed by everything that’s behind the Brexit vote.

I don’t want to over-sell the degree to which the Narrative Machine has been “weaponized” into an investable alpha source, because there are several critical aspects of network theory that remain to be implemented. Foremost of these is what network theory calls alluvial analysis, or evaluation of how different clusters “flow” into each other and away from each other over time. I’ve included two wonderful illustrations of this concept, both from a 2010 scientific journal article (“Mapping Change in Large Networks” by Martin Rosvall and Carl Bergstrom). I think the Quid technology is pretty good at what network theory calls “significance clustering”, the assignment of individual nodes into similarly colored and positioned groups – essentially a snap shot of the network at a given point in time. What we need now is a map of how those clusters evolve over time, because the meaning or organizing principle of the clusters themselves doesn’t remain constant.

epsilon-theory-narrative-machine-august-17-2016-mapping-change-large-networks-1

Rosvall and Bergstrom illustrate this beautifully in the second diagram here, where a network analysis of scientific journal articles show how neuroscience has become its own “thing” over time. We need the same alluvial maps for market Narrative clusters. I’m on it.

epsilon-theory-narrative-machine-august-17-2016-mapping-change-large-networks-2

So, yes … early days for the Narrative Machine. But, yes … a potential alpha source.

Which leads to an interesting question. If this is a new alpha source – the most valuable thing in the investment world – why am I talking about it? Isn’t this like announcing that you think you’ve found gold in California or the Yukon before you’ve staked a claim?

Good question. There’s some margin of intellectual property safety here because it’s not an easy alpha source to mine, even with cool new technologies like Quid. The internal logic of the Narrative Machine is the logic of strategic interaction (game theory), not the logic of stochastic processes (econometric inference). In plain English, I don’t think you can run a regression analysis of historical media network characteristics against historical market characteristics and get much that will be useful, at least not if you’re after edge and odds. The underlying theory here is Information Theory and the underlying math is the mathematics of entropy, and I’m reasonably confident that we’re not going to see an Excel plug-in for either of those anytime soon.

But yes, someone could “steal” this idea and run with it on their own. To which I say … fine. Better that than being another Van Leeuwenhoek, bogarting his research on his invisible world and setting back the advancement of germ theory and microbiology by a century or more. As in 1648 and 1776 and 1848 and 1917, we live in one of those rare moments in history where ideas are at stake and fundamental theories of the world are in flux. Let’s engage with that, and not hide in the convenient cubbyhole of narrow self-interest or the mentality of an agreeing machine.

We need a new perspective regarding the true nature of our economic and political clockwork, and that’s the real value of the idea of the Narrative Machine.

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