Weeks after the windup I was on a rooftop in New York City. The event was a founder / investor function hosted by Point Focal’s advisory firm, Sand Hill East. Andy Brown, its founder and CEO, and an early investor, asked from within the mingling,

“What is the one thing you learned from your experience?”

I knew immediately I couldn’t answer properly. It was too early in the stages of founder grief. I hadn’t finished coping with the failure. My reply was lost in the crowd. But I remember thinking the learning was cumulative, so it may have been a few things that led to the one thing.

The Point Focal story, documented here in thirty-two podcasts and 32,000 words, has ended. This, dear reader, is the final edition of Focus Signal. After developing a product, recruiting a team, raising capital, deploying to Fortune 500 institutions, and putting five years of life into bootstrapping a company into existence, when the runway ends, the landing is not smooth. Like Hemingway explains, gradually and then suddenly.

I knew I needed to go to the event, even if I didn’t know why. Sometimes you just go. Since that crepuscular evening I have spent most of my time in some state of reflection considering the question. With enough creative thinking - reading, writing, music production, exercise, and meditation - the answer surfaces.

There were a few things that led to the one thing that matters.

Time

Time is the most precious currency. In lifetimes and businesses, time is the only resource that cannot be replenished. You can raise more capital, you can find more customers, and you can build more products. You cannot create more time.

“Time kills startups. Solving problems creates businesses. Startups are in a race to solve problems before running out of time.”
- Solving an Earnings Challenge, December, 2021

Every decision should consider this dimension of scarcity. The time tradeoff. It is not a simple calculation because there are costs and benefits to speed and costs and benefits to deliberation. Some, not all, decisions are made better slowly. And some, not all, products are made better faster.

Time has a tipping point threshold. When awaiting luggage from baggage claim, as time passes it becomes more likely that our luggage will arrive. Until the point when as time passes it becomes less likely to arrive. In the early minutes, the probability of our luggage arriving is increasing. Later, beyond hours, days, and weeks, the probability is decreasing.

We need to understand the point when time’s impact on outcome inflects - from positive to negative in diminishing return scenarios like luggage arrival, and from negative to positive in situations where new information improves decision-making. Understanding duration, time’s curve, for product capabilities, customer satisfaction, and scale is to hold the fulcrum that solves temporal equations.

When time is the only currency, people are the only store of value. Ergo, with whom you work is more important that on what you work.

Systems determine outcomes by capturing all forms of leverage: technology, capital, and human. Because people create systems there is no substitute for working with great people - internally (teams) and externally (advisors, investors, partners, and customers). Startup currency and store of value, time and people, are related.

The most important function is recruiting people.

Focus

Focus is path optimization, waste allocation, and subtraction.

There are an infinity of paths to failure and one path to success. The likelihood of starting on the success path may be non-zero, but it is close to zero. Survival requires escaping failure paths for those more likely to lead to the success path. All that’s required to traverse the path maze is ruthless discipline, exacting precision, and sacrificial commitment.

Everything that does not lead to revenue is waste. The first catch is you do not know what will lead to revenue and what will not. Some waste is pure resource depletion to be identified and eradicated. And some waste is an optimization cost to be valued and happily paid. Amazon’s Fire phone was waste but so too were the exhaust fumes of its online book store: AWS. Allocate waste with care.

There are many things you can do, fewer things you should do, and fewer things still you must do. We exist in an environment transformed from the law of scarcity to the law of abundance, leaving us with few technological constraints. With sufficient creativity, most things are possible. But possibility is not rationale for execution. Subtract what must be done from what can and should be done.

Product inertia, investor gravity, and customer momentum are startup forces that pull us toward more. Toward yes. Focus is anti-inertia, anti-gravity, and anti-momentum. Toward less and no. Like organisms with survival at stake, what a startup excludes matters more than what it includes.

The second catch is that founders must do many things. Administration, financial management, HR, investor relations, crisis management, operations, and meetings. Painfully unglamorous and necessary functions. Even with singular focus, functions overwhelm. All failure paths have some combination of broad focus and suboptimal functional execution. The success path combines the narrowest viable focus with optimal functional execution.

Omit needless work.

Validation

Before growth, retention, efficiency, and margins there is revenue. Price multiplied by units. Revenue is validation. Everything else is something else.

Raising capital, hiring talent, pitching executives who gush at your product demo, signing beta users, and winning industry awards all fall into the non-validating, something else category. These milestones may be encouraging, useful, they may even be necessary signal. But they are not existentially sufficient. Misclassifying them as validation is perilous.

The third catch is that founders learn to ignore the disconfirmation of “no’s”. No, I won’t invest in you. No, I won’t work for you. No, I won’t try your product. No, I won’t take a demo. No, I cannot meet. No, you cannot have a discount. No, we will not partner with you. No.

Founders must counterintuitively ignore and learn from no’s. The no’s are incessant, so it is easy to to get excited by yes. Accept the difference between everything-yes-masking-as-validation and revenue.

Customers exchanging cash for product is the solitary validity metric.

The One Thing

Startups, like humans, are born alone and die alone. Everything in between that matters is time and people. Life is a gift. And on a different order of magnitude, being in a startup is a gift. The odds of being alive are infinitesimally small. The odds of being alive and being in a startup, through the law of multiplicity, are smaller still.

Life requires immense support. Consider the scope of evolutionary knowledge that have helped produce our living standards and lifespan. Startups require immense support too. Intellectual and emotional knowledge from teammates, advisors, and investors. Goodwill from partners and customers. All forms of courage and solidarity from family and friends. And a wife (or husband) who understands you entirely.

The depth of a failed startup abyss is deep. But it produces the same outcome as every step before failure. A new problem to be solved, albeit of a different kind.

Gratitude lifts you from the abyss. For the gift of having staked a claim on the possibility of creating something from nothing. For the reward of having been joined by believers who supported the possibility. And for the humbling feeling given to you from the ones who joined the journey. Gratitude.

We should all be so lucky to have new problems to solve. Progress of every kind depends on solving problems. As David Deutsch explains in The Beginning of Infinity, with the right knowledge, all problems with solutions not forbidden by the laws of physics can be solved, leaving a solution area under the curve approaching infinity. Happiness comes in knowing problems are inevitable and soluble.

The most common question from the rooftop investors was, “Do you feel relieved?”

Well, yes. Too close to the truth, I wondered about the question’s origin.

It came from their understanding of weight. They had lived through full lifecycles of windups as founders and therefore understood its intensely soul-crushing nature and the aftermath of silent relief.

Also from the evening, “He just wound up his business and he’s still smiling.”

The one thing is optimism.

We were standing on the edge of a Blade Runner themed movie lot in West Hollywood.

Stephen Wolfram was explaining why consciousness and free will should not be analyzed by hypothetically rewinding the universe to a prior state. Green lasers were cascading across my peripheral vision. He said too many things break down when we speculate about conditions that violate the laws of physics. Sonic audio waves from Grimes’ live DJ set were pulsing through our words.

The All-In Summit ’23 was about to come to a close after two days of ideation. It’s a strange word to define an event, “the formation of ideas or concepts”. But it’s the only word I can think of to capture the event, to borrow Stephen’s language, in an irreducible manner.

This cosmic EDM scene wasn’t what I envisioned when I told Martha we should apply to attend the All-In Summit. But uncertainty was one of the conceptual threads of the event. The nature of life is unpredictable. And possible outcomes are greater than we can imagine.

As Stephen told the summit, “the interesting way to lead life is to just let time exist and understand that you don’t know what’s underneath the universe”.

Background

As a fan of the pod and a first-time founder in the midst of a seed-raise, I came to the Summit to celebrate uncertainty. That state that connected my own life of market risk trading, data and analytics, and founding a startup. Reflecting on the Summit, it was the conceptual threads connecting the speakers that struck me most.

Particularly because the speakers had a wide range of backgrounds and ideas. Day one went from Ray Dalio to Elon Musk with Stephen Wolfram and Jenny Just, among others, in between. The breadth of content on day two was equally vast, spanning Larry Summers to Mr. Beast.

The days were surrounded by theme parties. Casino Royale in a bank vault, Barbie on the Santa Monica pier, and yes: the intersection of Grimes and Blade Runner. Tongue-in-cheek gatherings reminding us we are here to enjoy life, which is easy to do playing Skee-Ball in Barbie gear.

The All-In Pod, for those outside the audio loop, consists of four self-described “Besties” from Silicon Valley. Jason, Chamath, Sacks, and Friedberg. The reason 1,800 people flew from all over the world to a podcast gathering is because the show - which can be serious, helpful, controversial, and inspiring - is above all else, genuine.

The All-In Pod somehow attracts many cultures while being a reprieve from culture wars. And so four friends become a different kind of 1,804 friends.

Format

My aim isn’t to review the All-In Summit in full. There were some talks that didn’t resonate with me as much as others. A normal distribution of randomness. Other attendees will have had different perspectives and experiences. Fortunately, all the talks will be published by the All-In Pod.

My intent is to accurately reflect some of the ideas presented at the Summit. Where I’ve used quote marks around speaker language, I am quoting them directly. But the information I’ve captured from the event traveled from their minds to my notes before being processed and filtered by my own fallible memory and biases.

Errors and omissions will be present, for which I am wholly responsible. Feedback is welcome and as my understanding expands, edits and corrections will be made.

I am writing to process the event. To understand why certain concepts connected across speakers with diverse ideas. To appreciate what compelled the hosts, the speakers, and the audience to come together. And to reflect on their various incentives that combined to form a shared motivation.

I’m writing because there’s something about the ideas - these conceptual threads - that matter to Point Focal. And maybe to your business too. But I can’t quite identify the connection yet. So I’ll look to find it by writing these words.

Bear with me dear reader.

Concept Threads

Concept Thread One: Humility About Uncertainty.

From the mind who created the largest hedge fund to the mind with the largest YouTube following, there was a surplus of success on stage by many measures. And yet across the speakers there was a real appreciation for the limits of their knowledge. A humility about uncertainty.

Ray Dalio said sometimes we overlook the really big things.

Jenny Just’s Peak 6 trades hundreds of thousands of option contracts every day. And she doesn’t know which way the market is going.

Stephen Wolfram said, “it’s a better feature to know that we do not know the answer.”

Elon Musk didn’t know exactly what he was purchasing in Twitter. And he doesn’t know whether Russia will take Starlink down.

Larry Summers said, “There are two types of economists. Those who know they don’t know and those who don’t know they don’t know.”

Mr. Beast started a dark chocolate candy business for children. He said that turned out to be stupid because he didn’t know children don’t like dark chocolate.

And David Sacks didn’t know he was creating a podcast that would reach millions of people.

It’s a manifestation of Feynman’s axiom, “the more you know, the more you realize how much you don't know”. Some of our most brilliant minds were first and foremost explicitly humble about their ability to make accurate predictions.

One got the sense that this respect for uncertainty was responsible for both their success and for their understanding that there is an element of randomness inherent in their path to success.

This is a liberating thought not because it frees one from the hard work of innovation but because it illuminates the notion that there are many possible positive outcomes from hard work and innovation, most of which we cannot foresee.

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Concept Thread Two: The Middle.

Ray Dalio, speaking about geopolitics, said “structural problems created by elites cannot be solved with one side fighting the other side with irreconcilable differences. A strong middle is needed to bring the country together. A bipartisan engineering process that takes control of the extremes”. He framed the middle as a great reformation where if he were President he would establish a Manhattan type of project where important changes are engineered by a bi-partisan cabinet.

Jenny Just, speaking about market economics, said “financial volatility has been decimated. We’re in an odd place with a dot-com, AI feel… inflation may be slowing… but it may not be slowing… there is sector rotation… there is push and pull”. She said, “we’re right in the middle… without knowing what is going to snap this market one way or another”.

Respecting uncertainty, Jenny said she was not making a call on what exogenous event is coming. But from this economic middle state, she is long vol.

Vinod Khosla, an absolute giant in venture capital, said “investors bounce between fear and greed and never live in the middle. There is the hype cycle, followed by inflation, and then deflation. And it is difficult to see the middle. When the internet bubble burst in 2002, there was a dot com bust. But in reality, the internet didn’t change. Internet traffic stayed the same”. So within the hype-inflation-deflation cycles, there was fundamental value being added in the middle. And if you are adding value, fundamentally, you will be ok.

He said “you cannot time the market so it doesn’t matter at what price you enter. But upon deflation, respond quickly”.

This process describes active risk management. People ask Point Focal if our analytics generate alpha. That’s the wrong way to think about uncertainty and measuring risk. A better question is whether our analytics preserve capital. Can our models of investor behavior help asset manages respond quickly to deflation.

Whether you’re investing in startups or securities, exits are more important than entries.

Elon Musk talked about bringing Twitter to the middle. He is after the 80% of the world that live in between the extreme edges. He said “Twitter, I mean X, has been so far left for so long that a move to the middle feels far to the right”.

With his 157M followers and critics, and as someone with a knack for building businesses, it is telling that Elon wants the X experience to be in the middle.

Larry Summers said, “the thing about stuff that mean reverts is it mean reverts”.

The mean is a representation of the middle. Humanity, politics, markets, and companies mean revert. So we have Vinod Khosla reminding us that we cannot time markets, and we have Jenny Just reminding us that we do not know why financial volatility will not go to zero, only that it won’t, and here comes Larry to put a finer point on the things we know and the things we know we don’t know: the thing about stuff that mean reverts - like markets and financial volatility - is they mean revert. Larry said, “there’s never been a time when inflation was above 4% and unemployment was below 4% when the economy did not go into recession”.

Larry concluded by reminding the audience never to forget: “the best test for our country is whether we are the place to which people from every part of the world want to come”. That place may be on the edge of democracy, the edge of technology, and the edge of aspiration. But surely it is the middle of understanding.

Concept Thread Three: Demand and Supply.

Humility about uncertainty and mean reverting to a middle are concepts that translate to market behavior. More directly, Ray, Jenny, and Larry all spoke about demand and supply - market representations of greed and fear that can be observed and analyzed to manage risk.

Ray talked about debt and money creation which lead to the rise and fall of reserve currencies. “When debt and debt service payments rise relative to incomes, consumption gets squeezed and society realizes they have to print money.”

“When a government has a deficit they sell bonds. Buyers buy the bonds because there is an attractive return. When buyers realize they’re not getting a good return they will sell the bonds for equities or gold.” Gold is “the only asset you can have that is nobody else’s liability”.

Ray is describing a macro form of demand and supply. Economic forces that comprise greed and fear, mean reversion, computational math, and investor behavior. He talked about regime cycles - military, education, capital markets. Long term cycles that lead to regime changes, war, and peace. And like Jenny’s view of financial volatility, while Ray expects a new world order to emerge, he cannot predict who - what country or countries - will be at the forefront.

With respect to cycles, Ray said more important than the repetition is the cause and effect. This is what Ray is studying. These are his principles. Which led to my favorite line from Ray:

“Every conclusion I have is a function of measuring statistics and evaluating them as growth rates”.

This is risk management. This produced Bridgewater. And it is the framework Ray is using to analyze the world beyond Bridgewater.

Jenny said, “at any time, we will have three to four-thousand positions in equity stocks. But we're not high frequency traders. We are servicing customers and managing inventory”. Demand and supply.

The key to Peak 6 success is “managing inventory top-down, bottom-up, all day, every day”. If we don’t manage inventory well, we’re no different than a grocery store. The inventory goes bad, and we lose”.

Peak 6, consistent with Ray and Larry, do not make predictions. They do not call tops and bottoms. Because “they’re really hard to call”. Peak 6 monitors the cost of options - another proxy for the emotional state of the market. “When markets get stretched and become risky, the cost of options tend to get expensive. Today the cost of options is very inexpensive.” Which brings us back to Jenny’s observation that financial volatility has been decimated.

Like Ray’s view of geopolitics, conditions indicate a market regime change is coming, even if its form is unknowable.

Jenny provides another insight into demand and supply. These economic forces are oblivious to gender. When Jenny setup the first options floor off of an exchange she made a bet on her success: “that women could trade as well as men”.

The bet turned out well.

Jenny continues to bet on women through Poker Power, where she is teaching one million women to play poker. A game the Besties share a passion for that teaches discipline, risk assessment, negotiation, and decision making. And how to act on imperfect information.

Today women makeup 10% of professional poker players. This is a statistic Jenny is measuring. Through Poker Power she will be influencing its growth rate.

All of this ties into Jenny’s belief that the next “market correction” may come in the form of women becoming 50% of investors. Demand.

Three Session Reflections

I won’t try to recap Bill Gurley’s presentation on regulatory capture. It was a master class in how special interests stifle innovation. It’s a presentation that was 20 years in the making across Bill’s professional life as a business analyst and venture capitalist. His pace was incredible. The content is shocking. And understanding the impact of regulatory capture is profound. Bill’s talk has already been released here: 2,851 Miles. I promise it is worth your time.

Vinod Khosla’s view of the current state of venture capital was an extension of Bill’s presentation. In forty years Vinod “has not seen one example of a large innovation that came from a large company or a large institution. You have to go back fifty years to the early 70’s when Bank of America put debit and credit on a plastic card”.

“Apple’s iPhone is not an exception because it came from a founder-led company, distinct from a big company.”

Vinod posed rhetorical questions to the crowd: “Where are we going to get driverless cars? From Waymo or GM? Where are we going to get reusable rockets? From SpaceX or Lockheed Martin? Electronic transportation? Tesla or Avis?”

Vinod advised founders to “always be looking for off-ramps - where else can we go”? And he advised investors, when considering if an investment is still a good investment worthy of more capital, “to focus on the quality of the CEO”.

Stephen Wolfram’s thesis committee included Richard Feynman when Stephen received his PhD in particle physics from Caltech in 1980. I did not know, dressed in Rick Deckard’s jacket, that I was merely two degrees of separation from Albert Einstein. Had I known, I would have enjoyed wondering what Einstein would have thought of Grimes and her light particles in that Blade Runner moment.

Stephen’s discussion with David Friedberg, who runs the All-In Pod’s “Science Corner”, was a torrent of information. Stephen posited that despite complete knowledge of computational rules that define physics, we cannot infer all possible outcomes.

“Even with simple rules, consequences are hard to work out. In general, it’s not possible to predict output even with known rule sets and equations. Within science, there is a fundamental inability to say what will happen.” Friedberg responded by noting that we should therefore be careful about using simple heuristics.

Stephen suggests “the computational universe, the set of things we could compute, is vast. And surprisingly, even a very simple program can do very complicated things. It’s a bit of a trick that nature has figured out”.

“The number Pi is an example of a simple rule with complicated output. The output appears to be random, but we know it is created from a simple rule.” This type of observation relates to Taleb’s notion of being fooled by randomness.

Stephen explained that “artificial intelligence is limited to the computational universe - all the possible rules. So while we’ve given large language models four billion webpages that we humans care about, the LLMs can therefore only make things that are like what we care about which is a tiny part of the computational universe. LLMs can only construct stuff from the limit of what we’ve done”.

“The reason LLMs work is because they are telling us through science how we construct language - noun, verb, noun, etc. - assembling sentences that make sense. The logic is a formula of language producing reasoning that is impressive. LLMs have noticed patterns of language that can be reused. It’s a linguistic user interface.”

“We have a small set of inferences, bullet-points, and the LLM can turn it into a big report that you send to someone who can use an LLM to reduce it back into points. LLMs have become a transport layer for language.”

Friedberg noted that our “rate of communication is a bandwidth limiter”. Stephen responded, “while the structure of each of our brains, our neuron composition, is different, LLMs can package their communication”. And he added questions. “What else can we learn from AI? What LLM science and language semantics have we not worked out yet?”

Stephen said “we may learn that it’s simpler to understand human language than we thought. There are 50,000 common words in English. And we have 50,000 numbers that represent the probability of the next words. We’re asking questions in the space of how many word combinations can reveal meaningful answers. And the number of word combinations are greater than the number of atoms in the universe”.

“And yet when we consider the vector of numbers and the things that correspond to their arrays we can contemplate the idea of inter-concept-space. How many words might exist in between cat and dog? A huge amount of stuff. And AI is finding the inter-concept space in between words.”

Stephen’s work and his talk revolve around his concept of computational irreducibility.

Computational irreducibility: Computations that cannot be sped up by means of any shortcut are called computationally irreducible. The principle of computational irreducibility says that the only way to determine the answer to a computationally irreducible question is to perform, or simulate, the computation.

“As computationally bounded humans, we necessarily observe aggregate behavior. When we look at gas molecules, we see randomness because we are observers with certain characteristics. If we were different, we would make different observations. We are not capable of seeing gas differently. So our heuristic is the gas law: temperature. We sum up the gas molecules and take an average. We have to take the average because what is underneath is computationally irreducible. And we are computationally bounded.”

Stephen says “our brains are full of branching behavior. And we assume our branching brain perceives the branching universe persistently in time. Yet at any moment we are made of different atoms and we perceive ourselves as the same. We are computationally bounded observers in time and inevitably observe the laws of physics that we can observe, based on our nature”.

“There are likely things that we take for granted about he universe and about us as observers. Aliens will observe different laws.”

Stephen asked us to “imagine if our minds were each a small point in the matrix where an aggregate consciousness as a species could emerge”. He suggested “the future of civilization should be to expand into space. Expand our understanding and our consciousness”.

Friedberg suggested that “humans are the unconscious computer”.

In conclusion, Stephen said “the universe itself is a computer. It is computing. And time is the progress of that computation. The question is what do all of these atoms of space make. And the answer is they make Einstein’s equations”.

So What?

So what does Point Focal, a producer of portfolio analytics for asset managers, do with this Summit information?

For Point Focal, first and foremost, this notion of humility about uncertainty is core to our active risk management philosophy. We can observe, analyze, model, and forecast. But we cannot know. Because even simple rules can create not merely complex output, but complex output we could not imagine.

To help asset managers lose less money, we must combine this ethos of humility with adherence to “every conclusion being a function of measuring statistics and evaluating them as growth rates”.

Jenny Just goes all-in when she sees opportunity. But going all-in is a function of understanding probabilities produced from our computationally bounded minds.

Market behavior information expands the set of computational possibilities for modeling securities prices. It is the market inventory of U.S. equities. Demand and supply. Like Peak 6, though from a different perspective, we have to understand inventory levels top-down and bottom-up all day, every day.

We’ve created market behavior derivative statistics. We can measure them and evaluate their growth. We can observe amplitude scores mean-reverting. Like Jenny, we don’t need to call tops and bottoms to be effective.

By expanding what we know about the computational possibility of equities, we improve our bounded understanding of market behavior, which improves risk management.

This is what we’re dong with the ideas from the All-In Summit. What will you do?

Empathic Closure

Despite the two-day blitz through a mind expanding lineup of speakers, the best session may have been the last. Limited to the Besties and their 5th protagonist, Brad Gerstner, it was a letting down of the guard. Like the calm aftermath that follows an intense storm, it was the Besties in reflection.

After exploring venture capital, startups, fusion energy, gene therapy, modern education, social luxury beliefs, Starlink politics, Hollywood AI, and regulatory capture, it was the final unscripted discussion without a theme that may have mattered most.

One reason the All-In Pod works is because each of the Besties is flawed. We know this because we are constantly reminded of one Bestie’s flaws by three other Besties. They understand each other’s flaws perfectly. And they each own their own flaws.

J-Cal, Chamath, Friedberg, and Sacks explained why they produce the pod and what matters to them. Their friendship. And their families. They said all that truly matters when one looks back at their life is their loved ones. So spend more time with your family. Make time for your friends. And tell them all you love them. It is all that matters.

After an exchange of vulnerability, Sacks said, “I think all these emotions on stage are a bug, not a feature”. And in so doing, he expressed, I believe with self awareness, his own flaw, an apparent lack of empathy.

But then the other Besties, recognizing one of their own might need rescuing, explained how Sacks had been up until two in the morning before the final day of the Summit. He had been writing and publishing an extensive thread on X in defense of Elon.

During Elon’s talk, a video-conference using Starlink from his jet, Elon explained the impact of providing internet to Ukraine with Starlink, the only communication network that is functional in Ukraine. He explained the costs to Starlink. And the existential risk to Starlink’s business that has resulted from becoming a war target. Elon explained how not responding to a more recent Ukraine request for extended-territory internet via Starlink was an attempt to keep the war from escalating. For both decisions, to provide Starlink and not to provide Starlink, Elon has been attacked from all sides.

So Sacks went from his best Barbie party beach-themed Ken mode to write a 1,200 word geo-political piece into the morning hours to defend his friend.

My favorite part of the discussion with Elon was Elon’s laughter. Like the pod, it was genuine. Regardless of the topic or its gravity, Elon seemed to be able to find humor in life. And if the man leading energy transformation and space travel can find laughter amid the madness, maybe we can too.

Sacks may think emotions on stage are a bug, but he could not sleep until he had defended his friend. And in the end, there is nothing more computationally irreducible than a friend. In a vast universe we aspire to understand, friends - Besties - are what matter.

* And a perfectly timed, ominous background siren.

* Active manager performance context at 08:56 from product partner Tim Quast (ModernIR & Market Structure EDGE).

Data Partner Content

• Util ESG Impact Analytics

• Market Structure EDGE Market Behavior Analytics

• FINRA Off-Exchange Activity

• Brain News Sentiment

• Estimize Earnings

• Arria NLG (Pod)

But if you've heard someone use that Vegas line to describe their Vegas time, you know there is more to the story.

And so it is with equity trading. Whether you are the venue analyzing competitive intelligence, the sell-side optimizing trade strategies, the market-maker analyzing routing, or the buy-side seeking liquidity, knowing what happens off-exchange matters.

Like knowing you might wake up between Mike Tyson and a tiger in a Vegas hotel.

Off-exchange activity starts with Alternative Trading Systems (ATSs). In recent years new startups have entered the market (OneChronos, Purestream, and Blue Ocean). ATSs have merged (Luminex and LeveL). Big banks have launched ATSs (Citi-ONE). And brokers have consolidated ATS platforms (Weeden, Piper Jaffrey, and Sandler O'Neil into the concise, stealth ATS name “XE”).

You might wonder what allures startups, banks, and brokers to enter this incredibly competitive market to play a zero-sum game where the price of entry is SEC and FINRA regulation and compliance. Like high rollers to the lights of Vegas, venues of all shapes and sizes are attracted to the thirty to forty percent of U.S. equity volumes that trade off-exchange. We love analyzing the space because its intense competition and ever-changing market dynamics can spur trading innovation.

When one ATS wins market share another ATS loses market share. That’s if they’re lucky. It is more cut-throat than that because ATS off-exchange market share itself is not static. ATSs compete with over a hundred brokers with off-exchange single-dealer platforms (SDPs) and internal crossing networks. Some of them are large: Citadel, Jane Street, Robinhood. Some have both ATSs and SDPs: Goldman Sachs, Morgan Stanley. And together, the ATSs and brokers compete with a growing number of listed market exchanges almost wholly incentivized to reduce the amount of volume trading off-exchange.

The off-exchange game has a simple objective and complex methods. The objective is to match buyers and sellers. The methods include negotiating market price, volume, volatility, and market-impact. Integrating with other market participants and trading systems, often ‘frienemies’, adds more complexity. All of which must be navigated to optimize a matching objective measured in time slices of milliseconds1.

Want to launch an ATS?

We don’t. Though a few of our Focus Signal subscribers did, so rest assured you are in mad company here.

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Venue shape.

Point Focal’s off-exchange liquidity module helps market participants understand the space. And our new analytics helps one see the space differently.

A venue’s value is measured by how efficiently it connects participants. And this can be observed through volume and average trade size rankings, both proxies for market share. Depending on the design of a venue's matching logic, venues may rank higher on total volume or average trade size. Or their rankings may be a blended balance between the measures.

We're showing the shape of the venue landscape across these rankings. And we are shaping portfolios within these rankings. Shapes change across ATS and broker venues, and across market cap and sector. They morph further across price, volume, and volatility characteristics.

This is what it means to see a portfolio differently. Let's look at some shapes.

The following is a series of off-exchange venue and portfolio shapes. A new analytic lens. And for a certain kind of market practitioner, information value.

First a note on interpretation:

Scatterplots

• X & Y axes are average trade-size rank and total volume rank respectively

  • Both axes are inverted (wait, what?)

    • Up (volume) and to the right (trade-size) are highest ranks

• Markers are symbols, colored by market-cap, and sized by number of trades

  • Except the last chart in each series where markers are venues

• Markers are sized by trade count

• Vertical and horizontal lines are drawn at top-10 rank cutoffs

  • Markers above and right of the lines are top-10 in volume and trade-size

Vertical Bar Charts

• X-axis: volume and trade-size rank position

• Y-axis: symbol count in selected portfolio or index

The Startups

OneChronos and Purestream recent activity in the KRE Regional Banking ETF2.

OneChronos matches orders using periodic auctions with randomized start times. The auctions use mathematical optimization to seek optimal matches across all orders. Expressive bidding allows users to input specific execution instructions at the matching engine level precisely tailored to execution or investment strategy objectives3.

PureStream order types enable differentiation by separating the price and liquidity discovery process. Percentage rate based order types are used to match orders. The matched orders percentage rate is applied to each of the stocks subsequent trades reported to the “tape” to create a stream of trades4.

OneChronos and Purestream average volume and trade-size ranks across all ATS venues in recent KRE activity.

Both startup platforms are using unique, innovative matching strategies to capture market share. Going from zero volume to meaningful volume is a hard thing to do. This achievement alone means they are worthy of attention. Transforming standard off-exchange data structures to capture venue-specific strategies may improve our analytic lens. We consider this opportunity for further exploration.

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Two ATSs. One company.

LeveL ATS and Luminex recent activity in the S&P 500.

LeveL ATS is a continuous crossing US Equity Alternative Trading System (ATS), designed to transform dark pool executions with crossing innovation5.

Luminex is a completely anonymous, non-quoted trading venue that satisfies the liquidity needs of buy-side firms and prevents the main challenges of dark pools, such as intermediation, fragmentation, fall downs, information leakage, and concerns around ownership and control of trade data6.

LeveL and Luminex average volume and trade-size ranks across all ATS venues in recent S&P 500 activity.

The answer to why these platforms merged is explained by their shapes. Look again and note how their distribution shapes are mirror images of each other. In fact, if we were to fold the venues chart in half on its straight-line fit7, LeveL and Luminex might actually touch each other. Their shapes are complementary liquidity profiles. They each have what the other lacks. Symbiotic venues.

An Adverse Selection Protection Engine and the 3rd largest U.S. bank.

IntelligentCross and Citi-ONE recent activity in the Russell 3,000.

IntelligentCross is an ATS that uses AI to optimize price discovery. It matches orders near-continuously, within microseconds of arrival, to achieve maximum price stability after trades8.

Citi-ONE is a Citigroup Global Markets, Inc. (“CGMI”) ATS launched in 2022.

IntelligentCross and Citi-ONE average volume and trade-size ranks across all ATS venues in recent Russell 3,000 activity.

Citi has been through the ATS life-cycle before, from launch to exit. Their return suggests they have lessons learned and new strategic purpose. IntelligentCross is backed by Point 72 Ventures. Their Russell 3,000 shape, like balloons to a ceiling, reflects their continued growth.

Top non-ATS broker average volume and trade-size ranks in recent S&P 500 activity.

While this post is focused on the ATS segment of off-exchange activity, we can also see Citadel, Virtu, Jane Street, and Robinhood among a different venue segment, with different shapes.

What happens off-exchange can be observed. The players allocating resources to off-exchange activity is signal that there is signal off-exchange.

When market activity goes from opaque to transparent, information value and opportunity arise.

Viva las venues.

Pie charts usually suck. Humans aren’t designed to interpret relative sizes of angular shapes. 80% of pie charts should be bar charts. Most of the rest shouldn’t be charts at all.

It is a rare scenario when a pie chart is the best way to interpret data. And even then, the pie chart can usually be constructed in one’s mind, without financial technology.

Consider a pie chart representing the probability of a coin toss result. A circle split equally between heads and tails. Symmetry.

Now consider a pie chart representing what we know, “knowns” and what we do not know, “unknowns”. The unknown pie slice dwarfs the known pie slice. Asymmetry.

The giant unknown slice can itself be split into known-unknowns and unknown- unknowns. Things we know we don’t know. And things we don’t know we don’t know. Finally, a useful 3-slice pie chart. I won’t present it here because you can see it in your mind’s eye.

Known-unknowns and unknown-unknowns span a spectrum of blind spots from information disparity to information asymmetry. In asset management and in life, the greater the asymmetry, the greater the blind spot.

Blind spots are risk. And opportunity.

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Market Behavior

Knowns: volumes, prices, and related liquidity metrics.

Known-unknowns: market participant behavior and activity. Demand, supply, behavior, and divergence.

Unknown-unknown: amplitude scores.

Market behavior equilibrium is a measure of stock strength and weakness derived from market structure physics: regulations, incentives, participant behaviors, and activity. Amplitude scores measure a stock’s distance from equilibrium. Market behavior equilibrium information can be used in pre-trade preparation, through-trade execution, and post-trade performance reporting. Amplitude scores provide behavioral strength and weakness information that enhance program trading outcomes.

Monitoring equilibrium creates information value about price-setting behavior that affects volumes, prices, and volatility. Equilibrium can be observed as demand and supply rising, falling, diverging, and converging across market, index, portfolio, industry, and sector classifications, as well as by stock. It is a critically important dimension of trade flows.

In the visual below, amplitude scores identify the S&P 500’s extreme positive and negative demand supply divergences. Knowing market behavior characteristics across portfolios, sectors and securities highlights risky positions and enables managers to improve trading strategies to produce better economic outcomes.

Demand and supply trends signal equilibrium danger zones that further inform trade implementation. The likelihood of gains falls as demand rises above 6 on its 10-point scale. Similarly, the likelihood of losses falls as demand falls below 4.

High levels of supply over 50% signal bearish market participant behavior - an important input to trade strategy decisions.

A stock’s demand-supply distance to equilibrium can be observed and classified into market structure quadrants of strength, weakness, muted upside, and muted downside. The following analytic captures market structure equilibrium information value.

Market Behavior Demand Supply Quadrants

• Each marker is a symbol

  • Symbols are sized by intraday volatility

  • Symbols are colored by demand state

• Equilibrium: is where current demand and supply meet 30-day demand and supply

  • Y-axis: current demand minus 30-day demand

  • X-axis: current supply minus 30-day supply (inverted)

• Amplitude is distance from equilibrium

• Quadrants

  • Strength: high demand, low supply

  • Muted Upside: high demand, high supply

  • Muted Downside: low demand, low supply

  • Weakness: low demand, high supply

Market behavior information asymmetry.

Earnings

Knowns: sell-side earnings estimates, earnings reports, post-earnings performance.

Known-unknowns: alternative earnings estimates, revisions, and trends, statistical measures of earnings beat and miss materiality, and post-earnings drift performance attribution.

Unknown-unknown: post-earnings drift classification scores.

Our post-earnings drift quantitative model is built with earnings unknowns: alternative earnings estimates quantitatively superior to traditional estimates. Each earnings release is classified into one of ten deciles based on the statistical meaningfulness of the earnings report beat or miss.

In February, seven S&P 500 earnings reports were classified into the most extreme earnings surprise deciles, 1 and 10. The 2-day post-earnings drift performance difference between the deciles was 270 basis points (10th decile: +2.7%, 1st decile: 0.0%). The spread was 290 basis points over ten days (10th decile -1.3%, 1st decile: -4.2%).

More broadly, across nearly 1,500 companies for which we have alternative estimate data, our post-earnings drift model produced the following results.

• 439 earnings reports (10.5%) were classified in decile 10, with an average 20-day post-earnings-drift performance of 3.39%.

• 402 earnings releases (9.5%) were classified in decile 1, with an average 20-day post-earnings-drift performance of -0.27%.

• A difference of 366 basis points.

Post-earnings drift quantitative deciles and performance should not be unknown.

Earnings information asymmetry.

News Sentiment

Knowns: news stories and activity.

Known-unknowns: natural language processed (NLP) news sentiment scores and activity alerts.

Unknown-unknown: Portfolio performance of NLP classified sentiment scores.

Our news sentiment analytic combines NLP scored headlines and activity with traditional pricing and technical indicators. Combining AI level information with standard financial information is like adding the unknown to the known to create an unknown-unknown.

TDY is the top scored 30-day news sentiment stock from the S&P 500.

The 30-day average news sentiment score across the S&P 500 is 0.13 (symbol level sentiment oscillates between -1 and +1). The S&P 500 30-day performance is down 2.5%.

The S&P 500 top-10 news sentiment stocks have 30-day scores ranging from 0.29 to 0.37. Their 30-day average performance is 4.9%.

The S&P 500 bottom-10 news sentiment stocks have 30-day scores ranging from -0.11 to -0.24. Their 30-day average performance is -7.1%.

News sentiment information asymmetry.

For as long as markets have existed there has been information asymmetry. And as markets evolve so does its information. Our knowledge also evolves. Gradually, then suddenly. Small changes to information knowledge can have a large impact on risk management and performance.

A mouse in a maze seeks its known-unknown, the location of cheese. But a greater blind spot exists. Its unknown-unknown is the human measuring the mouse’s ability to find the cheese.

If the mouse saw its blind spot, it would probably be shocked.

Wouldn’t we be shocked to see ours?

It’s a bit like measuring stock performance from all time highs and lows. Possibly useful measures, but arbitrary nonetheless. Consider the duration of time a security spends at all time highs and lows. By their very nature, all time highs and lows are ephemeral. Yet we fixate on them.

Despite my ambivalence towards arbitrary standards, herein lies a year in review of my Substack, Focus Signal. It was actually launched in October, 2021. You’ll have to live with the fact that the year in review spans 15 months, which gives me an arbitrary sense of pleasure.

The point of Focus Signal is to document and share the building of Point Focal, a portfolio analytics business. The goal of portfolio analytics and fintech businesses is to reduce noise and increase signal. To that end 2022 was a year of progress.

Startup stopwatches count time to existential demise. So things never tend to move as quickly as you would like them to move. And the noise you suffer through is always more clear in hindsight. But if you attract enough signal you can keep going. And if you can keep going you can, well, keep going.

Signals, the Focus Signal micro-pod, reached twenty episodes in 2022. Looking back reminded me that nine episodes were recorded in 2021 and eleven were recorded in 2022. Already, not bounding this review to the calendar is helpful!

This spoken word micro-pod is an attempt to share the inner workings of building Point Focal. It has become a useful way to connect with you, the audience, and the feedback to the recordings has been wonderfully surprising.

As an introvert, there is something unnatural about communicating in this fashion. But I think this is also why I’m enjoying the medium. The fact that I can record the pod in isolated silence knowing it will reach you if, when, and where you want to be reached seems to bridge the space between introversion and extroversion.

Here are my favorite episodes:

11 Crossing the Chasm: Pod Sentiment, FinTwit Signal, and Trial Silence

Mostly because the part about Trial Silence epitomizes the defeatism inherent in startup rejection that must be overcome.

14 Zero to One to 1.1: Podcasting and How Music Production Emulates Product Development

Mostly because as an amateur musician I am fascinated by how Rick Rubin’s approach to music production can translate to business, financial technology, and analytics.

15 Walking Towards Uncertainty: This Pod, Explosive Growth, and Portfolio Manager Happiness

Mostly because Ma said it made her proud of me. What else can one say, really?

18 Work Life Imbalance: Appreciating the Stories of Others

Mostly because of an exchange with my wife, which went like this:

“Do you know Lex listens to your podcast?”

“Not really, no.”

“Well he does. And he asked me if I listen to it. Which of course I don’t. And then he said, ‘Well you should listen to it. And Mom, one episode was about ME.’”

The most popular episode:

12 Split Personality: Highs, Lows, and Nothing in Between

Maybe because the highs and lows of daily life are a universal experience.

And the longest episode (for market structure nerds):

20 Twenty!: 20, GTM, and STA Market Structure

Because it was the longest episode, 34 minutes.

My goal in 2023 with the Signals pod is twofold. Make the experience more comfortable. And publish about two episodes per month.

Making it more comfortable means reducing production friction. It’s the answer to the Tim Ferris question, “What would this look like if it were easy?” When I figure it out, I’ll let you know. If it’s more comfortable, it will become a better experience for both of us.

Twice a month seems challenging and possible. It’s a cadence that should be useful to you and achieve what one audience member, a music producer, advises in the studio: leave them wanting more.

What twisted minds would want more of this, I don’t know. But I embrace you. This approach also lets me continue to publish semi-spontaneously, which I believe is important. It’s true that Substacks grow faster when content is published on a routine schedule. But I prefer to publish when I have something worth publishing, rather than when an arbitrary period of time has elapsed.

It feels truer to me and better for you.

I’ve published six long-form Focus Signal posts. Mostly about the strategy and experience of building a portfolio analytics business. If you want to understand if you understand something, write about it. In a sense, these posts are a self-imposed challenge, shared publicly, to understand. And to error correct.

Reflecting on these posts, it’s the most recent three that I like most. This is encouraging because I’m hopeful the content is improving. I’m also fortunate to have some incredible data partners that make it fun to write about portfolio analytics.

Here are the last three posts:

ESG and Impact Analytics: The Skeptical and the Soluble

Util is our answer to the client and regulatory demands forcing asset managers to understand portfolio impact. One reason we love Util is because whether one loves or hates ESG, Util is the answer. This is becoming more clear as Tesla, born from one of ESG’s greatest critics, Elon Musk, is starting to talk about impact. From Tesla’s latest Impact Report:

"As the world needs to strive for a substantial positive impact, we won’t be referring to ESG. Instead, we’ll talk about Impact.”

“Many ESG ratings evaluate: ‘Does this ESG issue impact the profitability of the company?’ We need a system that evaluates: ‘Does the growth of this company have a positive impact on the world?’”

An Alt-Data Story Explainer: Market Structure Concept, Characters, and Plot

Stories are noise. Story explanations are signal. This was fun to write because it chronicles a fun partnership. One that has been an education and holds great promise. Because market structure sentiment implications on portfolio management are profound.

When 10% is Bigger than One: Leverage in Music Production and Product Development

An expansion on Signals episode 14: Zero to One to 1.1 where I tried to articulate why Rick Rubin’s approach to the creative process can be as useful in fintech production as it is in music production.

And the most popular:

The Secret of Free Data: How Little Insights Lead to Business Growth

Maybe because people like secrets. Especially when they’re hiding in plain sight. Or maybe because it’s a candid story of how Point Focal went from zero to one.

Our team often use these posts to answer questions from prospects and clients. How can ESG analytics be useful? Why should we care about market structure sentiment? How did you build off-exchange analytics?

Focus Signal has the answers.

These posts will come in the future as they have in the past. Semi-spontaneously, when they’re ready. But a general target is to produce more, higher quality long-form posts.

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Signal, launched in September, is the newest section of Focus Signal. These are shorter, analytic posts. They are the portfolio analytics being produced by Point Focal. The output of Focus Signal.

These pieces are meant to connect macro strategy and experience to micro insight. It’s likely to resonate with the practitioners in the audience - traders, portfolio managers, and research analysts. But also with individuals who have some aspect of these institutional roles within themselves.

I published five Signal posts, mostly related to new market structure sentiment analytics - how to interpret them (Sentiment Ebb and Short Volume Flow), what they can tell us (Short Volume and Market Behaviors), and how they can be applied to asset management (Informing Portfolio Turnover and Stacking Portfolio Analytics).

I am particularly excited about the Signal section of Focus Signal. The expanding scope and quality of our data and our improving technology stack offers new opportunity to create uniquely valuable content.

The Signal section can evolve in many ways. And I don’t want to try to define it’s end state. Part of the larger Focus Signal experiment is to write the story from inside the story. This is how Signal will be written.

On the look and feel of Focus Signal. Point Focal is fortunate to be working with a world class go-to-market team. We have an entirely new brand and visual identity coming soon. I’m looking forward to making the new design consistent across Point Focal and Focus Signal.

You will see new icons, branding, and colors in Focus Signal soon. It should be an upgrade given the current look and feel was produced by me. I hope you like it and come to recognize it.

Lastly, I thought it might be interesting for you to know alongside whom you are subscribed. Here is an anonymously classified summary of the Focus Signal audience:

• Family

• Friends

• Teammates

• Clients

• Trial Users

• Portfolio Managers

• Research Analysts

• Traders

• Partners

• Advisors

• Investors

• Stock Exchange Data Providers

• Alternative Trading System Operators

• SaaS Technology Providers

• Universities

• Competitors (Welcome!)

• Music Producers

• Future Point Focal Executives

• The Truly Anonymous

Thank you all for following along. I am grateful you care about this content and I appreciate you being part of the story. Wishing you the best in 2023.

- Thomas

Laws of physics determine planetary ebb and flow in the universe. And the wonder that unfolds is immune to our wishes. This whole spectacle in which we exist is unmoved by our desires.

Yet we have studied the fundamental mechanics of the universe deeply enough to understand some of its order. It was not a surprise when the winter solstice reversed the durations of days and nights on December 21st.

We do not need stories when we know the laws of physics.

Market behavior, like the universe, can be explained by stories or laws. In the case of the market, stories attract the most attention. They are born from clickbait, fueled by crowds, and validated by the great bias of hindsight. Our tendency to look back at events that were not predictable at the time and believe the outcome was easily predictable.

Market behaviors are governed by laws far less grand than universal physics. Yet they share an important trait: mathematical determinism. Meaningful explanations in the form of probabilistic analyses.

Observable for those who wish to study them.

Let’s turn our attention to the morning of December 21st as daily light began to outlast darkness. Across the over the prior month, short volume rose from 46% to 52%, a significant 13% increase across this broad market proxy. Meanwhile demand fell from 8 to under 5 on its 10-point scale.

Further, since 12/16, the percent of stocks with rising demand halved from 28% to 14%. While the percent of stocks with falling demand more than tripled from 9% to 29%. All the while, fast money behavioral flow grew from about a quarter to a half of all market liquidity. Algorithms amidst expirations.

And our supply breadth ratio reached 1.6, a level last observed in September. The ratio is calculated as S&P 500 constituents with rising supply divided by those with falling supply. The higher the ratio, the more willing market makers are to sell the broad market. On 12/21 over 300 S&P 500 stocks had rising supply. Less than 200 had falling supply. Growing weakness over declining strength.

Finally, over 50% of the broad market was positioned in the weakness quadrant of our market structure map. The weakness quadrant is down-and-to-the-left, where current demand is below 30-day demand and current supply is above 30-day supply.

Since integrating market structure behavior into Point Focal in Q1-2021, I cannot recall seeing this quadrant so crowded. The average S&P constituent was 20% below its 30-day demand trend and 7% above its 30-day supply trend.

Low demand and high supply is a bearish divergence. Crowded weakness.

This view of market behavior is not driven by human biased headlines. It is constructed with rules-based explanations derived from market laws.

Specifically, short volume activity made possible by the SEC’s REG SHO short sale exemption for market makers. Plus 605 execution quality, 606 order routing regulations, and related reports.

The REG SHO exemption exists to facilitate orderly markets. It exempts market makers from having to borrow stock to short stock. The law removes friction from the process of market makers becoming short.

605 and 606 regulations exist to facilitate fair markets. They introduce transparency into venue decision making behavior - market participant interactions.

For a more thorough examination of market structure laws, spurious headlines, and why all market participants are not the same, see An Alt-Data Story Explainer.

With the market up about 1.5% on 12/21 CPI news stories, a client informed of the above, wrote, “Pretty big sell off coming based on that analysis?”

I responded, “I prefer Tim's language, which is to say that given this weak market structure profile, stocks are statistically less likely to achieve or sustain gains relative to stronger profiles. But yes, until these deteriorating trends halt and reverse, we should consider this a cautious-to-bearish input to trade strategies. In practice, thinking probabilistically, today's strength is a selling opportunity until the setup changes.”

12/22 saw the market give back the entirety of 12/21 gains. But one shouldn’t judge decisions on outcomes when thinking probabilistically1. Decisions should be made and judged based on the quality of information available at the time of the decision.

Understanding the relationship between market participant laws and behaviors enables us to derive and observe new market information. Short activity across a class of market participants. And a market participant interaction network.

Additive, high-quality information helpful to market entry and exit decision making.

Market structure demand and supply.

Strength and weakness.

From individuals to markets to the universe, stories are told and laws are observed. In all their chaos and beauty, through processes as measurable as the length of light in our days.

“The atoms of our body, as well, flow in and away from us. We, like waves and like all objects, are a flux of events; we are processes, for a brief time monotonous”

Carlo Rovelli, Reality Is Not What It Seems

Stacking portfolio analytics is the art and science of performance.

Here is a stack of explanations.

Earnings results are a baseline layer of market intelligence. Deere & Co. (DE), beat earnings expectations Wednesday, 11/23. Knowing that DE beat Wall Street estimates is common knowledge. Layer 1.

Knowing that DE beat Estimize estimates, a quantitatively superior reflection of future earnings relative to Wall Street, is less common knowledge. Layer 2.

Knowing the historical 3-day performance spread between DE earnings beats and misses equals 385 basis points, and the 20-day performance spread equals 807 basis points is uncommon knowledge. Layer 3.

Knowing that DE EPS of $7.44 falls into a post-earnings drift quantitative model’s 10th decile is rare knowledge. The model classifies earnings releases by the statistical significance of upside beat and downside miss levels, relative to a universe of trailing 1-year earnings releases. When an S&P 500 constituent’s earnings release lands in the 10th decile of upside earnings surprises, the 20-day return is 4.25%. This is layer 4.

Layers 1 and 2 are interesting, descriptive analytics. Layers 3 and 4 are useful, prescriptive analytics. Because n the 20-day post-earnings period, buyers should tilt towards aggressive behavior and sellers should tilt towards passive behavior.

Portfolio analytic layers are additive quantitative evidence strengthening the probability that reweighting a portfolio towards DE will improve performance. And because post-earnings drift performance is relative, it is probabilistically prudent to likewise reweight portfolios away from companies landing in the 1st decile of downside earnings surprise misses.

Stacking portfolio analytics is stacking probabilities together to gain an edge.

Now, let’s stack market structure analytics on earnings analytics. Market structure sentiment is an entire category of uncommon knowledge.

Preceding the 11/23 earnings release, market structure demand for DE bottomed twice. Uncommon knowledge, but layer 1.

And short volume fell from 56% to 36%, a 35% drop in market-maker willingness to sell short DE. Layer 2.

In the post-earnings drift period, DE had rising demand and short volume 30% below its 30-day trend. Its rising demand and falling supply diverged sufficiently to land in the strength quadrant of our market structure distribution for the S&P 500. Layer 3.

Market structure sentiment is the best measure of price setting behavior. Market participants reinforced the statistical likelihood that DE would make gains through its earnings release.

When DE reports we stack 4 layers of market structure analytics onto 4 layers of earnings analytics. After earnings, the post-earnings drift model and market structure profile developments further stack probabilities in one’s favor.

Without stacking, we only know that DE beat estimates. By stacking, we know that DE beat estimates in a statistically meaningful way while possessing a strong market structure behavior profile.

Stacking portfolio analytics improves decisions that affect positions, weights, and aggressiveness. Entries and exits. Performance and risk management.

The final product of which is AUM. A non-trivial performance measure.

Portfolio analytic stacks move you from common knowledge to rare signal.

Investment managers (IMs) are fired and hired regularly. It’s why performance, marketing, and relationships matter. And not necessarily in that order. When large institutions like pension funds fire one IM and hire another IM, they need to transition their assets from one to the other.

From a legacy portfolio to a target portfolio.

To do so, they use some of the world’s largest asset service providers. Because raising cash by liquidating one portfolio while spending cash to invest in another portfolio is more challenging than it sounds. Consider transitioning several hundred million dollars across global markets spanning four asset classes. Now do it with the VIX over 30.

Not as simple as selling META to buy XOM in a Schwab account.

Enter transition managers (TMs): State Street, Macquarie, Northern Trust, Russell. They will transition assets from legacy to target portfolios in a market neutral fashion while minimizing market impact. And they’ll charge commissions or flat fees financial institutions are happy to pay.

Demand, the firing and hiring of IMs, is high. Supply, transition managers, is low. And the assets involved are large. Revenue is recurring because today’s hired IM is tomorrow’s fired IM.

Transition management is a small market, adjacent to asset management, with large network effects.

We’re building market structure sentiment analytics for transition managers. When we’re done, we will have a TM solution, and an analytic workflow that can be applied to any event-driven, episodic investment process, including program trading.

And we’ll have a market structure sentiment quantitative model.

We are modeling a baseline of selling a legacy portfolio and buying a target portfolio in a simple, market-neutral process. And we’re comparing the baseline to a model of selling the same legacy portfolio and buying the same target portfolio using market structure sentiment to inform position entries and exits and buying and selling aggressiveness and passiveness.

If market structure sentiment can meaningfully explain price setting behavior, our model will improve transition manager performance.

We envision a pre-transition report with the following portfolio information:

• Average market structure sentiment (demand) and short-volume (supply)

• Demand and supply distributions

• Average dominant behavior activity (Fast Trading, Active, Passive, Risk Management)

• Distribution of positive and negative demand / supply divergences

• Demand direction breadth percentages (topped, rising, declining, bottomed)

• Supply direction breadth percentages (rising, declining)

• Market behavior profiles percentages (big and stable, active divergence, momentum, short trades, liquid volatile divergence)

• Aggregations across market-cap, sector, and market behavior

Pre-transition reports will quantitatively demonstrate how the legacy and target portfolios differ across these market structure sentiment metrics. And they will provide a basic model of how using market structure sentiment to inform the liquidation and purchase of legacy and target portfolios can reduce market impact and improve performance.

A critical market structure sentiment metric is divergence. Whether securities have demand and supply (sentiment and short volume) moving together or apart. The pace at which divergence and convergence are occurring. And the extremeness of divergence.

For the sake of this Signals piece, we’ll use divergence to explain an initial market structure sentiment model2.

An Alt-Data Story Explainer, well, explains, why market structure sentiment matters3:

Beneath market activity is market structure. And beneath market structure is market structure data. 605 best execution, 606 order routing, and FINRA Reg SHO short volume. Market structure sentiment. Demand and supply. The data that explains the stories.

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Because we have 2 portfolios (legacy and target) and 2 profiles (positive demand / supply divergence and negative demand / supply divergence), there are 4 resulting market structure sentiment strategy profiles:

Let’s use a simple example of demand and supply divergences. Consider a legacy portfolio with average sentiment of 6 (on a 1-10 scale) and average short volume of 40%. And consider a target portfolio with average sentiment of 3 and average short volume of 60%.

• Legacy portfolio: demand = 6, supply = 40%

• Target portfolio: demand = 3, supply = 60%

Holding other factors constant4, relative to each other, the legacy portfolio has a more favorable market structure sentiment profile (high-demand, low-supply) compared to the target portfolio (low-demand, high-supply).

Therefore, on a relative basis, it is statistically more likely for the legacy portfolio to make gains during the transition event and more likely for the target portfolio to suffer losses. Since the legacy portfolio is being sold and the target portfolio is being bought, transition managers can reduce market impact and improve performance by behaving more passively than aggressively with respect to both portfolios during the event.

In this scenario, market structure sentiment can be used to model more passive liquidation behavior to capture gains by selling the legacy portfolio into market structure strength. Likewise, passive behavior will be modeled for purchases to reduce acquisition cost by buying the target portfolio into market structure weakness.

Note that if the target portfolio exhibited a positive market structure sentiment divergence (like the legacy portfolio), buying behavior would be modeled more aggressively, to reduce acquisition costs by avoiding paying higher prices (buying the target portfolio into market structure strength).

Every transition has a duration: the number of days during which the transition will occur. Let’s make this transition’s duration 5 days. The baseline model will sell the legacy portfolio and buy the target portfolio evenly over the 5-day period, with market-neutral sales and purchases occurring throughout the event. The market sentiment strategy (type 2 from the profile table above) will sell the legacy portfolio and buy the target portfolio more passively than the baseline model throughout the event.

The two models are presented below:

Models will be evaluated by analyzing the net dollar value amount of raising cash by selling the legacy portfolio and spending cash by buying the target portfolio. If the baseline model raises $297M over 5 days selling the legacy portfolio and spends $303M over 5 days buying the target portfolio, the cost of transitioning from one IM to another (before TM fees) is $6M.

If the market structure sentiment model raises $299M over 5 days selling the legacy portfolio and spends $301M over 5 days buying the target portfolio, the cost of the transition is $2M (plus TM fees).

This is our objective: a better economic outcome using market structure sentiment analytics.

 A post-transition report will include the following portfolio information:

• Market structure sentiment profiles and metrics throughout the event

• Liquidation and purchase amounts (cash raised and spent) throughout the event

• Baseline and market structure sentiment model comparative performance metrics

This is the beginning of a portfolio analytics model on which we will expand. We will add other metrics beyond divergence to the analysis. We will map the degree of divergence to the levels of liquidation and purchasing aggressiveness and passiveness. And we will assess market structure sentiment more frequently throughout the event.

We will also build a stand-alone market structure sentiment quantitative model that can be used by any asset manager. The model will compare benchmark performance of buying and holding a portfolio of securities to a strategy of using market structure sentiment analytics to reweight the portfolio.

We will share the analytics described herein as we produce them. I suspect they will create utility for investment processes we have yet to consider.

For now, we will pursue a small market.