David Senra on Invest Like The Best

Patrick O’Shaughnessey interviewed David Senra, the host of the outstanding Founders Podcast. I love David’s passion and storytelling. This interview was the best one I listened to this year. I listened to it several times and it was the first time I asked my son (now 9) to listen to an interview with me. It felt like one of those chats that could inspire an impressionable mind.

They discuss the premise, motivation, and lessons from David’s podcast. The premise of Founders is David studies famous entrepreneurs, scientists, artists or really any creatives that made a large impact and distills pitfalls and lessons from their stories. It doesn’t sound novel, except David’s personality and enthusiasm make you feel like you are hanging out listening to a friend tell a crazy about another friend (except that last “friend” is a historical figure)

The following notes are what stood out to me.

My son Zak also took notes (we made this an exercise because in 4th grade this year he’s learning to take notes). It was fascinating to discover where he wanted to pause the interview to jot something down that stood out to him. One of the reasons the episode might have been especially fun for him is we just finished watching The Men Who Made America series on the History Channel and Senra discusses many of the “captains of industry” or “robber barons” featured on the TV series.

Link: https://www.joincolossus.com/episodes/85503387/senra-passion-pain?tab=transcript

Books as mentors

Senra came from a challenged family. He wasn’t only the first male to graduate HS, he was the first to not go to jail! When he studied Warren Buffet, he recalled that Buffet said, “One of the best things ever happened to me is I picked the right heroes.” I think that is extremely important. So that is the role that books play for me.  I don’t have access to these people, I didn’t have mentors. I didn’t have anybody.

Senra’s career is the embodiment of what I described as The Engine Model

  • There are four passions in my life, entrepreneurship, reading history and podcasts, so Founders sits in the middle of that.

  • People say “Founders is podcast, Founders is business.” Yeah, but it’s an obsession first. It’s an obsession disguised as a podcast in a business.

  • The reason I’m so obsessed with just studying people that got to the top of their profession is because one third of your life is going to be spent working. Half of your conscious life, half of the time you’re not asleep, is going to be spent working. For a certain personality type, to not excel at that, to not be really good at that means that life is not going to be an enjoyable experience for me. If I had to guess, which comes up a lot, I think there’s some kind of deep-rooted fight against a sense of inferiority that is underneath it.

  • It’s interesting, this comes up a lot, there’s a line in the Francis Ford Coppola biography that I read, because I really love reading biographies of filmmakers, that’s the closest analogy to what I’m trying to do at podcasting. I read their words and I’m like, “That’s how I think about podcasting. That’s fascinating.” And there’s a line in the Francis Ford Coppola biography where, embedded in the story of the son is the story of the father. And his dad was this guy, he wanted to be a musician. He never made it, was super bitter. So he raised Francis Ford Coppola and they would just talk shit, his dad would just talk about anybody that was successful. And Francis is like, “Well, I don’t want to be the person criticizing the successful person, I want to be the successful person.”

  • And I think a lot of that came from how we started the conversation, some people have to say, “Hey, I want to be like that guy.” Other people have, “I don’t want to be like that guy.” And those are equally powerful motivators.

[This reminds me of Ambition As An Anxiety Disorder]

Originality and ego

  • To be an investor, to be an entrepreneur, it doesn’t work if you can’t trust your own judgment. So what kind of person who’s willing to take that risk? I know a ton of entrepreneurs that could even make more money, if they went to go work for Google or something like that. They’d rather make less money in their own business, than work for somebody else. But there is something bizarre that I don’t think you can explain. All you can do is notice it in other people, and then seek those people out like, “Oh, I’m not weird. There’s a ton of people just like me.”

  • But Edwin Land said that there’s no such thing as group originality or group creativity. He goes, “I do believe wholeheartedly in the individual capacity for greatness.” And he says, “Originality are attributes of a single mind, not a group.”

  • I actually don’t think that you build a great company without a giant ego. I don’t think that exists. Sam Walton has a good idea about that. He’s like, “Listen, your ego should use to drive you, but you should not be on public display.” And he’s like, “I hire people at Walmart with big egos, that know how to hide it, because there is some weird thing where it drives you.”

Confusing people liking the work for liking you

We talked a little bit about ego before we started recording, where it’s very prone to let your ego get the best of you. People admire you because the work. What happens is, you usually isolate yourself. You’ll work really hard. You’ll do a lot of work. That work draws the attention of other people because it adds value to their life. And then suddenly, over time, you confuse us. It’s like, “Oh, they don’t like the work. They like me. And then I could just show up without having to do the work and everything will be fine.”

[Reminds me of the Asimov line: Past glories are poor feeding]

On constant learning

The reason I say that Jordan’s biography changed my life is this idea of practice. How many people want to get to the NBA? A ton, millions. How many get? 400 maybe. How many people get to the Dream Team in Barcelona in ’92, which might be the greatest basketball team of all time? 15 people. A subset of a subset of a subset. Michael’s tired. He’d been playing nonstop, back-to-back. He’s like, “Man, I really want to take some time off. I don’t want to spend my summer for the Olympics, but I’m going to go.” He goes, “I want to see their practice habits. We’re all the best of the best, what am I doing that’s different than what they’re doing?” What happened was he goes, he watched the way they practiced compared to the way he practiced. The main theme of Jordan’s book is I believe in practice. I would rather miss a game than miss practice. That’s insane. He said something that gives me chills to this day. He goes, “I watched their practice habits,” and he goes, “they’re deceiving themself about what the game requires.”

Obsession and endurance

“If anything is worth doing, it’s worth doing to excess” – Edwin Land, Inventor

  • On my phone, my lock screen, is a picture of Ernest Shackleton, the famous polar explorer, who looks like hell. He’s got a huge beard covered in ice. He looks like he’s about to die. And his family motto was, “By endurance, we conquer.” Which is why I told you earlier, I’m only interested in people who do things for a long time. Because at every single step, these people are presented with opportunities to quit and they don’t. So he’s like, I don’t have to be the smartest. I don’t have to be the best. I don’t have to be the talented. This is what I believe in myself. I don’t have to be the best. I don’t have be the smartest. I don’t to be the most talented…if you do something for three or four or five hours every day that most people don’t do, you’re going to develop a value for other people in the world, and that’s all a business is. The best description of a business I ever heard came from Richard Branson. He’s like, “All businesses, it’s an idea or service that make somebody else’s life better. If you make other people’s life better, you’ll capture that value in return.”
  • I’m not a fan of moderation. I’m attracted to extremes. What do you want your life experience to be? Do you want to be exceptional? Do you want to push the boundaries of your capabilities? Do you want to walk around in a fog butting up against your potential but never actually realizing it? Then knock yourself out. Be moderate. I’m not interested in that. (Zak loved this!)

Understanding what you want — the soul of a business

I read this great book called Masters of Doom, which is obviously about the video game Doom. There’s John Romero and John Carmack, and John Carmack said something in what causes the rift of their partnership. He’s like, “Romero wants an empire. I just want to make great games.”

Senra relates to Carmack:

Founders is like a handmade product. And I was like, thank you because that’s what I think about. It’s a handmade product at scale because of the miracle of podcasting. I can do everything myself. I don’t have anybody helping me…role of the founder is the guardian of the company’s soul. There’s a cult around In-N-Out because the expression of the founder’s soul is manifest in the product. People have In-N-Out tattoos. Who are your entrepreneurial heroes? Everybody copies somebody, dude. You’re a human. I always have a maxim by saying in my podcast that the mind is a powerful place. What you feed it affects you in a powerful way.

The culture of a company as a reflection of the founder

The quote that comes to mind when I think of the founder as the guardian of the company’s soul is actually a quote about Steve Jobs. It’s in one of the books I read about him and he says he made and remade Apple in his own image. Apple is Steve Jobs with 10,000 lives. That gives me goosebumps because that’s exactly what a founder should be doing. It’s impossible to build a company, to spend all your life energy on it, and not have it imbibed with your personality, with your ethics. Everything that you think about your business and your life is going to seep into it. The good and the bad parts.

Patrick: It reminds me of a conversation I had with Tony Xu who started DoorDash. Tony’s a very mild-mannered, very humble, almost quiet person, which is why this quote from him stands out in my memory so strongly, which is I asked him something about culture.  How do you think about constructing the culture of a company? His answer is basically, “I think a culture of a company should be like 80 or 90% just the personality of the founder. That’s it. It should be the extreme characteristics of the personality of the founder. Because if you try to make it generic, nothing stands out and there’s no progress and inertia dominates.

Process as art (and marketing)

Patrick: It sounds like a common theme in all these stories, is process as art by revealing the process behind the product, because they’re so obsessed with that. That is a common marketing story. Do you see that over and over?

There’s no such thing as a business that is boring. Listen, it’s boring to you because you do it every day. If you explain to the customer the process, they’ll find it interesting. If there’s any part of your product that seems banal or ordinary to you, I promise you, no one is thinking about your business as much as you. The favorite business of mine in the world, you think about it less than probably five minutes a week. Nobody is thinking about it. You have shit in your brain that is interesting to customers, and then you could package that up and use that as marketing to get more customers.

The most recurring theme in Founders stories

  • We may or may not have talked about the most important, and that’s the best maxim in the history of entrepreneurship was said by the founder of Four Seasons, that “excellence is the capacity to take pain.”…Anybody that’s ever done anything difficult, whether it’s a company, anything, knows the euphoria and terror. It’s the entrepreneurial emotional rollercoaster. The reason that I think it’s so important to talk about is because it is supposed to be hard. There’s not a book you’re going to pick up where the guy or woman’s like, “Hey, I had this idea. I started it. Everything went great,” and the end of the book. It doesn’t happen.
  • James Dyson. It’s hard to find, but if you can get a copy, order it. He says, “Listen, it’s easy for me to celebrate my doggedness now. I made $300 million last year, but I’d be lying to you if there wasn’t times where I went inside my house, had my wife look at me in the face like I’m a failure and I’d cry myself to sleep, and I got up and did it again anyways.” [Dyson made over 5,000 prototypes in 14 years before landing on the bagless vacuum that made him a household name]

Because excellence is the capacity to take pain:

  • I apply this to like, “I don’t really feel like working out right now. I don’t feel like doing cardio.” I don’t give a shit, David, how you feel. How you feel is irrelevant. That’s an idea I got from Henry Ford. You read his autobiography, he goes, “I feel sorry for these soft and flabby men that can only do great work when they feel like it.” Essentially, Henry Ford is saying “fuck your feelings.” Henry Ford’s point was a business exists to serve other people. There are going to be days when you get out of bed and you cannot wait to get to work, and that’s great. There are going to be days when you don’t want to go to work, and that is irrelevant because the business is not about you. The business does not exist for your pleasure. The business exists to serve other people. [Kris: this is why you should probably care about the customers]

The kindest thing anyone has ever done for David

The kindest thing anybody’s ever done for me happened a few decades before I was born. My grandfather on my dad’s side was living in Cuba. He was just 38 years old. He had a wife and a newborn baby when the Cuban Revolution happened and Castro took power. He didn’t understand the language, had no money and no education, and yet took the gigantic risk … and the complete correct choice at that time in his life … to flee Cuba to go to America, to give his family a better chance and a better opportunity. That one decision changed the entire trajectory of my life. None of my interests that I happen to be naturally born into, the passions that chose me, that I did not choose, would make a lick of difference if I grew up in Castro’s Cuba as opposed to America. As somebody that studies dead people for a living, it really resonates how our decisions not only affect our loved ones now and our family now and our friends now, but they reverberate through the generations. If you think about it not in the context of what’s going to happen in your life this year or next year, but how the decisions you’re making will affect people that aren’t even born yet, you’ll make your decisions differently.

My 9-year-old Son’s Takeaways

  • David only had books as a kid. NOTHING ELSE
  • David’s extended family is all EVIL
  • “Many people can run a company but not many can create one”
  • In every one of Rockefeller’s biographies, J. Gould always pops up.
  • Jordan joined the Dream Team just to see how other countries practice.
  • ” I’m not a fan of moderation. I’m attracted to extremes. What do you want your life experience to be? Do you want to be exceptional? Do you want to push the boundaries of your capabilities? Do you want to walk around in a fog butting up against your potential but never actually realizing it? Then knock yourself out. Be moderate. I’m not interested in that.”
  • “Nobody can think clearer than Steve Jobs”
  •  “Hey, those are good ideas. Human nature doesn’t change. Let’s use them.”
  • David reads his highlights 5 times.
  • What I like about podcasting is it is completely permissionless.
  • Dyson:  “Listen, it’s easy for me to celebrate my doggedness now. I made $300 million last year, but I’d be lying to you if there wasn’t times where I went inside my house, had my wife look at me in the face like I’m a failure and I’d cry myself to sleep, and I got up and did it again anyways.” Excellence is the capacity to take pain. I apply this to like, “I don’t really feel like working out right now. I don’t feel like doing cardio.” I don’t give a shit, David, how you feel. How you feel is irrelevant. That’s an idea I got from Henry Ford. You read his autobiography, he goes, “I feel sorry for these soft and flabby men that can only do great work when they feel like it.” Essentially, Henry Ford is saying “fuck your feelings.” Henry Ford’s point was a business exists to serve other people. There are going to be days when you get out of bed and you cannot wait to get to work, and that’s great. There are going to be days when you don’t want to go to work, and that is irrelevant because the business is not about you. The business does not exist for your pleasure. The business exists to serve other people.
  • In every episode, Patrick asks “what is the kindest thing anyone has ever done for you?” [I told Zak that fact, I guess he thought it was worth writing down]

Notes From Invest Like the Best: Brian Christian

Link: http://investorfieldguide.com/christian/

About Brian: Author covering humans’ relationship with technology and AI

Q: What advice would you give to people, building careers. We’re in a political cycle now where things like basic income are being discussed. In your view, what are the most defensible areas of human activity, whether that’s some sort of creativity or asking great questions coming up with the objective functions that you then feed the machines? What would you recommend people focus on as they think about either early or late in their career, adding value?

A: There are sort of two ways that I can approach this question. My second book is called the Algorithms to Live By and it looks at things like career decisions from an explicitly algorithmic perspective.

1) Explore/Exploit Trade-off


There’s this paradigm, called the “explore/exploit” trade-off, which is: How much of your energy do you spend gathering information vs how much do you spend committing based on the information? There’s a number of decisions that we face throughout life, that take the form of a tension or a balance between trying new things and committing to the things that seem to be the best. Where to go out to eat, go to our favorite restaurant and we try a new restaurant. Reach out to a new acquaintance we’d like to get to know better or spend time with our close family or best friend. The same thing is true in investing, the same thing is true in managing your time and your career.

Generalizing the Problem

The structure of this problem is an iterated decision that you get to make over and over again. Do you continue to put energy into the things that seem promising, or do you spend your energy trying new things? A clinical trial can have that same structure, and indeed the FDA has been increasingly interested in looking over the disciplinary fence at the computer scientists and saying, maybe those algorithms that you’re using to optimize ads, could also be used to optimize human lives. The way a computer scientist, approaches this question is through something that’s called the multi-armed bandit problem.

The Multi-armed Bandit Problem


In the multi-armed bandit problem you walk into a casino that has all these different slot machines. Some of them pay out with a higher probability than others, but you don’t know which are which. What strategy do you employ to try to make as much money in the casino as you can. It’s going to necessarily involve some amount of exploration trying out different machines to see which ones appear to pay out more than others, and exploitation, which to a computer scientist doesn’t have the negative connotation that it has you know in regular English exploitation meaning, but just leveraging the information you’ve gained so far to crank away on those machines that do seem to be the best. Intuitively I think most of us would recognize that you need to do some amount of both, but it’s not totally obvious what that balance should look like in practice, and indeed for much of the 20th century, this was considered not only an unsolved problem but an unsolvable problem, and sort of career suicide to think about. During WWII, the British mathematicians joked about dropping the multi armed bandit problem over Germany in the ultimate intellectual sabotage. Just waste the brainpower and nerd snipe all of the German mathematicians. To the field’s own surprise, there came a series of breakthroughs on the multi-armed bandit problem through the second half of the 20th century.


Now we have a pretty good idea of what exact solutions look like given a number of constraints, but also what sort of more general flexible algorithms look like. The critical insight into thinking about this problem is that your strategy should depend entirely on how long you plan to be in the casino. If you feel that you have a long time ahead of you, then it’s worth it to invest in exploration, because if you do find something great, it has a long horizon to pay out. On the other hand, if you feel that you are about to leave the casino, then the return that you would get on making a great new discovery is going to be much smaller, because you have fewer opportunities to crank away on that handle once you find it. We should naturally transition from being more exploratory at the beginning of a process to more exploitative at the end. I think that’s an intuition that makes sense, but the math bears that out very concretely.

Observation of “Explore/Exploit” Trade-Off in Real Life


It’s interesting to see this idea that emerges in computer science in the late 50s through the 70s getting picked up by psychologists and cognitive scientists who are interested in human decision making. For example, Alison Gopnik at UC Berkeley who studies infant cognition, has been thinking about the “explore/exploit” trade-off as a framework for how the infant mind works. If you think about how children behave, we have all these stereotypes about children are just kind of random, they’re generally incompetent at things, and there’s a huge literature that shows that they have what’s called a “novelty bias”. They’re relentlessly interested in the next thing and the next thing and the next thing. Rather than viewing that as a kind of low willpower or attentional control issue, you can view it as the optimal strategy. It’s as if you’ve just burst through the doors of life’s casino and you have 80 years ahead of you. It really does make a lot of sense to just run around wildly pulling handles at random. The same is true for being in the later years of one’s life. We have a lot of stereotypes about older people being set in their ways and resistant to change. There’s a psychology literature that shows that older adults, maintain fewer social connections than younger people, and it’s tempting to view that pessimistically. In fact if you build an argument from the mathematics, you can see that older adults are simply in the exploit phase of their life and they are again doing the optimal thing, given where they are in that interval of time. You have psychologists like Stanford’s Laura Carstensen appealing to the “explore/exploit” trade off to make this argument that older adults know exactly what they’re doing and they’re very rationally choosing a strategy that makes sense given where they are. They have a lifetime’s exploration behind them, they know what they really like, they know the people and the connections that matter to them, and they have a finite amount of time left to reap the fruits of some new connection or new discoveries so they’re very deliberately enacting the strategy. The math should predict that, on average, older adults are happier than young people. Despite our preconceptions, and her research bears this out, that appears to be the case.


In business, the problem is very dynamic, which will classify it in the domain of the “restless bandit problem”. Since the research here is cloudier, researchers can invert the thinking to infer the conditions that lead to the business strategies we can observe.

Q: Interesting how this maps on to the life cycles of businesses. In the business context, “explore” might be innovation and “exploit” might be to run the same playbook to earn high returns on capital or something you know works. It seems like you always want to be handing off to a next batch of exploration or innovation, while thoughtfully maintaining something that you know works if you want to survive for very long time.

A: There’s a couple of things that I think are interesting in a business context. One is that implicitly the casino framing that I’ve described assumes that those probabilities are stable and fixed. Of course, we know that the world is not stable and not fixed that things change over time. This is true in our personal lives as well. Your favorite restaurant gets a new line cook and the burgers are not as good. These things shift. This is known as the “restless bandit problem”. How do you play this game when these probabilities are drifting on a random walk?

This is a very interesting case where the theory is not yet consolidated but humans, in practice, seem to have no problem. If you put people in a lab and give them a restless bandit problem, they have no trouble making choices within that environment but we don’t yet know what the mathematics of the optimal solution looks like. So here’s the case where the computer scientists and the mathematicians are asking the cognitive scientists, what are your models for how humans are actually approaching this because there may be some insight that we can use from the theory side. One of the implications of thinking in this way that is particularly relevant in a business setting is if the interval of time you perceive yourself to be on determines the strategy that you should employ, then it should be the case that if you observe someone else’s strategy, you can infer the interval that they’re optimizing over.

Inferring The Explore/Exploit Strategy in a Restless Bandit Problem

Let’s give an example from Hollywood. Most people have noticed, it feels like we’re living through this deluge of sequels, such as Marvel movies. It turns out that this is objectively true. There’s a sea change in Hollywood. In 1982, 2 of the top 10 grossing films were sequels. By 1990 it was six. By the year 2000, it was eight, and I think most recently it was all ten. From that, we can infer that Hollywood has taken a very hard turn towards an exploitative strategy. They are milking their existing franchises, rather than investing money speculatively to try to develop new franchises that will last them into the next few decades. From that, it’s reasonable to infer that movie ticket sales are declining, which turns out to be the case. Hollywood correctly perceives itself to be at the waning time of the golden era of cinema-going. If that’s true, then they really should invest all of their money into just squeezing everything they can out of the existing franchises. More broadly, so you can look at different industries and different corporations to see if they cut their r&d budget. If they’ve given that money to marketing that’d be an indication that they feel that the area has matured or plateaued.

My thoughts

    1. Ahem, asset management, cough
    2. Reminds me of a great Peter Chernin interview where he suggests that every business must be trying to grow new opportunities faster than the the old ones die out. While you must do your best to milk the old, it’s imperative to develop the new.

2) Predicting the Impact of Automation

The second avenue is totally different from this way of thinking, which is just what will the impacts of something like AI or UBI be on the economy. I’m reminded of a McKinsey report on which jobs they thought would be the most robust. The big picture thing that was interesting to me is that it cuts across the traditional class lines. It is not a white-collar versus blue-collar thing. It’s not an upper middle class versus lower middle class thing. It’s very sector dependent. The most resilient or robust jobs at the top end was gardener, legislator, and psychotherapist. I thought that was very fascinating that it’s this eclectic mixture of things. I don’t think of myself as a prognosticator about these sorts of things but my way of thinking about it is that there’s a lot of kind of human machinery around how capital moves and how laws get made. How licensing and permitting happen. It’s still done at a human negotiation level. “I know a guy. I’ll talk to Joe and we’ll sort it out.” I think humans will maintain oversight of these kind of flows of power and capital, even if the actual value is being created by software. So position yourself closer to the flow of that value than the actual creation of the value, which may be counterintuitive.

As far as the question of UBI, I don’t have a great intuition for that. There is already a restlessness in the labor force. A lot of the careers that employ some of the most numbers of people are the most vulnerable. People who drive cars or trucks, people who work in warehouses. A lot of those jobs are just one innovation away, and it’s not clear to me that there’s going to be a political response as well as just a pure economic response. I grew up in New Jersey where there was a robust toll collector union yet they had machines where you could toss your change in a bin and it would automatically sort your change and give you whatever you needed back from that. There was an effective effort to unionize the toll collectors so that you still had a human being in the booth counting out your quarters. That’s an example where it’s not for lack of technology. We had a coin sorting machine, but there was a political process that was directing the actual level of implementation. People will fight to use licensing requirements and regulations to maintain those things. Despite the actual technological capability having radically changed, it’s very hard to know which areas will look shockingly different than the world looks today. Which things will be in some ways shockingly backwards for their time because we’ve had for political reasons to hold the line.

(Reminds me of how rent flows to the owner of a relationship in a competitive market that has been flattened by technology)

Algorithms to make other types of decisions

The mathematics is very instructive, both in a specific way but also has a broader set of principles.

Optimal Stopping Problem

Difference from “explore/exploit” trade-off

One thing that comes to mind is the idea called “optimal stopping”. The multi-armed bandit problem in the “explore/exploit trade off” presumes framing that’s highly iterative. You can pull the handles again and again and again. You can go from one machine to another and back. There are many decisions in life where you are forced to make a single binding commitment that could be anything as banal as pulling into a parking space. It could be something like purchasing a house or signing a lease. It could be something like marrying your spouse. There’s a separate mathematics of cases where you need to find the right moment in time to go all-in, commit to an option, and no longer gather any further information.

37% Rule

There’s this very famous result called the “37% rule”. Let’s say you’re looking for an apartment. And it’s a really competitive marketplace. You’re in a situation where you encounter a series of options one by one. And at each point in time, you must either immediately commit, and then never know what else might have been out there, or decide to walk away and keep exploring your options but lose that opportunity forever. What do you do to try to end up with the best thing possible, even though you, you won’t necessarily know at the time, whether you found the best option that might be out there? There’s this beautifully elegant result that says that you should spend the first 37% of your search non-committally exploring your options. Don’t bring your checkbook, don’t commit to anything No matter how good it seems you’re just purely setting a baseline. After that 37%, whether it’s 37% of the time that you’ve given yourself to make the decision or 37% of the way through the pool of options, be prepared to immediately commit to the very first thing you see that’s better than what you saw in that first 37%. This is not just an intuitively satisfying balance between looking and leaping, this is the mathematically optimal result.

Broader insights on algorithms

Elegant solutions under a range of narrow assumptions about goals and acceptable risks

There are strategies like that that I think are wonderfully crisp in the recommendation they give, but they, of course, rest on this bed of many different assumptions about exactly how the problem is structured and exactly what your goals are. This rule, presumes that your entire goal is to maximize the chance that you get the very best thing in the entire pool, but it comes with a 37% chance of course that you have nothing at all, because you’ve passed. Many people would find that unacceptable. We can go down the rabbit hole of how do you modify this and the solutions get less and less clean as you wiggle the assumptions around.

Intuition for how complex decision-making is can be strangely comforting

More broadly, one of the highest level takeaways for me, from working on the book and just thinking in computational terms about decisions in my own life, is some decisions are just hard. The classical optimal stopping problem, due to a weird mathematical symmetry, is that if you follow the 37% rule you will only succeed 37% of the time. The other 63% of the time you’ll fail, and that is the best possible strategy you could enact in that situation. In a weird way, that’s some measure of consolation because often, in real life, we find ourselves not getting the outcome we wanted. While we can rake ourselves over the coals or try to reconstruct our entire thought process, I think it’s some comfort that computer science and mathematics can, in effect, certify that you were just up against a hard problem. There is some measure of comfort that if you have the kind of the vocabulary to understand the type of problem that you’re facing, and you have some intuitions about the general shape of what optimal solutions look like, then even when you don’t get the outcome that you wanted you can in some sense rest easy because you knew that you followed the appropriate procedure or the appropriate process for dealing with that situation.

Notes from Invest Like the Best: Ali Hamed

Link: http://investorfieldguide.com/ali/

About Ali: Partner at CoVenture fund

His approach

  • He looks at new asset classes that can be hard to value.
  • Alternative financing like asset-backed loans (loans against fruit inventory, app for fast-food chain which allows them to clock employees in and out and allow them to pay employees whenever they wanted for a slight pay haircut)
  • Fee structures depend on the dispersion of manager skill.

Coventure recognized many seed companies never get to Series A

  • Fail to build the planned software to get to market. So Covenutures helps them.
  • Software types who don’t understand the industry they are building a solution for
  • Don’t understand the team they need

How does CoVenture fit into this?

The lesson is that the capital was easier to find than the people who can execute so :

  • Giving young businesses guidance and connecting them to the personnel they need is very valuable.
  • Having a service which serves common needs to many prospective startups is how to scale this idea.

Thoughts on cost of capital

  • If one VC fund can convince its LPs to accept 1/2 the going return because it has the clout to get the best deals that’s another way of saying it has a lower cost of capital. Sequoia can offer lower rates of return because they are less risky than an upstart fund
  • These relative differences in costs of capital sustain significant advantages.
  • A fund may offer a startup cheap financing in exchange for warrants (similar to a convert). This is a bad strategy b/c the performance of the instruments is inversely correlated. If the company takes off and does well, the warrants will perform but a larger fund with a low cost of capital like Blackrock or Apollo will refinance the debt piece for cheaper. In the case where the debt is not refinanced the warrants will be worthless.

Conundrums for seed funds

  • They are expected to “stick to their knitting” and be contrarian. This is practically impossible since being contrarian requires you to exit the seed company in a year or so to a Series A fund which is by definition consensus.
  • Any seed fund of quality naturally wants to raise more money but will find itself capacity constrained so it will drift towards Series A deals which are outside their expertise
  • Pre-seed round is about trying to methodically uncover if you are creating customer value. Revenue can be falsely equated to customer value. For example, you can spend money marketing which will lead to more revenue but this is not the relevant KPI (“key performance indicator”) to test the hypothesis that you are increasing customer value. The seed round is then about trying to find out if the improvements to KPI can scale.
  • Important to have a strong understanding of the role of the round you are in
  • Judgment vs Empathy at the core of a solution
    • Empathy reflects a true understanding of the practical trade-offs that lie within a business problem.
    • Judgment is typically what an arrogant or ignorant outsider looking at the problem prescribes when crafting the solution
  • Technology has made starting companies cheap but scaling is more expensive.
    • Trade-off when raising capital: balancing getting off to a fast start to acquire customers and scale versus discipline and overleverage.

A link to another post with takeaways from this podcast: https://thewaiterspad.com/2018/01/24/ali-hamed/

Notes from Invest Like the Best: Jesse Livermore

Link: http://investorfieldguide.com/livermore/

About Jesse: Jesse Livermore is a pseudonym for the financial blogger behind philosophicaleconomics.com.

3 Methods for Drawing Meaningful Inference

  1. Intuition
    • Benefit: Low cost and readily accessible
    • Costs
      • Downside is noisy especially in ‘wicked’ learning environments
      • Not transparent
    • Traders are high in ‘cognitive reflection’ and stronger intuition
      • Careful deliberation is a hallmark. Studies have shown that people who take too long or too little to decide do worse.
      • Intuition is necessary to pull triggers, but deciding too quickly without careful deliberation leads to poorer inference
  2. Analysis
    • Benefits
      • Don’t need to gather data
      • A model of how something works can handle regime change by having a transparent mechanism from input to output
    • Costs
      • They are always incomplete and “so easy to be wrong”. The fact that we are prone to stories compounds the danger of analysis.
    • Using it responsibly
      • Leave margin for error
      • Validate

3. Data Analysis

    • Benefit: It is rooted in reality
    • Costs
      • Without context can be misleading
      • It is more costly
      • Requires sufficient “trial size” not just a naively high sample size
        • If your samples are highly correlated than your effective trial size is much smaller than you think. For example, all financial data drawn from a single regime or independent coin flips with an unfair coin
      • Data mining and multiple comparison
        • Patterns emerge randomly so this can occur in subtle ways, not necessarily because of fraud or nefarious incentives
          • Suggestions:
            • “Call your shot”
            • Out of sample test
            • Avoid overfitting by testing outcomes against variables that you know should not matter (for example, changing the day of the week an investing strategy occurs on should not change the result meaningfully)

Earnings are a distorted measure

  • Current strict accounting standards around depreciation understate earnings relative to history
    • Old accounting standards did not adjust depreciation for inflation effectively understating inflation and overstating earnings. The market is wise and understood that earnings were overstated and assigned lower multiples during a period of excessive inflation
    • Difficult to compare multiples over time because of this change in standards
    • Depreciation is not just about physical decay of an asset but the competitiveness of an asset. E.g: Inventory of Kodak cameras become obsolete much faster than their physical decay when digital cameras emerged. Any typical depreciation formula would have vastly understated the depreciation of the assets and overestimated the book value.
  • Inflation overstates earnings
    • He calculated the book value of the entire market and keeps track of retained earnings
    • The earnings being overstated means that the retained earnings that remain to actually be either re-invested or paid out to shareholders are understated once adjusted for inflation and compared to history. This means that published return on equity is likely understated because the money being re-invested is actually understated.
    • This is a known issue
      • Studied by prior economists
      • Big corps like Sears in mid-1900s argued for inflation adjusting depreciation because the overstated earnings were weakening their position in labor negotiations
  • Free cash flow handles many of these distortions more accurately
    • Free cash flow “plunges” during high inflation periods validating the distortions caused by inflation on earnings
  • P/IE Ratio (price to ‘integrated equity’)adjusts for all these shortcomings
    • outperforms all measures of valuation including many permutations of CAPE in correlating to future returns.
    • Highly correlated itself to CAPE and tells us that market is on the higher end of valuation which Jesse thinks is structurally justifiable
  • If you want to  dig deeper, OSAM published their joint findings

Why is it plausible that markets get permanently more expensive?

  1. Valuation is a function of the required rate of return to which liquidity is an input. Imagine a pre-Fed wildcat bank. You would not accept such meager real rates of return because you do not have the confidence in the liquidity of your deposit. So much of our required rates of return come down to confidence. The progress of finance has been towards greater networks levels confidence which creates downward pressure on required rates of return. The Fed put is an example of this.
  2. With low growth and inflation (demographics follow Japan, Europe), volatility will be to the downside but the Fed can also act more aggressively without fear of inflation. Higher structural valuations may be reflecting this market understanding.
  • Implications
    • Trend: we are seeing less trend formation and more whipsaws. Speculative but possibly due to Fed put. This has led Jesse to try to restrict his trend strategy to when it is most likely to work (ie fewer whipsaws). Historically, trend’s alpha has come from times of large market drawdowns. So he uses the trend strategy when it coincides with fundamental recession indicators. He admits the sample size is small so the research is thin and probably overfit. Best recession indicators:
      • Retail sales
      • Earnings
      • Unemployment trend
      • Housing starts
      • Industrial Production
    • He is agnostic on trend. Thinks it works but is worried about it.

Valuing the Market

  • CAPE and other statistical attempts to correlate valuation with future returns suffer from small trial sizes. Markets cycles so multiple years in succession are really just a draw from the same regime (overlapping data sets)
  • An alternative method of using the relative supply of assets to predict future returns. Derived from his work. My own notes on his full post are here.
  • Interesting inefficiency which hints at the validity of this: There are some egregiously overpriced preferred stocks carrying low yields, are callable, and sit in inferior positions in the cap structure. The only reasonable explanation is they are in relatively short supply. It’s a “rare baseball card”. The explanation issuance of preferred stocks has declined faster than investment demand for yielding securities. In other words, the demand for asset allocation in relative proportions has not changed as much as the composition of supply has changed.
  • Being biased towards flow-based explanations of pricing myself, I find this idea very compelling
  • His conclusions without proof: supply matters and there are inefficiencies. The presence of the inefficiency doesn’t surprise me since constrained supply means fear of squeezes and lack of scalability. Arbitrage or relative value trading is less likely to close the mispricing.

How using OSAM data, he tried to gain insight into how factors work

  • Value and momentum work very differently
  • His “Factors From Scratch” work with OSAM (O’Shaugnessey Asset Management)
  • My own notes on his post as well as the related OSAM work on “Alpha Within Factors”

Notes from Invest Like the Best Podcast: David Epstein

Link: http://investorfieldguide.com/epstein/

About David: Best-selling author of The Sports Gene and Range: Why Generalists Triumph in a Specialized World.  A former journalist at Sports Illustrated and ProPublica, David is also known for his talks on performance science and the proper use of data across many fields including sports, medicine and natural sciences.

Transcription: Otter.ai

Epstein’s Research Process

  • 10 journal articles a day for 1 year; hire translators for foreign journals
  • Consults with statistician 

A weaker  10,000 hours idea (Tiger vs Roger Problem)

  • Contrary Research Favoring Breadth

Showed elite athletes did not require a head start in deliberate practice. More likely specialization was delayed. A long sampling period exposed them to many sports which allowed them to better match their abilities to the sport.  Evidence: Success of Olympic talent transfer programs in other countries


Lots of variation in how people respond to stimulus. True of medicine. True of training. You baseline ability is uncorrelated with your ability to improve with training, which makes extrapolating difficult. “So much to gain from fitting people into the right sport”

  • Supporting research flaws
    • “Restriction of range” problem with the study of 30 violinists. When you squash the range of a variable that is correlated with the dependent variable you risk understating the correlation with the restricted variable. In this case, the sample was violinists who had already been accepted to a famous academy. We have squashed their innate talent even though it likely has a wide range. Likewise, if you studied the correlation of height to points scored in basketball for NBA players you find a jarring negative correlation but that is because you are selecting from a sample of abnormally tall players, to begin with. You’ve squashed the height variable, which would lead people to think that height has no impact on points scored. 
    • Inconsistent numerical data, no estimates of variances on variables, poor statistical inference


  • When to be like Tiger?
    • Kind learning environment
      • Fast, accurate feedback
      • Discrete turns
      • Well defined rules
  • When to be like Roger? 
    • Wicked learning environment
      • “Martian Tennis”: You see people out there playing, something’s going on, you don’t know the rules, it’s up to you to introduce them. And they could change at any moment without notice. And that’s the situation that we’re actually in for most of the things, the complex things that most of us care about.”
  • Most surprising study in Range: air force study is a natural experiment. Professors who were the best at causing students to do well in their own class do well on the test, (ie overperforming compared to the baseline characteristics they came in with) systematically undermined those students “deep learning” (performance in the follow on courses).
    • Professors taught narrow performance to optimize for their own exam to their detriment for overall learning. They undermined the students “making connections” framework. Professors failed to learn themselves because the students who would feel rapid progress would rate them highly. “really wicked feedback”
    • Professors themselves are incentivized to maximize for short term evaluations which have impaired their ability to teach frameworks that students can apply in novel situations.
    • Professors who did not teach to the test taught broader concepts relying less on “using procedures” knowledge. This type of knowledge is most effective in kind learning environments where possible tasks and choices are restricted. 
  • “Closed skills”: techniques that you can teach very quickly and see an advantage. these are temporary advantages as people with broader frameworks eventually catch up but have brought wider understanding as well.
  • Around the world, we are performing better on “culturally reduced tests” (meaning tests that are not influenced by formal learning). Our collective performance should stay stable on this portion of tests but in fact, our performance is increasing. Known as the Flynn effect. Flynn speculates “we have moved to a world where we are used to classifying things to grouping things instead of being stuck with lots of concrete knowledge and, and factual knowledge.” Pre-modern people did not have much need for classification, but the modern world relies heavily on this ability since we’re constantly laterally translating knowledge to different areas we’ve never seen. This ability to have knowledge that we don’t have from hands-on exposure is really important.

(Me: Don’t be fooled by a sense of progress when the task you are excelling at is not varying. Being able to match abstract models to a correct strategy is a more valuable goal and benefits from practice in dealing with variation. )

  • Learning hacks supported research but ignored by the media (3 out of 5)
    • Testing: Test people before they have a chance to study. It primes your brain and exploits the “hypercorrection” effect — our tendency to remember the correct answer to a question you tuned out to be wrong about
    • Spacing: Intervals between practice make learning stick longer. A useful technique is to learn several subjects at once. Switching provides natural breaks.
      • “Difficulty isn’t a sign that you’re not learning but ease is”. To maximize stickiness you actually want to re-learn something just after you have forgotten it! Your steepest learning occurs when the task is difficult.
    • Interleaving: Mixing types of problems will extend the time it takes to learn one type but improves broader ability to match approach to the type.

Grit is Misunderstood

West Point study: the survey which measured grit was more predictive than the conventional metrics for predicting who would complete Beast Barracks (physically demanding module of training). This grit survey was applied to other domains like the Spelling Bee championship contenders. Grit appears to have a measurable, effect independent of other variables. 

  • Problem with these studies is they suffer from the same “restriction of range” problem
  • The measured effect is significant but small. Much smaller than what companies are interested in testing for. 
  • Sample of people is dedicated to a short term task like winning a spelling bee or completing their training. Very difficult to generalize to a wider measure of this individuals’ determination when the task is less well-defined
  • When zoomed out, we find that attrition is a poor proxy for ‘lack of grit’. Attrition is occurring in a time when people in these studies are going through periods of rapid self-discovery and personality change during their early 20s (this is the peak change period in our lives) and re-assessing as they search for “match quality”. The degree of fit between work, interests, and ability. 
  • Grit is not necessarily stable. It seems to vary within the same individual depending on the context or task.
  • In general, the study of grit is has been contained to very short term, narrow environments

Avoiding Premature Optimization

Paul Graham admonishes against working towards some projection of future self when you are young since what you can conceive is too limited because your experience is limited. Too risky to throw yourself on a path based on such a limited hunch. 

  • Our personality is only .23 correlated between teen years and middle age
  • We learn by doing then reflecting, rather than introspecting to form a theory about ourselves. Frequent trial and error is a better way to decide which direction to go.
  • Harvard’s Darkhorse Project studies how people match careers. The students who matched best excelled in short term planning.
  • Economist Robert Miller refers to the “2 arm bandit process”. Metaphor on a gambler pulling levers in a casino, getting feedback, before focusing on a game. He advocates jumping into high risk, high reward fields early because you learn the most from them. That informational signal is a faster input into your decision path. 

Opportunity to recombine

Information including specialized information is disseminated more widely and quickly than ever and at an increasing rate giving people greater opportunity to recombine from all the available information. 

  • Parallel trenches: “everyone’s in their own trench and not usually standing up to look over at the next trench even though that might be where their answer is” (this is why he hires translators)
    • Gunpei Yokoi — Nintendo employee who used lateral thinking to recombine older, cheaper, “withered” technologies to create products including the GameBoy. The GameBoy competed with more advanced products on the basis of its ease and durability.
    • Yokoi viewed cutting edge technologies as zero-sum arm’s races fought by specialists. “Many more opportunities to take this stuff that was already well known that everyone was looking past and recombine them in new ways”
    • We are in an age where its feasible for a generalist to crowdsource specialists in novel ways which allow them to outperform specialists themselves (Kaggle has been able to solve problems that have stumped NASA)
    • Specialists perform better when the next steps are clear and the path is more obvious. The right mix of generalists and optimists depends on how well characterized the problem is. 
    • 3M has many interesting examples and lateral thinking is entrenched in their DNA. They maintain a “periodic table of technologies” so its teams can use their awareness to recombine. 
  • Superman or Fantastic Four
    • Metric that best predicted a comic book creator’s potential to write a blockbuster was the range of genres they covered, not reps or experience. 
    • In addition, they found that a team of writers with combined experience in diverse genres outperformed a single writer unless the single writer was fluid in at least 4 genres. “Individual in some ways is the best unit for integrating information” although a diverse team is next best. 
  • To a specialist with a hammer “everything looks like a nail”
    • Specialists continuing to administer procedure in face of evidence that it doesn’t work
      • Scandinavian meniscus placebos undermine the benefit of surgery 
      • Practices that make intuitive sense (“bioplausible”) but poorly supported by evidence
        • When outcomes are poor surrogates for health: stents for otherwise healthy people with a narrowed artery do not reduce their heart attack or mortality rates. A wider artery is not a perfect proxy for the desired outcome because “There’s a clogged artery, how could opening it up not work. It’s got to work except it turns out the body’s much more complicated than like a kitchen sink, and we didn’t design it. And it’s the disease is much more diffuse.” (Me: any counterintuitive but effective remedy that works by using a seemingly oblique strategy is at risk of confusing surrogate markers for the outcome. Hormetic processes, body’s use of iron, etc).
  • A better way forward
    • Need generalists to work with the specialists for a more zoomed out view which better aligns practice with objectives. Medicine seems especially prone to the errors and resistance to reform that can result when an inordinate amount of specialists populate a “wicked” learning environment
    • Medicine and similar “wicked” environments are “devilishly” hard. It will take generational change as the entire approach to “how information is evaluated and how scientific thinking works”. Need to de-specialize a bit and increase breadth. Statistical understanding requires more than “hitting buttons on a statistical program”
    • Freeman Dyson has said we need more birds in medicine. “Frogs are down on the ground looking at like a very narrow area of the ground, the birds are up. They don’t have a good definition on the ground, but they see the bigger picture. And I think we need to make the medical ecosystem more friendly to some of these birds who are looking at the outcomes we actually care about, not just those surrogate markers or did I fix the meniscus?”

Invest Like the Best: Andy Rachleff

Link: http://investorfieldguide.com/andy/

About Andy: Partner at Benchmark Capital and CEO of Wealthfront

Benchmark Capital started in 1995 by 5 equal partners (including Bill Gurley)


  • Turn your opponents biggest strengths to weaknesses
    • The biggest competitor at the time was Kleiner Perkins and ‘the best venture capitalist that ever lived’ John Doerr. Benchmark would woo portfolio companies using a team approach since not all Kleiner Perkins companies had access to Doerr.
    • The second strength of KP they flipped was the promise of doing business with other portfolio companies. Benchmark painted this advantage as an obligation they were free from if they joined Benchmark. Benchmark took a backseat to the portfolio companies management and did not demand to be the chairman of the board.
  • The other interesting thing they did was not allow the partners to ‘suck up the economics’ in the room. As soon as partner’s felt it was time to relax they needed to step aside for the younger team to be able to step up.
  • “Putting the gun in the other person’s hand”
    • Partner Bruce Dunlevie philosophy of trustfully dealing with people and if the person took advantage of him he would not work with them. This technique would usually engender trust and good faith in others

Product Market Fit

  • Products that are ‘bought not sold’. Delighted customers demand the product.
  • Running a business with such a product leaves lots of room for operational error and explains how a “25 year old can run a billion-dollar business”
  • The first book on the topic was Steve Case’s “The Four Steps to the Epiphany” which his eventual student Eric Ries would update and improve with “The Lean Startup” These books used the scientific method to approach business
    • A ‘value’ hypothesis needs to be proven
    • A ‘growth’ hypothesis is validated if growth is exponential and organic (ie word of mouth).
      • Growth hacking via experiments and A/B testing.
    • Typical businesses focus on the who, where and what and iterate on the what. Great technology companies ramp a new technology by finding the ‘who’. This is often not obvious and leads to non-consensus outcomes. This is now commonly understood (Me: reminds me of ‘theory of demand aggregation’)

His role as operator vs investor

  • Now as Wealthfront of CEO vs an investor a few points:
    • The skills aren’t necessarily transferrable
    • He speaks less on boards realizing how little perspective he has compared to management
  • “Crossing the Chasm” by Geoffrey Moore first book that discussed product adoption cycle and diffusion of innovation

Wealthfront features that grabbed my attention

  • Peer reviewed rules based strategies
    • Tax loss harvesting (added 1.8% pa). Automating it in software allows more consistent application of decades-old strategy (Me: Twitter discussions suggest this is highly overstated)
    • Tax loss harvesting within an index adds 25-50 bps pa. This includes selling index components that had losses and buy correlated names to maintain exposure
    • Portfolio line of credit leveraging risk-based margining. For accounts >100k this provides access to cheap loans
    • No hedge funds or expensive alts bc of the Grouch Marx “I don’t want to be a member of any club that will have me”. The best institutional investors are long term, not performance chasing (ie endowments and charitable foundations). The worst of the funds can’t access them so they would be the only ones open to listing on retail platforms. Classic adverse selection.

Business strategy not always best self strategy

  • In business, amplifying what you excel at has a better payoff than improving weaknesses. He asserts that this is also professionally true at the career level since differentiating expertise is a large determinant of a person’s value-add. He mentions that this is not the same strategy one should employ in their personal life, where boosting your weaknesses as a person is very valuable. In professional life, learning from success can certainly be more important than learning from failure. “I’m not hiring you because of what you can’t do. [I’m hiring you] because you have learned some tricks!”
  • Well-rounded people are interesting to talk to but not necessarily the best teammates in a business.

Invest Like the Best: Brad Stulberg

Link: http://investorfieldguide.com/brad/

About Brad: Performance coach and author of Peak Performance


  • Studying brains we find that people can summon extreme abilities if they have core beliefs which override the fear responses in their brain (is lifting a car off of a person trapped underneath). The importance lies in having core beliefs or purpose.
  • The Growth Equation: Stress + rest = growth
    • Need the right amount of stress/stimuli. Too much stress is overwhelming, too little leads to no adapting.
    • Just manageable challenges are those which are just outside your comfort zone. It’s self-defeating to onboard too many of these at once.
  • Mechanics of creativity
    1. Immersion: this work is stress
    2. Incubation: stepping away (this is the rest)
    3. Creative insight…this tends to happen after a period of rest…end of a vacation, in the shower, taking a walk.
  • Studies show deep work cycles are most effective in 45 to 90 min blocks followed by 15-20 min breaks.

Practical Tips

  • Whether you perform better in the am or pm is largely biologically determined. Manage your energy not time. If you are better at focused work in the am, then reserve that time for that.
  • Time in nature or outside is shown to improve stress, physical, cognitive markers.
  • Study of air force cadet squadrons showed that group performance was more influenced by the lowest common denominator as opposed to the leading performer. The group has more to gain from eliminating bad attitudes than from enhancing leadership.
  • Fatigue happens in the brain, not the body. Your central nervous system slows you down. How to overcome this? When interviewing peak performers you find that they are thinking about a larger purpose than what they are doing. So marathoner thinking of his family can override his brain’s fatigue signals.

Invest Like the Best: Jason Karp

Link: http://investorfieldguide.com/karp/

About Jason: Founder and CIO of Tourbillon Capital Partners

Growth of private markets

  • Smaller supply of scalable opportunities and increased competition
    • Less companies going public and staying private for longer. 50% of listed companies compared to 20 years ago (mostly M&A and lack of IPOs as opposed to bankruptcy)
    • Unicorns able to get unprecedented amount of private funding
  • Increasingly competitive public markets for short term performance.
    • Data from prime brokers shows that over 90% of flows driven by non-discretionary accounts (CTA, systematic, quant, passive).
      • He argues that this has caused large dislocation opportunities in past 5 years in valuations when historically values typically take no longer than 3-5 years to converge to fair valuations.
    • Short term pressures on advisors (quarterly performance, fee compression) incentivize move away from mark-to-market and costs of wooing performance-chasing allocators.
    • Information edges are gone or not scalable
      • Data points include the sheer number of funds and books referencing ‘value’ investing.
      • Growth of quant platforms like Quantopian.
      • The growth of data sets favors short term trading which is the domain of quants.
      • Ratio of sell side analysts to public stocks is at an all-time high
      • Reg FD neutralized many active managers’ edge b/c large commission paying funds could no longer get privileged info

How to compete in a competitive, expensive market

  • Look for real business growth. If a company is growing faster than it’s implied multiple there is a margin of safety that can ensure an investment in the event of multiple contraction
  • The deep value game is difficult since it’s a basket of adverse selection. A small minority will turnaround their distressed situation. It’s easier to look for ‘value’ names that are growing than it is to pick ‘deep’ value names to revert
  • Align investors with long term horizon as a structural edge. The only way to profit from longer-term dislocations. The current environment has a historic high correlation of growth to momentum which is a trend that continues to pay off, in turn, reinforcing the exit of value flows and increase in quant/momentum flows setting up a historic relative value opportunity.
  • Understand cyclicality and stickiness- He calibrates the riskiness of a business with the nature of its sales. Stickier businesses are less risky (ie high consumer daily engagement)
    • Fashion is unpredictable
    • Cyclical’s are dependent on GDP
    • Staples are more dependable

Invest Like the Best: Dan Egan

Link: http://investorfieldguide.com/egan/

About Dan: Managing Director of Behavioral Finance and Investing at Betterment

Uses insights from behavioral economics to nudge more adaptive behavior

Design a better dashboard

  • speed bumps on mobile platform to discourage impulsive trading
  • ‘tax impact preview’; the magnitude seems to be less influential than the outright presence of this speedbump; interesting side note is that this has a larger effect in Census areas known to be Republican
  • When messaging entire user base, they would sometimes prompt action (ie selling stock) in users who would have done nothing; they learned to send messages to people who were about to commit an action. This improved efficiency of the message by eliminating ‘false positives’
  • Displaying information which aligns focus with objectives instead of just defaulting to emotionally charged performance metrics or even the red/green triggers we are used to seeing
  • Recent related WSJ article pointing out the tyranny of what is displayed to you: https://www.wsj.com/articles/the-high-financial-price-of-our-short-attention-spans-1540174321

Designing a system informed by your beliefs when you are rational to pre-empt decisions you might make when emotional

  • Cranky judges: parole sentences are heavily influenced by time since their last meal. For such important decisions, we need a better system. Doctors’ diagnosis subject to the same effect!
  • Building custom tailored indices aligned with people’s stated goals

Notes from Invest Like the Best: Richard Craib

Link: http://investorfieldguide.com/numerai/

About Richard: Founder and CEO of Numerai; hedge fund which crowdsources machine learning algos

What does Numerai do?

Numerai runs open contests where they supply scrubbed financial data which is unlabelled and respondents submit ‘targets’. They do not need to submit their algorithms. Contrasting with Quantopian, the system doesn’t rely on trust. You preserve your IP which encourages collaboration and also has zero interpretability.

Problem is set up in a specific way, data is highly normalized, and doesn’t seem to be too non-stationary. Respondents compete on the predictive value of their machine learning algos.

Important criteria for crowdsourcing to be effective

  • Diversity of opinion
  • Non-overlapping (ie low correlation )
  • Decentralized


  • Model Edge
    • Users are using more unique methods than just neural nets, random forests, and support vector machines.
    • There is diminishing returns to model improvements but because you can crowdsource not hire it is cost effective to seek the best signals
  • Data Edge
    • 2 Sigma claims that extremely clean data is a huge driver of edge
    • RenTec reportedly does not use machine learning, and much of their edge is surmised to be a long, unique data history. In fact the data may be >> the talent. [Me: Interesting as a moat to think of unique data sets trading firms can have: executions, unstructured data such as voice trades that are passed on vs executed]
    • They do not introduce the complexity of unstructured data
    • Data normalizing is important as well as normalizing the targets to risk-adjusted returns
  • Ensembling the models via staking
    • The info content in how users staked their entries with NMR crypto exceeded the conclusions from Numerai’s extensive research into how to ensemble the models to construct portfolios. Craib, being friends with many of leaders in the crypto space, saw that bitcoin could be used to trustlessly pay contestants. NMR evolved as a smart contract to enable the entire staking mechanism, providing Numerai with a powerful ‘skin in the game’ filter. It also served to discourage spam since there is a cost to submit an entry.
      • The Sharpes of the strategies with staking was about 2 vs 1.5 for unstaked. The ‘floor sharpe’ was 1 since the data set was already high quality
    • They pay about 5k per week in prizes. $8mm to date.