Kathleen Mercury on Board Gaming With Education Podcast

Link: https://www.boardgamingwitheducation.com/games-in-schools-and-libraries/

About Kathleen: Educator with a special focus on teaching gifted students game design (Link)

Transcription: Otter.AI

I incorporated Kathleen’s presentation to these notes for the sake of consolidation.


Overview

Kathleen believes:

“Happiness comes from being able to choose the life you want to live.”

To empower students there are 2 anchor ideas:


Be Producers Not Consumers

…what I want more than anything for my students is for them to be creators, not consumers…The only thing I care about is what ideas they have, and giving them the tools where they feel empowered to take on big complex challenges where they have no idea of what the final product will be, but that they can build in and learn the skills and confidence that they can hopefully get themselves there. That’s what I care about because if I can get them to accept that and do that, then they can pretty much take on whatever challenges come their way for the rest of their lives.

Bias Towards Action

For those familiar with the Silicon Valley ethos of “Move fast and break things” this will be familiar. Despite, her midwest roots and home Kathleen’s thinking has been heavily influenced by the Stanford D-School.

…probably the biggest thing that’s helped me is the Stanford design school’s method of prototype development. I went to a design-thinking boot camp, and the design mindsets that were presented as far as when you’re wanting to design something for someone else, and how you should think about it. Here’s how you should approach it. And it was so different from what I was doing, but it was just one of those things where it’s like, oh my god this is 100%, what I should be doing and it completely pivoted everything that I was doing. For example “bias towards action”. Instead of just thinking about something just start doing it. Rapid iteration making prototypes fast and cheap so you can get them on the table so that you can fail quickly see what works, see what doesn’t work quickly and so you can make more versions of something even faster.

It’s designed to keep them moving quickly so that nothing becomes precious and nothing becomes so sacred that they won’t get rid of it. And I think for me as a teacher, that’s really helped me and also helped me as a game designer in terms of trying something getting it out there, seeing what happens getting feedback on it and making improvements to it as well.

Lessons From Teaching


On using games in learning

  • I think for a lot of gaming experiences in the classroom, having everybody involved at the same time, really, really matters for success.” (Party games are a good tool for this)
  • A good teacher can make a lot of things fun. Sparks a love of learning.
  • Bridging the abstract to concrete
  • Critical Thinking
  • Information more sticky/accessible. Increases connections.
  • Boosts engagement & connections (made me think of how a local teacher used Pokemon cards to bring the boys and girls in 1st grade together)

On kids having different abilities

  • Everyone deserves to learn at their level every single day that’s just one of those tenets that I just hold. If you’re doing something where their disabilities or inabilities become apparent to others. I think you have to be really careful about how you handle that. As far as you know what you’re willing to do to, you know, protect them to take care of them because if they’re stressed out and embarrassed.

  • Approach to gifted kids:

    1. If you don’t give gifted kids problems to solve, they will create their own.
    2. They need to learn how to struggle and work through it.
  • Heterogeneous groupings can protect kids by partnering up.

  • But homogenous groupings have advantages too.

For my gifted kids, a lot of times when that happens, they’re always like the ones that are like spread out amongst the other groups, and then they put all the spread out all the middle kids and then they spread out all this sort of low kids and pardon me for speaking in broad brushstrokes but I am. And so a lot of times they never get chances to work with each other. And one thing that research shows is that when you let kids have similar abilities work with each other. Everyone gains, because the kids on the middle step it up, and the kids on the lower end also step it up, even if it’s like one notch higher, you know, that’s okay for them, you know they’re using their abilities and what they know and trying to push themselves up to be more competitive as well

  • Why the emphasis on points in winning is redundant.

Points are used to ultimately communicate your position in the game to other people. And if we’re playing a game that is just to be, you know, a review or something like that I don’t care about the points at all. And so, what I will often do is even if they get points, or if one team starts to get a blow out. I will, you know, do something like say “this is a 20 point question”, and then somehow I manage to make it so that kids on the other team get those points, or I start awarding ridiculous points my cool you just got a puppy. So drop puppy up there on the scoreboard.  

Why teach game design?

  • Develop analytical, practical, and creative thinking skills

  • Autonomy and collaboration
  • Teaching game design is teaching to orient towards an internal scorecard not an external one

That quantitative checkmark feeds into a lot of the programming that we’ve already done with kids as far as you know letter grades and standardized tests and success is 100% and success is, you know, an A plus is, you know, and I think for a lot of my students especially having to sort of break that mentality. A lot of what I do in teaching game design is here is this problem that cannot be solved, or notions like that. Here is this problem that you will have to you have to define the problem. You have to figure out how you’re going to solve this problem, you’re going to design your tests with these resources in terms of you know how close are you to solving this problem and you’re gonna do this again and again and again, you’re going to make a prototype you’re going to put it in front of other people, they’re going to play it, you’re going to get their feedback, and then you’re going to take those ideas, and that, you know, good, bad, the ugly. Incorporate that into your next design so that when that hits the table hopefully it’s better. Thinking of it as an unfinished unending hopefully upwardly ascending sort of cascade. See that process as a real process reflective of what life will be, I think is really important, because for a lot of my kids, you know they’ve learned what successes is and it’s an A+. I’m trying to show them that if you want to do anything cool, there will never be A+. You will never be finished. You will always just have to try to do your best to put out your best possible effort, listen to other people, and hopefully make that idea better and so that’s why I teach game design.

The reason why I teach game design is a teaches them this process of thinking design, thinking hands-on, trying to create solutions and learning how to see successes incremental progress, not as I finished I’m done.

We do talk about how it can be finished and not perfect and that’s really important for a lot of them. That you can have something that is unfinished. And you can see it as successful because you did try to make it better, even if you don’t think it’s better. And that’s really really hard for them to accept because it goes against everything they’ve always done

  • An antidote to results-based thinking

I honestly try to minimize any type of objective points in any kind of game situation as much as possible, because no one should ever be blamed for losing for their team, and I honestly don’t want anybody to be, you know, the fourth batter to just hit the Grand Slam home run and they get all the credit, not the people who also got on first, second and third.

  • Be thoughtful about when points matter

It does make sense to have kids have scoring that matters, but I think you have to really ask yourself, is this that time.

  • Not having grades at all doesn’t really work

And if I had my choice I wouldn’t do grades at all, but this is the world we live in and I have to actually try tried one year to not give out grades and our gifted class. There’s some unintended consequences there but there you go. We tried it once. As much as we wanted it to work it didn’t really work.

Projects Kathleen and Dustin Are Pushing Forward

  • Game Database To Aid Teachers looking to use games to augment material

    I think that something you touched on and I’ve been kind of thrown around in my head is creating some sort of database where teachers are teaching a unit on something and they can go on there and see what kind of games they can use in their class to either tackle review or tackle preview and concepts of the whatever material they’re learning. It would be really good for teachers to find like a resource where they can just go to, and save time and kind of have this lesson plan that they can use.

  •  Formalizing standards

Look at the curriculum that I have and formalize it a little bit in terms of standards that it’s meeting. That’s something that people ask me about that I haven’t really ever have had to do. And I think it’s something that I’m interested in one because it will make it even easier for people to use these resources in their classroom but it also. I’m really like thinking about the idea of what are the things that people could do to get their kids to think like game designers to use design thinking, using games, what would be appropriate, you know the early elementary level, the later elementary level, the middle school level, the high school level. So that if somebody wants to do something with game design in the classroom, they’ve got a better chance of success. That they’re not over-shooting or under-shooting what their kids are able to do but also in terms of tying this, you know, more specifically to actual curriculum. Then it can be easier for their administrators to use.

Notes from Capital Allocators: Basil Qunibi

Link: https://capitalallocatorspodcast.com/2018/03/04/novus/

About Basil: Founder of Novus which does analytics on managers and portfolios trying to disaggregate sources of edge/skill and quantify obliques risks such as crowding and liquidity.


Early Days

Initial Research

  • Early 2000s, Basil began studying underutilized data sets :
    1. Public filings (ie 13F, 13D etc) domestic and abroad
    2. Monthly exposure reports from managers
    3. Position level reports when available which provided full transparency

Meeting Resistance

  • “People hear with their amygdala”
    • Amygdala is the emotional center of the brain. His analysis was perceived as a threat as opposed to being received with commensurate rationality. Often his analysis contradicted narratives or perceptions

Novus’ Start

  • Initial Novus Products
    • Individual Manager Report
      • batting and slugging avg, long/short attribution, geographic/industry exposures
    • Overlap Report
      • Calculate overlap between candidate managers as well as the client allocator.
    • Aggregate/Look Thru Report
      • Analytics on an allocator’s entire portfolio of managers combined
  • Novus Framework Product: aimed at distilling manager skill, positioning (based on private data on 1500 funds)
  • Product aims to be “Moneyball for Allocators”

 

Moneyball for Allocators: Decomposing Manager Skill

Systematic Factors

Factors that depend on the broader market.

  • Exposure Management: How does gross exposure variation influence return? On average detracts 200 bps/yr
    • Manager’s variation in this aspect is not persistent; deviation from mean is mostly luck
  • Capital Allocation: How does exposure to capital structures or sectors contribute to performance?
    • This factor is also typically negative for most managers

Intrinsic Factors

More persistent and where the potential for alpha lies

  • Security selection: items picked out of the sectors or geographies
  • Sizing: This is compared to a control of equal-weighted portfolio
  • Trading: Tactical trading seen in flipping positions

 

Using the Framework to Make Better Allocation Decisions

  • If the allocation thesis for any fund is simply returns it will invariably hit a bad run. Mapping a fund’s skill to the environment is a better basis to decide whether to cut or increase exposure to the fund than simply returns.
    • For example, if the majority of a fund’s monthly alpha comes from trading but the data shows that the volume in the fund’s positions has been steadily dropping, it may indicate a lack of opportunity to capitalize on the fund’s strength.
  • The framework allows an allocator to evaluate a fund based on its stated intentions. If they claim they have an edge in security selection they can be rated on that dimension.
    • This shifts the evaluation from “thinking in T to thinking in N”.
    • It doesn’t make sense to compare a fundamental value strategy vs a high-frequency strategy at the same time horizons.
    • Large sample size of trades without any single trades dominating the results is easier to evaluate than strategies that make a few concentrated bets.
  • Benefit of increasing transparency also accrues to good managers since the story is about more than returns and the data can reveal that a bad run is just bad luck (ie losses coming from extrinsic non-persistent factors)

4 Measures of Crowding

  1. Conviction: largest position sizes amongst managers; names reported as > 5% positions within a fund
    • Best performing factor over time
    • From Faber’s interview with fellow Novus co-founder Altshuller, they constructed a ‘Conviction Index’ with Barclays based on impressive and still persistent performance of stocks which rank high on a sort of high conviction positions by hedge funds (stock > 7.5% of portfolio concentration)
  2. Concentration: How tightly held are the shares?
    • This is also a positive factor
  3. Consensus: how popular is the name?
    • This factor underperforms over time
  4. Crowdedness: How consensus is the name AND how much daily volume do they represent? “How crowded is the theater; how big is the exit?”
    • This is actually a factor which performs well over time but has massive skew

Scaling up as AUM Grows

How you can expect AUM growth to impact manager performance?

  • Increasing number of positions
    • If the manager has skill in sizing positions this will ‘flatten’ the alpha
  • Moving into higher market cap names
    • If the manager has skill in small cap, this is style drift
  • Increasing current position sizes
    • This deteriorates liquidity; while adding it can be a positive feedback loop but this is a double-edged sword. This is the most dangerous form of scaling if liquidity is overestimated

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

Description

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

Background

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.

Solution

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

Psychology

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.

Business

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 Capital Allocators: Charley Ellis

Link: https://capitalallocatorspodcast.com/2018/07/29/ellis2/

About Charley: Charley Ellis is the founder of Greenwich Associates, author of 16 books, and one of the most sought-after industry advisors worldwide.

Otter Transcript (Link)


1. The Case for Indexing


The Evolution of Markets

Investing Environment 50 years ago

  • A device that reported the last, high and low prices and trading volume was cutting edge tech.
  • The money game used to be like stealing candy from children. 10% of trading at most was done by institutions.

Sparse Institutional Players

  1. Statewide branches were allowed but interstate branching was not allowed for banks. So every mid-sized and larger city had two or three trust departments.
  2. The second group would be the major insurance companies in Hartford
  3. There was a little bit of mutual fund activity up in Boston, a little bit New York and there might be some on the west coast.

Abundant Retail Investors

Were they hard to beat? No way. They were easy to beat. The secret to successful active investing is to have what’s called, it’s a little bit nasty term, but called “willing losers.”

  • Nice people who bought or sold once every year or two, usually an odd lot because that’s how much money they had. About half the time it was AT&T.
  • They bought because they’ve been given a raise or a bonus or an inheritance. And they sold because they’re sending kids off to college or buying a home or some other sensible purpose that had nothing to do with what’s going on inside the market.
  • They didn’t know very much, but that didn’t matter. They were buying a few blue-chip stocks that they read about in magazines.

Investing Today

The Talent Boom

  • Analysts

The number of people involved in active investment management, best I can tell, has gone from less than 5000 to more than 1 million over 56 years. A major securities firm might have had 10 or a dozen analysts back in 1962. What were they doing? They were looking for small-cap stocks and interesting companies that might be interesting investments for the partners of the firm. Did they send anything out to their clients? No, not anything. Goldman Sachs didn’t start sending things out until 1964 or 1965, and there was just one of the salesmen thought it might be interesting idea to put out. Today, any self-respecting security firm is worldwide with analysts in London, Hong Kong, Singapore, Tokyo, Los Angeles. 400, 500, even 600 people trying to come up with insights, information, data that might be useful to clients. Anything that might be useful. Demographers, economists, political strategists, portfolio strategist and every major industry team. Every major company will have 10, 12, 15 analysts covering that company. And of course, then if you go to the specialist firms, there are all kinds of people and then there are intermediaries with access to all kinds of experts in any subject you might like. We’ve got 2000 experts. And anytime you want to talk to any one of them, just let us know. Glad to provide so an unbelievable, flourishing amount of information of all kinds, all of which is organized and distributed used as quickly as possible. Instantaneously, everybody.

  • Global

The second thing is, Well, I hear about the CFA program. How’s that working out? Well, it’s off to a pretty good start. 135,000 people have passed the exams and another 250,000 people in the queue. The biggest crowd is the US, the second biggest crowd is China. The third biggest crowd is India. Its global. Yeah, of course its global. It’s all over the place — people want in on the good thing.

  • Trading

99% of trading is done by computers. Pros. We went from 3 million shares a day in 1960 to 5 billion today. And they know everything, you know, as soon as you know, and you can only buy from them and you only sell to them. How good a chance do you have of having this whole workout? And the answer is not very good.

Why so much competition?

  • Well, first of all, the investment world is probably the highest paid line of work for large numbers of people. It’s wide open to everybody. 
  • You don’t have to retire at 65 or 70. You can keep going to 80 or 85.
  • So the benefits around the edges that are quite nice. Anybody in the investment business knows once in a while, maybe once every 10 years, some unbelievably attractive opportunity, not really right for clients because it’s too small to specialize. But some really attractive opportunity comes up and says I would like you to invest in me. And it doesn’t always work out. But sometimes it can be beautiful. 

Paradox of Skill

So there you are professional investor, you have noticed that you’re getting better and better and better over the years because your skills keep getting better. You’ve got better tools to work with…You have research services like you’ve never had before…more sources of information, you get it very, very quickly, and can act anytime you want.

…So does everyone else

  • Flattening of skills

As more and more people get the same kind of computing power, the same kind of information, the same speed of access, more and more people get more and more equal to each other.

  • Flattening of info advantages

As recently as the 1980s, if you want to have a private meeting with senior management, all you had to do is show that you’ve done your homework that you were asking intelligent, probing questions, and that you had been coming back on a regular basis to this company. You were a serious investor. You could be invited to a dinner where the senior executives would talk about what their plans are for the future of the company. You could get a comparative advantage.

Today, the SEC now requires any public company that shares any useful information to any investor it must simultaneously make a diligent effort to be sure everybody gets that statement information. No more private conversations with management.

  • The increasing role of luck

All of us are in mixture of skills and good luck, when the old days, good luck when all that important, the skills really made a big, big difference. But the skills that they have might have diminished in their percentage or relative importance because they’ve got these fabulous tools in unbelievable supply. And it’s that that makes them all increasingly equal. Even though they’re getting better and better. They’re getting less and less different.

  • Playing bridge with all the cards face-up

Because everybody knows everything that everybody else knows, you may manage it a little differently, may make some mistakes a little differently. You may do some smart things a little differently. But it’s very hard to do significantly better than the other guys when they’ve got everything that you’ve got.

  • The genie isn’t going back in the bottle

Candidly, there isn’t any doubt in my mind that that transformation has already taken place so forcefully. And for really good, understandable reasons. It’s not going to reverse.

Indexing is the Logical Response

Unprecedented breadth and quality of competition means efficient pricing. Active management is a losing game when you consider that, adjusting for survivorship bias, 84% of funds underperform.

Benefits of Indexing

  • Top quartile performance, maybe top decile
  • Minimize fees which are a huge drag.
  • Lower taxes if you hold long-term

It’s passive and boring which helps you stay with it. Once you start transacting your behavioral biases undermine your goals.

“Passive” is poor label

He hates the word “passive” since it has labeled a great strategy with a very negative connotation word. Nobody wants to be called passive.

Caution on Smart Beta

  • These things do have real merit over the long, long, long term. But if you think about it for a minute, when will sales organizations ramp up their selling effort the most? And when will nice people who haven’t thought about it as carefully as they might be most tempted to say “let’s go with it”, of course, is after a very good period of rising prices. So if value has been working very, very well, the demand for interest in buying into and the supply interest in selling people on value factor investments will rise to a crescendo at the top and then people get disappointed.
  • The guys who have for years specialized in factor investing are going to find they can’t make as good a profit from doing it as they used to, because of the crowd. But they’ll still probably do a pretty good job for themselves and for their investors.
  • Beware those who are in it because it’s a good commercial opportunity. Intermediaries that are in it because they think “hey, this is a new way to beat the market” are going to create a self-disappointing experience and it’s a shame.

Indexing in China may be a mistake

  • The Chinese market is still dominated by retail investors which requires understanding how retail investors track past price performance and project future price performance.
  • You might find yourself being indexed with foolishness rather than an index with rigorous professional expertise.
  • This extends to indexing in emerging markets as well because there’s an unusual, different dynamic.

2. Concerns Over Indexing

Expensive Markets

There is concern that markets are not going to give what people need over the next 10 or 20 years. When Ted suggests that active management might outperform expensive public markets Charley replies with my favorite line in the interview:

Ted, Ted, Ted, you shouldn’t be talking that way.

He does not believe active will outperform.

How does Charley invest?

I’ve got two kinds of investment. One is index funds, which I’m happy with and just really comfortable with. And the second is an index fund equivalent in many ways –Berkshire Hathaway (he goes into his personal reasons for that).

Is there place for active management?

  • The bar for inclusion is exceedingly high. Charley, himself, has access to the brightest investors and sticks with indexing himself:

I’ve used myself as an example. I have probably as good a network of friends in the investments world as anybody, particularly in the active management world. I know, because of service on a whole bunch of different philanthropic investment committees, I happen to know an awful lot of guys who are really talented at picking talented people. And I meet with them on a regular basis. So you think, “hey, Charley, you are probably as good a position as anybody to be able to pick and choose terrific active managers.”

Why doesn’t he do more of the “clever” things that can be done?

Easy answer. I’m not good enough. I don’t know enough.

  • If not for Charley, who is active for?

Let’s be candid. You and I both have tremendous admiration for David Swensen. He should not be indexing. You know, why? He’s got all kinds of competitive advantage. Everybody loves David Swensen. Everybody loves the idea of working for Yale. He’s got the best team on his side, working with best managers anybody ever had. If you have a relationship with Yale, you know, it’s going to be a long term relationship, they average 10 years, their mentor relationship, something like 14 years. That’s the average, even though they usually invest with people at day one or before day one when they get into business. If you look at a list of their investment managers, you say, “geez, I don’t recognize most of those names.” That’s right. Nobody else does either. So a very unusual kind of investing. Guts, intellectual precision, and they do slightly better on asset mix. Half of 1%, maybe slightly better on manager selection, half of 1%, maybe got a team of 30 guys who are working to be sure they keep it up. And they got a network of friends and admirers all over the world, they get the best call, they do all kinds of vendors. So there are organizations like that and they’re not very many of them. Who else? There are pockets of managers who are extemely specialized and have their own money in the game, maybe some from outside investors. Basically a handful of managers with no real competition.

“If you are really an exception, you don’t have to index you can do something really different. But you have to be really good at your exceptional way of doing things. And you have to be not very widely followed or copied, because you’ve got to be almost alone doing it.”

  • How about private equity?

He makes a criticially important point here about the inversion in the supply/demand of capital :

The real competition today is not by the investment managers to get your investment money. It’s by the people who’ve got money to get access to the best investment managers. And that’s been true for the last decade and venture capitalist have clearly true in private equity. And that’s a really important differentiation.

He continues:

I don’t think there’s any very large pool of capital that has not addressed the following question. We have a commitment to a higher rate of return than we’re now getting. What can we do to increase our rate of return? Answer: Private equity. Great, so why don’t we put not 10%, but 20 or 30%. Let’s say 33% into private equity. And we’ll do it in a very imaginative way. We’ll have a couple of specialists who work on selecting the private equity funds employed by US, Canada, we can’t pay very high salaries because our fund structure doesn’t allow us to do that. But we’ll do the best we can. And we’re going to make a major commitment to private equity. Fine. So does everybody.

What is the PE landscape as a result?

They have more money than they’d really like to have. In fact, they’ve got cash balances they can’t use yet. They’re competing with other guys with a lot of money too. So the entry price for private equity has been going up and up and up.

Some people say “we’re not going to raise more money, we’re going to stay as small as we can, we’re going to specialize in our particular niche, and we’re not going to take new accounts.” So that takes them out of the equation. There’s another group that says, “Well, you know, if everybody wants us, we’ll have to be opening up more capacity. We’ll just take the money. Let’s see if we can find a way to invest it as we go along. But might be difficult, but we’re gonna try and you know, it’s always worked so far.”

Well, you could have a huge flood of cash going into private equity. You can ruin anything by raising the entry price.

The theoretical limit of indexing

  • What has to happen for the price discovery function to fail?

First, if you have 30% of the value of the market indexed, that’s not 30% of the trading activity, it’s a much smaller fraction. So what you have to do is have enough assets indexed to reduce the trading activity enough so that enough of that million plus people who are making their living as active investors comes down and down and down. Enough people need to decide “I’ve looked at it very carefully, and I’ve decided I’m quitting the business. I’m going into medicine, law, farming, ranching…”

What is it that people are going to go into that they think they’re going to find a more satisfying? Honestly, it’s not as good as it was, but it’s still the best game in town. It’s gonna be very hard to get people to give up on going into active investing.

  • If they do cut back a lot, how much do you have to cut back?

You’d have to cut back so far that there was not a residual group of people who are pretty darn good at price determination. My own guess would be somewhere around 85% would have to quit, just because it doesn’t make sense anymore.

Mix in the combination of interest of active managers, overconfidence, and people’s desire to be better than average and it doesn’t seem likely that the competition would abate enough to undermine price discovery.

3. The Pension Crisis

Scope of the Problem

Public pension plans are impossibly underfunded

If you look at what are the biggest problems we as a nation have in the investments world, it’s pensions or retirement security. You can see it easily in the state and city funds that are seriously underfunded. They need 7.5% rate of return which they’re not going to get because they’ve got 25% in 2.5-3% bonds. They’re just not going to get it.

Households are underfunded

If you look at individuals, half the population does not have a retirement plan. For those that do 401k is increasingly dominant, taking over from defined benefit system. The average person approaching age 63.5 which is the retirement age in this country is thinking:

“I’ve got 165,000 smackers in my account. Why my wife and I are going to Florida to play some golf, some tennis, have some fun. We’re gonna have great years. We’ve earned it. It’s been a long long working run, but we’ve earned it it’s going to work out just fine.”

Except…

Anybody with any knowledge about investing knows right away — $165,000 if you take money out, from 63 years old to 85 or 90 is not enough. You’re not going to have anywhere near enough per year, cobbled together with social security to make anything like a decent connection.

Something over 65% of your life time health expenses are spent in your life six months. Well, that’s where half the bank for personal bankruptcies come from all kinds of trauma that goes with that as well assisted living expensive and dementia. So we’re going to have a real problem with old age, retirement security.

Political nightmare

So what are people gonna say?

“God damn it. I worked hard all my life I played by the game rules as everybody laid them out. And I was supposed to be able to retire at a decent age and enjoy retirement. That’s part of the deal.”

But the answer they will get back?

“Sorry, but nobody else understands that to be the part of the deal. And you’re on your own.”

So you will have a giant generation that is angry, focused, and motivated to do something about this false promise.

If you think we’ve had divisive politics in the past, imagine what it would be if you had millions of people and their relatives all saying “It isn’t fair. It isn’t right. These guys got screwed.” I think we’re going to have a terrible societal problem, political problem.

How Did We Get Here?

The retirement problem is rooted in an era of different needs and circumstances.

History of the retirement age

  • Age 65 came from Social security which dates back to 1935,

which came from:

  • Railroad Retirement act in 1923,

and even before that:

  • Churchill and Chamberlain jointly put forward in the United Kingdom retirement at 70, but people thought that was unfair because the Germans used 65.

And here’s where we get to the root…

  • German’s retirement age dates back to early 1880s

Baron Von Bismark tried to unify the German municipalities via technology namely the telegraph and the railroads. The telegraph combined with the post office allowed instantaneous communication anywhere in Germany.

We’re going to bring coal and iron ore from the rural and other areas to where the steel mills are and we’re going to build steel mills and have tremendous industry. And then railroads are going to be able to bring people from the cities out to the countryside for weekends, vacations  can be normal, and we will bring from the countryside, fresh fruit, fresh vegetables, all kinds of wonderful things that for people to eat, it’s going to make everything terrific. That’s great.

But where are you going to get the workers to work on the railroad?

Offer lifetime employment.

You get them to come out of the forest because they can get lifetime employment. That’s terrific. What do you call that? That’s guaranteed. This is a commitment. It’s the honor of Germany. Okay. Let’s go.

So what happened?

Well after a couple of years there were accidents on the railroads. Trains ran into each other, people were killed. Public outrage and scrutiny.

What’s going on?

Well, let’s send a study group and find out what the heck is going with these accidents. Well, we found out what the answer is in the work. Laying tiles, lifting heavy ties, brailles, shoveling coal, all kinds of heavy work. They’re saying to the older guys in their late 50s and 60s, your too old for this kind of work. You take the easy job. You’ll be in charge of the switches.

Then what happened?

So the switches are being manned by guys in their early 60s. A beautiful summer’s day and no trains coming in for the next couple of hours, why not take a little nap? And they’re just taking a nap, forget to wake up, and the accident happened. 

The solution?

Guarantees for life. Pay them not to work. To be cost effective find the min-max where it costs not too much to solve most of the problem. And the answer was 65. Most people don’t live to 65 in those days in Germany, but those who do are really doddering, so they will only last for another couple years after 65 anyway.

An obsolete model

We have inherited and retained a retirement model that is a poor fit for our post-industrial circumstances.

    • People live longer now. The ratio of non-working to working years has increased.
    • People are able to work longer as manual labor’s share of the economy has declined.

Dealing with the Crisis

Extend your savings

  • Take social security later…instead of 62 if you wait until 70.5 you make 76% more inflation-protected for the rest of your life. If you wait, you have fewer years in retirement, so they’re willing to give you a larger amount.
  • Continue funding your 401k in your 60s. These are the easiest years to save money. So you can ramp up your savings, dump it into the 401k as fast as you could. (also there are catch-up allowances)

Do all of these things and your chances of being in serious financial trouble in retirement go from awful to not too bad. So if we act soon, we could make a big, big difference in what could otherwise be one of the worst problems our society has ever faced.

Why has this been so challenging to solve?

The big problem is nobody’s paying attention to it. It’s too late. Congress is dealing with politically urgent issues. We need to agree to raise the retirement age to 70 but it’s easy to say that when you are not a ditch digger or coal miner.

Flirting with Models: Wayne Himelsein

Link: https://blog.thinknewfound.com/podcast/s2e7-wayne-himelsein/

About Wayne: CIO of Logica Capital

Transcription: Otter.ai


Overview

Every trade is implicitly long or short volatility or optionality

  • There is variability in every asset and its distribution dictates whether you are long or short.
  • Every trade is either a bet on convergence or divergence. Convergence trades are short volatility

Quant vs Discretionary

“There’s good and bad in all of it. So the best you can do for yourself by going with what you know because you’ll be able to ask better questions and be more comfortable with what’s happening day-to-day.”

  • Myth of quants building a black box then “going to the beach”

“The market is always changing. In fact, it’s funny even the idea of factors and categories, if you think of something like value and growth. These two big facets of the market, even those are evolving. [Consider] that you buy a value stock, and it turns around and starts moving in your favor. Well, now it’s a growth stock. So literally, the categories are changing on us. So if you bought a value book, and you leave it for six months, you’re now a growth book, if you were right on your picks.”

  • Using quant to “mechanize” what works vs mining for patterns

“Finance algorithms that developed from logic and experience that simply seek to mechanize what is already well understood, have a chance at success. Those that begin in data analysis, categorization, quantification, or statistical or numerical gymnastics do not.”

Opportunities in volatility trading

Traders have different “assumptions across the volatility surface, the strikes up and down and across the calendar upwards and outwards, There are different prices for every option. Because of all this modeling and people having demand for different options at different calendars in different strikes, there’s going to be cheaper and more expensive….Take advantage of the weirdness and pricing and model variants across the option surface.”

An inverse relationship between signal strength and opportunity size

  • As your signal strength declines you need to diversify more. “To have more probabilities repeated more often, [so] more positions”
  • Hoffstein: “Information ratio is equal to your information coefficient times square to breadth. If you have to lower your information coefficient, but your breadth goes way up, you can actually end up with higher information ratio”

Re-phrasing a bit: expectancy scales with number of trials but volatility scales with square root of number of trials. If your bankroll is large and your business diversified, it follows that your focus should be on hunting for high expectancy games, not minimizing risk.

Evaluating a strategy

  1. Use daily returns to get more data points. Monthly returns mask too much.
  2. Are you achieving your premise?

    “So you’ve said yourself, I know where I want to neutralize, and I know where I want to get my alpha. And if that’s where you get your alpha, you have to know that number one, you have alpha there. So if you look at your growth tilt and measure that against Fama growth factor, do you beat it? If not, you’ve got no edge.”

    • Map the strategy.
      • Compare the exposures to time series of different exposures to see how it behaves. This requires using mathematical tools that do not rely on linearity (ie regressions).
        • “I don’t ever listen to what [the manager] tells me. I just run it versus we have in here about 180 different exposures that we have time series for factors or exposures [to find out] “what is inside this thing?”
      • How intentional are the exposures?
        • Managers will tell you that they’re doing something but don’t even know what they’re exposed to. “Did you know you have a 30% exposure to momentum? Oh, no, I didn’t. I’m actually a value investor.” (Me: sounds similar to performance attribution frameworks behind “hedge fund replication” strategies)

Risk

Beta is a poor quantity to use to balance your portfolio

  • Beta equals correlation times vol ratio
    • It’s easy to compute which makes it popular
    • …but since its inputs are non-stationary, non-linear and themselves volatile it’s garbage in/garbage out.
    • Important to understand if a beta-hedge portfolio will bleed longer or shorter as correlation increases. (Me: This is why gross exposures are important to constrain)
  • How to balance a portfolio without relying on beta?
    • Geometric approaches that account for non-linearity
      • Clustering distance approaches
      • Stochastic dominance

Market neutrality is a “funny” concept

  • What does it mean to even be neutral?
    • “What do you want to be neutral to? Are you directionally neutral? Are you factor neutral? You can [initiate] a directionally neutral portfolio that has equal long shorts, with a complete growth, tilt, or a value tilt or some other factor tilt like a volatility tilt.

Overcrowding

“If we find a good pair trade, rest assured, many others have found it. And there’s just gobs of computing power, and PhDs and all the rest doing the same thing. And so we’re all going after the same edge. When things start to go wrong, the differences between the different groups is that they manage the risk differently. And one of the best means of managing risk in these markets [is to manage leverage]. The overcrowding risk is that everybody’s in this trade, and it’s a good trade. That’s why everybody’s in it. So you’ve done the right thing. But as some of these bigger shops start to unwind, it becomes everything going the wrong way. Others are needing to exit because they have LPs to answer to or they have risk that they’re managing to, so as long as you’re in it, you’re exposed to that. And it’s difficult to manage because at the get-go, you made the right bet.”

Walking away or sticking with a “broken” strategy?

Difficult question since the pricing may be more favorable as anomaly gets stretched but unclear whether the relationship will revert and on what timeline. There’s career risk is sticking with it vs the weight of the historical evidence for the opportunity.

“The more your measure won’t determine whether something’s out of favor, the more time you might give it to try to fix it”

“Comes down to a personal decision. How much time am I willing to spend tweaking and contorting to try to figure out whether I can fix it. And we all have our limits. It comes down to a business question as well. It’s not just tweaking and contorting and trying to fix it. But how much time can you spend defending it? How sticky is your capital? Even if it does come back still be in business?”

An easy example was the trade that shorted both the triple long and triple short ETFs on the same reference asset. The trade was over once the cost to borrow the shares exceeded the edge in the trade. This was easy to measure and therefore abandon when it became too crowded.

Hedging non-linearity or skew

  • “The only way to get rid of the left tail is to balance it with the right tail. And to have that obviously, you have to have the right offset temporarily. You need the time association to match that when this thing goes down, the other thing goes up. So you need to understand the time relationship between the two.”
    • Stop-losses are “synthetic left tail mitigator”. They are not fully reliable because of:
      1. Gaps
      2. Discipline
    • Tradeoffs between hit rate and cost of the hedge. Need to define what type of exposure you are ok with to target the right option hedge. Just like insurance has cost levers like premiums, coverage amounts, durations, and deductibles options portfolios can be custom tailored.
    • Flight to quality assets like gold, USD, treasuries in a permanent portfolio
    • Managers who engineer defensive market-neutral portfolios

Final words on hedging

  • Depending on the nature of the crisis hedges behave differently. Since we cannot predict the nature nor timing of a crisis it’s best to be diversified across hedges.
    • “Back to the larger insurance analogy, you have your medical and you have your dental and you have your vision. And so I don’t know where I’m going to get hurt. But either way it’s covered.”
  • Tolerating the cost
    • “Optionality being potentially the heaviest cost again, to me, it’s not expensive when you get what you want. But since it is more often a bleed than a payoff, perhaps people should have more treasures and gold and a little bit less optionality. But definitely all concurrently.”

Thought experiment

You can only own 1 asset and never trade it again, what do you pick?

SP500. The only reason people underperform the market is they want to control volatility and liquidity needs. But if we remove these concerns the best thing is to just own the market in perpetuity.

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 Capital Allocators: Tali Sharot

About Tali: Professor and author of The Influential Mind, The Science of Optimism, and The Optimism Bias

Humans have evolved to maximize positive emotion which is a reward for engaging behaviors which promote survival (sex, eating, social acceptance).

We have built-in biases which push us towards maximizing this emotional well being.

Comparing alternatives requires us to put a value on which actions will improve our well-being, but this is a significant task requiring us to continually weigh our immediate happiness vs future happiness. This is difficult comparison since it requires exchanging immediate, visible gratification for longer-term, invisible, and often compounded benefits. The cost of these decisions is not immediately visible, but the benefit is.

Tali’s research seeks to understand the mechanism by which our built-in biases confound these comparisons so that we can make the costs and benefits of our options more readily available or design nudges which push us towards better long term behavior in cases where we reflexively choose poorly for short term benefit in defiance of what we might actually want.

Optimism Bias

  • We paradoxically hold private optimism vs. public despair
    • “Machines are going to take everyone’s job; except mine”
    • “The market is going to crash, but I’ll be ready and willing to buy when they do”
  • We tend to learn less from things which give us negative feelings
    • We ignore them
    • We explain them away more easily rather than attributing our role to them.
  • We seek opinions which agree with our priors.
    • She cites a study where a group is discussing the value of real estate. When people were agreeing the pleasure centers of their brain light up.
These last 2 points conspire to boost the well-documented confirmation bias.

Is the optimism bias adaptive?

Whether it’s adaptive or not depends on the consequences.

  • It’s not adaptive when it encourages you to take reckless risks
  • On the other hand if you are very motivated in a task because you are overconfident it may be a self-fulfilling prophesy.

Home Country Bias

  • Driven by preference for control rather than the uncertainty that comes with investing in the unfamiliar. Sense of control is also shown to independently be a source of positive emotion.

Reducing Bias

The prescription for dealing with biases varies across individuals. How responsive individuals are to social rewards, anxiety-reducing rewards, risk tolerance all lives on a spectrum.

How can we combat confirmation bias?

  • Confirmation bias compels motivated reasoning. To counteract that find outside points of view that don’t share your priors.
  • Be aware of group dynamics especially our preference for agreeing.
    • For example, before a group discussion surrounding a decision, it is good practice to ask everyone to write their opinion down before the discussion.
  • Be aware of our tendency to confuse confidence for competence. [This reminds me of mimicry in nature. For example, several snake breeds are imposters of the venomous coral snake. Just as a true expert will spot an imposter, a coral snake will intimidate their copycat cousins.]
How can we encourage incremental actions whose benefit is unseen or far in the future?
  • Feedback. We can provide rewards for near-term milestones.
    • She gave the example of a display showing how many people at the hospital washed hands so employees are encouraged to increase the score. Seeing the score increase serves as a psychic prize.

Flirting with Models: Benn Eifert

Link: https://blog.thinknewfound.com/podcast/s2e2-benn-eifert/

About Benn: Founder of QVR Advisors specializing in option-based strategies


Selected notes from his conversation with Corey Hoffstein, co-founder of quant management firm Newfound Research

Can you maybe explain the difference between what you would consider to be more of an option strategy versus what actual volatility investing is?

  • A common option strategy is call overwriting or put writing. They are both long equity exposures. That exposure is going to be the biggest risk factor. I would contrast that with volatility strategy which tries to isolate features of the distribution of returns, but not the direction of returns.

You mentioned that you guys focus somewhat heavily on relative value strategies in the volatility space. Can you explain what constitutes a relative value strategy? What’s a trade that you might put on?

  • Create value buying cheap exposures and selling expensive exposures at the same time, trying to hedge out the main directional market risks that would dominate a traditional asset allocation.
  • Identifying trades is really an important part of the process as they move around all the time over different cycle frequencies. Imagine, for example, long volatility in large-cap energy companies versus short volatility of smaller energy companies. That might be an opportunity at a point in time driven by a series of large transactions in the equity market. A large fund may have done a bunch of overwriting in their large-cap energy names, which suddenly made them very cheap. You really have to monitor, see the prices move and understand why there are dislocations and other relative value opportunities that might cycle over time.
  • Pension fund overwriting or cash-secured put selling are types of opportunities that might last for several years while those strategies are popular. You can imagine them becoming too popular over some number of years, then the pendulum swinging the other way. I wouldn’t say that there’s risk premia that you would expect in the space to just exist in perpetuity in a relative value sense.
  • There’s a large need to provide liquidity for end-users of options and distribute risk from where options are being heavily supplying to where they’re heavily demanded. These markets really developed on the back of end-user demand and their need to transfer risk. The key thing in relative value investing and in volatility, is that the marginal price setter for the probabilities and the market prices that prevail is not a volatility investor who is thinking about nuances in implied volatility. For a specialist volatility investor, many of the best opportunities really arise from either explicitly or implicitly providing liquidity to meet the needs of end-users, and to warehouse basis risk between what they’re buying and what they’re selling.

Analogs and Differences from traditional investing?

  • Selling vol and overwriting are expressions of carry styles
  • Rather than using traditional factor language to describe volatility trading he prefers a “Star Wars” analogy: Derivative users do things in big herds. And they typically have very large size relative to the absolute return community. Those flows are very sticky and implemented in similar ways with similar benchmarks, for example selling one-month index options. This creates congestion in one segment of the overall options market creating a ‘disturbance in the force’ — this creates really steep term structures, market makers get stuffed with short term options, they don’t have the risk limits to hold. And the relative value community’s job is really to distribute that risk much more broadly, throughout the ecosystem. A nice risk-reward profile is the payment to provide the liquidity to that market. Concentrated flows need someone on the other side to warehouse and distribute that basis risk.

How do you think about identifying trades in this space? How do you think about managing trades? How do you think about exiting trades? How does the book come together? It seems like a very overwhelming landscape to try to get your arms around.

  • Our investment process as a collection of bottoms-up strategy sleeves. So an individual strategy sleeve would really be a theme that’s driven by some particular type of dislocation or some particular type of underlying flow that end-users are generating. In a cross-sectional portfolio, opportunities are more fleeting, as opposed to being structural flows that are very consistent over long periods of time. It involves building out quite a lot of infrastructure, to identify those opportunities quickly.
  • In the example of a fund running a big overwrite sale on their long, large-cap, equity names portfolio, that would feed through quickly into the prices of options within that universe, and you’d see a significant reduction in those prices, relative to the prices of the small-cap energy names. You’d see it probably wasn’t driven by underlying realized volatility dynamics, it wasn’t that the spread compressed(because the names in the short baskets started becoming very volatile, and their prices started rising). You’d have various other ways of quantitatively triangulating that which would trigger an investigation into a type of the trade to add to the portfolio.
  • Where the dislocations are potentially more persistent, it might be more a question of measuring those dislocations. How do you track the ebb and flow over time? Is it a particularly attractive opportunity set? Do you want to have maximum risk on? Is it a less attractive opportunity set? Do you only want to have 30, or 40% of risk on? The identification of those type of opportunities is a starting point in the design of the strategies.
  • Again much is driven by what end-users of derivatives are doing in really big size and affecting markets. It’s not hard to see if you are an active market participant. You spend a lot of time talking to market makers and talking to the end-users of derivatives so you see it very quickly.

How do you think about the trade off between systematic versus discretionary and volatility investing?

  • I think in terms of a spectrum between, on the one hand, fully discretionary, and gut feel based investing all the way to the other end of the spectrum of fully automated back to front, systematic trading. Most volatility managers lie somewhere in between on that spectrum. It’s really hard to get that last mile to full automation. Since options are non-linear, you need to manage the very small risk of automation failures which also makes full automation elusive.

Are there any examples that come to mind where either an opportunity was systematically identified and you had a discretionary override? How about the opposite, where you thought there was an opportunity and the systems were not flagging it?

  • Back in the early days of Abenomics, in Japan, when the Nikkei was incredibly depressed, there was an interesting dynamic showing up in skew on Japanese equity indices. So skew is the relative price and an implied volatility sense of upside, call options versus downside put options. And in Japan, it actually started to go positive, which is very unusual. In other words, upside call options, were trading at a higher implied volatility than downside put options. A lot of folks in the volatility community got really excited about how silly it was, that an upside call option would trade at a higher implied probability than a downside put option, and really aggressively sold upside call options. But the key thing to remember back then was the Japanese equity market had just been incredibly depressed for a long time. There was a tremendous macro narrative building around big structural reforms and a great unconventional monetary policy. What followed was a very volatile rally! It was really a sucker’s trap to look at skew based on the historical data set because you were selling an upside crash scenario.
  • Another example was the model not appreciating how cheap the options on VIX were when the sizing in XIV became extreme and creating a very negatively convex profile in VIX due to the size of the rebalance. If you have a fund that requires a mechanical response that has to buy volatility when vol is up, it creates a problem if the size in the market became too large. It was just a market microstructure time bomb waiting to happen. The timing of that type of event happening was uncertain. But the sizes of those positions made it almost inevitable.

When you see a very steep VIX futures curve, in your opinion, is that an expression of the markets viewpoint? Or do you think that’s just an expression of a market imbalance?

  • Typically, it’s more related to risk premium than it is some kind of unbiased forecast of future volatility. If you look academic research or practitioner research there are some fundamentals to that term structure and some expectation element but often quite a lot of element of risk premium.

If you were doing due diligence, on a volatility strategy, describe red flags (besides leverage and are they selling tail insurance) and other concerns.

  • I would want to drill into sophisticated, top-down risk systems that stress all of the main risk factors in the portfolio to very extreme levels, and see that the risks were acceptable. There is no portfolio that makes money under all circumstances which is fine. But if there’s a major risk factor in the portfolio, you should be able to take it to a very extreme unprecedented level and see that the portfolio is not going to be getting liquidated at that level.
  • It should be contained in a level that’s acceptable to the end investor.
  • I’d want to understand the assumptions they’re making in those stress tests.
  • I would really want to see the actual positions and hear them explain what other parts of the market and what other market participants are doing to understand what the squeeze risk looks like.
  • I would want to see that they had at least contemplated thoughtfully and analytically how the strategy should be expected to perform going forward. And really a thought about how the market changed in the past 20 or 30 years versus right now. Markets in general change over time but volatility and options markets have changed dramatically.

Knowledge Project: Adam Robinson

Link: https://fs.blog/adam-robinson-pt1/ and https://fs.blog/adam-robinson-pt2/

About Adam: Author, educator, founder of the Princeton Review, and hedge fund advisor


Purpose and happiness

Modernity may not be making us happier

  • Quantitative measures that suggest the “average person today lives better than the average king centuries ago”
  • Technology is engineered to hijack your attention.
    • Either confirm your biases or enrage you (reminds me of how we ended up with Ben Hunt’s “widening gyre“)
  • Gandhi: “There’s more to life than making it faster”
  • Top 5 google search: “Teach me how to love myself

Conclusions for himself after battling depression

A life paradox: to be happy, to find love, to be successful is to not look for it. It’s to be fully engaged in your life. Only two places to direct your attention in life:

  1. the task at hand
  2. others
  • His metaphor is breathing. When he is home he is focused on his work. Breathing in. When he goes into the world, his focus is on others. Breathing out.
  • The problem with self-help books is they focus on yourself, but you find yourself in your value to others.
  • Engagement in others
    • “Create fun and delight for others”. Lean into the moments. If this is your goal, you turn life into a fun game which holds unseen rewards for you and others.

2 ways to change someone’s thinking:

  1. Change their question
  2. Give a more inspired answer
  • Usually easier to supply number 2 then re-direct someone’s question.
  • Every institution is an answer to a question. Slavery is the answer to the question “how can I extract economic value with compensating a person?” The answer is horrific but the question is valid. The answer “Wikipedia” is an inspired answer to the same question.

An algorithm for finding opportunity

  • Appreciate limits to logic
    • Niels Bohr: “You’re not thinking, you’re just being logical”
    • Paradoxically using logic to show that logic is not the key to life’s answers: The fact that after thousands of years of pondering we still haven’t been able to use logic to reveal a universal understanding of life’s meaning
  • Insights are spontaneous
    • After the insight, we reverse-engineer the logic.
    • GK Chesterton: “You can discover truth by logic only if you have discovered it first without it”
  • A hint that you are on to something is the insight will surprise you.
    • The surprise is evidence that something is wrong with your model of how things work. It is the breadcrumbs to a learning moment.
    • Look deeply at situations that don’t make sense

Applying this algorithm to investing opportunities

  • The best opportunities occur when something seems inconsistent.
    • This reminds me of Josh Wolfe’s observation that all best investments were always polarizing at the outset. If the idea was obvious and everyone agreed it would have been fully priced or overpriced.
  • Markets are smart. When people or in disagreement with prices or confused they are in denial or are missing something from their model
    • Fundamental value investing relies on Ben Graham’s undefined notion of “intrinsic value”
      • It is defined by “the value justified by the facts”. This is a meaningless definition. Like “gravity is when things go down”.
      • In order to think fundamental investing works is hubris. You must believe:
        1. There is a true value
        2. You can ascertain it
        3. Others will come around to your view in a reasonable timeframe
      • What about Buffet and Munger?
        • They hold things forever.
        • They are geniuses.
        • It is a stretch to attribute their success to this idea of fundamental investing.
    • Technical analysis
      • Exercise in confirmation bias and data mining
    • Adam’s approach: Game theory
      • He doesn’t try to predict market prices. He follows the smart money
      • The market is a predatory ecosystem. Books like Peter Lynch “One Up on Wall Street” give retail the illusion they can win in what is a ‘gladiatorial pit’
      • Keynes who was also a great investor described investing: “How do we anticipate the anticipation of others?”
      • What pattern of behavior have you seen that correlates with a different future?
        • People placing bets are wagers on a view of the future
        • His favorite investing book is not an investing book: 1962’s Everett Roger’s “The Diffusion of Innovation”
          • A trend at its core is the spread of ideas
          • Roger’s decomposes the lifecycle of an idea. Early adopters are ridiculed, the masses begin to come around, the idea is enshrined and seen as ‘self-evident’
      • His ordering of traders and how they express their views. Traders near the top of the order will be “right” on a lagged basis. The giant caveat is that these orderings may not have applied as strongly before the 2000s because he claims the world was different (different investment flows, presence of EU, etc). But he makes the case they still held. He looks for strongly divergent views between asset classes to make probabilistic bets on the future. He prefers this because it is the expression of bets vs say using economic statistics. You don’t trade statistics, you trade assets.
        1. Metals traders sentiment is proxied by the copper/gold ratio. They are the “Forrest Gumps” of the investing world —simplistic. They are the closest to economic activity. They are very far-sighted because of mine timelines. They have never been wrong in the past 18 years on the direction of interest rates. In September 2018, during this conversation, the copper/gold ratio implied that interest rates should be at 1-year lows instead they were at 1-year highs. He thinks the metal traders will again be right, they are just early. (9 months later as I write this, interest rates have gone back to 1-year lows!)
        2. Bond traders sentiment proxied by the ratio of LQD/IEF. Basically, credit spreads
          • When they disagree with equity traders, the bond traders tend to be right and early
        3. Equity traders
        4. Oil traders sentiment reflected in XLE vs SP500 spread. The price of oil is less reliable because of sovereign intervention
        5. FX traders sentiment reflected in commodity currency crosses
        6. Economists: Always wrong as a group
        7. Central Bankers: not in touch with the real economy; rely on models only. And economists
      • 3 Ways a Trend Can Form
        1. A stock very sharply reverses a long-standing trend. The trend needs to have been in place for a long time (long is ambiguous; he says ‘months’ or ‘years’). The stock will retrace after its sharp move but if it runs out of gas then the early adopter of the new direction are starting to win converts
        2. Parabolic moves precede a change in direction and a new trend in the opposite direction (reminds me of dynamics of a squeeze)
        3. An asset in a long-established, tight range starts to break out. The less patient hands have been transferring their position to hands that have more conviction evidenced by them willing to wade into a dead name.

      His ranking model jives with how I think about trading.

      • The science part of trading is the constant measuring of market prices and implied parameters.
      1. Rank which markets are the most efficient
      2. Find the parameters which are in conflict with one another
      3. The parameters in the less efficient markets that conflict with more efficient markets represent an opportunity set
      • The art part is to then investigate why those parameters are priced “inefficiently”.
        • Flow-based?
        • Behavioral?
        • Is there an aspect of the inefficient market that is unaccounted for and the not normalized for in the comparison to the efficient market?

    His work in education and founding the Princeton Review

    • Cracking the SAT
      • Questions get more difficult as you proceed thru the test. On a later question, if you narrow to 2 choices, pick the one that makes less sense. The lack of intuitiveness is what makes the question hard. On easy questions go with the intuition. He created a mythical first-order thinking character named “Joe Bloggs”.
        • “He asks the student what would Joe Bloggs do?”
      • Princeton Review was very advanced in using large data sets to A/B test for every possibility
        • Study of NYC school data and using custom questionnaires of teens across the world testing into Stuyvesant HS
          Cutting to the conclusion: Groups that overcame socioeconomic factors to improve on tests (test improvement is more sensitive to those factors than outright performance) had the following factors:

          • immigrant father
          • US-born mother

          Speculation to why this mattered? One parent to impart work ethic, one parent to impart language. The gender didn’t matter, it just turned out that the father typically tended to be the immigrant. This combo even outperformed both parents being born in the US.

          • Students improvement was inversely related to their confidence in their ability to improve. Want drive and counterintuitively pessimism!
      • The best lesson here was students learned they can improve! In the US we “deify” intelligence, but we should deify hard work. So when you don’t achieve something it is viewed as a choice.
        • Kipling: “If you don’t get what you want, you either didn’t really want it, or you tried to negotiate over the price”
        • Hard work is not passively learned. It’s modeled. You need to model experimentation and persistence. Need a detached, scientific process to experiment for solutions. (example of urging the kids trying to raise money on the streets to try different approaches, since their script was failing. If they find a good script they should share it with the whole organization. This is how “do you want fries with that?” became part of McDonald’s process)
        • Schools favor rote learning. LSAT selects for people who can memorize the answer to a test but not for people who can see multiple sides of an issue. GMAT interestingly favors people who can handicap the right answer fastest since getting the right answer to each question to the decimal is impossible in the time allotted. The test design favors understanding the concepts.
        • Men outperform women in both verbal and math of standardized tests because women are on average more prepared. This leads to paradoxical behavior — they are more rattled by questions which try to trip you up because they don’t expect to feel unprepared, whereas men are more used to feeling that way and simply choose an answer and move on.

        Improve Learning and Decisions

        • Distill variables that matter
          • Study of horse handicappers found that the accuracy of their predictions did not improve from the original 5 variables they desired and as they were given more variables there confidence went up (confirmation bias effect) although their accuracy did not! The handicappers with only 5 variables were well-calibrated. They were close to 2x better than chance at predicting winner 20% vs 10% and they estimated their confidence as such. When they were given more variables their accuracy remained 20% but confidence grew to 30%!
        • Importance of rehearsal
          • Re-reading rote notes is not useful for studying. You need to connect your notes to things you know, re-phrase your learnings, and practice tests and questions.
          • The closer your practice is to the desired application of the knowledge the better. Practice a language, music, writing code, writing, playing sports in the context you want to be good at. Rehearse your speech in as similar a condition as possible. Record yourself. [this has been my experience in taking music classes that force me to perform on stage]
          • Playing over chess games to copy what your idols did. Reminds me of Austin Kleon’s advice to start out copying your heroes. Not to plagiarize but to get in their heads.

Notes from EconTalk: Anja Shortland

Link: http://www.econtalk.org/anja-shortland-on-kidnap/

About Anja: Researcher and author of  Kidnap: Inside the Ransom Business


Economist Russ Roberts interviews Anja Shortland

Kidnapping for ransom as a business

The hint that kidnapping was in fact a business: 97% are resolved peacefully

How can the chance of a peaceful resolution be so high if all these things must go right:

  • Both sides must negotiate a price from a wide range
  • How to payment, typically unmarked cash, to the kidnapper?
  • Trust that the kidnapper will acknowledge payment
  • The kidnapper to trust they will not be arrested during the hand-off
  • The kidnapper must expect that the hostage will not be a witness

“The only reason for this kind of trade to go smoothly is what economists call the shadow of the future. So, people behave well this time ’round because it will help them in their business in future interactions.”

“This will only work if the kidnapper understands that he’s better off keeping the promises than breaking the promises. And that works because there must be a mechanism for information about good and bad behavior to be transmitted to future victims. So, if you have a kidnapping gang working in a city, then local gossip will probably ensure that people know whether or not they can trust the kidnappers. However, how does that work for transnational hostages? How does it work for the tourist that gets picked up in a bar late at night? How does that work for the aid-worker? How does that work for the expatriate?

Enter kidnap insurance

“There’s a very limited number of insurers, syndicates, underwrite kidnap-for-ransom, and they exchange information about trustworthy kidnappers and rogue kidnappers.”

  • Insurance actually ‘orders the market’, creating moral hazard in the process.
  • Corps buy ‘kidnap for ransom’ insurance with conditions:
    • Insured cannot know about it
    • Corporation provides security
  • In some areas, kidnapping occurs because corp didn’t know who to pay protection money to
  • Lloyd’s of London brokers a market of insurance companies willing to ensure special risks (like a basketball player’s knee)
    • The market settles into a civil equilibrium
    • Small supply. Crisis responders (often ex-special forces) retained by the insurer will have specific experience with a class of kidnapper
    • Insurers share info and more coordinated than the heterogenous kidnappers which keep prices down. However, when gov’t come in splashing the pot it changes the dynamics of the game as it raises the expectations of kidnappers b/c of public pressures and gov’t large resources and because unlike insurers they are in a one-off game (France hopes the next victim is Swiss)
  • Each kidnap market has local conventions
    • Example: Pirates want money dropped in canisters next to the ship so that kidnappers can stay high enough to avoid capture himself
    • Businesses that provide secure common ground for handoffs(almost like escrow!)
    • Trustworthy middlemen — again ‘shadow of the future’; reputations and long-running exchanges (reminds me of my open-outcry trading past. In the pits, your “word was your bond”)
    • While any one transaction can go wrong on average the market hovers around a going price.
    • If kidnappers make mistakes, then they are out of business.
      • “Sometimes you have very emotional kidnappers. Sometimes you have stupid kidnappers. But stupid kidnappers will reveal information. And ultimately it is in the insurer’s interest to eliminate stupid kidnappers–well, eliminate kidnappers where possible. But if you have stupid kidnappers who make mistakes, you can remove them from the market by dropping some hints to the police.”

On the game theory of negotiation

  • Manage kidnapper’s expectation of ransom size (hide the fact that the captive is insured)
  • “Squeezing the towel” process as the concessions offered to the kidnappers turn in to a slow drip
    • Eventually, the concessions are below the kidnappers’ cost to hold the victim. For example, the longer a hostage in custody the more expensive (via bribes) to keep it secret
  • Can’t reward kidnapper’s bad behavior or threats (“parenting lesson”)
  • Negotiators help the kidnappers see things through a more rational perspective. And, they educate them. And say, ‘Yes, we don’t want you to hurt Uncle Ted.’ And, ‘You’re not going to get anything out of hurting Uncle Ted.’ And they just help the kidnappers see how that strategy is not going to be helpful.