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.

Notes on OSAM’s Factors from Scratch

http://osam.com/Commentary/factors-from-scratch

How does the ‘value’ factor generate excess return?

  • Method for decomposing the return into earnings growth and multiple expansion
    • Limit to large caps (conservative, since the factor is weakest here)
    • June 1964 – Oct 2017
    • Cheapest quintile of stocks rebalanced annually at the end of June
      • Rebalance averages about 38% turnover
      • Turnover requires technical adjustments to normalize the decomposition: “rebalancing growth” and “unrebalanced valuation change” [details in the paper]
  • Findings
    • Value stock fundamentals do deteriorate in the holding period. This is reasonably expected since these are the cheapest stocks. But the prices turn out to be overly pessimistic, as the multiple expands during the holding period more than enough to compensate for the decline in earnings.
    • The excess return becomes highly diluted after 1 year as the bulk of the market’s re-rating of the stock occurs within the first year. 1 year maximizes the “gain to time invested ratio” and also strikes a reasonable balance with the costs to rebalance
    • The market’s re-rating of the stock higher proves to be vindicated as fundamentals do stabilize. The value factor is capturing the ‘overreaction’ discount and the rebalance sells the re-rated names into the market’s stabilizing bid.
    • Value is difficult or impossible to time since it only correlates reliably with future returns at market extremes (ie dot com era).
  • ‘Value’ in recent context
    • Values has underperformed since the financial crisis although the underperformance is not unprecedented
      • Value is even cheaper today on relative basis compared to the overall market but both value and the market are about 50% more expensive than historical averages
      • Value is not currently stretched despite underperformance
      • Value may be underperfroming since cheap stock’s implied underperformance turned out to be even higher than their subsequent realized underperformance.
      • Likely that this is bad luck as opposed to the market having become better at handicapping future performance.
      • From Asness: ‘In stock selection, value is still not super cheap (i.e., super-cheap would be if the cheap stocks were way cheaper versus the expensive ones than normal).78 It would be fair to wonder why not, especially given the poor long-term value returns. Well, with any strategy, you can lose because either prices or fundamentals move against you. Unfortunately, more of this current drawdown has been about fundamentals. Value, at least using the behavioral. explanations, is a bet that prices over-extrapolate current prospects. The better companies deserve to be priced higher versus fundamentals, but even so, they’re priced too high (and vice versa). However, sometimes prices are correctly reflecting this information, and sometimes they are actually underreacting to it (meaning what looks expensive is actually an ex ante good deal). Prices may over-extrapolate on average, that’s why value works long-term, but not all of the time. Value wins more than it loses, but when price differentials underdo it (meaning, unlike most of the time, cheap companies aren’t actually cheap enough versus the expensive ones) is a time that value fails.79 Importantly, we find no signal from this analysis for timing value going forward. Value is not predictably bad or good following periods where fundamentals move against it.’

How does the ‘momentum’ factor generate excess return?

  • Momentum factor constructs equal weighted basked of top quintile of names based on prior 6 month returns
  • Findings
    • Recent returns are a better predictor of earnings growth than simple expensiveness
    • Decomposing returns we find that the resultant earnings growth exceeds the size of the multiple contraction
    • Unlike ‘value’ which leads to excess returns for many years (albeit at a declining rate after year 1, the momentum strategy is mean reverting. The excess return actually overshoots in year one and subsequent years actually show underperformance.

Combining ‘value’ and ‘momentum’

  • Value converges to fair value after initial overreaction which leaves them overly offered, momentum diverges above fair value at the tail end of the holding period as shares become overly bid.
    • This makes them complementary timing-wise over the 1 year holding period
    • They work best in opposite market environments

Digging further into factors

https://www.osam.com/Commentary/alpha-within-factors

The above describes how, in general, these factors work (distilled to their essence on average you are betting against overdone price declines in companies facing headwinds or trouble). [The strategy is innately convergent and supplies liquidity]. However, when digging into this general dynamic further, OSAM finds lots of dispersion under the hood. Since that is the case, it makes sense look for what differentiates the names which are favorably re-priced vs those which continue to underperform their price outlook.

To illustrate they show AAPL vs IBM from 2014-2018. Both stocks were in the cheapest quintile of P/E in 2014.

IBM earnings declined, AAPL’s grew and the stocks were predictably punished and rewarded respectively. They validate this is not an anomaly by looking at names historically that are priced similarly and then looking at their performance as a function of earnings growth over the next year. The names with faster-growing earnings outperform those with slower growing or declining earnings and the effects increase are amplified by the degree of growth (fastest growing, perform better than just faster growing on average etc). The paper’s appendix goes further by decomposing the stock’s returns into contributions from multiple expansion and earnings growth.

These findings unsurprisingly apply to the stock market in general — companies whose earnings grow faster, have share prices that grow faster. However, they find this dynamic to be much stronger in cheap (aka value) stocks meaning the rewards for being able to predict earnings growth are higher in the value arena.

This chart shows the ‘excess return vs historical average’ binned by rank of earnings growth. While the names with fastest growing earnings are the relative best performers, we can see how much volatility there is in the entire value factor with periods like the most recent 5 years and periods in the late 1980s being notably poor for the factor.

Zooming in:

How to capture this return with info available at the time?

“Is it possible to reliably identify the top-growing stocks in the value factor using presently-available information? The answer is surely no, especially if presently-available information is limited to price and financial statement data. The forces that determine future earnings outcomes in businesses arise out of complex, idiosyncratic chains of causality that are not fully captured in that data.”

A more reasonable and still very valuable goal is to “tilt” exposure towards the names which are more likely to be indicative of real value vs the ‘value traps’ which bring the value factor’s average down.

  1. Minimize selection bias by using a composite of measures to identify value
    • For example, P/E will be understated if a company takes a one time gain (for example if it sold a balance sheet item for much higher than its accounting value). Measures of value are vulnerable to any accounting variable which is not reflective of the ongoing business. By using a composite of measure the risk of a single accounting aberration having undue influence is mitigated.
  2. Addition by subtraction by removing the value-traps
    • Momentum: a measure of trailing total return; higher is better.
    • Growth: a measure of trailing change in earnings; higher is better.
    • Earnings Quality: a measure of accruals; lower is better.
    • Financial Strength: a measure of leverage; lower is better.

Because low scores in these indices have a disproportionately large impacts they choose to cut of say the bottom 10% as the best trade-off between the desire to avoid the worse outcomes reliably and the desire to maintain a large enough universe of names and diversification. Asymmetrical filter: Scoring poorly on those measures is a better predictor of poor performance than good scores are predictors of positive performance

  1. Create an equal weighted portfolio of the remaining top half of names: “value leaders portfolio”

Summary

The results as the portfolio becomes tilted to higher quality value names:

When the process above is used to filter value-traps and we further narrow the universe to the ‘value leaders’ we find that our equally weighted portfolio had much higher exposure to the faster-growing names than a strategy ranked according to simply cheapness (ie “Value:Top Quintile”)

This is the excess return to the traditional value factor in different historical periods:

This table shows how the tilt to the top quintile exceeded 20% in every period

This table shows the returns to strategies decomposed to multiple expansion (ie the point spread re-rating) vs earnings growth

The leaders strategy improves the generic value strategy by eliminating the names which drag on EPS growth (at the lesser expense of having less pronounced average multiple expansion)

The outperformance of the value leaders strategy is notable for three reasons:

  • First, it requires only a modest amount of intervention. The percentage of original value stocks retained in the final strategy–38%–is relatively large. Moreover, the strategy is rebalanced annually, rather than quarterly or monthly. These characteristics suggest that the strategy is able to accomplish more with less.
  • Second, it’s occurring entirely in the large cap space, a space in which factor signals are comparatively weak and, according to some, non-existent.
  • Third, it’s associated with a significant shift in allocation towards the value factor’s top growth bins, a shift that we know is efficacious, given the extreme levels of outperformance produced by stocks in those bins.”

OSAM notes that “In practice, we therefore use methods that are more focused and refined. We also take advantage of the benefits of concentration and size: factor investing is more powerful when applied in a concentrated manner and when used outside of the large cap space.”

Notes on Philosophical Economics: Predicting equity returns from supply/demand not valuation

http://www.philosophicaleconomics.com/2013/12/the-single-greatest-predictor-of-future-stock-market-returns/

Premise

If there is too much supply of a given asset relative to the amount that investors want to hold in their portfolios, then the market price of the asset will fall, and therefore the supply will fall. If there is too little supply of a given asset relative to the amount that investors want to hold in their portfolios, then the market price will rise, and therefore the supply will rise. Obviously, since the market price of cash is always unity, $1 for $1, its supply can only change in relative terms, relative to the supply of other assets.

Aggregate investor allocation to equities is the best predictor of future returns. This equals:

Market Value of All Stocks / (Market Value of All Stocks + Total Liabilities of All Real Economic Borrowers)

A description of various economic data used to calculate the total liabilities

His constructed chart

Assumptions

  • The rest of the world holdings of domestic assets cancels out US investors holdings of foreign assets.
  • The supply of cash and bonds that investors in an economy must hold perpetually increases with the economy’s growth. The cash and bonds in investor portfolios are literally “made from” the liabilities that real economic borrowers take on to fund investment–the fuel of growth. Chart shows how this grows at 5-15% per year.
  • For investors’ allocation to equities as a proportion of total assets, equity prices must rise commensurately or new share issuance must fill the gap. Equity issuance has actually been declining since the 1980s. Thus stock prices must levitate if the investor preference for equity is unchanged while the economy grows (which can only happen via cash and debt growth)

Price is a supremely important determinant of return!

Price balances supply/demand of allocators and “there’s absolutely nothing that says that this process has to equilibrate at any specific valuation. History confirms that it can equilibrate at a wide range of different valuations. For perspective, the average value of the P/E ratio for the U.S. stock market going back to 1871 is 15.50. But the standard deviation of that average is a whopping 8.4, more than 50% of the mean. One standard deviation in each direction is worth 243% in total return, or 13% per year over 10 years.”

There is no explicit link which mandates price and value must be sensibly related which highlights the risk of owning equities.

“Consider the classic buy-and-hold allocation recommendation: 60% to stocks, 40% to bonds (or cash). What rule says that there has to be a sufficient supply of equity, at a “fair” or “reasonable” valuation, for everyone to be able to allocate their portfolios in this ratio? There is no rule.”

Markets contain both ‘mechanical’ and ‘active allocators’ with active allocators varying allocations based on perceived risk and expected returns whereas ‘mechanical’ allocators are systematic investors who simply allocate on a pre-defined or regular basis typically without regard to price. They are a minority but significant part of the market.

Decomposing drivers of return:

Mostly price change, not dividends. The price change is a function of multiple changes and earnings changes.

Return = Change in price + dividend return

…but decomposing the price return:

Change in price = Price Return from Change in Aggregate Investor Allocation to Stocks + Price Return from Increase in Cash-Bond Supply (Realized if Aggregate Investor Allocation to Stocks Were to Stay Constant)

So the mechanism of return conceptually is reframed:

“In the previous way of thinking, the earnings grow normally as the economy grows. If the multiple stays the same, the price has to rise–this price rise produces a return. When the multiple increases alongside the process, the return is boosted. When it decreases, the return is attenuated. The multiple is said to be mean-reverting, and therefore when you buy at a low multiple, you tend to get higher returns (because of the boost of subsequent multiple expansion), and when you buy at a high multiple, you tend to get lower returns (because of the drag of subsequent multiple contractions).

In this new way of thinking, the supply of cash and bonds grows normally as the economy grows. If the preferred allocation to stocks stays the same, the price has to rise (that is the only way for the supply of stocks to keep up with the rising supply of cash and bonds–recall that the corporate sector is not issuing sufficient new shares of equity to help out). That price rise produces a return. When the preferred allocation to equities increases alongside this process, it boosts the return (price has to rise to keep the supply equal to the rising portfolio demand). When the preferred allocation to equities falls, it subtracts from the return (price has to fall to keep the supply equal to the falling portfolio demand)”

“If you buy in periods where the investor allocation to equities is high, you will get the dividend return plus the price return necessary to keep the portfolio equity allocation constant in the presence of a rising supply of cash and bonds, but then you will have to subtract the negative price return that will occur when equity allocation preferences fall back to more normal levels. This is what happened to investors in the 2001-2003 bear market. This way of thinking about stock market returns accounts for relevant supply-demand dynamics that pure valuation models leave out. That may be one of the reasons why it better correlates with actual historical outcomes than pure valuation models. ”

How can this explain the earningless bull market of the 1980s

It can explain, for example, the earningless bull market of the 1980s. Unbeknownst to many, earnings were not rising in the 1980s bull market. They actually fell slightly over the period–which is unusual. But prices didn’t care–they skyrocketed. The P/E ratio ended up rising well above 20, despite interest rates near 10%–a then unprecedented valuation disparity. Valuation purists can’t explain this move–they have to postulate that the “common sense” rules of valuation were temporarily suspended in favor of investor craziness.

But if we look at what investor allocations were back then, we will see that investors were already dramatically underinvested in equities. If prices hadn’t risen, if investors had instead respected the rules of “valuation” and refrained from jacking up the P/E multiple, the extreme underallocation to equities would have had to have grown even more extreme. It would have had to have fallen from a record low of 25% to an absurd 13% (see blue line in the chart below, which shows how the allocation would have evolved if the P/E multiple had not risen). Obviously, investors were not about to cut their equity allocations in half in the middle of a healthy, vibrant, inflation-free economic expansion–a period when things were clearly on the up. And so the multiple exploded.

This framework has a much higher correlation with future returns than any of the popular valuation based models

This table shows the R-squared stats for different methods

Refactoring Austin Kleon’s Steal Like An Artist

Get started

  • Nothing is original.
    • Original is usually what you call things when you don’t know what preceded it
    • Screw imposter syndrome. Fake it til you make it.
    • All the world’s a stage

Creativity Loop

  • Collect ideas
    • Thoroughly study the thinkers and artists that inspire you. Branch to their inspiration
    • “Build your own family tree”. “See yourself as part of a creative lineage”. Hang pictures of them in your studio. You are not alone.
    • Surround yourself by people more talented than you. You are the average of the 5 people you are closest with. “If you are the most talented person in a room, you need to find another room”.
  • Remix
    • We learn by copying. When copying you are seeking a glimpse into your hero’s mind.
    • In failing to faithfully copy you will find your own voice. That unique part is what you amplify.
    • Your remixed ideas will be blended in a unique way
    • This is a reason to not discard periphery interests. You cannot predict the interactions of your interests. “Don’t worry about unity”
  • Produce what you want to see exist
    • Don’t write what you know. Write what you like.
  • Share
    • Be grateful for obscurity when you have it. It’s a safe space to take risks. Use it.
    • There’s no penalty for revealing your secrets. You are not a magician.
    • The internet is not just a destination for your work but a place to iterate and develop your work.
    • Share your dots but don’t connect them.
    • Don’t look for validation. You can’t control how your work is received. Plus people may not come around to your work while you still care about it (or until you’re dead)

Tips for the journey

  • Bring your body into your work.
    • Move. Your nerves are not a one-way street.
    • Analog tools engage your senses.
    • The computer is great for editing but not creating. It edits ideas before they can blossom. Cartoonist Tom Gauld, “things are on an inevitable path to being finished. In my sketchbook, the possibilities are endless.”
    • 2 workstations if possible: a digital one and a purely analog one
    • Take time to be bored and let your mind wander
  • Routine
    • Day jobs that leave you time to work on your projects give you structure. They also surround you with people to learn from.
    • Work gets done in the time available. No holidays, no excuses. Don’t stop.
    • “Inertia is the death of creativity You have to stay in the groove”
    • “Get a calendar. Fill the boxes. Don’t break the chain”
  • Embrace constraint
    • Jack White advice — don’t wait until you have all the equipment or time
    • “Nothing is more paralyzing than the idea of limitless possibilities”
    • Green Eggs and Ham was the result of a bet that Seuss couldn’t write a book with under 50 words
    • Creativity is about what we choose to leave out as much as what we choose to include

Notes from Alpha Exchange: Harley Bassman

Link: https://www.youtube.com/watch?v=X8wioRF0434&t=26s

About Harley: There is but one “Convexity Maven” in the world, a moniker that belongs uniquely to Harley Bassman. A 35-year career in financial markets has left Harley steeped in all things relating to the price of and characteristics of optionality.


Dean Curnutt of Macro Risk Advisors interviews “Convexity Maven” Harley Bassman

  • Is there too much short convexity out there?
    • Not in listed option markets where there’s a clearinghouse and vol is explicit traded and monitored
    • Risk is in the implicit convexity similar to portfolio insurance
  • Bassman on volatility surfaces
    • Term structure reflect flows; SPX has option sellers near term and insurance company buying in the longer term
    • Skew in bond markets has flipped since GFC. Pre-GFC puts were richer than calls as large asset managers hedged their bond exposures buying puts. Since GFC, the market recognizes that low interest rates are more coincident with financial stress which has re-priced the upside higher.
    • Forwards will typically price in line with long term options
    • Structured note issuance has vol-suppressing influence on surfaces
      • Europe has more structured note issuance b/c older more income-demanding demographic (looks more like covered calls)
      • Auto-callables in Asia suppresses downside vol (until roughly 10-15% knockout levels)
  • Bassman on a low interest rate worldWith central banks setting policy rates negative, the market is setting pricing across the curve very low.
    • Germany is -.20% out to 10 years yet have nominal positive growth and breakeven inflation is priced at 90 bps, so an extremely negative real interest rate out 10 years.

    Demographic motivated argument for secular stagnation

    • Negative short term rates are not unprecedented and typically accompany short-term market stress. Insurance premium to secure assets
    • Longer term negative rates are a symptom of market expectations for slower growth due to demographic headwinds.
      • In US boomers are getting older. Japan is further ahead and Europe behind Japan.
      • Declining labor force participation is biggest concern since growth = total hours worked x productivity
      • Labor force participation and yields are correlated over long periods
      • The trend of each decade is bluntly explained by demographics but it’s slow moving and difficult to trade
      • Immigration necessary to balance the ratio of workers to retirees. Immigration very important.
    • Trump is a symptom of low wage growth
      • Bassman believes QE1 was necessary to save economic system but later rounds of stimulus should have been fiscal not monetary. Monetary has caused asset inflation without wage growth. Inflation therefore was uneven and regressive leading to Trump and dissatisfied public
    • MMT
      • It’s coming. 2029 boomers will be fully retired and Republicans will not want to cut spending so there will be no check on Democrats
      • Japan a good example that MMT can work in the short term if you borrow in your own currency. The issue is that MMT will not be restrained even if inflation starts to emerge so is likely bad idea in grand scheme
      • The fallout can take decades but it’s not sustainable to print money at a faster rate than the economy grows

    Trade idea

    • Since bond vol term structure is flat, buy long dated (10 year) vol to hedge against longer term seismic shift while levering coupons on CEFs, MLPs, REITs and/or sell puts in 1 to 3 years bond options since demographics will limit rate upside to 3-4%. Can lever the near dated trades while owning the vol protection. This is a version of long time spread since near-dated levering or outright option selling is all short vol.
    • Outright tail protection too expensive and path dependent to be relied upon