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 from Invest Like the Best: Jesse Livermore

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

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


3 Methods for Drawing Meaningful Inference

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

3. Data Analysis

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

Earnings are a distorted measure

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

Why is it plausible that markets get permanently more expensive?

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

Valuing the Market

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

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

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

Notes from 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

Notes from Invest Like the Best Podcast: David Epstein

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

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

Transcription: Otter.ai


Epstein’s Research Process

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

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

  • Contrary Research Favoring Breadth

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

Why?

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

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

Learning

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

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

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

Grit is Misunderstood

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

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

Avoiding Premature Optimization

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

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

Opportunity to recombine

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

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

Masters in Business: Robert Cialdini

Link: https://ritholtz.com/2018/11/mib-robert-cialdini/

About Robert: Psychologist and author of Influence: The Psychology of Persuasion


Influence

  • There are hardwired heuristics which have been adaptive traits for humans who are social and cooperative animals.
  • They can be hijacked or counterfeited by unscrupulous actors. Often a combination of hijacks is being used.

6 Heuristics

  • Reciprocity
    • Giving a small gift (restaurant gives a  mint with a check); nobody wants to be called a moocher
  • Commitment and consistency
    • We prefer to be internally AND externally consistent. Leads us to defend our publically stated positions even if we no longer believe them, hence advice to “keep identity small”.
    • By publically declaring our intentions we will have additional motivation to follow through. Also, by asking others to state things or write them down we increase their chance of adhering (ie asking people when and where they will vote, not just will they). Also, by asking for favors which flatter a person’s self perception we compel them to oblige by hijacking their need to feel consistent.
  • Social proof
    • Peer pressure. Works best when claiming that others with whom you closely identify are promoting x. For example, getting out the vote by sending individuals door to door in their neighborhoods. The peer pressure is coming from a neighbor and peer.
  • Authority
    • Endorsed by an expert
  • Liking
    • We trust people who like us. A salesperson should convince you that they like you as opposed to getting the customer to like them. You trust that people who like you are looking out for your interests.
  • Scarcity
    • Act now. Time is running out. “If you are put on hold, try again, everyone is calling to get this great deal”