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

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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?”

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

About Andy: Partner at Benchmark Capital and CEO of Wealthfront

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

Strategies 

• Turn your opponents biggest strengths to weaknesses
  • The biggest competitor at the time was Kleiner Perkins and ‘the best venture capitalist that ever lived’ John Doerr. Benchmark would woo portfolio companies using a team approach since not all Kleiner Perkins companies had access to Doerr.
  • The second strength of KP they flipped was the promise of doing business with other portfolio companies. Benchmark painted this advantage as an obligation they were free from if they joined Benchmark. Benchmark took a backseat to the portfolio companies management and did not demand to be the chairman of the board.

• The other interesting thing they did was not allow the partners to ‘suck up the economics’ in the room. As soon as partner’s felt it was time to relax they needed to step aside for the younger team to be able to step up.
• “Putting the gun in the other person’s hand”
  • Partner Bruce Dunlevie philosophy of trustfully dealing with people and if the person took advantage of him he would not work with them. This technique would usually engender trust and good faith in others

Product Market Fit

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

His role as operator vs investor

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

Wealthfront features that grabbed my attention

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

Business strategy not always best self strategy

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

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

About Brad: Performance coach and author of Peak Performance

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Findings

• Studying brains we find that people can summon extreme abilities if they have core beliefs which override the fear responses in their brain (is lifting a car off of a person trapped underneath). The importance lies in having core beliefs or purpose.
• The Growth Equation: Stress + rest = growth
  • Need the right amount of stress/stimuli. Too much stress is overwhelming, too little leads to no adapting.
  • Just manageable challenges are those which are just outside your comfort zone. It’s self-defeating to onboard too many of these at once.
• Mechanics of creativity

1. 1. Immersion: this work is stress
   2. Incubation: stepping away (this is the rest)
   3. Creative insight…this tends to happen after a period of rest…end of a vacation, in the shower, taking a walk.

• Studies show deep work cycles are most effective in 45 to 90 min blocks followed by 15-20 min breaks.

Practical Tips

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

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

About Jason: Founder and CIO of Tourbillon Capital Partners

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Growth of private markets

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

How to compete in a competitive, expensive market

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

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

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

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Uses insights from behavioral economics to nudge more adaptive behavior

Design a better dashboard

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

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

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

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

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

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What does Numerai do?

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

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

Important criteria for crowdsourcing to be effective

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

Observations

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

Link: http://investorfieldguide.com/kitces/

About Michael: Leading expert on financial planning and building advisories

Transcript

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Financial Advisory Fee Model History

Commission Model

• Until 1975: high commission — stock brokers making $200 in 1975 dollars for execution; fees were set by price control in aftermath of the 1920’s free for all where clients were ripped off during the bull market preceding the Great Depression1975: May 1st — ” May Day”: deregulation of stock commissions. Some brokers thought they could raise prices but Bay Area-based Charles Schwab uses computers to undercut and in the next 20 years commissions fell 90%.

Fee Model

• 1980s to 2011: Mutual fund model rises as stockbrokers go out of business. The rise of independent broker dealers as the creation of the financial product unbundled from distribution. Financial advisors would recommend mutual funds that were not manufactured by the wirehouse they worked for creating less conflict of interest from stockbroker model. Mutual funds assets rise from 1/2 trillion to 5 trillion! Advisors lobbied for 12b-1 fees which allowed them to charge a recurring fee on assets to support their advisory business since commission dollars were now unsustainably low.

Internet Era

• Technology platforms all funds to be distributed direct-to-consumer. Etrade: “It’s so easy a baby can do it”
• 1998: Schwab One Source Program pioneers the no-load fund
• Advisor model becomes the AUM model where once again the value prop to consumers improved as advisors were now constructing diversified portfolios for clients instead of jamming them into a single loaded fund. The rise of the fee-based account and RIAs. The value prop being constructing diversified portfolios tailored to the client’s goals
• Mutual funds in decline as advisors no longer incentivized to sell them and their inferior structure to etfs. Advisors competing wanted to actually cut high fee funds from client portfolios. We are witnessing the acceleration of this process now as we are actually seeing net flows out of mutual funds in aggregate. It took 15 years to get to this point, he expects another 10-15 to finish the trend.
• Advisors are disintermediating funds — “wholesale transfer pricing”. When there is competition up and down the value chain the owner of the relationship, in this case the advisor stands to survive.
• Roboadvisors have competed for assets of self-directed investors much more than displacing human advisors. They were the biggest threat to Schwab and Vanguard ironically and turns out they were the first to respond with robo-advising of their own

The new model: Barbell

1. Economies of scale and tech. From 1995 until now fees dropped yet another 90%. Commoditized funds and etfs fees going to zero. Large firms like Fidelity, Schwab, Vanguard ok with this because as fund managers they capture AUM fees on the back end.
2. Niche advisors that add value in ways that often has nothing to do with asset management.

Understanding the New Model

• Advisor fees average about 1%, although closer to 75bps on a weighted basis (larger accounts get discounts).
• Robo advisors started at 25 bps which was a venture backed hypothesis price. They have been steadily raising fees and it looks to settle in around 35 bps. So the gap between a robo-advisor and a full service human advisor is about 40 bps.

Advisors recognize they need to add enough value to justify the premium.

How are they doing it?

• Comprehensive financial planning (taxes, estate)
• Upgrade talent. The bar is going up dramatically in terms of credentials. A basic license no longer viable qualification to compete.
• Specialized expertise: most advisors advise about 100 clients and the top 20% are 80% of the revenue, meaning you can serve tiny niches
  • Niches:
    • Seniors
      • Social security timing (when to take it)
      • Medicare and health plan guidance
    • Narrow cohorts
      • Doctors at a certain hospital with complex hospital negotiation process
      • Competitive bass fishermen (endorsements and prize money guidance)
      • Expats from certain countries

Does the data support advisor fees declining or them fighting to add more value at constant revenue?

Instead of average fee declining, we are seeing advisor profit margins decline as they add value and costs to ‘defend the 1%’ fee. Profits are not exploding despite markets on record highs

Michael’s view on flat fee versus AUM fees

On AUM fees:

“the only business model that I’m aware of anywhere in the history of any industry or your average revenue per client automatically goes up at a real rate above inflation simply by keeping your client because your [fee] lifts with the return of the markets and return the markets is generally risk premium plus inflation so we get this natural lift in a world where every other industry that ever existed has to actually go back to their people to ask for an increase and get them to buy in”

This makes the flat fee business difficult to compete.

But why are flat fees the future?

This will be the dominant structure in 20 years because the size of the addressable market will widen.

Currently, only about 1/3 of households have over $100k in investable assets. But 1/2 of these assets are in 401k plans which cannot be advised. This leaves less than 20%.

Of that proportion,

• 1/3 are DIYers
• 1/3 are “validators”. They have an idea of what they want to do but need some guidance but unwilling to pay AUM fee for validation
• 1/3 are “delegators” and thus great clients

Current AUM model only addresses about 7% of the population

So fee-for-service has an opportunity because 50% of the population need financial guidance but neither the asset level nor will to delegate to an advisor.

He says the model may shift to “1% of assets to 1% of income”