Levered ETF/ETN tool

Use this tool to estimate how much a levered fund would need to buy or sell to maintain its mandated levered exposure. You should make a copy of the sheet for your own use.

A few points to consider:

  • AUM changes faster than the position size by the amount of the leverage factor
  • Inverse funds require 2x the adjustment of their long counterparts! So a levered inverse SPY fund would require 2x the adjustment of a levered long SPY fund.
  • For more detailed explanation of why funds must adjust their positions see my explanation of shorting.

Preview below:

The difficulty with shorting and inverse positions

Shorting is hard

Shorting assets is intrinsically difficult because
  1. while your position goes against you it gets bigger
  2. and when you win your position is getting smaller
Consider the impact of a $1mm fund that is designed to mimic a $1mm short in stock X.
  • X down 50% scenario

    • The fund earns 50% return. So now the fund has $1.5mm aum and the short is only $500k. For the fund to match the return of X going forward the fund must now triple its position.
      • Note this requires selling into a declining market (negative gamma)
      • The fund must keep its initial Position/AUM ratio constant. So initially this was 1:1 but then became 1/3 which is why it needs to triple the position
  • X up 50% scenario

    • If the stock increased 50% the fund loses 50%. Its AUM is $500k and the short is $1.5mm. The fund must cover much of the short.
      • The fund must buy in a rallying market (negative gamma).
      • The new position/AUM ratio is 3:1 so the fund must buy back $1mm or 2/3 of its position so that its AUM is $500k and its position is $500k. In this case the fund is insolvent.

Inverse ETFs and ETNs

The above dynamic is also how an inverse ETF or ETN work. The ETN must match the inverse return of a reference asset. So if all the AUM is exposed to the asset then we calculate the fund PositionSize/AUM.
  • NAV = AUM / Shares Outstanding
  • The down case

    • As the reference asset moves lower the fund must sell more of it to maintain the PositionNotional/AUM ratio. In this case, as the reference asset moved lower, the fund AUM increased due to profits while its position size decreased as the price of the reference asset declined. The fund must sell enough of the asset to rebalance the initial PositionNotional/AUM ratio. Selling into a declining market. This ensures the ensuing percentage move in the reference asset corresponds to the percentage change in NAV.
    • Redemptions are stabilizing as they require the position rebalance to be smaller as the AUM declines and the reference asset is purchased
  • The up case

    • As the reference asset rallies the fund must cover its notionally increasing short. PositionSize is increasing while AUM declines, so the reference asset must be purchased to reduce the position size and again normalize the notional/AUM ratio.
    • In this case, redemptions are de-stabilizing as they reduce AUM which further moves away from its initial value and the redemption also prompts an in-kind purchase of the already appreciated reference asset.

In sum:

  • For inverse etfs to maintain a constant exposure in return space to their reference asset they must rebalance such that the dollar size of the underlying position is a fixed ratio to the AUM.
  • The inverse nature means that the AUM and position size are always moving in opposite directions requiring constant rebalance (negative gamma). This creates a downward drift to the product NAVs.
  • As the reference asset rallies, position size gets bigger and AUM drops due to losses. As reference asset falls, position size shrinks while AUM increase due to profits.
  • Redemptions can stabilize rebalance requirements in declines and exacerbate rebalance quantities in rallies as redemptions reduce shares outstanding and in turn AUM while in both cases triggering the fund’s need to buy the reference asset which again is stabilizing after declines but not after rallies. In other words, profit-taking is stabilizing while puking is de-stabilizing.
  • I extend this explanation to levered funds here.

Adam Robinson’s Game Theory Approach to Markets

Distilled from his interview with Shane Parrish on the Knowledge Project

Markets are smart

When people or in disagreement with prices or confused they are in denial or are missing something from their model

Dunking on 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”.
  • Thinking 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.

Dunking on 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? Who’s the sucker? Who’s better capitalized?
    • Behavioral? Confirmation, anchoring, recency biases? Others?
    • Is there an aspect of the inefficient market that is unaccounted for and therefore not normalized for in the comparison to the efficient market?

Links to learn about private markets

Canon (IMO)
http://reactionwheel.net/  (Jerry Neumann, angel and professor)
http://www.paulgraham.com/articles.html  (Paul Graham, founder of Y Combinator)
https://continuations.com/ (Albert Wenger of USQ Ventures)
https://stratechery.com/ (Ben Thompson)
The smartest writing I’ve seen after the canon
http://kwokchain.com/ (Kevin Kwok)
https://www.ben-evans.com/ (Benedict Evans)
https://sivers.org/ (Derek Sivers)
https://startupboy.com/ (Naval Ravikant)
A listen:
Non-Venture Business
Amazing resources
And this long-form Breaking Smart from Venkat Rao (here’s my notes)
More blogs that are worthy to follow:
https://superlp.com/  (Chris Douvos)
https://avc.com/   (Fred Wilson)
https://a16z.com/ (Andreesen Horowitz team)