Market Design with Blockchain Technology

Katya Malinova and Andreas Park

We first presented this paper in June 2016 ...

... and for 1 year people told us that trading of blockchain "stocks" was years away

 

How did these guys put it ...?

 

Initial Coin Offerings are now a reality

source: coinschedule (data from Sept 5, 2018)

What is different?

1. Multiple trading protocols are possible

User-facing exchange mask

Fully Decentralized, "OTC",

Peer-to-Peer Exchange

What is different?

2. High Level of Transparency

See transactions between "addresses" (="IDs")

What is different?

3. You can tell who owns what

To sum up: What is different?

  1. Exchange-trading and Peer to Peer is possible
    • current world peer-to-peer -- through intermediaries
      • a dealer/market maker is on one side of trade
      • parties know who they are trading with
    • technology enables frictionless value transfer
  2. Past transactions are visible
    • may be able to see frequent "traders"
  3. Current holdings are visible
    • may be able to tell who the "whales" are

=> Informational environment changes drastically

 Key: wallets/addresses = IDs  but NOT = traders

Research Question

How does the design of ledger transparency and identifier-usage with possible P2P interactions affect trading behavior and economic outcomes?

  • possible ledger transparency regimes:
    • visible to all
    • hidden (from some)
  • possible identifier-usage regimes:
    • mandate single IDs per entity
    • allow multiple IDs
      • allows to obfuscate holdings (Buterin 2015)

Who benefits and loses under which regime?

Literature

  1. Economics of blockchain protocols and transaction costs
    • there is a large literature in computer science, e.g., Eyal and Sirer (2014)
    • Gans and Halaburda (2015); and Halaburda and Gandel (2016)
    • Budish (2018), Saleh (2017), Biais, Bidiere, Bouvard, Casamatta (2018)
    • Huberman, Leshno, and Moallemi (2017), Easley, O'Hara, Basu (2018)
  2. Smart contracts and other uses of blockchain
    • Cong and He [2017], Yermark (2017)
  3. Blockchain and financial securities/markets
    • Boehm et al [2015]; Harvey [2016], Raskin and Yermack [2016; 2017]; Aune, Krellenstein, O’Hara, and Slama [2017]

Model Ingredients

  • Risky asset, normally distributed 
  • Two large investors

    • Each period one is hit with size Q=1 liquidity shock.

    • Other can absorb the shock at zero cost.

  • ​Continuum of 1/    small investors
    • ​trade with probability       at "public" price
    • each period, mass 1 wants to buy, mass 1 wants to sell
  • Infinitely many trading periods
N(0,\sigma^2)
N(0,σ2)N(0,\sigma^2)
\rho\le1/2
ρ≤1/2\rho\le1/2
\rho
ρ\rho

Disclaimer:

  • no asymmetric information 
  • => our results need not be applicable to all asset classes
\rho
ρ\rho

Model Ingredients:
Trading and Timing

 

  • When hit with a shock, the "liquidity trader" (LT) may:
    • trade peer-to-peer (OTC)  (with small and/or large peers)
      • other large: "liquidity provider" (LP)
    • trade with a risk-averse intermediary at

       
      • Intermediary's inventory I "shifts" the public price
    • net-trades with intermediary = inefficient transfer of risk 
  • Unfilled positions clear with intermediary at end of stage game.
p(q)=\frac{\kappa \sigma^2}{N}\ (-I+q) \equiv \frac{\ell}{2} (q-I)
p(q)=κσ2N (−I+q)≡ℓ2(q−I)p(q)=\frac{\kappa \sigma^2}{N}\ (-I+q) \equiv \frac{\ell}{2} (q-I)

Model Ingredients: Costs

  • Data processing/complexity to contact q
  • Quadratic cost to contact mass q of IDs:
    • cost c is a loss to aggregate welfare
    • pay        and trade quantity
  • Linear mining/validation cost:
    • pay       to trade with     IDs
\rho q
ρq\rho q
\frac{c}{2} q^2
c2q2\frac{c}{2} q^2
\gamma q
γq\gamma q
q
q q

Direct

  • LT to LP: Buy quantity Q at price p?
    1. LP buys Q from intermediary and moves the "public price" P to​
       
    2. LP to LT: "sell you Q at price ≫ p?"
  • Front-runner pays validation costs.
P+ \ell/2 \times Q
P+ℓ/2×QP+ \ell/2 \times Q

Idea:

  • keep "risk" of transparency within trading model
  • for investors, can think of other costs, e.g., stealing of investment strategies

Indirect

Model Ingredients: Transparency of Ownership

  1. Full transparency = common knowledge of who is large
    • assume single ID (since validation costs increase in # of IDs)
  2. No transparency
    • only single ID allowed
  3. No transparency (ownership cannot be inferred)
    • continuum of IDs (to obfuscate ownership)

Requires a system design choice:

  • allow an entity (individual, investment fund) only a single ID per instrument
  • possible with private blockchain

Benchmark:
fully transparent (single ID) ownership

Options for Large Trader

Trade with small investors and intermediary

Trade with large investor
 

  • costs:
    • complexity + validation
    • intermediation
  • costs
    • reveal info about the trading needs
    • => [model choice]:
      LT may get “front-run” by LP.

​​

Repeated setting:

Front-running is punished by “grim trigger” & trade forever with small and intermediary.

Single shot:
LP always extracts all surplus (or would front-run).

The Benchmark Equilibrium

  1. In a repeated game, "social norms" have bite and front-running can always be avoided.
  2. LT always trades with LP.
  3. LT and LP share the cost savings.
  4. Price concession
    • For small discount factor (≈ infrequent interaction) price concession is necessary.
    • For large enough discount factors (≈ frequent interactions), price concession = 0 is an equilibrium.

Opaque single ID ownership

Equilibrium

  • The optimal mass of IDs to contact is independent of the intermediary's inventories/public price.
  • Mass x* depends on:
    •   : probability of small traders accepting the offer
    •   : the (il-)liquidity of the intermediated market
    •   : complexity/data processing costs.
\rho
ρ\rho
x^*=\max\{0, \frac{\ell \rho}{\ell \rho^2+c} - \frac{\rho\gamma}{\ell \rho^2+c}\}
x∗=max⁡{0,ℓρℓρ2+c−ργℓρ2+c}x^*=\max\{0, \frac{\ell \rho}{\ell \rho^2+c} - \frac{\rho\gamma}{\ell \rho^2+c}\}
\ell
ℓ\ell
c
cc
  • When the  validation cost is not too large,              , the liquidity trader trades with both continuum & intermediaries
\gamma < \ell
γ&lt;ℓ\gamma &lt; \ell

Opaque multi-ID ownership

Closest and native to "public" blockchains:

  • anyone can participate anonymously
  • can create as many accounts as I want
  • described by Ethereum founder as simple solution to achieve privacy
  • private blockchains can choose to organize like this

Acceptance Probabilities in Opaque Settings

small traders

large trader

small traders

large trader

small traders

large trader

filled

unfilled


Opaque Single ID

Opaque Multi-ID: LP accepts

Opaque Multi-ID: LP rejects

\rho
ρ\rho
\frac{2\rho}{1+\rho}>\rho
2ρ1+ρ&gt;ρ\frac{2\rho}{1+\rho}&gt;\rho
\frac{\rho}{1+\rho}<\rho
ρ1+ρ&lt;ρ\frac{\rho}{1+\rho}&lt;\rho

Decision problem LT

accept offer

"target" small investors only

  • (small) price concession to entice the large trader (but also paid to and "wasted on" small traders)
  • larger amount => complexity costs
  • no price concession
  • smaller amount
  • => expensive interaction with intermediary
  • => smaller complexity cost

Decision problem LP

"target" IDs of both: large and small

front run

  • incurs validation fees when front-running

Equilibrium & More

Result 1: There exists an equilibrium with no front-running where

  • LP accepts
  • price concession = 0

provided

  • the discount factor is large enough
    • = frequent interactions.
  • or the intermediated market is sufficiently liquid
    • = front running not very profitable (small quantity and low price advantage)
  • or validation costs are sufficiently high
    • = sunk cost for front-running too high.

Equilibrium & More

Result 2 (numerical): For small discount (=infrequent interaction) factors, the equilibrium with no front-running where LP accept does not exist. Then:

  • In equilibrium, LT offers p = 0 to the continuum, and
  • LP's IDs reject the offer.
     

=> "over-trading" with intermediary

  • Observation: an increase in the validation cost may curb front-running.

 

 

Comparing the designs

Observations

 

  • Trades with intermediary => socially inefficient 
    • better if large traders interact
    • otherwise: intermediary faces imbalance 
  • Small with large traders => complexity costs
  • By construction, payoffs under the full transparency benchmark are highest.
  • The trade-off for opaque regimes are:
    • complexity cost vs
    • intermediation cost

Comparing multi- vs single-ID opaque designs

 

  • Finding 1:
    • When large traders do not trade with each other, the welfare is the same in both opaque systems, irrespective of the ID-ownership setup.
  • Finding 2:
    • When large do trade with one another with multi-ID ownership, the welfare in this setting is higher than in the single-ID setting.

Payoffs to Large Traders

Finding 3:

For the average equilibrium stage payoffs of large traders.

  1. In multi-ID, when large traders do not interact, eq. payoffs lower than in opaque single-ID.
  2. In multi-ID, when large traders interact and p=0, eq.  payoffs larger than in opaque single-ID.
     

Finding 4: (Numerical)

There exist parametric configurations such that large traders trade with each other at p > 0 in the multi-ID ownership setting, but their average equilibrium payoff in the opaque single-ID setting is higher.

 

Summary

  1. "Back office" settlement has important front office implications!
    • with peer-to-peer there are critical design choices
      • Who can see the ledger?
      • How are virtual identities managed?
  2. Findings:
    • Transparent ledger with single IDs is welfare optimal and has lowest wealth redistribution (almost by construction)
    • Between (A) public blockchain solution with multiple IDs and (B) private, non-transparent ledger with single IDs:
      • public blockchain privacy solution has higher aggregate welfare
      • but does not necessarily lead to higher payoffs for large investors.

Market Design with Blockchain Technology - MBS Sept 2018

By Andreas Park

Market Design with Blockchain Technology - MBS Sept 2018

I used this set of slides for a microstructure workshop at Machester Business School in September 2018. The deck has been designed for a 25 minute presentation.

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