The Data Toll

Can an exchange price its rival out of view?

Katya Malinova
McMaster University

Andreas Park
University of Toronto

Munich · 18 September 2026

The research programme

Two papers on barriers to venue competition

THIS PAPER

Separate data fees

Make the rival’s data harder to buy

Squeeze its liquidity

SECOND PAPER

Integrated broker–exchanges

Crypto-style platforms
Explicitly fragmented liquidity

What changes when the broker
is the execution venue?

Today: the data toll

Motivation

Why data fees matter

Motivation

Data matter and the Cost of Data is Controversial

“There’s No Market in Market Data”

Market Structure Partners / AFME · 2025

“Exchanges hit back at ‘inaccurate’ and
‘misleading’ accusations around market data costs”

The TRADE · 2025

Who pays for seeing the market?

MSP report · The TRADE headline

Motivation

Brokers pay, exchanges earn

authors' calculations from TMX annual reports

TSX: market-data income relative to trading-fee income

2006 = last year of trading monopoly

Motivation

Regulators are rethinking access

Canada

CSA consultations and industry committees
Data fees, retail access, common terms
CSA Consultation Paper 21-403;
CSA Staff Notice 21-334

Europe

ESMA authorises EuroCTP
One consolidated view of shares and ETFs

United States

SEC market-data infrastructure reform
Broader consolidated information
SEC Release 34-90610

CSA, Apr 2024 · ESMA, Jul 2026 · SEC, Dec 2020

Motivation

Competition needs visibility

A challenger's data is worth buying only if others buy it too: they post the orders that make its book worth seeing.

Motivation

This paper: data fees as a competitive weapon

This paper

Three results

1

Challenger data need critical mass

Zero adoption can persist because the data are valuable only when others subscribe.

2

The data toll

The dominant venue can use its own data charge to squeeze or exclude the challenger.

3

Traders lose; brokers can amplify the problem

Lost adoption reduces trader surplus; broker incentives and fixed fees impede access.

The model

Key Model Ingredients

The model

Two venues, a continuum of traders

Venues

dominant venue \(d\): everyone has its data
challenger \(c\): data costs \(d_c\)

Traders

private value \(v\sim U[0,1]\), trade \(q\) at cost \(c\)
 

volume \(q^*=v-c\), surplus \(S=(v-c)^2/2\); traders with \(v>c_0\) are active: mass \(\kappa=1-c_0\)

The critical component is the cost \(c\):

- it determines participation in the market

- it determines trading volume and surplus

- it determines whether a trader subscribes to the challenger's data 

\[\max_{q\geq0}\;\underbrace{vq}_{\text{trading benefit}}-\underbrace{cq}_{\text{trading cost}}-\underbrace{q^2/2}_{\text{diminishing returns}}\]

The model

Seeing the challenger's book lowers trading costs

cost with challenger data, when a share \(\alpha\) of traders subscribe:

\[ c_0 \;\longrightarrow\; \underbrace{c_0-\delta\alpha}_{\text{more subscribers, deeper challenger book, lower cost}} \]

Network externality

your data is worth more when others buy it too

Who subscribes?

high-value traders: \(v>v^*\) subscribe, so \(\alpha=1-v^*\)

α = share buying challenger data
δ = strength of the liquidity benefit
\(d_c\)=challenger fee ; dominant venue's data fee comes later

Higher-value traders trade more, so they gain more from the challenger data.

Result 1 · Coordination

The challenger's subscription rate is fragile

Result 1 · Coordination

Buying data is a coordination problem

I will buy the feed…

…if enough others buy it too.

A higher fee raises the number of other users needed to make subscribing worthwhile.

Result 1 · Coordination

The same equilibria as a fixed-point problem

Actual
adoption

Expected adoption

Above the diagonal:
adoption grows

Below the diagonal:
adoption falls

Same posted challenger fee. A higher metered toll shifts the blue response down.

Result 1 · Coordination

The fee the marginal subscriber will pay, given that a share \(\alpha\) of others subscribe

\[ d_c \;=\; \underbrace{\delta\kappa\alpha}_{\text{value rises with adoption}} \;-\; \underbrace{\tfrac{\delta(2-\delta)}{2}\,\alpha^2}_{\text{marginal subscriber has lower } v} \]

A concave hump

the maximum \(\bar d_c=\dfrac{\delta\kappa^2}{2(2-\delta)}\) is the viability bound: above it, nobody buys

Below the bound: three equilibria

• zero adoption (stable),
• critical mass \(\alpha_L\) (unstable),
• \(\alpha_H\) (stable)

Result 1 · Coordination

A challenger needs critical mass

Challenger-data adoption

Sustainable
data fee

0

Posted fee \(d_c\)

No adoption

Critical mass

High adoption

unravels

adoption grows

Dashed line: the same subscription price for every buyer.

Result 1 · Coordination

What moves the hump

Stronger network benefit, bigger market

higher \(\delta\) or \(\kappa\): raise the viability bound, lower the critical mass, raise \(\alpha_H\)

Higher challenger fee

higher \(d_c\): raises the critical mass, lowers \(\alpha_H\)

total volume \(= \underbrace{\kappa^2/2}_{\text{no challenger data}} + \underbrace{\delta\alpha^2}_{\text{gain from adoption}}\)

coordination failure (everyone stuck at zero) costs the market \(\delta\alpha_H^2\) in volume

Result 2 · The data toll

The data toll

Result 2 · The data toll

The Dominant Venue also charges for data

Fixed subscription

Pay once per period

\[d_d\]

Same payment with one feed or two

Metered usage

Pay more when using more

\[m\times q\]

Raises the cost of another trade

Participation

Participation + trading intensity

fixed subscription \(d_d\) and a metered charge \(m\) per unit traded; both exist in practice ("metered is "per quote" so  "per trade" is an approximation)

Result 2 · The data toll

Can own data fees weaken demand for rival data?

Raise the price of the dominant venue’s data…

Metered charge

Raises marginal trading cost
\(\kappa(m)=\kappa_0-m\)
Lower viability; higher critical mass

Fixed subscription

Cancels from the relative data choice
for traders active either way
Works through participation

Result 2 · The data toll

Why a metered charge weakens the challenger

\[\underbrace{\text{saving per trade}}_{\delta\alpha}\;\times\;\underbrace{\text{trading activity}}_{\text{falls when }m\text{ rises}}\]

Fewer trades to save money on

Less willingness to pay for the challenger data

Intuition for the incremental surplus from subscribing.

Result 2 · The squeeze

A metered fee squeezes the challenger

Challenger-data adoption

Sustainable
data fee

0

Posted fee \(d_c\)

No metered fee

Higher metered fee

Critical mass rises

High adoption falls

Dashed line: the challenger’s posted subscription fee stays unchanged.

Result 2 · Profitability

Does the data toll pay for the dominant venue?

Result 2 · Profitability

When does squeezing pay?

Lost trading activity

Recaptured order flow

The second effect can outweigh the first

Result 2 · Order-flow recapture

Traders who drop challenger data reroute their orders

Buy challenger data

Drop challenger data

Subscribers route more flow to the challenger than non-subscribers do: σ > λ.

Result 2 · Local incentive

When does a further fee increase pay?

Fixed fee

A further increase lowers total profit

Metered fee

A further increase can raise total profit

when active non-subscribers remain
and the challenger had captured enough subscriber flow

Reserve · Local profitability

The local profitability threshold

\[\sigma>\widehat\sigma=\lambda+\frac{1-\lambda}{1+\delta}\left[\frac{\kappa_M[(2-\delta)\alpha_H-\kappa_M]}{\alpha_H^2}+2\delta\right]\]

σ: challenger share of subscriber flow
λ: challenger share of non-subscriber flow

Closer to the viability bound makes the threshold lower

At an interior metered data-revenue optimum; active non-subscribers remain.

Result 2 · Global exclusion

A larger fee can eliminate adoption

Challenger-data adoption

Sustainable
data fee

0

Posted fee \(d_c\)

No metered fee

Higher metered fee

No positive intersection

The challenger’s fee has not changed

Result 2 · Global exclusion

Either fee can exclude a fragile challenger

Different channels — the same loss of challenger-data adoption

Fixed subscription

Fewer traders participate
↓
Less support for challenger adoption

Metered charge

Trading becomes more costly
↓
Less willingness to buy challenger data

Near the challenger’s viability limit,
recaptured trading revenue can make exclusion the best fee choice

Given challenger offer; under the paper’s trading-fee and routing conditions.

Result 2 · Global exclusion

A fragile challenger can be worth excluding

Near its viability limit

Only a small squeeze is needed to remove positive adoption

Either tariff can make exclusion the best response

Fixed fees work through participation. Metered fees directly lower the benefit of the challenger data.

Result 3a · Trader surplus

The challenger benefits traders

Result 3a · Trader surplus

Traders lose from lost visibility

At a given dominant-venue tariff

\[\underbrace{W(\alpha_H)-W(0)}_{\text{trader surplus gained}}\;=\;\frac{\delta\alpha_H^3}{2}\;>\;0\]

Losing adoption removes better trading opportunities

Closed form when subscribers would trade anyway. The positive ranking holds more generally.

Result 3b · Broker incentives

Brokers can amplify the problem

Result 3b · Broker incentives

Brokers are gatekeepers

The broker pays the bill

The client gets the full benefit

Result 3b · Broker incentives

Agency acts like a data-price markup

\[\underbrace{\beta}_{\text{share internalised}}\times\text{client benefit}\;>\;\text{data bill}\]

Internalise half the benefit…

…act as if data cost twice as much

With a severe enough agency wedge, positive adoption disappears.

Result 3b · Broker incentives

Fixed distribution fees favour large brokers

More clients over whom to spread the cost

Result: Size bias

fixed per-firm fee \(F_c\) \(\to\) cutoff \(n^*=\bar n\sqrt{1-\alpha}\):

• large brokers subscribe,
• small brokers do not and retail sees an incomplete market

Takeaways

Takeaways

Takeaways

Three results — and why fee design matters

1

Liquidity needs traders who can see it

Challenger data need a critical mass of subscribers.

2

The Dominant Platform may be able to squeeze the challenger by raising its fees

Locally, metered fees can pay by squeezing adoption; fixed fees do not.
Globally, either fee can make exclusion profitable through different channels.

3

Traders lose; brokers can amplify the problem

Lost adoption reduces surplus. Agency and fixed distribution fees impede access.

Local comparison: at the fee that maximises data revenue.

Takeaways

Does a consolidated tape fix it?

a voluntary tape whose new content is the challenger's data faces the same adoption problem

Same hump

the tape's extra cost plays the role of \(d_c\)

Maybe seeded

professional adoption may seed challenger liquidity, but nothing guarantees it

Display

no requirement that brokers show the tape to clients

Supplying a tape does not by itself ensure adoption or display.

Policy implications

A consolidated tape still needs to reach traders

Data available

Data displayed

Venue used

Access, adoption, and broker incentives all matter

The model identifies barriers to access. It does not prescribe an optimal regulatory tariff.

Reserve · Literature

Where this paper fits

Networks & liquidity

Katz & Shapiro (1985, 1986); Pagano (1989)

Expectations, critical mass, concentration

Exchange competition

Parlour & Seppi (2003); Foucault & Menkveld (2008)

Fragmentation and competition for order flow

Selling market data

Cespa & Foucault (2014); Brogaard, Brugler & Roesch (2024);
Hendershott, Rysman & Schwabe (2025)

Data pricing, subscriptions, and trading

Strategic pricing

Salop & Scheffman (1983, 1987)

Cost-raising strategies

Here: separately priced data gate the liquidity externality

Reserve · Global exclusion

What “exclusion” means

Eliminates positive challenger-data adoption

The challenger may still execute non-subscriber orders

Best response to a fixed challenger offer

Within either fixed or metered pricing
High adoption selected whenever it exists

Reserve

Every exchange is a monopolist for its own data. Yet data is how venues compete.

dominant
venue

traders

challenger

everyone sees the dominant book

who buys the challenger's data?

A challenger's data is worth buying only if others buy it too: they post the orders that make its book worth seeing.

Reserve

Challenger data fee

share of traders who buy challenger data \(\alpha\)

viability bound \(\bar d_c\)

challenger fee \(d_c\)

\(\alpha_L\)

\(\alpha_H\)

0

adoption unravels

adoption grows

filled dot: stable
open dot: unstable (tipping point)

Reserve

The fee that adoption can sustain

Challenger-data adoption

Sustainable
data fee

0

More users make
the data more useful

The next buyer trades less:
less benefit from the feed

Curve: willingness to pay of the marginal subscriber — a different trader as adoption grows.

Reserve

Does the squeeze pay? Start at the data-revenue maximiser

Raise the fixed fee a little

profit falls

Raise the metered fee a little

profit rises iff some active traders do not subscribe, and the challenger's share of subscriber flow \(\sigma\) exceeds a threshold \(\hat\sigma\)


 

Why: higher \(m\) lowers adoption; volume moves from subscribers (route \(\sigma\) to \(c\)) to non-subscribers (route \(\lambda<\sigma\)).

\(\hat\sigma\) is low when \(\lambda\) and \(\delta\) are small, and falls as the challenger nears its viability bound.

The toll pays when the challenger is taking sufficient trading income from the dominant venue's subscribers.

Reserve

Traders lose when adoption fails

Without challenger data

surplus at cost \(c_0\)

adoption

With challenger data

strictly higher, for any fixed dominant tariff (revealed preference)

gain on the intensive margin \(= \dfrac{\delta\,\alpha_H^3}{2}\)

The toll transfers surplus from traders to the dominant venue and destroys some on the way.