Market Provisioning: Disruption impact scale


How should sell-side firms work with their large buy-side clients who are now disintermediating through direct or  alternate access to liquidity pools?

How can algorithmic traders sustainably capture profits in the long run as data exclusivity is diluted?

How should firms think about exploiting blockchain innovations both internally and externally?

What talent strategies should broker-dealers adopt as human advice becomes a strong differentiator?


The core market-making value  proposition will shift to advice:  algorithmic trading will expand  to include “real-world” data.

Magnitude: 5

Timing: 4

The quality of human advice will become critical  in the near term as emerging information  platforms facilitate price discovery and central  counterparties reduce risk.

Algomi’s bond information network maximizes connec-  tions between market participants to increase trading  opportunities and velocity in large, illiquid voice trades

Incumbents will refocus on research and advisory services as their tradition- al edge from network size and balance sheet strength is diluted.

New data from the IoT will mesh with advances  in cognitive technologies to fundamentally  reshape algorithmic trading in the medium term.

A broader range of specialized algorithmic strategies  will emerge that use data from various sensors.44 These  strategies will exploit market imperfections using big  data analytics.

Call to action

Work with emerging platforms quickly  to gain access to new liquidity pools  and networks and proactively redefine  the value proposition for buy-side clients. Invest in deep-learning cognitive  technologies that can replicate and  improve human decision making.


Blockchain will drive speed and  efficiencies across the trading  lifecycle; differentiation in front-of-  fice capabilities will emerge from  unique data and analytics.

Magnitude: 4

Timing: 3

Distributed ledgers will revolutionize securities  clearing, settlement, and reconciliation in the  next five years.

The Australian Securities Exchange  has partnered with Digital Asset  Holdings to develop a block- chain-based system for clearing and  settling trades in Australian equities

Faster event detection and lower decision  latency will become the key focus areas for  specialized traders.

SNTMNT, a Dutch startup, specializes in social  sentiment analysis in the financial markets.

Call to action

View blockchain as both a strategic threat and opportunity.

Acquire in-house expertise on the technology through new  talent or through inorganic expansion. Algo traders should  attempt to carve out exclusivity in data access with a focus  on analytical differentiation.

The disaggregation of advice from execution leads to industry transparency

Significant changes in offering structures and operating models will upend traditional business economics.

Business economics

New information networks  to squeeze market making  revenues, but profits will rebound for algorithmic traders.

Magnitude: 4

Timing: 3


Leaner operating models await in response to  spread compression and lower commissions in  the medium term.

Savings from blockchain adoption may be huge—Au-  tonomous Research estimates that clearing and settling  trades using blockchain technology could lead to cost  savings of $16 billion within five years.

Expertise-driven algorithmic traders could see  sharp increases in profits in the next five years,  but will have a tough time keeping them there.

Real-world data-driven strate-  gies will capture trading profit  pools, but declining exclusivity  of data access and increasing  ubiquity of analytics will  threaten the sustainability of these earnings.

Call to action

Monitor the change in revenue  structure due to reduced market  frictions and craft operational  playbooks to respond to these shifts  in competitiveness. Algorithmic  traders should invest in developing  strategies that are more likely to  sustainably capture profit pools.

Industry dynamics

Increasing automation, along  with blockchain innovations,  will radically restructure the  competitive landscape. While  transparency may rise, there will be new concerns over asymmetric  information and systemic risk.

Magnitude: 4

Timing: 3

Immutable transaction records on distributed  ledgers and machine supervision will bring  regulators and market participants closer.

Market monitors will be able to  attain an organized, systemic view  of asset ownership and trading  activity, equipping them to diagnose  problems and recommend remedial  measures much faster.

Questions of fairness will arise from asymmetries in information access and computing  ability between algorithmic traders and other  participants.

Public concerns over specialized  traders’ access to exclusive out-of-market data and advanced  cognitive computing could invite  future regulatory scrutiny.

Call to action

Collaborate with other incumbents and regulators to devise  systems that achieve economic and compliance goals.

Use machine intelligence to monitor internal risk-taking  behavior to manage conduct risk. Algorithmic traders  should invest in strong compliance programs that oversee  whether strategies pass both a legal and ethical test.

Customer experience

Investors will get unprecedented access to traditionally  opaque markets, while leading  intermediaries will seek to  enhance value.

Magnitude: 3

Timing: 5

Greater direct participation in markets is imminent.

Inter-dealer broker ICAP’s Sponsored Access Matching  solution enables buy-side firms to directly access liquidity  pools in fixed-income markets by leveraging partnerships  with broker dealers.

Broker-dealers will expand value-added  services to attack client pain points.

By providing access to cutting-edge risk management  suites and advice on upgrading  compliance capabilities, large  established broker-dealers will seek to exploit their scale advantages.

Call to action

Create clear plans to give clients direct  access to the marketplace; prioritize the  long-term relationship and take them there.

Target maximizing share-of-wallet by  investing in value-added services that  appeal to critical clients.


Broker-dealers will reinvent the  way they distribute research and  algorithmic traders will attempt  to target mainstream customers

Magnitude: 2

Timing: 3

In the next three to four years,  research will go “live”—becoming  almost real-time and dynamic.

Palantir’s Capital Markets solution allows users to  change assumptions and re-run complex analyses in  real-time and present “living reports,” allowing clients to  drill down into entities and engage with underlying data.

Over time, algorithmic trading firms with  specialized strategies will likely attract more  traditional investors.ù

Complex strategies based on real-world data will  attract a greater share of committed assets from  pension funds and ultra-high net worth investors.  Investment firms leveraging these specialized strategies will establish themselves as a separate asset  class in the alternative universe.

Call to action

Create capabilities to give greater control  to research consumers, with analytical  input used to leverage dynamic data.

Real-world data-focused algo traders  should differentiate themselves from  traditional high-frequency traders as a  separate investment class.