dtcc

Blockchain Technology Can Scale to 100 Million Equity Trades per day, DTCC Study Claims
The Depository Trust & Clearing Corporation (DTCC), widely considered to be the apex post-trade market infrastructure, announced on October 16, 2018, a benchmark study which concluded that distributed ledger technology (DLT) can be leveraged to support average daily trading volumes in the U.S. equity market in excess of 100 million trades per day.

DLT Efficient Enough to Process Peak Rates Equity Trading Volume
Multinational IT behemoth Accenture conducted the study in association with other technology service providers, including Digital Asset (DA) and R3.

The study concluded that the blockchain technology could process close to 115,000,000 daily trades, or 6,300 trades per second for five continuous hours. The study acknowledges the current state of public blockchains supporting cryptocurrencies, which roughly process transactions in the single or double digit per second range.

The finding that a private blockchain can process as many as 6,300 transactions per second will give the naysayers something to think about. However, it’s worth pointing out that the study only tested the grass level functionality.

Other factors such as the security of data, resiliency, operational needs, and regulations concerning DTCC’s existing clearance and settlement system will also be considered if the technology has to be brought to the forefront of equity trading.

Murray Pozmanter, Head of Clearing Agency Services at DTCC, shared his optimism concerning the results produced by the study. He said:

“As an early adopter of DLT, we are encouraged by the results of the study because they prove that the technology’s performance can scale to meet the needs of markets of different sizes and maturity.”

Prototype Designed to Capture Market Activity
Speaking on the behind the scenes aspects of the study, DTCC commissioned the research to Accenture for technical expertise and developing a prototype of the U.S. equity clearance and settlement system. R3 and DA were given the task to provide support on performance tuning.

The primary objective behind the study was to gauge DLT’s potential in processing the massive trading volumes of the U.S. equity markets; and evidently, the results seem to be promising to say the least.