OMS Challenges for the Buy-Side
As hedge funds turn to a more active, quant-style of trading, they often need to run pre-trade compliance checks on hundreds if not thousands of names within seconds.
But what happens if an OMS is not built to keep up with the rapid pace of executions and compliance checks? There’s no doubt that the move toward quant trading has raised the stakes for buy-side OMSs that serve hedge fund clients executing baskets with thousands of names.
Some OMSs easily handle big blocks of 100 trades, but they may stumble over executing a large portfolio of names. For example, a quantitative hedge fund may be required to trade 1,000 names every five minutes. (While this is not in the category of high frequency trading, it does involve running compliance checks on many names concurrently.)
At the same time, hedge funds must comply with a myriad of pre-trade compliance rules, order marking, restrictions and short locates.
For example, let’s consider a market-neutral hedge fund that needs compliance checks to run in real time. Its top three priorities are the ability to mark positions long or short, check for short locates, and ensure that pending orders are not on a restricted list. With short sales, the OMS needs to locate the stock within its prime broker universe before selling it short. For any hedge fund that wants to trade quantitatively, the OMS has to mark every trade accurately based on firm-wide or aggregation unit wide positions.
Order Marking: Long and Short
Many OMSs have upgraded their technology to accommodate the rise of quant trading, but some legacy OMS systems are not designed to handle the demands of quantitative traders.
Quant funds trading long lists of names have to be checked in real time with a very quick turnaround time. In fact, the needs of a discretionary trader holding 50 names, who trades 20 stocks at any given time, are far different than a quant trader holding 500 to 1,000 names, who is trading all of them every day.
Why Legacy OMS Platforms Struggle
“You’re not talking about 20 names. You’re typically talking hundreds to thousands of names in a portfolio with the quant funds,” said Spencer Mindlin, an analyst with Aite Group, who focuses on capital markets and technology, particularly front-office trading technology.
A quant fund could be benchmarking to a large, small-cap index like the Russell 2000, optimizing a basket to outperform the index or performing some type of alpha generation strategy,” suggests Mindlin. Some systems have historically “fallen over since their architectures are built with legacy technology and are transaction-based,” explains Mindlin. Each order generates a heavy transaction that needs to be persisted. Each order could turn into a three- to-five second process for each transaction in the basket. The system also has to go through its post-trade analysis or booking. If the trader executed a large basket that needs to be allocated across multiple different accounts, it can be painful having to wait for it to complete, he says.
An OMS that was built on legacy infrastructure wasn’t designed to handle a fund trading multiple baskets of thousands of names. “That’s where the newer systems pull away from the pack,” says Mindlin.
In the front-office functionality, there is often blocking and tackling of pre-trade risk and impact analysis as well as compliance checks on each on individual names within the basket, he says.
But if you are using a system that was not built from the ground-up to service the trading of baskets of orders, the performance could be poor, says Aite Group’s analyst.
“These systems haven’t refactored their code or optimized their database,” according to Aite’s analyst. There’s a trend towards using newer databases that are optimized for high speed and large scale, says Mindlin.
To perform pre-trade analysis on a stock that’s part of a Russell 2,000 index or a global international basket, the OMS needs the ability to quickly analyze industry, country, and currency exposures, as well as liquidity differences of hard-to-trade small-and mid-cap vs. large cap stocks. A quant hedge fund that’s trading 1,500 names through an algorithm, could decide to manage 200 names independently. It could roll the 200 names into a basket and then do post-trade analysis on costs and market impact, says Mindlin. But, imagine if a trader commits to a basket with over 1,000 names, and then decides to cancel it? Unwinding the basket can take valuable time to process.
Multi-Manager, Multi-Strategy Funds
The demands for throughput and compliance checks are even more intense when a hedge fund operates on the basis of a multi-manager and multi-strategy structure. Take the case of a multi-manager quant hedge fund that was looking for an execution management system or EMS, and soon realized that it also needed an OMS for pre-trade compliance. With 50 different trading desks feeding trades into the EMS, the fund needs an OMS to handle and coordinate the compliance checks across all the desks.
As hedge funds adopt a quantitative style, executing multiple baskets with hundreds if not thousands of names, it’s critical that the OMS be able to keep up with the rapid pace of quant trading. In the end, OMSs are expected to meet the complex compliance needs of today’s active hedge funds, and falling short is not an option.
How FlexTrade Can Help
At FlexTrade, we offer a multi-asset Buy Side OMS that is built from the ground up, and provides real-time integration with short-locate services offered by major prime brokers. The OMS supports a multi-manager, multi-strategy fund structure and runs compliance on more than 1,000 rows in one second. It integrates with the FlexTrade EMS as well as pre-trade, post trade and real-time analytics.