Order Management Systems: Industry Challenges & Trends
Order management systems (OMS) pose several challenges to the way in which an institution is able to leverage them to ensure effective trading. These include challenges such as ensuring compliance with increasingly stringent and ever changing regulations related to the order management process. Another challenge is ensuring the accuracy of the pre- and post-trade analytics of the OMS. Several OMS have reliability issues with critical external interfaces to electronic communication networks (ECNs), market data feeds and clearing houses. There may be inconsistencies in the performance of OMS across different asset classes. Firms face constant problems in achieving accurate book keeping/logging and effectively managing the market data. To add to all this, there is a rush to broaden the functionality of the OMS by including features including comprehensive decision support systems, integrated market data and algorithmic trading.
Financial services firms spend millions of dollars on their OMS to manage all of these changes. The success of these integration, upgrade or implementation efforts lies in the timely rollout of these applications in accordance with their business requirements. These systems must be tested thoroughly to verify that business requirements have been met and that business processes are functioning correctly and reliably.
In a simple world, when an investor places a trade order, whether online or over the phone, the order goes to a broker. The broker then looks at the size and availability of the market to decide which path is the best way for it to be executed. A broker can attempt to fill an order in a number of ways:
Order to the floor – For assets trading on exchanges, the broker can direct the order to the floor of the exchange, or to a regional exchange. In some instances, regional exchanges will pay a fee for the privilege of executing a broker’s order, known as payment for order flow.
Internalisation – This occurs when the broker decides to fill the order from the inventory of financial assets that the brokerage firm owns. This type of execution is usually accompanied by the broker’s firm making additional money on the spread.
Order to third market maker – For assets trading on an exchange, the brokerage can direct the order to what is called a third market maker. A third market maker is likely to receive the order if they provide the broker with an incentive to direct the order to them or the broker is not a member firm of the exchange in which the order would otherwise be directed.
ECN – ECNs automatically match buy and sell orders. These systems are used particularly for limit orders because the ECN can match by price very quickly.
Order to market maker – For over-the-counter, the broker can direct the trade to the market maker in charge of the stock the customer wishes to purchase or sell. Some brokers make additional money by sending orders to certain market makers.
Once the appropriate order routing path has been identified, the broker needs to bring the request to the appropriate market in order to find a party wishing to assume the opposite position. Once both parties are found, the second portion of the transaction occurs, which is commonly referred to as clearing. While all brokers maintain their own books recording the entire amount of buy and sell orders transacted by clients, the actual act of clearing all these transactions is handled by a clearing institution.
Modern-day OMS are evolving into sophisticated systems that provide automated order management, portfolio modeling, reporting and compliance features tightly knit together to create a complete investment management solution. There are several new trends in the market, however, that are driving changes in OMS:
In this section, we will explore these trends and the challenges facing OMS.
Latency is becoming such a differentiator and competitive advantage in a lot of markets, that firms are looking hard for any chance to make them a little faster than their competitors. Successful trade execution demands price discovery in milliseconds while the data still reflects the actual market. The time interval between when a trade order is sent and when that same order is acknowledged and acted upon by the receiving party is getting shorter. This includes both the trade-order flow latency and market-data latency.
There is a growing realisation that faster internal networks and state-of-the art client-side messaging transport have the biggest impact on reducing latency.
Best execution is one of the most critical challenges in trade order management. It involves a lot of steps including prioritisation of factors, analysis and selection of execution venues, linkage to execution venues, creation of ‘best execution’ policy, execution of orders, periodic review of factors and venues. Best execution is increasingly also becoming a compliance requirement. The challenge here is that best execution policies and order routing algorithms vary vastly from firm to firm, often best execution policies are unstructured content that guide business decisions to be made. Firms are now struggling to formalise these policies and incorporate the rules into OMS. Also, in their bid to achieve best execution, firms will need to carefully monitor data latency in addition to price and ask themselves whether they should upgrade their systems to cope with the heightened pressure for speed.
TCA is an emerging trend that involves the comparison of a firm’s trading history with the universe of executed trades, thereby assigning execution quality metrics to brokers.
The need right now is for a credible framework for effective analysis. An effective TCA framework should include comprehensive pre- (cost queries, algorithmic recommendations, risk bid analysis) and post- (end of day, longer-term reports, consulting) trade analysis by industry, country, past performance and risk characteristics. The post-trade reports need to provide detailed analytics that should help determine the trade’s impact, measure the overall performance of the transaction and analyse the execution quality.
The Markets in Financial Instruments Directive (MiFID) in Europe has provisions for many areas including conflict of interest, conduct of business, best execution, equity markets transparency, record keeping, client reporting and outsourcing. Reg NMS in the US has provisions for many areas including order protection, market data amendments and non-discriminatory access to quotations etc.
As these compliance regimes near implementation, most financial services firms find themselves scrambling to comply with the regulation’s requirements. Most firms have been working to boost their order-routing capabilities and infrastructure to comply with the regulations and many are fine-tuning their surveillance approach. However, a lot still needs to be done in this area.
In the advent of increasing trade orders and enriched market data connectivity, intermediation of broker groups increases turnaround time for the institutional clients. Buy-side institutions, under regulatory pressure, seek best execution and greater control over their trading strategies. In this scenario, DMA proves advantageous as the buy-side can seek ‘best execution’ in terms of greater efficiency and transparency in the trade execution process. With DMA, the buy-side institutions can rent a broker’s infrastructure and clear via the broker and at the same time take control of the order flow. FIX messaging standards ensure standardised communication across various counterparties. The real motivation for aggressive DMA trading on the buy side is reduced commissions and trading fees. Through real direct market access, investors eliminate any potential conflict of interest with delayed orders or proprietary trading activities.
With the increasing volume of trade orders, it becomes difficult to seek the best price and allocation strategies. Algorithmic trading allows investors to obtain the best possible price without significantly affecting the stock’s price and increasing the purchase costs. These algorithms determine the optimal time for placing an order that will cause the least amount of impact on a price of individual stock or portfolio. Algorithmic trading enables dynamic monitoring of performance until execution is complete.
Establishing reliable external interfaces to critical systems, such as ECNs, market data feeds and clearing houses, has been a major challenge for some time now. Minimising message delays is another critical challenge that trading firms are facing.
It is no longer a profitable practice for brokers and trading firms to operate in product silos, it is not just hedge funds but traditional investment managers that need to be able to trade across all the different asset classes. Initially, firms have been bringing together their exchange traded equities and derivatives on a single OMS, but this is now going further to include some over-the-counter instruments, as well as the overlay of foreign exchange transactions. Ensuring the performance of OMS across all asset classes including equities, derivatives, fixed income and commodities is becoming increasingly important.
Recently, OMS have been evolving into sophisticated solutions that provide automated order management, portfolio modeling, compliance and reporting features tightly knit together to create a complete investment management solution. Hence, there is a rush to broaden the functionality of OMS by including features, such as portfolio management, portfolio rebalancing and post-trade support.
OMS – given their mission critical nature and ever-expanding functionality – pose real challenges to financial institutions. These challenges are aimed at eliminating the vertical silos of technology that traditionally supported each asset class as well as to ensure regulatory compliance. The emergence of algorithmic trading and DMA provide impetus for organised automated trading. Financial institutions must consider various parameters, such as order routing, performance management, transaction analysis, event monitoring and exception management, scheduling and third-party affiliation in the selection of OMS solution vendors.