Securities exchanges involve large quantities of data. It’s not just the data of the actual transaction. It’s also the over-the-counter data from information sources such as Bloomberg, as well as the after-the-fact data such as derivatives and commodities contracts. Increasingly, that data is accessed on mobile devices.
Large exchanges such as New York and London Stock Exchanges have their own data standards. So do leading information sources like Bloomberg. But there’s no unifying standard for the communication of data among all the relevant parties. And in many cases there are no real standards that smaller stock exchanges or alternative trading system (ATS) firms around the world can use to facilitate communication with major exchanges.
The lack of a universal legal entity identifier (LEI) is a well-covered issue. In a similar vein, absence of standardization and shared methods for other components of securities-trading data and financial-markets content impedes information flow across global trading venues.
The content that could be distributed in standard formats, structures, and methods could be pricing data, reference data, yield curves, contracts (derivative instruments), correlated newsfeeds, sentiment ratings, and accompanying or related but unstructured data.
Big Data, Big Problems?
Securities trading uses three types of data:
Transaction data—This is the information involved in the actual transaction. It starts with the tick data coming in from listed securities. That’s a fairly well-defined and narrow data set, though it involves companies doing business in countries around the world. It also includes the over-the-counter data from information services such as Bloomberg and Reuters.
Trade-order generation, routing, and trade execution also use supporting data such as pricing history, ratings, asset class specific data, corporate events data, and even correlations across news and market events.
Large exchanges such as the New York and the London Stock Exchanges conform to certain protocols or standards. Leading information providers such as Reuters also have standards that they make available, though the structure of their data can vary widely.
But many smaller players in countries around the world don’t communicate data in a standardized format. So if traders in New York want to access data from an over-the-counter exchange in Johannesburg, they may face complications in data retrieval and transformation to get it in a format they can use.
After-the-trade data—This is information after the trade execution and includes contractual terms and conditions, payment/fulfillment details, asset-specific issuer and constituent data, instructions for clearance, and taxation.
Whenever there’s a transaction, specific financial accounting rules apply depending on the asset type and jurisdiction of the firm. The transaction history, along with historical price and quote data and the execution details, are part of an audit trail and, in some cases, trading and execution strategy.
Mobile data—This is information transmitted to mobile devices. The demand for transmission of time- and mission-critical data to these devices is on the rise. A growing number of users involved in securities trading make decisions based on information they access on a mobile device. The messaging systems used on devices such as iPads and BlackBerrys are optimized for multimedia data and may not be appropriate to presenting enriched trading data. Furthermore, network security protocols aren’t necessarily compliant for securities trading.
The set of standards and protocols that could be the lingua franca of global trading would extend beyond data representation to include methods and object models, structure for hierarchies and insertion and relationships for object models, enriched content, and embedded data sets such as time series for certain economic indicators. The standards would be suitable for development and deployment across various platforms, including mobile.
The securities community already relies on a number of standards for the exchange of data. Financial Information Exchange (FIX), a protocol for real-time communication of data related to securities transactions and markets, and the Society for Worldwide Interbank Financial Telecommunication (SWIFT), a network and set of standards for communications related to transactional banking, are leading examples. The NYSE uses the Common Message Switch (CMS) as its own protocol for communication. The Federation of European Securities Exchanges (FESE) adopted the Market Model Typology as a standard for post-trade equity data in February. The Financial Information Services Division(FISD) of the Software and Information Industry Association (SIIA) promotes standards, as well. However, these efforts remain fragmented.
The consolidation of exchange ownership in fewer hands in recent years, as well as the increasing appetite of content owners to transmit their services to mobile users, should provide enough incentive to create the next-generation communication platform for securities.
Sinan Baskan is Vice President, Capital Markets for SAP. He is responsible for developing integrated solutions for Capital Markets and Business and Ecosystem Development. His team has developed and delivered solutions for e-Trading, Risk Analytics and Regulatory Reporting and Compliance for financial services customers. Prior to his current position, Sinan was Vice President of Risk Technology for the Americas at HSBC Corporate and Investment Bank. He has held positions in engineering and product management at Sybase (1993- 2005) and rejoined Sybase in 2007. He started his career at Philips Research Laboratories and at IBM Research Division.