Data Management for New Trading Opportunities
As high-frequency and quantitative trading techniques mature, trading firms are finding it harder to make money. Market practitioners are recognizing that speed of market access alone is no longer sufficient to stay ahead of the pack, as low-latency connectivity enters the mainstream.
The emphasis is returning to the quality of the trading model. In order to gain an edge, innovators are using more sophisticated techniques to improve the effectiveness of their trading models. These techniques range from analysis of unstructured data to generate an assessment of market sentiment, to the use of emerging machine-readable news services.
In addition to a focus on the quality of the trading model, firms are using new capabilities to identify cross-asset opportunities: across linked instruments, linked execution venues and linked geographies. And they’re applying many of the techniques used in listed markets like equities, futures and options to over-the-counter markets. The market has already witnessed firms deploying event-driven and algorithmic models for foreign exchange trading, and fixed income and derivatives capabilities are emerging.
Key to success in all of these new trading approaches is a sophisticated data management strategy, as firms are forced to draw upon new data sources to drive their models and trading applications. These new sources range widely in type: from traditional but unstructured data, such as text-based company reports, to machine-readable news (MRN) and unfamiliar data sets used in OTC markets, like synthetic pricing and valuations models.
This briefing looks at how trading firms are using innovative techniques to take trading to the next level, and the data management challenges that they need to overcome to be successful.