The following white paper is sponsored and written by Informatica, HP, and Mellanox Technologies.
The securities trading market is experiencing rapid growth in volume and complexity with a greater reliance on trading software, which is supported by sophisticated algorithms. As this market grows, so do the trading volumes, bringing existing IT infrastructure systems to their limits.
There has been some real action of late in the standards space for corporate actions. In the US, the SEC issued a requirement for corporations to report in XBRL, and the XBRL US organisation has announced a project with the DTCC and Swift to tie the standard into ISO 20022, all of which could optimistically see true corporate actions automation from issuer to investor.
In the post-credit crisis financial services industry, risk management, compliance and transparency have emerged as focus points for review with provision of accurate and timely data recognised as a critical element of success. Fundamental to data provision is the accurate identification of both financial instruments and counterparties – without which you cannot truly measure your performance or exposure.
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.
Only a few years ago – but crucially, prior to the financial crisis – surveys which reviewed progress towards corporate actions automation highlighted the key drivers for automation as reducing costs and inefficiencies. Whilst important, these were essentially technical objectives and correspondingly the debate, and its participants, inclined heavily towards the operational parts of the business. There are important signs that this is changing.
New white paper from Cinnober:
Algorithmic trading and the related term high frequency trading are generally defined as using computers to generate orders that are entered on marketplaces. Lately the term, rightly or wrongly, has been widely associated with its negative impact on marketplaces from a technical as well as a business and fairness perspective. Others argue that the positive effects of algorithmic trading – such as increased liquidity and the elimination of market inefficiencies – outweigh its potential negative effects.
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