Quant Model environment

I have received quite a few queries on specifics of quant models that we seek to build. While it would be difficult to give a list on such a dynamic initiative, I can certainly share with you by way of the changes that we anticipate in view of the current global practices in this industry.

Algorithmic trading (“placing a buy or sell order of a defined quantity into a quantitative model that automatically generates the timing of orders and the size of orders based on goals specified by the parameters and constraints of the algorithm”) has permeated the sell side of the securities trading business. It is being embraced, albeit at a slower pace, by the buy side of the securities industry. The nature of the relationship between the buy side and sell side, has changed, most notably in the way in which traders on the sell side articulate and transfer their market knowledge to the buy side.

What further changes may be anticipated?

Business developments

• While technology advances, in particular the enhancement of securities order management systems, have enabled efficient adoption of algorithmic trading technology, availability of cheap money globally has provided a hitherto unknown unsurpassed level of liquidity. Liquidity and the consequential increase in transaction flow has been the catalyst of the growth of algorithmic trading; equally a tightening of liquidity through the anticipated rise in interest rates globally over the next two years may restrain the velocity of its further growth.

• Pre-trade transparency requirements of the EU Directive on Markets in Financial Instruments (MiFID), especially for equities, is almost certainly likely to see further developments in Order Management Systems (OMS). The value of OMS to the business is dependent on, and will be judged in part by, the effectiveness of the algorithmic applications which accompany them.

• The equities line-of-business has provided much of the growth in algorithmic trading, while there has been some resistance to its growth in the foreign exchange market. However, algorithmic trading applications are increasingly featuring in cross-asset, complex and structured transactions; for example, where an equity transaction is accompanied by a derivative transaction in the underlying equity and simultaneous foreign exchange. Indeed, much of the leakage of algorithmic trading into the foreign exchange market arises through cross asset transactions. The growth of electronic trading through ECNs is already promoting the use of algorithmic trading by institutions on cross-asset transactions.

• The ability of major sell side institutions to consolidate and support cross-asset transactions from an organisational, operational and technology perspective are pre-requisites, if institutions are to manage high volume flows of cross asset transactions and to apply algorithmic trading to enhance the quality of execution of the transactions.

• Algorithmic trading may not, as yet, be extensively deployed for execution of derivatives transactions but it is, again, increasingly used to execute transaction futures or options instruments as one of a series of executions in complex cross asset transactions.

• More simple articulation and de-mystification of algorithmic trading will lead to a growth in the volume of algorithmic trading by the traditional buy side of the business in Europe, where technology innovation is embraced more slowly than in the USA.

• The desire for increased anonymity at the point and time of transaction execution is a continual stimulation to the growth of algorithmic trading volumes.

• Increasing competition between markets and exchanges is increasing the fragmentation of liquidity. MiFID is projected to increase this trend. Algorithmic trading is the ideal and certainly the only cost effective way of identifying and tracking liquidity across a range of markets and exchanges.

• Hedge funds will continue to play a significant role in the development of algorithmic trading. They provide some 40–50% of the equities transaction volume on major US and European Exchanges, 70% of convertible bond volume and 80% of distressed debt volumes. They have lead the charge and are at the “bleeding edge” in deployment of algorithmic trading on the buy side of the business either directly or through the use of their Prime brokerage relationships. The proliferation of hedge funds means more competition amongst them. They have to differentiate themselves through the range and innovation their products and services, including their trading strategies.

• Algorithmic trading applications are essential to the hedge funds' armouries for development of their business strategies. Automation, maintenance of low execution costs, increased dynamic use of data and analytics, are all essential to the development of this element of the buy side of the financial markets. They can form the basis for delivery of new hedge fund applications.

Technology developments

Many of the required technological developments and capabilities for algorithmic trading are self-evident from what has been said but deserve specific reference:

Availability of technology tools is essential.

Tools must service across asset classes including highly efficient router libraries.

The technology must be capable of creating and holding new sets of strategies and, above all, it must be able to conduct enormous computational strategies.

Underlying this, the technology must allow for the creating computational platforms for both historical data and data streaming purposes.

The technology has to accommodate complex data structures, filter data and fuse data structures almost on demand. This would require the creation of independent data structures.

The demand for high-quality, clean and filtered data, which is essential for equities algorithmic trading, will spread to other asset classes, fixed income and derivatives trading, where there is not the stream of transactions.

In summary, the algorithmic technology has to be able to deliver -

A flexible and dynamic technical environment, which can deliver value at risk computations and analysis.

For hedge funds, specifically, collateral management data and features to mange the funds' leverage (borrowing) capacity and capabilities.

Real time graphic spreadsheets with strong easy visualisation and articulation of data and trends.