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The Art And Science Of Trading

By Jacqueline Loh, Former Head of Asia Trading, Schroders
Jacqueline Loh_16Technology has completely changed the way traders go about their jobs. The modern trader is tech savvy and able to handle the various types of platforms and trading venues now present on his/her desk. Trading desks are much quieter places; phones hardly ring while communication with brokers takes place on electronic chat. Matches for the other side of the trade are done electronically. When they are found (often heralded by a pop up alert on one’s computer) negotiations too take place electronically. As trades are electronically sent from the traders’ order management systems (OMSs) and execution management systems (EMSs) to the executing venues and back again, errors are minimised, leaving the trader to focus on the task of trading. The ability of trading systems to electronically capture timestamps and live prices has spawned a whole new world- the science of trading performance measurement.
As trading algorithms continue to improve, more of trading has become automated. The more liquid names eg, where order sizes are less than 5% median daily volume(MDV) can be traded by algorithms in most markets. Orders which are half a day’s volume or more will require trading by hand and looking for blocks. For most markets in Asia, trading in small cap stocks will still require much manual intervention. In Japan, where high trading frequency (HFT) prevalence is an added challenge, it is even more important to stay in the market for only short periods of time.
Instinct vs technology
Electronic venues continue to evolve, making it easier to look for matches even in the less liquid names. Although pre-trade analysis might be of some help in valuing a block of stock of several days’ volume, pricing is still subjective and requires exercise of a trader’s judgement. The traders’ broker relationships also add value here, in sourcing and placing blocks of stock with information disseminated to the right people and controlled impact costs.
Bayes Theorem, pattern recognition and machine learning will drive the new generation of trading algorithms. However, I think block pricing under different market conditions will still require some human skill and judgment.
Trading by instinct is good and will always remain so. However, the use of transactions cost analysis (TCA) can further refine that skill by identifying the situations in which that has worked well and situations where it hasn’t. As human beings, it is natural for us to keep remembering our successes, but not so much our failures. We will always tend to remember the time we beat our benchmark by 100 bps but perhaps not the 10 times we underperformed by 10 bps.
The ultimate end game
It’s all about getting better. The end game must be about increasing trading quality for the benefit of clients. The art of trading is in the trading of small cap and illiquid stocks, the science is in the use of quantitative forms of performance measurement to keep moving forward. Performance measurement is always an emotive issue. Yet, performance measurement is a very necessary evolution of trading processes. It is an admission that traders are not infallible and we are always looking for ways to improve. The objective of using TCA in any review is never to devalue the broker relationship but to numerically record the successes made possible by that relationship. Equally, it gives the buy-side trader an insight into the successes (or not) of those strategies employed, and the trader’s inherent strengths and weaknesses.
The future will see the trader making increasing use of technology to achieve scalability and higher execution quality. Scalability will be achieved as EMSs become more advanced, allowing enhanced real time monitoring of larger portfolios of stocks over wider parameters. Development of advanced algorithm portals will offer insights into how algorithms interact with the market. As the science of trading performance measurement grows in increasing sophistication, frameworks for comparing algorithms will be developed, identifying best in class algorithms, as well as the trading environments they work best in. Machine learning and artificial intelligence platforms will also allow more different variables to be taken into account in algorithm design. The modern trader’s skill will lie in adapting his/her strategies accordingly to achieve optimal results in a circle of continual improvement.
When science meets art in the guise of the modern trader, the benefits are higher standards in the industry and satisfaction of a job well done for the trader.
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