Debate: The pros and cons of algorithmic trading

Algorithmic strategies explained

	Richard Balarkas (RB) is a managing director of Credit Suisse in the investment banking division, based in London. He has responsibility for the Advanced Execution Services sales product in the equities department.
	Daemon Bear (DB) is head of equity trading (Europe) at JPMorgan Asset Management. Daemon has been with the firm since1998. His previous trading roles were with Worldinvest Investment Managers (now New Star Asset Management) and GT Management Asset Management (now Invesco).
	Alasdair Haynes (AH) is the CEO of ITG Europe and is responsible for the strategic development of ITG in Europe. He joined after a 20-year career in investment banking, having held senior positions with Bankers Trust, UBS and HSBC.
	Tom Middleton (TM) is head of the algorithmic trading team, covering more than 17 markets across Europe and Africa for Citigroup Global Markets in London. Tom has been in the team since its inception, developing algorithmic trading strategies that adapt to the idiosyncracies of a diverse set of markets.
	Peter Urry (PU) is head of dealing at JO Hambro Capital Management and is a 30-year veteran of the securities industry. Previously, he was a proprietary trader for one of London’s first hedge fund managers, IFM Trading, and a sales trader in risk arbitrage products for Barclays Capital.
	Tim Wildenberg (TW) runs the European direct execution services team at UBS Investment Bank. He has been responsible for establishing UBS’s equity electronic trading service and recently has been working on rolling out UBS’s leading edge quantitative trading tools for client use as part of the Direct Strategy Access Service.
	Stephen Wilson (SW) is head of exchange-traded products at Reuters.
	Steven Wood (SW) is an executive director and global head of trading investment at Schroder Investment Management Limited. He joined Schroders in 2002 from JPMorgan. He is a member of the Euronext advisory board, the Liquidnet advisory board and the Deutsche Börse advisory board.

AH, ITG Europe Yes – and for the purpose of this discussion the focus will be on equities only. Algorithmic trading strategies are mathematical models that automate trading processes using predefined parameters or benchmarks. Algorithms achieve alpha in an environment where speed and the prevention of information leakage are paramount. These types of algorithms were first developed for this purpose approximately 10 years ago. Algorithms can be classified into two broad groups: structured algorithms and opportunistic algorithms. Structured algorithms – for example, VWAP or implementation shortfall – make greater use of cost and risk modelling; opportunistic algorithms – such as pairs or volume participation – make greater use of heuristics.

TW, UBS We define algorithmic trading as any form of automated rule-based trading where decision-making is delegated to a computer model. That includes hedge funds running prop strategies, the sell side executing its normal order flow via a rule-based engine or the buy side using algorithms to execute their business without going through a normal sales-trader. The key development is the increasing use by the buy side of algorithms to execute their business as an alternative to the traditional route. Typically this type of algorithm is designed to target a trading benchmark: for instance VWAP, TWAP, implementation shortfall or market-on-close [see Algorithmic strategies explained ]. Although algorithmic trading has been around in some form for a while, decimalization in the US – which triggered a decrease in average trade size – and the increase in US market venues a few years ago, has recently prompted an explosion in it as traders have increasingly found it impossible to achieve high-quality execution without using the technology. More recently it is the use by the buy side of broker-built strategy servers that has become the recognized and most widely used definition of algorithmic trading.

AH, ITG Europe There are a number of different tools and uses. Even within the trading space there are clearly different types. There are heuristic-type algorithmic tools, which are used in opportunistic methods – for example, volume participation strategies. And then there are more quantitative and analytical tools, like implementation shortfall (the difference between the decision price and the price actually achieved). It’s a very wide subject. Algorithmic trading clearly is not the same product as direct market access (DMA), nor is it a completely separate tool – whereas algorithmic trading is a strategy for trading, DMA is a destination and routing issue, not a strategy.

TW, UBS Algorithmic trading services are very different to pure DMA, which is a much simpler product.

RB, Credit Suisse I think we’ve moved through three phases of algorithm development. Volume, time-based strategies – TWAPs, VWAPs – that was the first phase. The second phase was price-based, shortfall-based and reduced impact. And now we have idiosyncratic, personalized or customized strategies. But all three are still perfectly valid, because fundamentally as a trader you’re thinking, ‘How fast have I got to trade versus how much impact is it going to have?’, and if the price is moving in your favour one day, the simplest way to make sure you trade for six hours is to TWAP it.

TM, Citigroup It is very clear what the first type of algorithm is going to do: all of them can be broken down pretty simply into two parts. First is a scheduling part – how much do I want to trade in the next 30 seconds or in the next 10 minutes? Second is how I’m going to achieve that capture of liquidity. For instance, VWAP will trade according to a historical volume profile, possibly with dynamic adjustment, TWAP will trade similarly to VWAP, but with a flat volume profile, and so on.