That is the question. In these times of increasing automation and regulation, how can an honest broker-dealer make a dollar or euro? Some would consider chucking it in and keeping it simple, whilst others are moving towards complexity. Which trading options will work and how do you play the markets for advantage in the future? Chris Skinner takes a look.

The issue these days is that there are few market areas where you can really buck the system. By ‘buck the system’, I mean find alternative trading strategies to really gain competitive advantage. The last real move in this direction was the creation of the credit derivatives market ten years ago by JP Morgan. Credit derivatives are now a $12 trillion market – about the size of America’s annual GDP – but even that is becoming a commodity. Therefore, there appear to be three ways in which the market will split. The first is to create much more complex trading strategies using technology tools to win. The second is to create much more complex products using technology tools to win. The third is to move towards simplicity and outsource the trading operation to those who can afford the complex technological capabilities to manage this on your behalf. All three rely on technology tools to win.

These strategies are not mutually exclusive but can be mutually inclusive, depending on your overall market objectives. Let’s look at what each of these strategies mean in practice.
The first focuses upon creating much more complex trading systems using algorithmic trading. Algorithmic trading has historically been the domain of engineers and scientists who started the market towards more automated trading back in the late 1990’s using quantitative trading strategies and highly automated systems. Alternative titles also included automated trading and program trading. This market began simplistically with buy-side hedge funds and sell-side brokers looking to find arbitrage and other complex plays in the equities and FX markets.

Today, the tools are being used more and more to create cross-asset class plays in all markets and are getting really clever. For example, if the value of MFT increase by more than 0.5% within a 30-minute window during which IBM shares decrease by more than 0.5%, then buy MFT and sell IBM whilst taking a futures option on IBM purchase and MFT sale for one month. In addition, if Oracle shares rise whilst SAP fall during the same 30-minute window, follow with Oracle buy and SAP sell with a hedged futures option. Finally, if these movements occur after a 3 point rise in the NASDAQ, then shift FX strategy from Euro to US Dollars, with a sales option on the dollar.

In fact, you could add as many parameters as you wanted until you were happy you had played all the different criteria that might be required during that period. But it doesn’t end there.
The more you find trading strategies that work, the more you store those in the good stuff and they become inbuilt into the software algorithms. Equally, if your strategies didn’t work, you highlight those and that also becomes inbuilt into the software algorithms as the bad stuff. The result is that the software learns what works and doesn’t work through its ‘learning’ capability and builds that into future trading.

The impact of this is that it provides asset managers the ability to test strategies and moves their trading advice away from their brokers and onto their systems. This led to me thinking, “so what’s the point of active trading when the software tools are getting so good that they will ultimately always know the best trades to make with perfect timing?”

In other words, if we create a ‘perfect’ market where all trading is so sophisticated using algo tools that you can only keep up if you use algorithmic trading, then those without algo tools are dead meat.

Well, not necessarily. And this leads to the second phase of market development which, as mentioned, is complementary to algorithmic trading.

If everyone has algo tools that learn trading strategies and always strike perfect trades with perfect timing, then those in the human world have to go and find something else to do, such as more and more complex credit derivatives.

When the JP Morgan team created credit derivatives in the late 1990’s, they were looking for a way to create something that would be hard to copy. A bit like swaps, arbitrage and other futures and options, it was really a way to find another market.

Sounds easy, but if it was then every bank’s investment team would be winners. What the JPMorgan team succeeded in doing is creating something that was hard to copy because, by the time others knew what it was, they had taken the lion’s share of the market.

By way of a brief explanation. The way credit derivatives work is like an insurance policy for a bank’s credit risk. For example, the bank takes a basket of loans that mix $1 billion to Panama, $5 billion to Mexico and $2.5 billion to Columbia, and then offers the option on these loans defaulting as a credit derivative. The investor only has to pay on the derivative if the borrowers default. Meanwhile, if the loans are paid off, then the investor has made a packet of cash from the premiums the banks pays to offset their credit risk. That is why, in simplistic terms, credit derivative are an insurance policy for a bank’s loan book and is one of the major reasons contributing to the acceleration in lending over the past decade.

Banks love credit derivatives because it takes possibilities of substantial loan losses off their balance sheet and onto someone else’s, such as General Motors. That is why it is a market that has ballooned over the past decade from nothing to $12 trillion – bigger than the USA’s GDP – but is also now a market that has commoditized.

Like any great trading initiative – swaps, structured finance, arbitrage – the more popular the initiative, the more money it makes, the more others want to get in on the action, the more competitors start to buy the people who make the money, the more commoditized the market becomes. After ten years, credit derivatives are reaching market maturity and so the next big play is now being looked for. That big play is likely to be around complex cross-asset class trading strategies that combine equities, bonds, options, derivatives and FX.

These incredibly complicated products can only be dreamt of by having the technological capabilities to support such complexity. And, as technology automates trading generally, the complex products for human traders can only be created by using the sophisticated automation tools now available to him or her.

There are huge dangers in this of course, as witnessed by the collapse of Enron, WorldCom and Parmalat. That is why the regulator will watch these new trading strategies and products very carefully to ensure it is not just fleecing the markets. However, the only way in which markets can operate is to always offer risk and rewards, and the greater the risks, the greater the rewards. That is why the markets are getting so complex, in order to create greater risk and reward opportunities.

Perhaps the approach is best summed up from a line out of Michael Lewis’s book Liar’s Poker, which described life in Salomon Brothers in the late 1980’s. In the book, Salomon’s head of law, Donald Feuerstein, took great delight in finding “chinks in the regulator’s armour” which he could use in ways to buck the markets and make greater returns. In other words, the more markets are commoditized and regulated, the more complex and sophisticated the products and strategies which need to be used to generate returns. In today’s world of turbo-charged algorithmic cross-asset class trading strategies, that can only be delivered through massive investments in systems.

Which brings us to our third strategy. The technological turbo-charging of the investment markets has meant that you have to have a huge cheque book and pen to be a player these days. The ability to offer near real-time, systematic, automated, algorithmic trading of micro-derivatives of derivatives across asset classes means that the second-tier sell and buy-side players are either too technology naïve or budgetary handcuffed to even be a player. Those firms will end up giving their trading to a third party to manage. After all, if you can outsource your customer service centre to Mumbai, why not do the same with your investment banking division if you can’t afford to be a top player?

Therefore, there will be a range of firms who find algo trading and derivatives are beyond their comprehension. These firms will ditch their trading desks and get someone else to do it for them. By way of example, Acme Trading Desk Services Ltd offers outsourced trading desk facilities and soon finds that Acme supports Hedge Fund A on a Monday, Fund Manager B on a Tuesday, Investment Manager C on a Wednesday, Insurance Group D on a Thursday and maybe takes Friday off because the systems need a bit of an overhaul.

This vision is not too far away, and is in fact becoming even more likely thanks to legislation such as MiFID and CP176 from the UK’s Financial Services Authority that relates to soft commissions and bundled brokerage.

The combination of regulatory drivers, technology chargers and market makers’ margins, will deliver a future world in financial trading that will only be played by those who have the depth of pocket and technological know-how to be a player.