Conference Dailys TRADETech Daily 2018 - Page 15

THETRADETECH DA I LY THE OFFICIAL NEWSPAPER OF TRADETECH 2018 Miles Kumaresan, head of trading and FinTech at Nordea Asset Management, has brought his technology expertise and experience to tackle the ongoing issue of sourcing liquidity in the bond space through equipping traders with the right tools for the job. T here is nothing more challenging and relevant now than the aggregation of fragmented liquidity in all asset classes, especially in fixed income,” says Miles Ku- maresan, global head of trading and FinTech at Nordea Asset Management. “I’m consumed with this problem.” Enhancing Nordea’s ability to source liquid- ity in the fixed-income markets, particularly in the credit space, is the most prominent conundrum facing Kumaresan. Having spent the bulk of his career with investment banks and hedge funds, Kumaresan is now applying his experience with the Copenhagen-based asset manager, having joined the firm in February 2016 with the dual responsibility for Nordea’s trading and technology teams. “I was never a born trader,” he says. “I was one of these guys who could create tools to help with execution. Everything that we are doing now is to create tools; where they already exist we will use them, but if they don’t exist then we will create specialist tools for the traders, either in liquid or illiquid brackets. This is what we do.” It is, of course, a futile exercise to imple- ment technology tools without the right teams in place to use them. As well as its 26 traders located in Copenhagen, Bergen and Stockholm, Nordea has invested in a five-per- son trading research and development team and a five-person IT team focused on trading, a reflection of the company’s recognition that technological innovation is critical to staying competitive. Building a diverse team An efficient trading operation requires a di- verse set of traders, ranging from classical to quantitative, to enable it to thrive. Kumaresan says that Nordea has a strong base of expe- rienced traders to which it has added three junior traders with strong quantitative and IT skills, brought in last year. “Everyone in trading has their skills enhanced continuously wherever they are lagging,” he adds. “This is how we were able to retain all of our traders during transition and leverage off their depth of experience. Stacking the desk with only cheaper junior talents was never an option.” The first thing to take care of in the process is risk rather than reward, according to Kumaresan. Operational mistakes can occur extremely easily with manual repetitive tasks and some of these errors can be very costly. “You need to automate all the operational instructions at every part of the pipeline, starting at the order source, all the way down to it hitting the trading desk,” he says. Nordea’s trading structure has evolved throughout the two years Kumaresan has been with the firm, the primary of which is the di- vision of teams by instrument liquidity across asset classes first and then by specialisation. Orders traded in the new cross-asset “Liq- uid Trading Desk” are suitable for automatic routing to brokers with either zero or very low touch, using what Kumaresan calls a pro- prietary systematic model that plugs directly into its FlexTrade execution management system. This frees up the high touch trading teams to add value on demanding orders. The asset manager also has specific teams dealing with high touch trading where spe- cialist knowledge and human involvement can be required. But, even here, traders are given tools in line with the technology-driven income. Finding liquidity in the fixed income markets, specifically in credit, has become one of the most enduring puzzles across the buy-side. Credit liquidity is fragmented and discontinuous across both price and time partly due to the infrequent trading property of corporate bonds, explains Kumaresan, which is not helped by the fact there are over ten thousand ISINs in Europe and twice that number in the US. “Fixed income is still in the dark ages in a technological context,” says Kumaresan. “Before the crisis, banks had all the balance sheet needed to price a wider range of bonds in size. Now you have the same trading struc- ture even though the sell-side can no longer carry big inventory.” The more advanced segments of the sell- side have been trying to make up for the shortcoming of diminished inventory through technological and quantitative innovation— for example, by automatically pricing a large universe of bonds in order to offer tradable “Everything that we are doing now is to create tools; where they already exist we will use them, but if they don’t exist then we will create specialist tools for the traders, either in liquid or illiquid brackets. This is what we do.” focus of the company. Fixed income traders, for example, use tools that aggregate real-time and historic market data sets to assist with price and liquidity dis- covery. In equities, the company is developing a model to price liquidity in very large orders to help traders with large blocks. The liquidity conundrum Equities may have its fair share of challenges in aggregating liquidity across lit and dark venues; however, liquidity in equity markets is a relatively predictable phenomenon, mostly due to price transparency. Foreign exchange, meanwhile, still has its antiquated mores—in particular the last-lo &7F6PFBF7F'G2FRG'VRƗVFG7GW&RFR&VƗVFG&&VƖW2fV@7G&V֖r&6W2V&W6&VƖWfW2F20( Ɨ2&r7FWFR&vBF&V7F( vFЦfF2bF2B&V6֖r7&F6vpf'v&B&WFrFR&R2&fFW'2`V7ƗVFG( FRWr&VƗGbFR&WB7G'V7GW&Pv'&G2WrG&Fr&Fv( 60V&W6( ħW7B&V6W6RvRvWBFR&GbW"&FW'2FRFRf'7BFFW2@ǒFBFR&WB2ƗVB"FBFP&6W2vRvBvW&RvBB2W&VǒFR&W7@&6RvfVFR6&7V7F6W2&VƖWfRFB'F7V&ǒ7&VFBFR'W6FR&V6֖p&6RW'2FRW2v6vRfPGW&FW&W7BF&VvFG&BЦr27&F6FvVW&FRBGG&7BƗVFGF27FfRvb6W&6rƗVFG6V@&V6R&VF7'WFfRWfVB7&VFB( Ф77VRFUG&FTWw26У