By Theo Hildyard , Software AG
@theohildyard

The Trade Tech conference in Paris this years was packed with buyside firms looking for ways to utilize real-time intelligence. The old-fashioned image of long-only fund managers was fading fast during the conference, with buyside execs getting serious about reducing slippage, lowering transaction costs and making use of data and deep analytics.

In panel after panel the discussions centred on the need for the buyside to get smarter about decision making by empowering investment managers with real-time tools.

Real-time transaction cost analysis (TCA) was high on the list of most of the panellists. Real-time TCA is increasingly important to the buyside; they want to know how much slippage the trade volume will create, and how much that eats into alpha.

Panellists agreed that TCA needs to be done pre-trade, along with the assessment of risk. And both of these need some context, i.e. historical analysis of relevant Big Data.

There is conviction that risk and compliance analysis needs to be immediately available to many in the firm – and not just the risk and compliance officers on the ground. This requires a lot of data to be analyzed to “very high resolution” using mathematical models.

Regarding both risk and slippage, there was some discussion whether traders should trade larger volumes over a much longer time period in order to lessen slippage. Many traders want the trade done ASAP, feeling that there is less risk this way, and trade far too aggressively, said one panellist.

Others said that real-time TCA is needed as traders are increasingly required to “justify every trade,” as are portfolio managers that make the investment decisions. This can be done using historical data, but not quickly enough. Historical data analytics can be key, however, for helping to reveal whether an order is better suited to an algorithm or a “high touch” broker.

One panellist said not to overlook post-trade TCA: “Most people think of post-trade TCA as why a trade went wrong. That misses a trick; it can also be able why a trade went well.”

Unbundling transactions costs was also discussed as one of the keys to managing execution costs; separating research costs from execution, for example. Once separated, the costs can more easily attributed to specific funds, i.e. some funds may really heavily on research but all funds pay a bundled execution fee.

Some panellists felt that funds are driving toward buyside-to-buyside trading to save on costs, but most felt that brokers perform a valuable service and can help to mitigate risk.

Another discussion focused on social media as a trading tool. Panellists agreed that real-time news is key but – surprisingly – most felt that social media and Twitter sentiment was not really relevant to fund trading. There has reportedly been analysis done that positive sentiment on Twitter does not predict price rises. Also, interestingly, negative sentiment predicts volatility but not direction.

Long-only funds found that it is better to follow the footprints high frequency traders left in micro-market structures– i.e. the footprint left in the market by HFT is useful information! For, although funds trade less frequently than HFTs, they do trade a lot – there is constant portfolio rebalancing and tweaking going on.

My conclusion after attending these panel discussions was that, although there is increased interest from the buyside to use real-time tools to help them save costs and prevent slippage, there is little appetite for them to try and circumvent or replace their brokers. After all, the brokers have already made a lot of investment in technology – the algos, the risk tools – that benefits the buyside.