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The derivatives market undergoes transformation as it moves closer to the exchange

On the regulatory front, a handful of new policies are set to affect the options market and will likely enhance transparency, execution, and market data. Meanwhile, the cash equities business has hit a level of maturity and we are now trending toward better tools for block crossing and less resource intensive methods of trading.

foreign exchange

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Driving competitive advantage through FX TCA

“The most valuable commodity I know of is information” – to quote Gordon Gekko from the 1987 movie classic Wall Street. This line has never been more significant than in today’s data-fuelled financial markets, where detailed analysis of information can provide that all important competitive edge – both now and in the future. To achieve this, firms are looking towards Transaction Cost Analysis (TCA), which enables them to reduce costs and hone trading strategies.

This isn’t new. TCA has been established in equities for many years and while compliance was initially the main driver, it has increasingly proven to add alpha to the execution process. As a result, it’s not surprising that the TCA focus is now spreading beyond equities, with foreign exchange (FX) trading trading now coming into the spotlight.

It is not simply a case of applying the equities model to FX, however. The idiosyncrasies and inherently complex structure of currency markets have presented challenges to TCA’s progress in FX. Added to this is the fact that FX has often been outsourced to third parties, or may be a secondary or subsidiary trade linked to another asset. So not only has accurate TCA for FX trading proved more demanding than in equities, but the impetus has not been there in the same way.

However, times are changing, and recent events emphasize the need for smarter use of TCA in FX. As shown in ITG’s recent report on tradable data between London and New York before and after the 4pm fix, the costs from the order arrival time until trade execution are on average 17 basis points, 20 percent of the time. This is crucial as 17 basis points of implementation shortfall for up to 20 percent of all days can potentially cost asset managers millions of dollars of value lost from their funds. Not an insignificant amount for any investor.

With greater scrutiny on the role and efficiency of the 4pm London fix, the time has come for the asset management community to take full advantage of the data available to inform their execution strategies. Forward-thinking investors are already beginning to position FX TCA as a critical business function, enabling better trading outcomes and enhancing performance. Last month’s data can be a significant input to this month’s decisions on when and how to trade, maximizing the benefit from their FX trading. This takes on even greater significance when you consider the percentage of the collective pension funds which directly or indirectly participate in the currency markets. Even incremental improvements must be pursued.

To make these improvements, asset managers need to squeeze the very most out of all the information at their disposal. For this to happen, TCA providers need to anticipate what an asset manager might require from their data analysis in the future. A combination of growing regulatory pressure and a need for higher returns is triggering clients to demand even better execution. This will see them asking more testing questions above and beyond the standard analysis.

New technologies are also driving new trading strategies in FX, resulting in a need for further analysis on topics such as algorithm selection and the use of trading venues. Such granular analysis sits alongside the broader questions of investment process such as the most effective time of day to trade a given currency pair, the optimal frequency of book squaring, or the decision on when to use an Electronic Crossing Network (ECN) rather than calling a bank on the phone.

The key to unlocking future success is for the TCA provider to work closely with the asset manager ahead of execution. This enables both the provider and the asset manager to get a better understanding of the investor’s objectives and set these against the prevailing market conditions before deciding on its trading strategy. It really is a classic case of using data to support the decision making process. As we move forward, asset managers who achieve competitive advantage will be the ones that adopt this approach in order to evolve their trading strategies in FX, using that “most valuable commodity” – information – to answer the increasingly testing questions for tomorrow, not just the ones for today.

european markets

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Examining the WM/Reuters London Close through the Prism of Foreign Exchange Transaction Cost Analysis

Please find this article referenced in the Wall Street Journal. Responding to many client requests, the FX team at ITG Analytics reviewed trade data surrounding the WM/Reuters London Closing Spot Rate Service (“the fix”). By observing the factors that influence…
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asia pacific markets

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How Analytics Can Help Make the Most Out of Asian Liquidity

The term ‘TCA’ has now become so common across the industry, and some would argue commoditized, that its value is in danger of becoming misunderstood. While most buyside firms use some form of broker post-trade analysis to measure how they’ve performed against their benchmark, the firms who are out-performing versus their peers are using a broader approach of pre-trade, real time and post-trade analytics to answer questions about how and why trading costs are incurred, and what actions can be taken to reduce them.

canadian markets

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Canadian Order Flow Trends, Q3-2013: What’s The Value of Your Market Data?

Our analysis of Canadian equities order flow this quarter indicate that HFT activity has scaled back slightly:  total order flow reduced, Order-to-Trade ratio decreased, Volume Traded-to-Order ratio increased, and the rate of order activity has slowed down. We also add to the debate over the cost of real-time market data – we describe an objective method to intrinsically value market data using three core factors.  How much should market data be valued?