While Best Execution and Transaction Cost Analysis (TCA) are well-established in equity trading, other asset classes have been slower to adopt such techniques due to limitations in market data and market structure characteristics. In Over-thecounter (OTC) markets there has typically been no requirement for central reporting, making it difficult to demonstrate best execution in the same way as for equities. This is beginning to change due to pressure from regulators and end investors who require higher standards of information. Market structure changes, with more electronic platforms taking increasing shares of trading, are also enabling more precise analysis. Over the last three or four years, Foreign Exchange (FX) TCA has become increasingly mainstream for asset managers, while one recent survey shows that in the past year, Fixed Income TCA has become the fastest growing category of analysis1. These trends are expected to continue, not least in the light of MiFID II regulations.
As the next advance in FX TCA reporting, our clients in the investment community have requested size-adjusted spread (SAS) benchmarks that account for risk and liquidity on a pre-trade and a post-trade basis. However, one of the more frustrating aspects of over-the-counter trading is the lack of transparency around these spreads. An accurate size-adjusted spread based on aggregated electronic foreign exchange quotes would replace the old method of supplying expected spreads: manually-filled matrices for each trading region with spreads for given currency pairs and sizes. Buy-side traders depended on this information to both hold banks accountable for their agreed spreads as well as manage their own expectations for costs. Now that buy-side firms are more responsible for currency risk, they need a system that will digitally re-create those matrices and give them a benchmark that will show that they add value to the investment process.
“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.
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 trading costs using ITG TCA® for FX’s rich quote data we found trade patterns that were unique. Consistent with academic literature,we show that volume and volatility around the fix spikes and the spread costs tighten temporarily. In addition, we see mean reversion of the FX rates on days when there is substantial price pressure shortly prior to the fix. Our analysis does not prove the allegations of manipulation brought about by some market participants.
In this interview from the Winter 2012/13 issue of Best Execution magazine, Jim Cochrane talks about the challenges of achieving best execution in the FX market.
While pricing more thinly traded currencies or emerging markets may be challenging, traders can now insist on certain measures in order to better understand the quality of their executions. Sean Hefkey offers some timely suggestions.
The challenges of creating algorithms for FX trading are many, with no central limit order book, depth of book or volume information to draw upon. Firms are using new market microstructure knowledge and market data to move away from the historically manual FX processes to more automated, anonymous electronic trading.
In the historically unregulated FX marketplace, trading practices have carried on in the same fashion for many decades, with a few major dealers dominating. By its very nature, FX trading transcends borders and therefore, has not come under the regulatory scrutiny that other asset classes have.
The benefits of multi-asset system are undeniable—reduced possibility for data entry error, consolidated compliance, enterprise-wide risk management, and standard benchmarking for trading—not to mention seamless integration of data and workflow.
For funds holding securities that trade on foreign exchanges that close before the US market, the usual method of computing Net Asset Value can result in stale fund prices. Some speculators profit from stale pricing to the detriment long-term shareholders. To solve the “mutual fund timing” problem and comply with SEC guidance, mutual fund companies are using fair value models to adjust the closing prices of foreign securities. Two challenges arise in implementation: (1) Fair value pricing creates tracking error relative to a benchmark index that uses stale foreign closing prices, and (2) Fund groups differ in their use of fair value models, distorting short-run peer comparisons. I argue that the implementation of fair value pricing across the financial industry would be expedited and simplified if public benchmark providers were to produce fair-value adjusted indexes. Such indexes are straightforward to produce and use, as demonstrated here, and would help coalesce pricing around a common industry standard.
I am very grateful to Richard Leibovitch for his invaluable comments and suggestions. I also thank Konstantin Zalutsky for expert research assistance. The information contained in this communication is for informational purposes only and has been compiled from sources, which we deem reliable. ITG Inc. does not guarantee its accuracy or completeness or make any warranties regarding the results from usage. All information, terms and pricing set forth herein is indicative, based on among other things, market conditions at the time of this writing and is subject to change without notice. Additional and supporting information is available upon request. ITG Inc. is a member of NASD, SIPC.
Mutual funds provide liquidity on a daily basis, allowing investors to transact in fund shares at the fund’s Net Asset Value (NAV). For funds holding securities that trade on foreign exchanges that close before the US market, the usual method of computing NAV, at 4:00 p.m. Eastern Time using closing prices for the day, can result in stale fund prices. In the case of US mutual funds holding Japanese stocks, as much as 15 hours can elapse from the close in Tokyo at 1:00 a.m. ET (3:00 p.m. in Japan) to the US close at 4:00 p.m., as shown in the time line below. Although European stock prices are less stale, the same issues arise as European exchanges close between 11 a.m. and 12 noon, ET.