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The Art and the Science of Leveraging Cloud Infrastructure: Making Cloud Computing Work in the Trading Environment

Having demonstrated an initial, and understandable, wariness of cloud services, trading firms and other players such as hosting providers in the institutional trading environment are slowly coming to embrace the technology. With storage demands for mid-size firms growing at approximately 90 terabytes a year, perhaps this is not surprising. Building proprietary infrastructure that can keep up with this level of growth is comparable to painting the Golden Gate Bridge: a never-ending task.

Measuring the Trading Activity at the Open and Close Auctions Around the Globe

ITG Financial Engineering has recently completed its R&D work on international stock specific intraday volume profiles, extending its robust estimation methodology to common stocks and several other security types to cover more than 50 markets around the world.1 The intraday volume profiles, which include the estimated percentages of the daily volume to be traded in each 15-minute interval of the continuous trading session and at the  and closing call auctions, are estimated for individual securities traded on each market and can be used for efficient execution of large orders.

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.

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.

We call this broad range of tools Trading Analytics, and this article looks at some common Asia liquidity events to show practical examples of how Trading Analytics can provide data that answers real questions, helping traders take advantage of liquidity while minimizing costs.

TRADING AROUND THE OPEN AND CLOSE

More buyside traders are now involved in investment meetings, acting as advisors in the discussion of how to get the most alpha for a given investment decision. Using Trading Analytics to form an opinion in advance of the trade on how to structure its timing can have a significant effect on the alpha outcome. Understanding intra-day volume profiles and the corresponding volatility can be critical to finding liquidity that comes from trading at the times when others also are. The chart below shows average profiles of how much is traded in different Asia Pacific markets in the first and last 30 mins of the day (including auctions).One common theme is that traders often avoid the more volatile early trading period for fear of price movements, preferring to hold their trading back to the more stable sessions through the day. However, doing this too much, particularly in markets such as Taiwan and Japan where high volumes are done during early trading, can push overall costs higher and increase risk. Missing out on significant early liquidity requires the trader to execute more later in the day where they may need to cross the spread more, and increases the risk of non-completion of an order – often overnight
volatility is higher than that seen during early trading sessions. It also increases risk of over-participation in the Close which may push a stock price, a topic that Asian regulators are particularly sensitive to.

Fig. 1

While an overall understanding of market profiles is helpful, Trading Analytics really becomes useful at a stock-specific level, understanding in advance when a stock may deviate from the market norm. A recent query run by a trader using ITG’s pre-trade portal for stock-specific volume analysis shows that Hong Kong stock Cheung Kong [0001] normally trades significantly more during the last 15 mins of the day than the market average. When the trader received an order from the portfolio manager (PM) late in the day, he made the decision to hold more of the stock back for the closing session to take advantage of this, while finishing more of Hang Lung [0010] when he could, and completed the trades favorably.

Fig. 2

RED FLAGS AND UNEXPECTED LIQUIDITY EVENTS
ITG’s Transaction Cost Analysis of real institutional trading costs consistently shows that many outlier trades – both high and low cost – occur at times when both volume and volatility in a stock are high. To handle this, traders need a real-time ‘red flag’ system to accurately identify these conditions at a stock level, and make decisions accordingly. This allows early conversations with PMs to adjust strategy or manage expectations, as well as enabling the trader to change the trading instructions to take advantage of the situation and increase the chances of a positive outcome. We call this ‘market surprise’ monitoring, which when combined with real-time P&L analysis is a further example of how Trading Analytics can help traders maximize liquidity opportunities or make informed risk management decisions to improve their daily trading.

MAKING THE MOST OF THE HOLIDAYS
Another common liquidity event features the many and varied holidays across the region. Knowing which result in liquidity lulls, and which in volume spikes, is useful both for staffing trading desks and planning investment decisions around them. For example, Trading Analytics drilling into Opening volumes after Hong Kong holidays shows significant and unusual levels of liquidity after the locally specific holidays of Chinese New Year, National Day etc driven by large backlogs of orders from overseas.The same is not true for local holidays in the more domestically driven Japanese market, nor for the period of global holidays over Christmas and Easter. Detailed data can help both traders and PMs decide whether to stay away from these volatile spikes or take advantage of unusual liquidity.

TAKING ADVANTAGE OF LIQUIDITY EVENTS
These are just a few examples of how Trading Analytics moves far beyond standard TCA to provide traders and PMs with data to improve trading decisions and maximize alpha. Trading Analytics also encompasses data-driven tools to help answer a range of questions covering portfolio capacity analysis, regression testing on turnover, how best to time orders and how to treat different market cap stocks in various markets. In Asia Pacific’s often illiquid and volatile market conditions, this can make a significant and long term difference to both the traders, and to fund performance.

ASK US A QUESTION:
ITG’s Trading Analytics team invites you to ask us a question about the Asia markets that could help improve your trading. Using the ITG Peer Database
and a wide range of market data and analytics tools, we provide evidence and data-driven insights that can be acted upon to improve trading outcomes.

Email: info@itg.com with your questions

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 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.
Nevertheless, our results suggest that the price movements create a real cost for asset managers. Utilizing order matching or algorithmic trading strategies around the fix can possibly remedy some of the cost burden. With or without collusion, the fix has become an irrational trading period that can cause prices to diverge from an equilibrium state without a concurrent change in fundamental risk.

METHODOLOGY AND INTRADAY PATTERNS

The WM/Reuters London Closing Spot Rates are published by the WM Company and Thomson Reuters using a sampling of data during the one minute period beginning
30 seconds prior to the top of the hour. Trillions of dollars in assets contractually tied to these rates trigger billions of dollars in turnover, especially at the end of each month. In our empirical study, we look at tradable quote data during the overlap of London and New York trading before and after the fix rate is calculated. Since the WM Company is responsible for the “fix” relies on Thomson Reuters traded rates, we focus on eight deliverable currency pairs where Thomson Reuters is dominant: GBP/USD, USD/CAD, USD/DKK, USD/MXN, USD/NOK, USD/SEK, USD/TRY and USD/ZAR.

Figure 1 summarizes the intraday patterns of volatility and spreads between 11:00 and 17:00 GMT aggregated across all selected currency pairs in 2013. As expected, we observe volatility spikes around the London Close fix at 16:00 GMT, just as we do at 13:30 GMT (major scheduled US economic reports) and 15:00 GMT (FX option expirations, economic reports).

FIG 1

During the two spikes at 13:30 and 15:00 GMT, spreads widen in synch with volatility, which appears to be consistent with the risk-averse behavior of FX dealers as they charge more for the increased uncertainty in the markets. What is interesting to note with respect to the London Close fix at 16:00 GMT is that the trading risk premiums,proxied by the spread values, do not respond as expected in a typical high volatility environment. Goettler et al. (2009) , modeling such a behavior, noted that even at the times of high volatility, traders with “intrinsic motives” will increase liquidity and tighten spreads. There is certainly enough motivation to manage currency risk in the countdown atmosphere to keep spreads tight without considering front running or manipulation.

TRADING ACTIVITY AND MARKET PARTICIPANTS PRICE IMPACT OF D-QUOTES

Trading at the fix during the final minutes before the rates are determined can be intense, especially at month’s end (see Figure 2). Asset managers are accountable for matching WM/Reuters London Closing Spot Rates when they use an international index as a benchmark. This creates acute pressure to match the fixing rate or outperform it without adding undue risk. Many clients shed responsibility for this risk by accepting a foreign exchange bank’s guarantee to match that rate for a set fee, usually two basis points or less. On the other end of the spectrum, some institutional investors enter the closing period with large positions and actively participate in trading around the fix. In the first case, the bank’s foreign exchange trader’s role shifts from that of a risk-neutral market-maker to a cross between an informed trader and a market-maker. In the second, the asset manager is the informed trader with inside information (the large position that is about to be traded). In this two-player race, each has incentive to increase the frequency of their trades as time runs out on the 4pm London close. Our review of the data confirms this pattern.

FIG 2

RETURN PATTERNS

Although the spreads compress around the fix, indicating that it has become cheaper to trade, there are still costs created by increased volatility. As a result, institutional investors can see significant adverse price action in the form of implementation shortfall even if they are trading on their own behalf. Figure 3 below illustrates the average FX rate moves for the 10% largest positive and negative returns between 15:45 and 16:00 UK time across calendar year 2013 for the selected currency pairs.Regardless of whether or not the base currency was being bought or sold, we see significant average rate drifts from 15:45 until 16:15. The absolute average currency moves are in the ballpark of 10 to 25 basis points across all considered currency pairs. Most of these moves end quite abruptly after 16:00 and partially mean revert in the first few minutes after the fix.

FIG 3

The mean reversion patterns after the fix appear to be partial but very consistent in the above charts. We see a price reversion of 2 to 3 basis points on average which corresponds to size-adjusted spread costs for deal sizes that are distinctly larger than $15mln for all selected currency pairs. Focusing on the USD/CAD pair, we see from Table 1 that the mean reversion does not occur 100% of time; however, the probability of a large price reversal (>1bp or >2bps) is high. For instance, after having observed a very large negative price move in USD/CAD within the last 15 minutes before the fix (the average return is 13bps in Figure 3) one can expect to see a rate increase of more than one basis point in 80 of 100 cases. Table 1 also confirms that the price reversal is quite rapid and typically does not last beyond two to three minutes after the hour.

TABLE 1

Final Thoughts

Does the price reversal around the London fix prove manipulation or “banging the close”? It is impossible to tell at this point. If institutional investors are actively trading in a herd-like manner in the FX market, how could one distinguish their trading from the banks compensated for assuming the FX risk of their institutional clients? How could one separate those trades from noise traders hoping to catch one of these directional moves? A telltale characteristic of conventional trading patterns around the fix is that the institutional investors normally start legging into the trade earlier than the banks that trade on behalf of their clients. Bank traders with access to nearly unlimited liquidity would trade much closer to the actual fix in order to
reduce their price risk. The idea is highlighted in the work by Kumar and Seppi5 (1992), which models “punching the settlement price.” The institutional investor and banking communities seem to be equally important contributors to the move observed in the charts.

The costs to pension funds and mutual funds cannot be ignored. On aggregate, 17 basis points of implementation shortfall for up to 20% of all days can cost the asset management community millions of dollars of un-invested funds. Can we propose any solutions to this dilemma? Can the market reduce volatility and the adverse price movement? Two tentative solutions that have been proposed by the banking industry deserve consideration: order matching and algorithmic execution. Using a third party dark pool, an intermediary can match off trades which would alleviate volatility and prevent information leakage. The other idea, employing algorithms, could help clients match or outperform the fixing rate while protecting their anonymity. A combination of the two, matching all available orders and pushing the excess positions out to an algorithm that creates a fair fixing rate would be another solution that would require less regulatory intervention. Alternate fix times had been suggested in the past, but WM/Reuters London Closing Spot Rate Service has asset values contractually tied to it. Other fixes are available, but they would have to be written into the contracts as benchmarks. That solution does not solve the problem of directional moves. Central bank oversight would also help, if only to remind noise traders or traders seeking excess profits that their trading behavior might be called into question.

In the end, banks are given private information in return for providing the service (matching the fixing rate). If the customer’s order is large, and no collusion is involved, it is likely that the market will move in the direction they are trading. It is also likely that a majority of index funds will be on the same side of the trade, exacerbating the situation. Without a matching exchange, this cannot be classified as manipulation. It is a momentary use of market power while providing a riskbearing service. Additionally, it would be impossible in an unregulated, OTC market to differentiate one bank’s trading pattern from another bank that is actively trading the fix for a client. Both scenarios would involve frantic trading patterns on behalf of each trader, informed and uninformed, attempting to beat the clock and, with some luck, the WM/Reuters London Close fixing rate.

From a transaction cost analysis perspective, the costs from the order arrival time until trade execution are on average 17 basis points 20 percent of the time. We would also warn our clients that volatility is at one of the three peaks of the day and trading during periods of high volatility is not recommended. If an institutional investor is using the fix to offset currency risk without being contractually obligated to that rate then they are paying a lot for the service ($1,700 per million), especially if they lack contractual obligations. Clients with a mandate to target the fix rate should either
use a matching service to avoid information leakage and thus reduce volatility, or work towards a reform of the system that has created an expensive, irrational trading environment.

What Makes an Exchange a Unique Institution?

I recently received my copy of the Winter 2014 Journal of Trading. Quickly scanning the journal’s cover, I began flipping through to an article on real-time TCA visualization.  I stopped, when I came across the title which I reuse for this comment.  The Journal piece is an edited manuscript of a panel session of the same title held during a conference, organized by Robert Schwartz of Baruch College in New York.  The participants, led by Andy Brooks of T. Rowe Price Associates, are well-known in the industry, and I recommend a read by anyone who did not see that crew in action.

The full version of this article can be found in the Journal of Trading

Big Data, Big Decisions: The Coming Sea Change in Technology Investments

Market participants do not need to be told that they are working in an era of Big Data. They experience it every day. However, developing an appropriate response is going to change the daily experience in a number of important ways. The relationship with technology will inevitably change. Internal relationships will be altered. And analytics will dominate any list of required capabilities.

Living in the Post- MiFID II World
 

On January 14th, Michel Barnier, the European Commissioner in charge of financial services in the European Union (EU) welcomed the agreement in principle reached on rule changes to the Markets in Financial Instruments Directive (MiFID II/ MiFIR).  Barnier declared that although the speed of implementation was not ambitious enough, the agreement still represented “a key step towards establishing a safer, more open and more responsible financial system and restoring investor confidence in the wake of the financial crisis” (see: http://europa.eu/rapid/press-release_MEMO-14-15_en.htm?locale=en).

Commission Spending Under the Microscope

The unbundling of research and trading has been a discussion topic for many years both globally and in Asia. While in theory there are many good reasons to unbundle, the practical implications have often made it difficult for asset managers to do so. However now several important business factors are pushing Asia-based fund managers to review their processes and consider how they value research and trading, while using more sophisticated tools to manage and report on who and what they pay.

The Cost of the D-Quote

Traders commonly use market-on-close (MOC) or limit-on-close (LOC) orders to participate in the NYSE closing auction. An alternative mechanism is the D-Quote.Unlike MOC/LOC orders, which must be submitted prior to 3:45 unless offsetting a Regulatory Imbalance1, D-Quotes can be submitted or modified until 3:59:50,regardless of the current imbalance. Given the greater flexibility of D-Quotes, why don’t traders always use D-Quotes when participating in the close?

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