Tuesday 5th May 2015
NEWS TICKER: FRIDAY, MAY 5th: MYOB will return on Monday next to the ASX, selling 228.3mshares at $3.65 in the company’s IPO. The company raised AUD833.1m, giving it an implied market capitalisation of AUD2.13bn. Bain Capital will retain 58% of the firm’s stock. “We saw a significant level of participation from eligible retail noteholders in the offer, with approximately 57% of holders exchanging their notes into shares. We see this wide range of investor interest as a strong vote of confidence in MYOB.” MYOB chairman Justin Milne says. ASX trading in MYOB shares is set to begin on 4 May under the code MYO. MYOB was listed on exchange from 1999 to 2009 – The volume of US municipal bonds soared by 42.1% in April, according to Thomson Reuters’ data; the ninth straight monthly gain. Issuers brought $37.76bn to market in 1,210 issues, up from $26.58bn in 939 issues in April 2014. Low interest rates, and the reluctance of the US Federal Reserve to raise rates over the near term has resulted in a dash by municipal issuers anxious to secure low cost funding as many refinance their debts. Other than refinancing, new issuance per se looks to be tailing off. New money transactions declined by 5.6% to $12.68bn from $13.43bn, while combined refunding and new money transactions increased 42.5% to $7.17bn from $5.03bn in April last year. Negotiated bond sales increased 62.4% to $28.97bn from $17.84bn, competitive deals rose 15.4% to $8.62bn from $7.47 billion and private placements plunged 87.2% to $162mn from $1.26bn. Sales of revenue bonds increased 49.9% to $22.84bn in 421 deals from $15.24bn in 306 deals. General obligation bond volume jumped 29.9% to $14.73bn in 788 issues from $11.34bn in 633 issues. Tax-exempt deals were up 42.4% to $33.88bn, while taxable deals were 24% higher to $3.30bn.Fixed-rate issues increased to $36.75bn in 1,167 issues from $24.85bn in 891 issues the previous year. The volume of deals with bond insurance more than doubled in par amount wrapped to $2.54bn in 161 deals from $1.06bn in 104 transactions. California claimed the top spot among states with $21.47bn of issuance thus far in 2015, up from its No. 2 ranking in the same period of last year with $12.03bn. Texas dropped from first to second with $17.85bn, an increase from $12.31bn the year before. New York remained in third place with $11.91bn so far this year, up from $10.29bn year to date - This morning Lloyds Banking Group said that in Q1 it had made a net profit of £913m and underlying profit was up 21% on the same period last year, to £2.2bn. Moreover, the group said that it was raising its net interest income target above the original target of 2.55%. Graham Spooner, investment research analyst at The Share Centre, says: “These results are good news for investors as they are ahead of forecasts and demonstrate a continued improvement in the company’s performance. The part UK government owned bank additionally reported that it has been benefitting from a resurgent British economy which has led to reduced bad loans and fuelled demand for mortgages. Lloyds announced its first dividend in February since being bailed out and investors should acknowledge that the increasing signs of recovery will boost hopes for a significant dividend growth in the near future. Analysts have become a little more positive on the group and its long term restructuring plans, which appear to be happening faster than expectations. However … the sector [remains] under pressure, as a result of regulatory issues and ahead of the next government sale.” - The Straits Times Index (STI) ended 0.24 points or 0.01% higher to 3487.39, taking the year-to-date performance to +3.63%. The top active stocks today were SingTel, which declined 0.23%, OCBC Bank, which declined 1.84%, DBS, which gained 0.19%, UOB, which gained 0.29% and Keppel Corp, with a 1.02% fall. The FTSE ST Mid Cap Index gained 0.47%, while the FTSE ST Small Cap Index rose 0.18%. The outperforming sectors today were represented by the FTSE ST Real Estate Holding and Development Index, which rose 1.00%. The two biggest stocks of the Index - Hongkong Land Holdings and Global Logistic Properties – ended 2.02% higher and 2.23% higher respectively. The underperforming sector was the FTSE ST Consumer Goods Index, which slipped 1.04%. Wilmar International shares remained unchanged and Thai Beverage declined 3.38%.
ALGO trading: a moveable feast Photograph © Robertds/Dreamstime.com, supplied March 2013.

ALGO trading: a moveable feast

Tuesday, 19 March 2013
ALGO trading: a moveable feast Algorithms have come a long way since the early days when traders used simple volume weighted average price (VWAP) routines to facilitate execution in large cap stocks. Technology has helped, of course; computers can now process so much data in near real time that programmers can incorporate feedback from the market to alter the way algorithms execute or route orders on the fly. Attitudes toward algorithms have evolved, too. Any lingering reservations—not uncommon among old-school traders—about how algorithms might perform during market dislocations were put to rest during the 2008/09 financial crisis. Today’s trading tools are smarter and more flexible than ever—and so are the users. Neil A O’Hara reports. http://www.ftseglobalmarkets.com/media/k2/items/cache/f8c1ec925a18c92698c05bff8c327469_XL.jpg

Algorithms have come a long way since the early days when traders used simple volume weighted average price (VWAP) routines to facilitate execution in large cap stocks. Technology has helped, of course; computers can now process so much data in near real time that programmers can incorporate feedback from the market to alter the way algorithms execute or route orders on the fly. Attitudes toward algorithms have evolved, too. Any lingering reservations—not uncommon among old-school traders—about how algorithms might perform during market dislocations were put to rest during the 2008/09 financial crisis. Today’s trading tools are smarter and more flexible than ever—and so are the users. Neil A O’Hara reports.

The proportion of trades now executed by algorithms is a movable feast depending on who is asked. A buy side desk may say it uses algorithms for 30% of its trading, counting only the orders it handles using direct market access or other electronic broker pipes. The other 70% of orders are called in to brokers, where the sell side trading desk will likely enter the flow into algorithms for execution. “Most US equity trading now uses some sort of algorithm,” says Scott Daspin, a managing director in the global execution group at ConvergEx. “The average block size continues to fall and appropriate execution requires a trading tool.”


Growing confidence in algorithms has encouraged buy side traders to exploit more complex routines. While some players still rely primarily on VWAP—quantitative shops doing mass optimisations of market neutral trades, for example—this long-time favourite has given way to implementation shortfall routines designed to minimize market impact. Traders specify the degree of urgency and the algorithm tries to optimise execution within that time frame. Based on measures of liquidity in the name and where the liquidity is concentrated, the algorithm will select the best routing among venues and decide whether and when to cross the spread to obtain a fill. “Clients are moving toward implementation shortfall as their primary benchmark,” says Daspin.




Average investment holding periods have come down in recent years, which has reinforced the focus on implementation shortfall. The shorter the time horizon, the more market impact costs affect the expected return on the trade. If a portfolio manager expects a 5%-10% uptick in price from a positive earnings announcement next week, the difference between 50 basis points (bps) and 150bps in market impact matters more than for a stock expected to rise 30% over two years.


For sensitive trades that are not urgent, traders may prefer a dark aggregator algorithm designed to tap liquidity only in dark pools where the footprint of a large order is harder to detect. Some dark pools are darker than others, and some admit participants whose activities may be toxic to large orders so traders can exclude certain venues or order types on a particular venue. Jeffrey Bacidore, head of algorithmic trading at ITG, has seen attitudes toward dark aggregators evolve, too. At first, traders would designate where they were and were not willing to trade, but now they take a more nuanced approach. “Shutting a dark pool out completely means there is absolutely no liquidity in there a trader ever wants to participate in,” he says. “That can’t be true.”


ITG and other providers have built more sophisticated algorithms that expose bigger size in clean pools but still show some interest, albeit with stronger safeguards against gaming, in more suspect pools. The buy side does not have the resources to monitor every venue in detail, which has led firms to lean on brokers to deliver an acceptable end result. “Brokers have to justify their decisions and provide good performance,” says Bacidore. “Clients find it hard to stay on top of the landscape. They have outsourced that to brokers and hold us accountable.”


The buy side learned long ago that while brokers always claim to put clients’ interests first a broker’s own interests will take priority if the client suffers no harm, at least in theory. In the spirit of “trust, but verify” the buy side is demanding more transparency about how algorithms route orders and where they are filled. Convergex has just opened its kimono through a Web portal on which clients who enter a ticker symbol and size can see a forecast of the expected market impact, how long it will take to complete the order, where the trade will route and where fills are expected. When clients enter a live order, they can see in real time where the order goes and the fills come from.


“When we demo this technology to people we don’t know, they fall off their chairs,” says Daspin. “We can practically see them reaching for the phone to ask their existing brokers how orders are routed.”


The degree of transparency ConvergEx offers allows buy side traders to tweak their execution strategy based on hard data about which venues give the best fills in a name. Sometimes it requires just a change in the parameters entered into the algorithm, but Convergex will customise the algorithm if need be. “The beauty of transparency is that people can make the algorithm exactly what they want, which is not the same thing for everyone,” says Gary Ardell, head of financial engineering and advanced trading solutions at ConvergEx.“Transparency helps clients get the right tool for the right job.”


The heightened transparency may tax the capacity of some buy side shops to make good use of it, however. Paul Daley, head of product development at SunGard’s Fox River Execution Solutions, says many clients struggle with the sheer volume of data generated in the full routing disclosure his firm provides and prefer to rely on monthly summaries instead. The snag? The summaries only includes trades done through Fox River, so users cannot compare the results with trades done through other brokers who do not offer similar transparency.


Clients who use the complete data dump can see where orders went, whether they were ever routed from one venue to another, how they were executed and whether they took or provided liquidity. “Over time, people are getting more into the logic of why a broker went to a particular venue, not just where it went,” says Daley. “People will use the tools and get their hands around the data.” He expects buy side trading desks to hire quantitative analysts with a grasp of trading who can use their programming skills to mine the data and suggest improvements in how the desk interacts with the market.


The buy side trader’s role continues to evolve from the jumped-up order clerk of yore toward equal partnership in the investment process. Traders don’t have to watch the market all the time any more; they can focus on higher value-added tasks like picking the best execution strategy and leave implementation to the machines. “A human does not have to look at the screen, see the bid move up a penny and decide whether to cancel and resubmit the order,” says Bacidore at ITG. “The algorithm has already done that if it makes sense. The trader looks at the objective and works more closely with the portfolio manager behind the trade.”


The nature of product development for algorithms has changed, too. Ten years ago, Bacidore says the main concern was to ensure the routines were robust and would not break down or go haywire. Today, those safeguards are a given and developers spend more time figuring out how to source liquidity as efficiently as possible. They also know other technology-savvy market participants like high frequency traders will try to reverse engineer or game their designs, a constant threat to buy side clients. “We have to have cutting edge technology,” says Bacidore. “We must be as smart and efficient as the best people in the market if we are to deliver good results to our clients.”


While technical improvements in single name algorithms will continue, the bigger challenge is to perfect algorithms that can handle baskets of stocks. It’s a daunting task: not only must the algorithm process data on all the individual names but the trading in one name affects how other names are traded. In a market neutral (equal dollar amounts to buy and sell) basket of 1,000 names, for example, the algorithm must maintain balance so that buys and sells don’t run too far out of whack. “The algorithm takes into account portfolio level objectives and constraints,” says Bacidore. “It comes at a cost, though—they can’t be too dynamic.” If a block showed up in an illiquid name on an institutional dark pool, a human trader might grab it but the algorithm might not because a large fill would unbalance the basket.


Another difficulty is the lack of industry consensus about how basket algorithms should work. The objective is clear: to minimize risk and maximize return on the trade—but opinions differ over what that means in practice. The uncertainty has hampered development efforts, according to Daley. Fox River could build an algorithm that made sense to its developers but if clients reject the logic it would be wasted effort. “We all agree what a VWAP algorithm is,” says Daley, “but we don’t necessarily agree what a basket algorithm is. There is a tremendous amount of unsatisfied demand in that space.”

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