Bank of America Merrill Lynch’s Josephine Kim lays out the reasons why the close price benchmark is so important and how experienced traders can utilize technology to meet the benchmark.
Close Price Benchmark
Mutual Funds Interest in the closing price is due to the structure of the market. Mutual funds are marked at the end of the day at the closing price. This is the published net asset value (NAV) that indicates that the mutual fund will be marked once a day, and where all the purchases and sales of that mutual fund will be marked at. This is a relic of the market structure of mutual funds. Now, mutual funds are not traded each year or all day long – buys and sells are made at any point during the day and the price is given at the market close. If mutual funds are traded on any given day, the mutual fund trader is going to attempt to get the closing price if at all possible.
Transition Trades Another way in which a trader would try to beat the closing price is via a transition trade. This is when a pension fund moves assets from one manager to another by selling the assets in one manager’s holding in order to buy the assets from another. The pension fund may go to a specialist transitional manager, who takes all the holdings from the current manager, sells them, uses the proceeds to buy the holdings for the new manager, and then passes those holdings back to the new manager. This process has a mark on valuation and the closing price is that mark.
Index Rebalancing The final type of trade that allows traders to beat the closing price is index rebalancing. Asset managers may opt for a guarantee on their trade. Brokers will bid aggressively for those trades with the same tradeoffs occurring. Index rebalancing trades have an additional nuance in that everyone in the market typically knows whether the street has to buy or sell a stock for the rebalance, so brokers will put out research ahead of the rebalance.
The brokers then have to make a tradeoff, knowing there is potential demand to buy or sell these stocks from many different buyers and sellers. Traders must decide whether to trade early because they know everyone is going to buy the stocks when the index weight goes up, or trade them later (or even the next day), because everyone is going to sell. In the last few years, more and more investors have the requisite information to make the right decision on rebalancing trades.
Implications of Missing the Close Price The implications of missing the close price benchmark are straightforward. If the trader purchases below the close price, the mutual fund investor buys the fund at a higher price than their investment, which creates transfer of wealth from the new purchaser to the shareholders of the fund. If the mutual fund is not able to beat the close price but requires funds in excess of the new investment, then the existing shareholders subsidize the purchase.
In a transition trade, missing the close price on the funds to sell and the funds to buy creates numerous problems. For transition trades, brokers will very often give an assessment of the expected range of slippage ahead of time, based on the liquidity of the portfolio they are trying to liquidate and the portfolio they are trying to invest in. The broker clearly hopes to land within that range. If they are outside that range however, the transitional manager might refund some of their fee or maybe even negotiate a profit share ahead of time to incentivize them to minimize slippage. Unless the transition manager is able to buy at a discount, missing the close price means a loss to the fund. This slippage can have major implications for the value of assets retained by the client.
Schroders’ Head of Asian Trading, Jacqueline Loh, shares her thoughts on trading in Asia, offering comments on which markets are primed for change, how to find value in dark pools and whether unbundling is as useful as people say it is.
Fragmentation arising from multiple sources of liquidity is a necessary step in the evolution of best execution and in the long term, fragmentation will increase the quality of trade executions in Asia. What it means for the buy-side is investment in infrastructure spending to develop new order routers and the like, so we can electronically seek out and have exposure to multiple liquidity sources. For the sell-side, it means acceptance that there will be more competition for the same block of business in the marketplace. It means different things for different buy-side firms as well.
When I think about the investor ID markets in Asia, I am not sure any model is particularly productive because ID markets make it administratively more difficult to trade. IDs can make best execution very difficult to implement, especially if cash and stock checking is the primary consideration. Some of the ID markets, namely Taiwan and Korea, allow trading through omnibus accounts and that seems to be the way it is evolving. The ID markets are slowly going away, but having said that, the most productive example is probably China because the brokers seem to have a handle on exactly how much cash and stock you have in your account, and therefore how much you can sell and buy. You cannot overspend or oversell, and it is relatively easy to take part in IPOs.
Trade allocation used to be a problem with investor IDs; for example, explaining to compliance and regulators why the prices are not exactly the same between accounts. In these cases the use of omnibus accounts really help. Executing through omnibus ID means you know exactly what is in an account and do not experience many of the issues associated with overselling or settlement. It is a lot cleaner.
With retail-heavy markets, anonymity is the primary consideration for us. We tend to trade more using electronic means and make use of dark pools in retail-heavy markets. In addition to that, the algos we use will be more price-specific, rather than volume-participation models, which are more price impacting.
Best Execution, in the Dark?
You would think that dark pools would have more success in markets where spreads are currently wide and there is a need to be anonymous, which would imply ASEAN markets. In practice, however, it has had more success in Hong Kong, and that is because there are more users of electronic trading there. Perhaps the users are a little more sophisticated as well insofar as they are willing to take accountability for their executions. Which is, in fact, what defines electronic trading.
In our experience, dark pools make a difference in terms of liquidity, however, the question is what creates that difference? Is it the electronic trading system feeding through the dark pool that provides the benefit or is it the dark pool, itself? I would say it is the former, but that may depend on each user. routers. I hope the Securities and Exchange Board of India will consider further change including allowing stock crossings and clarifying the rules regarding P-Notes.
Brian Ross of FIX Flyer talks to Buy- and Sell-side presenting the latest lessons on high frequency trading and algorithms from the Indian market.
India’s capital markets are experiencing increased interest from local and global firms and new rules are set to attract high frequency trading (HFT).
The capital markets regulator, the Securities and Exchange Board of India (SEBI), the exchanges, brokers and many investors are in favor of abolishing Securities Transaction Tax (STT). Eliminating STT will have a positive impact on market turnover, will help high frequency traders to be more profitable and, at the same time, narrow spreads should drive up trading volumes.
STT has been levied for all trades, domestic or foreign, on all transactions in either equities or derivatives markets since 2004. At the time, the purpose was to generate tax revenue and to protect market integrity by slowing down the pace of technological advancements of a few, well-funded players. Revenue generated by STT amounted to around USD 1.5bn in 2011.
It is widely expected that STT will be eliminated this spring, bringing new opportunities for HFT in one of the world’s biggest and fastest growingcapital markets.
To better understand the situation, we asked five panelists who are leading the charge in HFT in India, to share their insights with us.
You never forget your first algo. When you first got involved in algorithmic trading, what problem were you trying to solve? What was your decision process, and what technologies did you use?
Sanjay Rawal, Open Futures: We started off using algos for trading purposes and the first one we built was for a specific type of arbitrage that was getting difficult to run using manual input. We used third party software for the exchange connectivity and wrote our algo in C#.
Vishal Rana, IIFL Capital: My first experience with HFT was trying to create a straight-arb model on a real-time basis. Although it was a simple model, the most difficult thing was to clean the data. We got the data dumps and it took a lot of effort to clean it. Most of the coding was done using C++.
Rohit Dhundele, Edelweiss: At the onset of the project, the easiest yet most important task was gathering the business intelligence to be subsequently converted to algorithms. Some of the more intricate decisions were the selection of order, execution and risk management systems to ensure a stable back-bone to the platform. Other equally important criteria were a flexible programming environment and a friendly interface for users. To achieve these objectives, we had to decide whether to build or buy this technology.
At Edelweiss, we realized relatively quickly that there is a sweet spot between the two extremes of in-house vs. outsourced solutions. We have since been following this model – combining the best of both worlds, which has helped us deliver customized solutions within acceptable turnaround times, whilst still protecting our IP.
Sanjay Awasthi, Eastspring Investments (Singapore) Limited: In the Indian markets, propelled as they are by rapid information dissemination systems, anonymity becomes a key factor in determining efficient trading. It was this need for anonymity that propelled us towards algorithmic trading. Continued use and familiarity lead to further benefits by way of better execution control. Algorithmic trading has thus become an important part of our execution arsenal.
Chetan Pandya, Kotak Securities:
The first algo I worked upon and put in production was calendar rolls for derivatives. Our trading desk had huge positions to roll from the current month to the next and manual execution was leading to slippages and erroneous executions at times. Using the 2 legged order of NSE we created a simple algorithm which would roll the position at desired spread.
My first observation regarding algorithmic trading was to appreciate the difference between an individual trading manually versus a machine trading automatically. There are so many things that come naturally to a human being but needs to be told to the machine. Sometimes I wonder whether an algorithm can fully replace a human being ever. There are those nuances of the market and events that lead to erratic market behaviour that cannot be fully programmed for reaction.
Also, I had to ensure that there is no room for error when you are trading using an algo platform, primarily because of the sheer number of orders that it can process in a single second and also the inability to spot something going awry with the naked eye given the sheer speed. Hence, I had to also think of risk management capabilities of the Algorithmic platform while needing to ensure that risk management does not lead to inefficient execution due to latency.
In terms of technology, we were limited to applications that conformed to our market regulations. Once we had the base framework and architecture ready, we integrated it rapidly with our existing applications for order routing and downstream workflows.
Simo Puhakka, Head of Trading for Pohjola Asset Management, shares his experience trading in the Nordic markets, giving his opinions on interacting with HFT, using TCA and knowing whether you can trust your broker.
The prospects for High Frequency Trading (HFT) are really up to regulators. It will be a free market, but as we all know, regulatory changes affect the whole trading landscape. For example, we can see what is happening in France and the debate that is going on in Sweden, which are quite hostile towards HFT, so those countries.
Personally, I think that HFT is a good thing for the market, as long as you have the proper tools to deal with it. There are a number of small firms that have been suffering from HFT
since MiFID I because they lack the proper technology and tools to measure and deal with it. We have not suffered in our dealings with HFT, and I would actually say in many cases, it is the opposite. HFT firms seem to add liquidity and when you have the proper tools to deal with it, you can take advantage of it.
Speaking of tools, we started building our own Smart Order Router (SOR ) a year and a half ago. The goal was to create an un-conflicted way to interact with the aggregated liquidity. In this process we went quite deep into the data and turned processes upside-down with the result that we have full control of how we interact with the market.
On the other hand, I welcome technological innovation from the sell-side; for example, brokers now disclose the venues where they execute trades on an annual basis. The surveillance responsibilities that brokers have are beneficial. Many of the small, local brokers and buy-sides, however, are now finding it challenging to upgrade their technology.
Trusting your Broker
Our approach was to take control of our order flow and only use our brokers for sponsored access. We chose full control because, in some to deliver what I am asking.These questions first arose a few years ago, and we realized we needed to create a transparent, fully-controlled, non-conflicted path to the market. How you interact with different venues – even lit venues, where you have more transparency – will affect your choice of strategy. In most cases, you are better off without brokers making decisions for you. The root of the problem is, when you send an order to the broker, what happens before it goes to the venue? What control do we have over the broker infrastructure, including their proprietary flow, internalization, market making and crossing, not to mention the routing logic?
When we dug into the data, we were quite surprised to see that, although a broker was connected to all the dark liquidity, many of the fills were coming from that particular broker’s dark pool, suggesting there are preferences in the routing logic. Brokers want to internalize flow, which is not a problem, if you are aware of potentially higher opportunity costs. When it comes to dark liquidity, that is an even bigger problem, since our trades were often routed to the broker’s own dark pool or those it has arrangements with.