Regulatory prohibitions on direct market access within Asia-Pacific markets add to the legal and operational challenges and costs faced by international investors. The promotion of best practices across APAC markets is necessary to facilitate the standardization and control of algorithmic electronic trading strategies (algos).
Both high transaction costs and transaction taxes persist as a barrier to market development, which adversely affect Asia’s competitiveness relative to global markets. Even the region’s free-market ‘petri dishes’ (Hong Kong and Singapore) have unacceptably high transaction costs relative to other developed markets.
There are also regulatory challenges. Current obstacles to becoming members of exchanges (for example, requiring a local presence) persist, resulting in many international brokers routing their trades through locally incorporated brokers. This increases credit risk, adds potential operational inefficiencies, and detracts from transparency in technical platforms.
As for market access, China comes out first on both the buy-side and sell-side, with India coming in second on both sides. Other markets that feature prominently on the buy-side are Singapore, South Korea, and Malaysia; on the sell-side, it’s Japan, Singapore, Malaysia and Indonesia.
China and India remain the standout destinations for future Asian algo trading, with rapidly growing interest in certain asset classes: OTC; ETFs; debt; and index futures. Alternative Trading Systems would help cut impact costs by operating as non-displayed venues. This is because details of orders are only put into the public domain after a trade is completed, which minimizes the potential for leakage of investor intentions alongside mid-point matching – both buyers and sellers benefit from such improved prices.
Regulatory developments in Hong Kong
Hong Kong’s Securities and Futures Commission’s (SFC) electronic trading rules came into force on 1 January 2014. The SFC’s electronic trading rules surprised the market in that they require the sell-side to make judgments about the buy-side’s ability to use electronic trading tools such as algos, while also requiring the buy-side to make judgments about the sell-side.
Requiring market participants to conduct this level of due diligence may backfire, resulting in a smaller roster of sell-side algo providers.
Hong Kong is primarily a long-only market. International players open up regional offices to access the China market, the region and to enjoy a favourable tax regime. There is no high frequency trading (HFT) on the Hong Kong exchange (HKEx) per se, but there is a large market in derivative warrants. The secondary market is weak and HKEx has been overly focused on its position as a gateway to China IPOs.
However, since Hong Kong is a regional financial hub, respondents trade in a number of markets. Singapore, Japan and South Korea are the most common markets on the buy-side, whereas Japan, India, Singapore and Taiwan are preferred on the sell-side. Spot FX is popular on the buy-side, as compared with FX derivatives on the sell-side.
Regulatory concerns over HFT appear too focused on minimum resting periods. Arguably, a greater focus on and enforcement of market abuses would probably be a more effective allocation of scarce regulatory resources. In summation, while Hong Kong remains a regional financial center providing market access throughout APAC and into China, competitive concerns remain for the SFC to address, including: the HKEx monopoly appears to be stifling certain types of algo trading; the local brokerages are engorged due to local presence requirements; and stamp duties are hindering the development of the secondary market.
Algo usage in Singapore
The Singapore Exchange (SGX) leads APAC in terms of cross-border derivatives trading. Importantly, the SGX lists Asian benchmark indices on its own exchange. By offering such global access, Singapore is at the center of the ASEAN Trading Link. However, liquidity remains a major challenge and trading costs remain high.
There is a large presence of international firms, which offers global connectivity with Singapore as its base. Although China remains the most popular market, Singapore is also a major gateway to Indonesia and South Korea. Most market participants build their own proprietary algos in Singapore and across Asia. However, ‘white-labeled’ brokered algorithms are also active in Singapore.
Clearly, many strategies are being used in Singapore over a wide range of asset classes. The biggest challenges in Singapore’s algo market are: liquidity; alternative trading venues; tighter spreads; and lower fees. Lower trading costs would help improve volumes (liquidity) at SGX. To promote increased competition and liquidity and allow further development of the algo market, Singapore needs a true ATS.
Both the Asian buy-side and the sell-side recognize the need for better regulation of algorithmic trading. Asian regulators need to collaborate to allow greater substituted regulatory approval.
The changing nature of algos in Asia
Rob Hodgkinson, Director APAC, First Derivatives looks at the changing nature of algos across Asia, and what it could mean for trading structure.
Algos are typically structured as
(a) Execution strategies (TWAP, VWAP etc),
(b) Proprietary (prop) trading strategies (Statistical Arbitrage, Mean Reversion, Pairs Trading)
(c) HFT algos (ultra-low latency, FPGA based solutions, etc)
There has been an increase in ‘packaged strategies’ for DMA clients such as TWAP, VWAP to facilitate trade execution (ie more flow through ‘vanilla’ algos), and also an interest in more competitive prop trading strategies (seeking enhanced backtesting and strategies based on more proven, verifiable mathematical models). The focus on HFT strategies has largely dissipated due to expensive infrastructure, diminishing spreads and lower returns.
There has also been increased competition to provide more efficient trading platforms and a move to provide common infrastructure to facilitate sell side competition. This includes tick as a service, co-location solutions, common algo testing and infrastructure.
In particular as the strategies themselves become more competitive, and regulations become more stringent, and punitive in that fines are widely being applied for market misconduct of algorithmic strategies, we are specifically seeing a move to much more robust use of:
1. historical data models for backtesting (both for PnL model verification and strategic stress testing to ensure models fully comply under all circumstances)
2. more dynamic simulation models to facilitate random market moves, shocks, and verification for strategy conflicts primarily within a participant, but also between participants. The testing infrastructure sought should provide multiple trading venues, not just one market venue.
This is leading to more competitive strategies and more efficient venues that can provide more cost-effective trade execution as spreads narrow, but regulatory compliance increases.
The trading desk flow is becoming more automated as orders are directed to multiple venues, and there is now a strong requirement to ensure compliance on order flow across all venues to avoid financial penalties yet ensure competitive trading strategies. The increased compliance and surveillance focus is highly apparent.
Transforming FX through TCA – an Asian buyside perspective
By Richard Coulstock, Head of Dealing, Eastspring Investments Singapore.
TCA beyond equities
We began our equity TCA project about seven years ago, so we’ve been working at it for a while and have subsequently been asked by clients, senior management, audit and compliance, “OK, you’ve done this on the equity side, why don’t you try and replicate it into the non-equity asset classes?” and “how do you monitor custodial FX and your FX processes in general?”
We began by asking our custodians and counterparties from the FX side to do some self-analysis. After a while we started receiving monthly and quarterly reports from each counterparty about FX trading — this meant that we had a process in place to measure and monitor FX that we could explain to regulators and to clients.
You also have to think about what benchmark you use in the FX world and also the fact that the benchmark might change depending on each individual funding you’re trading for. Our spot FX trades fall into three broad categories:
1. Currency trades in unrestricted currencies where you can trade with any counterparty you like.
2. Trades in restricted currencies, where you go through a custodian, but you get interaction at the time of trade; a phone call, Bloomberg message etc.
3. The third category is the most problematic; the custodial trades – where the asset manager only finds out what’s being done on a post-trade basis – so there’s very little transparency. You may just be given a rate and the trade date.
Where FX algorithms could help
For a long only manager such as ourselves, we would like to be more in control of our executions across all asset classes, including foreign exchange.
FX execution analysis can give us insight into how best to handle trades in the market and algorithms can help us put certain strategies into effect. Following execution, we can again use the analysis to determine where we can make further improvements. For example are we trading at the best time of day or are we executing in sizes that won’t move the market away from us. Use of FX algorithms is a learning process with the goal of achieving more optimal execution using appropriate analysis.
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