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How S&P Global's Algorithmic Trading Insights report will accelerate Asia’s embrace of algorithmic trading

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A new report from S&P Global Market Intelligence aims to give a nuanced view of algorithmic trading performances and usage trends across regions, trading strategies and the impact of market volatility on trades.
How S&P Global's Algorithmic Trading Insights report will accelerate Asia’s embrace of algorithmic trading

The global algorithmic trading market is expected to hit $41.9 billion by 2030, growing at a CAGR of 12.9%, according to a report from Acumen Research. Algorithmic trading is being adopted at a rapid clip across Asia. A key driver for its growth is the impetus towards reducing transaction costs in a region where trading has traditionally been expensive.  

Acknowledging the rising interest in algorithmic trading, S&P Global Market Intelligence has created a report that provides deep insights into the algorithmic trading space which chronicles significant trends across regions, performances of trading strategies, and the impact of market volatility.

Creating the Algorithmic Trading Insights report

The report has emerged out of demands from S&P Global Market Intelligence’s clientele. Michael Richter, global head of trading analytics, S&P Global Market Intelligence said, “Algorithm analysis was becoming a key part of the transaction cost analysis (TCA) use case for most of our clients. There was an increasing demand for an in depth understanding of how algos performed, looking at the data grouped by strategy and counterparty.” For instance, clients wanted to understand how a particular broker’s liquidity seeking algo performs against a similar product from a rival.

After creating apples-to-apples algo analysis reports, S&P Global Market Intelligence decided to broaden its remit, and provide a view of the market, using anonymised peer data. Richter explained, “Several clients send us algorithm and strategy data for analysis. We thought of including those data points to produce a report that provides colour on algorithm performances, strategy types, and counterparties across the industry. We wanted to make this information available and provide visibility for the wider market. This is the first report of its kind.”

Given the broad array of data and metrics available, the report aims to improve strategy selection and counterparty usage, allowing consumers to make much more informed decisions.

Key insights from the report

Trends in trading during periods of volatility: In a volatile market environment, the buyside utilises high touch services from the sell side in search of natural liquidity. Richter observed, “There seems to be a huge focus on minimising market impact and footprint whilst trading. In recent periods of volatility in Asia, we have noticed a trend where algos are still heavily used for the low ADV (average daily volume traded) demand cashflow trades, while the usage of participation algos such as POV (percentage of volume) and VWAP (volume-weighted average price) strategies fall away.”

Traders are becoming smarter with strategy selection: Implementation shortfall and liquidity seeking algos have resulted in a shift towards getting the order done as soon as possible around a given price level. The time to execute an order has reduced by 50% on average.

Richter said, “Liquidity seeking algo performances across the board are particularly strong, especially where there is an emphasis on dark liquidity. A trend in algorithms and strategies is that they have been skewed to back-load a lot more to take advantage of the volume that comes into play later in the day.” S&P Global Market Intelligence has a metric within its TCA suite of benchmarks called VWET (volume weighted execution time), which shows how an order is implemented in the market. A higher VWET indicates a more back-loaded execution. Richter said, “For particular strategies that have historically seen VWETs at around 55% for a certain type of algorithm, we are now seeing a shift to 65%.” This is the result of a trader and the sell side working together to configure certain strategies to provide the best possible outcomes in adverse and volatile market conditions.

Despite critiques of algo trading, there have been improvements in execution quality: A common critique of algo trading is the increase in volatility. Addressing this concern, Richter said, “From a TCA perspective the trends have been positive over the years as algo usage has increased. The buyside have been able to identify the strategies that have worked very well for them. They also have full transparency around the routing logic of these algorithms to see which venues are providing optimal fills. Once venue execution quality can be established, decisions around routing logic and which venues to exclude can be made. Likewise looking at strategy selection around certain market capitalisations, volatilities and momentum environment, the buy side has become a lot smarter about the algorithms they interact with.” This has resulted in an overall boost in execution quality when it comes to trades.  

Besides, regulators are increasingly aware of the risks and implementing rules to minimise adverse occurrences. Richter said, “It is up to the buy side to use that toolset to reduce these impacts and maybe turn them into an advantage from an execution perspective.”

Algo trading is gaining ground in Asia

While algo trading already had traction in Hong Kong, Singapore, and Japan, it’s starting to gain momentum in South Korea and India. Speaking about these developments, Richter said, “India was a particularly difficult market because you could only transact in different lot sizes. It was holding back some of its potential in terms of active trading. Algo usage allows people to invest in a lot more straightforward manner. Obviously, that benefits the exchange and Indian companies.”

He said that more companies were advancing with technology, and understood that to attract investors, they must make trading efficient, and minimise costs that could drive investors away.

What the future holds for algo trading

The number of algo providers is likely to decline as the technology gains traction and the use of AI becomes more prevalent, making it harder for relatively resource strapped platforms to compete.

However, there are niches that can be tapped given the different profile of clients and the diversity of their goals. Richter said, “One of our clients may want to be aggressive and incur market impact. Another may be purely focused on achieving the open price.”

The types of strategy being deployed are also likely to change. The Algorithm Trading Insights Report will continue to track developments in the space as they happen, leaving its users with valuable, actionable information.

For a deeper understanding of the growth and adoption of algorithms, their performance, and usages now and in the future, subscribe to the Algorithm Trading Insights report.

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