Analysis: Greater automation, stronger ETF cluster will augment SGX’s prospects
The Singapore Exchange (SGX) will need to embrace greater use of automated trading systems and cognitive technologies (i.e. artificial intelligence), as well as grow its exchange-traded fund (ETF) cluster if the bourse if to remain competitive amid rising regional competition across the Indo-Asia Pacific (Indo-APAC).
With a number of local technology enterprises choosing to list in Hong Kong and New York, as well as many Singaporean issuers choosing the Australian Securities Exchange (ASX), this has raised concerns over Singapore’s competitiveness, given that capital from exit events needs to be cycled into Singapore’s capital markets in order to support the city-state’s entrepreneurial ecosystem.
In 2017, the World Economic Forum (WEF) ranked the city-state as the third most competitive country globally after the US and Switzerland — Singapore had held its second-place ranking since 2010 — and comes amid concerns over the country’s seeming inability to build a pool of globally competitive enterprises.
With the US stock market dominated by automated systems — JP Morgan estimates that humans account for only 10% of equities trading volume — Singapore and other East Asian stock markets may eventually reflect this, especially as the investor base of these ageing societies grow.
According to a March 2017 piece by The Straits Times, on the Singapore Exchange (SGX), algorithms are reportedly responsible for up to 34% of daily stock turnover in H2 2016, with two-thirds of these trades performed by algorithms affiliated to institutional investors and the rest of the volume accounted for by retail investors.
This is contrary to the popular observation that retail participation in Singapore’s bourse is lacking; retail investors were linked to 40% of shared trades by value on an average day, with close to half of retail investors’ trades conducted via fund managers and the remainder split between other retail investors and algorithms.
Deloitte’s view
Recent developments have seen cognitive technologies reshaping capital markets. Robot-journalism can augment liquidity in markets which see trading dominated by automated systems, while investor news consumption behaviour can influence stock prices, a development that comes amid greater automation of the finance sector.
Ms Tay Hwee Ling, Global IFRS & Offerings Services Leader, Deloitte Singapore, elaborates: “Robo-traders use circular logic and complex algorithms that focus on changes in the market prices and indexes to manage investments. Similarly, robo-journalists analyse market date to churn out investment advice for high-net-worth clients and investors.”
“There are several robo-platforms competing to provide investors with specially designed portfolio planning and asset management tools, with an enhance control over their portfolio. This is not limited to financial technology start-ups; it is a useful enhancement for traditional wealth management institutions as well.”
“The local banks in Singapore have robo-advisory services, for example OneWealth (OCBC) and Wealth Adviser (DBS), that offer automated investment advice and monitoring to their customers. With the help of fintech automated platforms, individuals can invest in a globally diversified portfolio of ETFs suited to their risk appetite.”
She adds, “While robo-traders and robo-journalists can compile data and trends, they cannot execute the analytical/instinctive process that people can, including the consideration that the capital markets are impacted by soft factors such as political events, anomalies and company developments.”
“Greater information transparency can drive financial activity, and may even be perceived as necessary to a well-functioning capital market. Companies with no research coverage tend to underperform their peers — this is typical for smaller listed companies.”
“ Robotics allows the chance for smaller companies to surface as an investment option. However, the typical investors’ preference and appetite are generally dependent on the general sentiments towards the capital market, rather than purely on the availability of analytical information.”
Meanwhile, the SGX has been on its way to re-establishing trading volumes, with the launch of initiatives in 2016 to augment liquidity and boost trading. Asked to what extent ETFs and automated trading systems would serve to boost liquidity and enhance the efficiency of distribution, Tay elaborated: “Many investment managers are augmenting traditional stock-picking methods with advanced analytical techniques and alternative data sets to stay ahead of the curve.”
“Those investment managers who are seeking organic growth via consistent alpha generation should be on the lookout for creative approaches utilising technology and alternative data sources for making investment decisions.”
“The new generation has shifted the focus from the stock-picking skills of portfolio managers and traditional financial analysis to augmented processes including quantitative and analytical techniques, such as artificial intelligence (AI) and new alternative data sources that can provide investment insight.”
“Investment managers are using a myriad of technologies in their investment decision process — including AI and other advanced analytical techniques to improve their traditional processes. In fact, many hedge funds and family offices are using AI not only for investment decision making, but also for finding better ways of executing trades, and hopefully boost liquidity, notwithstanding that there are several other factors that impact investment decisions”, Tay commented.
Responding to queries as to why the Singapore securities market had seemingly been unable to leverage its fundamental strength in attracting and distributing international capital to tap the pools of international private capital managed by Singapore-domiciled funds, Tay observed: “As Singapore is an international financial hub in the region, institutional investment portfolios in Singapore tend to be diversified across the key markets in the Asia Pacific.”
“The Investors’ decisions on which markets to invest in tend to be proportionately diversified across the key markets of Hong Kong, Australia, China, Japan and Singapore,” she added.
Stashaway’s take
Freddy Lim, the co-founder and chief investment officer of Singapore-based digital asset management firm Stashaway, believes that developments in US equities involving robo-traders are unlikely to be mirrored in Asian securities markets like Singapore, Hong Kong or Tokyo.
Despite the reduction in high-frequency trading, a September 2018 market research report published by Transparency Market Research indicates that the global algorithmic trading market, valued at US$ 8.373 billion in 2016, is estimated to expand at a compound annual growth rate (CAGR) of 10.2% from 2018 to 2026, growing to US$21.8 billion.
Most of this growth will be concentrated in North America, primarily due to strong technological advancement and considerable application of algorithm trading in several end-users such as banks and financial institutions. But it is also coupled with a growing AI risk to capital markets.
Lim comments: “Absolute trading in Asia markets attributable to robo-investing are much lower than US and European markets. We don’t have specific figures on this at the moment. Having said that, we do expect the share of trading relating to indexed and/or robot-investing to rise rapidly as public awareness in Asia is growing rapidly.”
“In addition, there is also strong regulatory support in the region. In our opinion, it is not unrealistic to see robots’ share of trading grow rapidly to the neighbourhood of 35%-65% of total trading over the next 3–5 years,” he adds.
Information from an October 2017 IDC report indicates that total assets under management (AUM) under robo-advisory in Asia-Pacific is estimated at US$30 billion and will grow to US$500 billion by 2021. This will see the robo-advisory opportunity grow from 0.2% in 2017 to 1% in 2021. China is a market of particular interest, as robo-advisors are expected to manage around US$450 billion in assets by 2021, accounting for 90% of the estimated market in the Asia-Pacific.
This field that has substantial room to grow in Asia given the growth of the regions middle class. In the US alone, an account in Robo-Advisor Pros indicates that as of August 2018, Vanguard Personal Advisor Services had US$112 billion in AUM, followed by Schwab Intelligent Portfolios (US$33.3 billion), Betterment (US$14 billion), Wealthfront (US$10 billion) and Personal Capital (US$7.5 billion).
In 2017, the Monetary Authority of Singapore (MAS) announced moves meant to foster the growth of digital advisory firms like Stashaway, while in late August 2018 OCBC Bank announced the launch of its robot-advisory service, OCBC RoboInvest.
In response to the launch of OCBC RoboInvest, Michele Ferrario, the CEO and co-founder of StashAway, had commented in an interaction with the Singapore Business Review: ”For StashAway, this is also good news as it will attract more attention to robo-advisors. We don’t see this as a threat as we are confident that we offer a superior product at a lower price.”
Ferrario highlighted its investment framework, use of technology and lower costs as key differentiators, with the ability to maintain portfolios diversified across asset classes, geographies, types of issuers and maturity.
As for the potential of ETF issuers creating their own indices and its relevance to Indo-Asia Pacific? Lim notes: “There is significant growth in the AUM of ETFs tracking traditional assets in the Asia-Pacific region, although the adoption of smart-beta strategies and factor-based investing has been relatively constrained by lesser liquidity in some parts of the region.”
“A large number of the indices are already provided by well-respected institutions such as MSCI so we see little value addition in “re-inventing the wheels” by having ETF issuers create their own indices.”
However, despite the potential for information asymmetries to emerge amidst the greater automation of the finance sector and the infiltration of fake news into financial markets — investor news consumption behaviour has been found to influence stock prices — Lim sees these information asymmetries being levelled off, with the investment public and different sectors of the investment community operating within certain degrees of parity.
Lim notes: “In more developed markets, the persistent push by regulators for more transparency has already levelled the playing field when it comes to how information is distributed. For one, it is illegal to trade on insider information. In these markets, automation merely reduced the speed in which publicly available information can be accessed by investors.”
“Having said that, there still exists some information asymmetries in less-developed markets. Where regulation has not caught up, automation and digitization would certainly help plug the gap,” he adds.
On a more local basis, he also believes that in Singapore and the broader Indo-Asia Pacific (Indo-APAC) region, the growth of robo-advisors will augment investor choice. More automated trading solutions along the lines of AlgoMerchant, Bambu and StashAway will emerge in the future in response to growing demand for more options, as well as the proliferation of ETFs.
“A rapidly growing number of ETFs are being launched to help investors access essential asset classes such as stock indices, REITs and local government and corporate bonds in Singapore and rest of the Asia ex-Japan region. The presence of robot platforms further reinforced these initiatives and would certainly help accelerate the growth of these indexed tracking products.”
However, with cognitive technologies reshaping capital markets, Lim also sees high-frequency trading being impacted. The proliferation of automated solutions, cognitive agents and machine learning are likely to enable further refinement and calibration, creating opportunities for investors focused on long-term value creation and those focused on short and medium-term plays.
“High-frequency trading was the first arena where pattern recognition and advanced machine learning were applied on a large systematic scale. This space has evolved from being focused on speed to continuous evolution. In today’s landscape, algorithms can become obsolete very quickly and hence suffers from diminishing rate of returns.”
“Having said that, we see significant potential for cognitive technologies and deep learning to be applied to the field of fundamental investing where trading strategies identified can be more relevant for significantly longer periods of time.”
