Discussion about the rise of robo-advisors has generally been theoretical – focusing on issues such as whether they will support or compete with wealth managers, or more generally on their potential to disrupt the industry. Views differ, but private banks, asset managers, regulators and investors are all taking robo-advisors seriously.
What is missing from the debate, however, is a measure of their performance. Do algorithm-driven portfolios actually deliver what they promise?
In June 2018, we completed the first round of robo-advisor participants, with the final results here.
FSA started the second year of the feature in the following month, with participants including Aqumon (based in Hong Kong), Smartly (based in Singapore) and Algebra (based in Malaysia).
On July 1, 2018, we made a hypothetical investment of $1m in each of these robo-advisors (except Smartly, which started with S$1m). Return results will be published monthly until August 2019.
The purpose is to highlight how robo-advisors allocate and how they perform, particularly when there is a downturn.
Additionally, we want to help self-directed individual investors to make a comparison with their own results. Wealth managers can check robo-advisor performance against client portfolio results and the rest of the industry has useful data to form a judgment on the robo-advisor phenomenon.
FSA Robo-Advisor Showcase
Performance on 1 March 2019
Note: Three portfolios for each robo-advisor are presented – cautious, balanced and aggressive. However, because the firms operate in different markets and offer various products, the robo-advisors are competing against their own benchmarks, not against each other.
Hong Kong-based Aqumon, which launched last year, uses machine learning, a type of artificial intelligence, as part of the process to screen investment products and form allocation views to re-balance portfolios.
Aqumon can provide up to 10 risk profiles for each client, according to co-founder Don Huang. Based on individual risk appetite, the service then creates a portfolio that will have eight-to-10 ETFs for Hong Kong-based investors. In China, the robo-advisor makes use of mutual funds, as the costs of using ETFs are much higher than in Hong Kong and the US.
The service does not have a subscription fee and has a 0.8% annual investment advisory fee.
Note: Smartly did not provide any benchmark indices for its portfolios. The initial hypothetical investment in July 2018 was S$1m. The firm only provides portfolios denominated in Singapore dollars.
Singapore-based Smartly, which was launched in 2016, provides 10 risk profiles, with 1 being the most conservative and 10 the most aggressive.
Looking at various quantitative and qualitative criteria, the platform has around 20 ETFs to choose from and may focus on equities, bonds, commodities, real estate or cash. It only selects physical ETFs, which means there are no derivatives involved.
When creating portfolios, Smartly uses the “Black Litterman” model, which is applied in algorithms to measure the limitations of how much can be allocated to a particular ETF or a particular asset class or region in general. Portfolios are re-balanced regularly, as often as once a month.
The annual fee is between 0.5% and 1%, and there are no additional fees for re-balancing, withdrawals or trading.
Algebra is a robo-advisor offered by Malaysia-based Farringdon Group. It was launched in July 2017 and offers both sharia-compliant and conventional portfolios. FSA features three non-sharia portfolios.
The basis of Algebra’s portfolios is a smart-beta, stock-picking strategy developed by Singapore-based Farringdon Asset Management. The portfolio consists of around 50 US stocks from the S&P 500 universe. They are selected based on the analysis of portfolios of 10 highly-rated active US equity fund managers. From each manager the algorithm chooses five stocks in which their fund is overweight, to include in the Algebra portfolio. The three model portfolios presented here contain a different allocation of fixed income to manage the risk profile. The annual fee is 0.85%.