Are robo-advisers the way forward for retail investors?

THE growing popularity of robo-advisers, here and around the world, shows little sign of abating - what with the market welcoming new entrants every few months or so.

The most recent newcomers in Singapore have charmingly varied propositions: endowus, for example, is an advisory firm helmed by investment veterans from Goldman Sachs and Morgan Stanley, while MoneyOwl is a social enterprise venture between NTUC Enterprise Co-operative and Providend Holding.

Like many segments of the economy, the traditionally people-fronted investment advisory space is growing increasingly digital - with algorithms and bots taking over the interface with retail clients and the decision-making for them.

The question is, will this development best serve the needs of the retail investor, now and in the future? Are robo-advisers what the new (and current) generation of investors want or is the human touch still what they seek? Or could the ideal solution be a hybrid of both?

Robo-advisers are, simply put, a digital platform with an online interface that uses algorithms to make investment decisions for clients, based on their investment goals - typically on a low-cost basis.

They first gather information on clients' investment needs, risk appetites and financial situations, before building an investment portfolio which they feel would best suit their clients.

Clients may be able to tweak some of these parameters but extensive customisation is not available. The platform will, however, be able to recalibrate portfolios, to an extent, in response to market conditions.

Robo-advisers tend to, therefore, be suited to investors who have a more hands-off approach to their investments, either because of a lack of time or expertise, and who don't need a highly personalised or people-fronted interface. Robo-advisers tend to appeal mostly to younger working Singaporeans, but are not limited to these.

Generally, robo-advisers, which burgeoned on the back of the growth of exchange-traded funds (ETFs), tend to most often invest in these.

Varied approach

The exact investment approach varies according to each robo-advising company, so investors will need to pick the one(s) that suit their own investment philosophies. Web portals such as Seedly (blog.seedly.sg) have done good comparisons of the various robo-advisers in the market.

Our intention is to discuss the impact of robo-advisers as a whole on the retail investor base. For a start, is their advent going to best serve the needs of the retail investor?

Yes, in that they fill a need in the market by being ideally suited to a certain type of investor that did not exist before. Many of their characteristics suit the millennial generation, but even older folks stand to benefit in several respects.

Firstly, robo-advisers tend to charge relatively low fees, an important consideration in the Singapore market, in particular, where investment costs among retail funds remain high.

Secondly, robo-advisers offer a decent amount of diversity on your investment. They can construct a portfolio across a wide range of assets (including the aforementioned ETFs which each have their own range of underlying assets) even if your initial investment is small.

They also invest one's money based on their algorithms - omitting the element of human error and emotion.

These characteristics make robo-advisers especially appealing to new or beginner investors and those who tend to adopt a passive approach to their investing (for example, someone who doesn't want to have to constantly monitor the market or worry about how their portfolio is doing once they've invested).

But, while appealing, is this investment approach what retail investors truly want?

Again, the features of robo-advisers make them more attractive to some and unappetising to others, but even those who feel robo-advising suits them should also be aware of its shortcomings.

Robo-advisers use algorithms to automatically allocate and rebalance assets; in some cases, these could be simple rules of thumb while, in others, there are human investment managers who oversee complicated algorithms. Critics say that many of these algorithms remain untested in a crisis, and react poorly to surprise events. Investors will need to decide if they're willing to risk mis-selling in such conditions.

Investors also need to realise that obtaining the needed information to make such decisions may not always be easy.

How an algorithm works - how exactly investments are chosen, how a portfolio is rebalanced according to market conditions and how a robo-adviser reacts to shocks - may not be data easily gleaned (or understood). Clients need to determine what level of transparency they are comfortable with.

And, even if one has easy access to such information and understands it, that will not change the fact that one's investment portfolio with a robo-adviser is essentially uncustomisable. Again, this may not be an issue with the investor who is happy with investing his money and then not having to think about it, but this will not sit well with the investor who would like to have a bit more say in, or be a bit more active in determining the individual components of his portfolio, depending on his mood or the season.

This would also be a problem for the investor who might be satisfied with the overall makeup of his portfolio but who would like to either exclude a particular asset or fund, or include a particular one, for whatever reason.

Ideal solution

Some have also said that robo-advisers suit new or beginner investors less well than people think. They say that the lack of understanding when it comes to investing or the products involved means one would likely do better with a human adviser, who could answer all of one's questions and correct one's misconceptions and provide guidance as market conditions change.

A human adviser, they add, could also be more comforting than an online interface when one's investment looks to be going up in flames during a market crisis.

The Holy Grail, said a Deloitte report, would be self-learning artificial intelligence algorithms shifting allocations based on market conditions and individual needs.

Until that comes about, or perhaps even if that comes about, a hybrid offering might be the needed middle ground for many investors, bringing human confidence in digital solutions to acceptable levels.