Since the emergence of “Modern Portfolio Theory” and the “Capital Asset Pricing Model” in the late 1960s, institutional investors have taken a quantitatively driven approach to portfolio construction, looking to create portfolio diversification and obtain better risk-adjusted returns by balancing their asset-class exposures. This journey has seen several important advancements in thinking about how to optimally achieve desired results.
Funds that want to take the total portfolio approach first need to get the total portfolio view. To do that they not only need data – and lots of it – but a rock-solid understanding of exactly how they’re going to use it.
Robo-advice lifts member contributions
Adopting a robo-advice platform will increase member contributions as well as investment-option performance, while improving diversification, according to a study of French savings plans.
While the buzz surrounding its introduction just over 10 years ago has abated, an increasing body of research is building on the value of robo-advice. More than a marketing tool for index managers or various asset allocators and other agents, robo-advice, when properly constructed with a minimum of distortions – especially commissions – has potential to both grow and improve the efficacy of the savings system.
What is generally considered the first robo-adviser is Betterment, started by Jon Stein, a fintech entrepreneur, in 2008 and opened for business in 2010. Stein remains chairman after appointing Sarah Kirshbaum Levy as chief executive in December last year.
With an estimated US$18 billion (A$23.4 billion) in assets under management, Betterment is the third largest such platform in the US, having been overtaken by financial services giants Vanguard, with US$230 billion on its robo platform, and Schwab, with US$63 billion, according to US publisher ‘Robo Advisor Pros’. The total US market was said to be worth just under US$1 trillion in 2020.
The latest research, labelled a ‘working paper’, was published this month (June) by researchers Marie Brière, head of the Amundi Research Center affiliated with the Paris Dauphine University, and Milo Bianchi of the Toulouse School of Economics, TSM, and IUF, University of Toulouse Capitole. The lead editors are both Amundi executives – Pascal Blanque, the manager’s CIO, and Philippe Ithurbide, a senior economic adviser.
Key findings in the paper, which involved behaviour from a sample of 17,000 subscribers, who had signed up to consider the service and went on to invest in the proposed portfolio within their plan at 713 companies, include:
- Investors with smaller portfolios are more likely to invest a larger proportion of their portfolio with the robo
- Wealthier investors are more likely to be ‘robo curious’ rather than to be ‘robo takers’. That is, they are more likely to acquire information about the robo without subscribing to the service
- There is a positive correlation between the distance between the robo’s recommended portfolio and the investor’s current allocation. The further away the recommendation of the robo, the more likely it become that the investor accepts it. The researchers note this is in contrast with one of the ways many advisers use to gain trust with their clients and prospects by accommodating the client’s beliefs and strategies. The effect is stronger when the robo proposes riskier allocations
- Robo takers increase their trading activities after they subscribe to the robo and, importantly, increase their investment in the company’s saving plan
- Increased activities are associated to a change in risk exposure. After subscription, robo takers increase their equity share by 55 per cent relative to the average equity share of 15.7 per cent
- Robo takers reduce their weightings to bonds and money market funds and increase the weightings to balanced and equity funds.
- Robo takers experience an increase in annual returns of 80 per cent, relative to the average return of 6.7 per cent. After adjusting for the increased risk in the portfolio due to higher equity weightings, the researchers say the “robo treatment” increases returns by between 3-4 percentage points annually.
Brière and Bianchi say: “Together with the increased investment in the saving plan and considering that the management fees associated to the [Amundi] robo are much smaller, these results suggest that the robo can have a significant impact on investors’ wealth accumulation in the long run.”
The researchers had access to the data from Amundi, Europe’s largest fund manager, which offers funds management to French savings plans between 2016 and 2018. This allowed them to study differences between pre-launch behaviour and post. The service was launched in August 2017 and the study is based on the experiences of about 8,000 companies with access to the Amundi offering for their employee savings plans.
The working paper also sources 37 separate pieces of research, including two smaller studies by Brière and Bianchi also published this year.
The main finding of one of these other studies is no surprise; it finds that robots have low operating costs and allow access to a broader set of investors. The other, however, finds that there is considerable mistrust of algorithms in the savings community.
They say in the working paper: “An important dimension concerns the dynamics of human-to-robo interaction in the context of financial services. Building trust is key for financial services, and at the same time mistrust in algorithms seems particularly severe in this context…
“In addition, investors may value a trusting relationship with their advisor even beyond financial performance. Human-to-robo interactions are important when investors decide on whether to accept the robo service and possibly change their portfolio allocations.
“They are also important over time, when they may be induced to pay attention to their portfolios even if not used to do so, or when they may be advised to rebalance their portfolio in a given direction even if tempted to do otherwise…”
Even in 2008, when Jon Stein started to build the systems behind Betterment, the technology itself was nothing new. Human wealth managers had been using automated portfolio allocation software since the early 2000s. But until 2008, they tended to be the only ones who could buy the technology, so clients had to employ a financial adviser to benefit from the innovation.
Today, most robo-advisers put to use passive indexing strategies that are optimised using some variant of modern portfolio theory. There are now others which offer more specialist strategies, such as for ESG or even those which mimic hedge funds.
