Neither one thing nor the other – this new approach has the potential to change the landscape of investment management, but still has a long way to go to one day equate to a full-service consulting.
Although it is seen as a revolution in the way we handle investments, the truth is that it already existed, but with another focus. Essentially, robo-advisors are algorithms that, based on mathematical criteria, indicate and / or send orders to buy and sell securities for a given investment portfolio. The concept of algorithmic trading, for decades exploited by hedge funds, is also based on series of computerized decisions for the purchase and sale of all types of assets, from stocks to currencies. Although there are particularities in this type of approach – algo-traders trade normally in the short term, using fairly sophisticated mathematical modeling – the fact is that the idea of using software to make a human decision about investments has been around for quite some time.
Innovation, therefore, is not in form, but in the target audience. For the first time, retail banking sees alternatives to the traditional account manager – beyond that, it can impose itself as the master of its own decisions, given that the current level of automation of the investment process serves well the basic allocation needs. It also serves to provide more peace of mind in possible dividend reinvestments and eventual rebalancing of the asset portfolio. Because they are based on predefined algorithms, they ignore any kind of emotional involvement in decision making because they are based exclusively on quantitative mathematical parameters.
Two undisputed advantages are the low cost and the minimum capital value for initial allocation in hiring the robo-advisor service. However, the trade-off is clear – there is no free lunch. The current robot-advisors operate on the premise of efficient market, where no investor can surpass the market, given the same level of risk. It does not consider possible strategies that can increase the return, without necessarily adding much risk to the portfolio. Efficient market theory advocates diversification as a means of eliminating diversifiable (unsystematic) risk, but it does not eliminate the risk arising from macroeconomic crises or cyclical problems affecting the market as a whole. Finally, when portfolios are formed with many securities where there is risk asymmetry (as in fixed income assets), the mathematical model may lose some meaning. The rebalancing is also very limited, since in order to avoid incurring higher transaction costs, rebalancing is usually carried out only for new contributions, which means that the portfolio can spend long periods completely unbalanced in the face of changes in the market values of the assets.
Although there are heavy criticisms of the inability to promote a human touch to the relationship, this is a debatable point either because the new generations (Y and Z) are widely used to this approach and expect this kind of automation, or because even within Generation X many also look favorably on this type of approach. However, the idiosyncrasies of every human being, especially when involving desires and pondering subjective investment alternatives, are best interpreted by another human brain. Although an algorithm can quantitatively analyze the best allocation in a risk and return matrix, or can design a good financial planning, the premises and fundamental parameters will still be for a long time defined by a human being.
Finally, we must consider the lack of history, especially in periods of stress such as the crises of 2008 and 2001, in order to assess the resilience of these models. Evaluating purely from the years when the market has generally achieved good returns gives rise to legitimate criticisms as to its actual efficiency, since most asset portfolios would have obtained positive returns even without great intelligence in their allocations.