Robo Advisors – Rise or Demise?
Wealth management has typically only been for those with higher-than-average income, with fees for these services being prohibitive for the general population.
However, wealth management is becoming increasingly accessible – companies using robo-advisers now provide similar services for a fee of less than 1% of the money being managed. In recent years, the ‘robo-advisers’ have grown rapidly, mainly due to their low-cost offering, technology adoption and ease of access toa wider range of investors. This low-cost model has allowed investors with even smaller amounts to be able to use such services compared to the traditional advisors where large minimum investment was required.
Financial planning is the key ingredient or even the foundation stone of the advisory process. In this regard, it is crucial to fully understand the clients’financial goals, risk capacity and risk tolerance. To ensure the suitability of the investment advice it is crucial that the risk profiling follows a rigorous methodology. According to Kitces the optimal portfolio solution is not acombination of risk tolerance and risk capacity. It’s the portfolio that can bestachieve the client’s goals, constrained by risk tolerance to ensure that neither the portfolio, nor the goal, exceeds the client’s tolerance in the first place.
So, following the planning, asset allocation is the core building block of the financial investments. Robo-advisors are better able to profile the customers and match them to the market due to which they can provide superior asset allocation and re-allocation, exposure to multiple markets at a much faster pace than traditional managers and advisors. As reported by ValueWalk, Machine Learning hedge funds already significantly outperform generalized hedge funds, as well as traditional quant funds.
While the robo-advisors of today might have some limitations but I believe they will continue growing so in future they will have an edge over the traditional managers and become a normal way of investing for most people. The robo-advisors and associated algorithms are still in evolution and adopting the latest artificial intelligence as well as the machine learning technologies while there are still some areas that require constant improvement and growth. Although, the amount of Assets Under Management (AUM) needs to substantially increase for robo-advisors to be able to break even, but they have the advantage that they can cater to increasing number of customers at very little extra cost, in contrast to traditional managers.
According to Moodys’ some large asset managers like BlackRock and Point72 are investing large amounts in robo-advisors and big banks like JPMorgan Chase, Morgan Stanley, Bank of America and WellsFargo are adding these platforms to their offering portfolios. An advanced robo-advisor that is fully evolved, in future, will have the qualities which are also expected of a good wealth manager. While humans have a behaviour bias at onepoint or the other, the algorithm on the other hand, should not have any such bias.
With artificial intelligence and deep learning still near their infancy and growing, the robo-advisors of future would have the ability to think independently and focus on what is relevant rather than getting bogged downby what’s irrelevant. These robo-advisors will have sufficient experience as well as exposure to several market cycles, as they would be based and tested upon several years of historical data, and be tuned into the psychology of the marketby interpreting the data using the proven methodologies.
Collecting and processing the information necessary for complex decision-making is not just time consuming but expensive too. It is beyond a human being’s cognitive limits to fully define the future states of the financial assets and probability-weighing them. Even the chess grandmasters are unable to fully evaluate more than five chess moves ahead and the human cognitive limits are quickly breached.
This is where, with advanced artificial intelligence, machine learning and more cognitive computing employed, the robo-advisors of future will have an advantage as the decision making is far more complex.
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