23 Nov Helping investment advisors compete against “free”
Taking on the “robo-advisors” won’t be easy. That’s why providing “product pitchers,” “asset allocators,” “model matchers” and “stock pickers” with the right tools is so important.
What do investment advisors, newspapers, television stations and popular musicians have in common? They all face competitors who charge little, if anything for their offerings. Media players’ competition comes from “free” blogs, YouTube, file-sharers and the like.
Investment advisors’ latest big competition comes from “free” (or “nearly free”) robo-advisors. The new bots supply risk profiling, portfolio construction, and automated re-balancing for less than 0.5 % of assets under management. Sometimes much less. Wealth management firms need to act fast to take on this growing threat.
That means automating and scaling basic IA functions, to give advisors more time to provide clients the services that robots can’t. We’ve identified four advisor roles, and the technology capabilities they will need to keep their businesses moving forward.
Product Pitcher
Product Pitchers are old school professionals that believe in equipping clients with the “right tool for the job”. Their strength is dealing with a limited number of products, and knowing them inside and out. The Product Pitchers’ best complement would be an internal robo-advisor that helps them provide clients the information they need, so that they have more time to build deeper and more effective relationships.
Asset Allocator
Asset Allocators are more sophisticated versions of Product Pitchers. Their competitive advantage is their ability to leverage their market knowledge to generate alpha, in part by performing advanced portfolio scenario analyses. Asset Allocators need access to advanced, real-time market analytics platforms (Bloomberg, Thompson Icon) coupled with a robust reporting platform that demonstrates the value they add (attribution analysis).
Model Matcher
Model Matchers’ effective use of their firm’s recommended allocation and other investment models enables them to provide better and quicker service to a larger number of clients. Their challenge is their need to provide clear messaging which differentiates their value proposition from competitors who offer similar levels of service. Model Matchers are big potential beneficiaries of automated portfolio rebalancing tools and robust CRM solutions, which eases their high work volumes.
Stock Pickers
Stock Pickers have long-standing roots that date back to the days when stockbrokers/Investment Advisors, were the gatekeepers to financial markets. They continue to actively manage portfolios, picking the stocks and allocations needed to create their own model portfolios. Like Product Pitchers, Stock Pickers know their portfolio companies inside and out and appeal to a more mature clientele who want their advisor to be their “personal” portfolio manager.
Their weakness is that they rely on their ability to “generate alpha,” that is to perform better than a passive portfolio, a feat that has proven very difficult even for the most accomplished institutional investor after accounting for fees. One way could be to provide Stock Pickers with portfolio management systems that have advanced modelling and rebalancing capabilities. This would enable them to focus on a goals-based approach, that prioritizes matching the right portfolio to the each of the client’s goals, rather than stocks that outperform their competitors.
Key takeaways
The key takeaway stemming from the rise of robo-advisors and commoditization of basic portfolio construction, is that IAs need tools to enable them to scale their practices, to help them take on the growing threat. This will enable advisors to spend more time in areas where they provide the greatest value to their clients. Technology firms for their part need to design, test and update new tools regularly – because robots adapt fast.
However, it is the wealth management firms themselves that in a way bear the greatest burden. They need to source, identify and implement best practices – and to constantly update and improve on them.
If they don’t, someday, somewhere, a robot will be pushing their “lights-out” button.