As the shift from active to passive investing in the discretionary equity space has continued to accelerate, it has become evident that traditional active asset management firms—i.e., long-only, fundamentally driven stock-pickers—are facing a serious crisis of relevance.
One reason for the shift from active to passive is that, collectively, traditional active asset managers have fallen short of investor expectations in failing to deliver risk-adjusted, net-of-fee performance.
Maybe this is just a cyclical reversal, but perhaps there is a more enduring reason: that the world has changed while many traditional managers, proudly clinging to their artisanal approach, have not been able to adapt to the changing dynamics, and this lack of evolution has hampered risk/returns.
For decades, the identification and collection of information was almost as important as the analysis of that information. Recently, regulatory changes and technological advances have shifted the paradigm. Access to timely information is now ubiquitous and free—in a word, commoditized. This, in turn, has created new challenges, highlighting the human limitations of processing so much information in a conventional analog fashion.
Merely trusting your money to the smartest guy in the room may not be the most sustainable model; the smartest guy in the room can certainly benefit from a state-of-the-art investment process that incorporates the technologically advanced tools and analytics.
I believe most traditional active managers have yet to adjust to this new reality and need to add more “science” to the “art” of investing to harness the new challenges. As in many other endeavors, technology will be crucial for the future of active management.
Merely trusting your money to the smartest guy in the room may not be the most sustainable model; the smartest guy in the room can certainly benefit from a state-of-the-art investment process that incorporates the technologically advanced tools and analytics.
So, are fundamental active mangers investing sufficiently in technology and in the right areas to preserve that future? The answer is that while they are spending large sums on information technology (IT), the spending has not been sufficiently focused on alpha enhancement.
According to Gartner and Institutional Investor, the investment management industry spends two times the global industry average on information technology (IT), about 8% to 9% of revenue (versus 3% to 4% for the global average).
Looking more closely, the majority (50% to 70%) of this IT spending has been focused on middle- and back-office operations, which we could qualify as defensive (or necessary) spending. Approximately 10% to 30% focused on client-related activities.
Only the balance (10% to 30%) is spent on the front end or actual investment process (and, of that, one-fourth is for market data, which is not really technology!). This offensive spending has been receiving attention from some pockets of the industry, such as quantitative managers, but on aggregate it has lagged defensive areas.
Moreover, investment in the front office has largely focused on risk-management systems (especially after the Global Financial Crisis) or other non-alpha-generating areas, such as compensation and benefits.
In my opinion, the most significant opportunity in investment-related spending is in technologies designed to enhance the existing investment process, slowly moving away from using technology to access data to incorporating more advanced analytics focused on investment decision-making and alpha-generation. No technology solution will fix a subpar investment process, but having the right systems to support the process is key.
Industry consultant Boston Consulting Group stated in a recent report on the outlook for the asset-management industry: “It will become increasingly clear that competence in advanced data and analytics will define competitive advantage in the industry in the not-too-distant future.” 1
Technology Integration to Enhance Outcomes
At William Blair, we have taken a proactive approach to technology integration in the investment process. About a decade ago, we started to build a custom-made research and portfolio-management software platform to reflect our existing investment process. This platform, called Summit, integrates all our intellectual capital with select external vendors’ data and captures all events related to our investment process.
Our primary goal in building Summit was to foster investment-team collaboration and transparency to better align all members of the team with investment objectives. A secondary consideration was the collection of data to improve the monitoring of research activity and decision-making.
No technology solution will fix a subpar investment process, but having the right systems to support the process is key.
A side benefit of introducing this system was that it forced us to truly analyze our existing analog investment process, exposing biases that we may have been unaware of, and allowing us to improve.
The next step was to introduce more advanced tools designed to enhance investment outcomes. More recently, we have been focused on such analytics. Our use of systematic stock factors, for example, has greatly enhanced the scalability and efficiency of our investment process.
The integration of systematic analysis enhances the objectivity of our fundamental investment process. We have also extended these tools into our investment strategy and risk management applications to apply a coherent framework throughout the process.
Furthermore, we created customized dashboards and alerts to push information to our investment professionals instead of forcing them to navigate the massive information available in the system manually. This was a critically important paradigm shift. We also developed a location-agnostic workflow that lets us collect, analyze, and share information from almost any place across devices.
With the advent of the cloud, software-as-a-service applications, and data warehousing, systems such as these are even more accessible and relatively low in cost. These applications can be used to identify opportunities and risks that would be far more complex or time-consuming for humans to identify in a conventional manner.
Standard visualization tools like Tableau are good examples of what is readily available now. They do not make the decisions or judgment calls themselves, but assist in the process.
Going forward, the goal is to leverage the technology to evolve the investment process itself. In my opinion, this is the holy grail of technology adoption in the investment arena.
This will require applying business intelligence, machine learning, and artificial intelligence to big data (both structured and unstructured) to visualize the investment process and potential outcomes in a way that most investment professionals cannot see as clearly today.
By allowing a faster and more efficient analysis of the information available and continual improvement of the decision-making process, existing and future technologies can improve human judgment.
1 Global Asset Management 2016: Doubling Down on Data.
GICS Reclassification: Impact on Emerging Market Investors
