This post is an exerpt from a recent conversation between Katina Stefanova, Chief Investment Officer with Marto Capital, Marc Mallett, Vice President for Product Management at SimCorp, and Patrick Green, Principal at InvestTech Systems Consulting.
David Grana: Can technology and operational efficiency increase returns and generate alpha in a portfolio?
Patrick Green: The improvement in decision outcomes is one way technology and operational efficiency can contribute to returns and help generate alpha in a portfolio. The improvement of information latency is another.
From a technology perspective, there is a large amount of emphasis being placed on big data and the ability to integrate all the newer sources of information with big data and being able to predict market trends more rapidly. This focus has led to greater ability to make better and quicker decisions. These advancements have also required firms to reassess various roles within portfolio management, and to create new positions that are using these advanced, statistical data mining tools. And applying machine learning algorithms to dig deeper and find patterns. The quantity and complexity of data is greater than ever in our industry. The ability to ingest, process and analyze these volumes exceeds the limitations of the traditional market expert model of the past. We have been helping clients select and implement these tools so that they can have this new type of data visualization. We are helping them create these models looking at all of the different, complex big data sources to create the predictive models that are helping to generate alpha.
Information latency and accuracy are areas where operational improvement are helping firms generate alpha. The idea being that there currently is a crowded market for investment and trading ideas coupled with benchmark issues. If several names in the traditional benchmarks grossly outperform others, then most active managers won’t beat that standard measurement for performance.
There was a study done by Blackstone and McKinsey several years ago, where they finally found a way to quantify operational quality and link it to alpha enhancement. They did this by looking at a handful of institutional asset managers, and while holding their systems and operational processes constant, they systematically analyzed and improved each system and processes for order management, settlement, account servicing, securities lending, compliance rule of trade execution, custodian and broker communications, fund accounting, etc. They found that within 6 to 9 months, each firm would begin to see visible increases in alpha.
This could range anywhere from 50 to 250 basis points. And it all comes down to information latency and the ability of people and systems to act and deliver in a timely manner. If you are waiting for correct cash balances or positions in order to be able to trade, or your collateral management is manual, this leads to information latency and managers are making decisions after everyone else in the market and thus, falling behind.
Katina Stefanova: We tend to automate existing processes and models of how roles and responsibilities operate.
You have the traditional hedge fund or asset manager model, where it is highly person dependent. It is what I call the pyramid model, whereby you have one manager at the top with various junior, or mid level people, in the middle. And they manage different amounts of capital that is allocated to them.
When technology comes into play, the first thing you do is utilize it to enable the existing model to operate better through new data sources, faster calculations of investment ideas, insight machine learning, etc. But the initial model doesn’t really change. What is beginning to happen within our industry is that technology is entering and allowing people who are more creative to completely re-envisage how this industry operates. Technology is truly utilized when we start thinking out of the box. This hasn’t fully happened within the asset management industry, but it is beginning to.
On the hedge fund side, the closest example would be two sigma. This is mostly a technology company versus money management company. There are also players like Alibaba, who manage hundreds of billions of assets under management and are contenders in our industry coming from a completely different angle. I expect that with technology, the most radical change will be the re-design and re-envisaging of how money is actually managed. New roles will appear and there will be different kinds of exponential opportunities that emerge.
Here at Marto Capital, there are a few elements that we do dramatically differently. One of those is that we architect the investment process from beginning to end so that we have no portfolio managers. This is the first truly designed workflow process where you have specialists in research and in portfolio construction. This allows you to build in risk and transparency all the way through to eliminate key risks. We can also generate scalability in a way that would be very difficult within the traditional model.
Technology also helps from both an efficiency and transparency perspective. There have been new models created, as well. Those include a back-office, tri-party shadow model that firms like Bridgewater implemented and are now available throughout the industry. The final, radical shift in technology is when we start thinking of money management as technology on the forefront and individual knowledge in the background. This addresses many of the challenges within our industry and most likely over time will lead to a compression in fees.
Marc Mallett: Although many firms may not realize the direct link between efficient operations and portfolio growth, to us at SimCorp the relationship and opportunity is clear. An integrated platform based on a single source of truth, or what’s known as an IBOR, provides a timely, accurate and transparent view into what the firm owns, what it is worth and its exposure across the entire book of business. Thus, managers can gain much needed operational efficiency, make more informed business decisions and drive performance.
There was a white paper issued by a consultancy firm called forward look inc., with research still ongoing. It is interesting, since it echoes some of what Patrick mentioned. The paper describes a 10-year research study that clarified how state-of-the-art, integrated technology enables buy-side firms to protect inherent alpha in their investment strategies, drawing a direct link between operational effectiveness and portfolio performance. By allowing better control of enterprise data, investment managers can quickly and readily analyze that data to improve business performance and gain competitive advantage. By investing in state-of-the-art technology, investment managers can retain 51-242 basis points of inherent alpha by minimizing implementation shortfalls arising from suboptimal investment operations.
We see a lot of technology today that is disparate and disjointed with data needing to move from one platform to the next. In many investment management firms, front-office personnel start their day by questioning, validating, reconciling and manipulating their position, cash and analytics data. They know the data in their front office tools is updated on some periodic basis and that the data may be stale, inaccurate or both. A recent survey by TABB Group found that more than half of senior front office personnel take at least an hour out of their day to unravel errors caused by bad data which detracts from the time devoted to actual trading.
Buy-side firms exist to preserve and grow their client’s capital and to support their client’s investment objectives, not to integrate and reconcile disparate applications. Nor do they exist so their highly talented investment professionals can waste their time questioning the timeliness, accuracy and quality of their data.
