Managers of active funds, including hedge funds, are under increasing pressure to back up their investment ideas and performance with qualitative data. By using data science, they can better justify the level of their fees.
That’s according to a survey entitled “The Art of Alpha”, all about investment data science done by custodian bank Northern Trust among 300 CIOs and portfolio managers of asset managers around the world.
The survey found that 58 percent of managers are already using data sets to identify both risks and opportunities. For example, 56 percent of those surveyed automatically execute trades based on signals and/or trends. Also, half of managers use data to deepen or guide an investment theory.
According to Gary Paulin, head of global strategic solutions at Northern Trust, asset managers are adapting as institutional clients integrate data science into their manager selection decisions.
Intuition over logic
Fund houses have so far often relied on marketing, star managers or gut feeling, rather than the ability to deeply argue and defend their process. As a result, many active fund houses have ended up becoming “index huggers”. These involve fees for so-called active management, while the tracking error is usually so low that they remain strongly in line with the benchmark. But in the industry, this is called “stuck in the middle” and its survival is threatened.
Asset managers are trying to reverse this dangerous market position by either merging with the aim of gaining scale, or by specialising in a specific area. Also helping is that data analytics is becoming more readily available through the availability of affordable software-as-a-service packages, Paulin said.
Until recently, fund manager performance was weighed by large institutional investors primarily through “back-testing”. They took into account a portfolio manager’s top weightings, the way he or she classified holdings, and often explicitly considered the composition and experience of the team. Thus, a manager could be penalised for his “style drift”, being the pursuit of investments that went against his or her own strategy.
Better understanding
According to Northern Trust, the use of data analysis allows for a better understanding of a fund and/or granted mandate. It also greatly increases the need for a good explanation of investment decisions taken. Paulin argues that using available data(points) will also enable an increasingly sharp focus on skills, ESG factors and cyber security. “This trend will accelerate over the next three to five years,” says Paulin.
Those companies that can codify their investment process can make it scalable and easier to sell. They can use data to shift to better planning for portfolio transitions.
This requires sticking to an investment process and ignoring short-term distractions, such as style changes. However, it also means that investors can be more confident in building targeted portfolios based on conviction, supported by available data.
According to Paulin, ideas with high conviction tend to perform well, but the alpha is eroded by the rest of the stocks in a portfolio that is often justified for the sake of a manager’s compensation. Data science can help solve some of these problems by making asset managers more aware of the process and better able to match their conviction with the size of their positions. Also, the availability of data and analytics can help to address emotions.