Since the emergence of “Modern Portfolio Theory” and the “Capital Asset Pricing Model” in the late 1960s, institutional investors have taken a quantitatively driven approach to portfolio construction, looking to create portfolio diversification and obtain better risk-adjusted returns by balancing their asset-class exposures. This journey has seen several important advancements in thinking about how to optimally achieve desired results.
Funds that want to take the total portfolio approach first need to get the total portfolio view. To do that they not only need data – and lots of it – but a rock-solid understanding of exactly how they’re going to use it.
… as Corona crisis forces investment data rethink
by Wietske Blees*
Making well-informed investment decisions isn’t easy at the best of times but throw in a pandemic and a decade worth of quantitative easing that’s fuelling a growing disconnect between equity markets and underlying fundamentals, and reliably predicting future growth prospects becomes a significant challenge.
A recent ‘Leading Minds’ webcast hosted by Fund Business found that traditional investment data sources such as earnings guidance are being called into question, as companies remain uncertain about future earnings potential. As a result, investment managers are forced to think more critically about the data sets they have available, increasingly cross-checking insights using different or alternative data sets and using human judgement to sense check investment ideas.
Mark Monfort, head of data and technology at Prosperity Advisors, said that in the current climate, it is become significantly more difficult to make confident predictions. “Your sources of information might remain the same, but those sources of information that you trust you’ll find may not work while the market whipsaws. I do think that there is an opportunity to dive deeper and look at existing sources and relationships in a new way to understand them better.” Monfort said while that part of that meant conducting deeper economic analyses on traditional sources of data, it also meant considering new datasets that had not necessarily been looked at before. For example, an equities manager could look to bond data to get a better picture of what head or tail winds a particular company might face.
Nick James, data scientist at Arowana, said that funds that were able to tweak investment styles and combine quantitative views with fundamental insights would likely have an edge in the current environment. At Arowana – which applies a ‘quantamental’ investment approach – data remains the backbone of the investment approach, but the fund has upped its reliance on independent expert opinions to sense check its investment ideas over the course of the pandemic. In practice, that means the fund will look to traditional data sources first, applying systematic tools such as screening, portfolio optimisers and a variety of algorithmic techniques to make sense of traditional price and accounting style data. In addition to that, the team will look at alternative data sources to get a better understanding of a particular problem or trend. The final piece involves a heavy reliance on independent experts.
“What we look to do is marry the quantitative signals and fundamental signals from any candidate investment, so we look to see whether they’d line up in terms of the reliability of the data sources,” James said. “In times like these anomalous events, markets can be very hard for systematic investment managers and I think we are able to get the best of both worlds with this marriage of the two techniques.”
Understanding Corona
Understanding the investment implications of COVID- 19 is not merely an economic exercise, either. Fund managers are increasingly looking to medical and epidemiological data sets to inform investment decision making. With that in mind, bringing epidemiological expertise in house can provide investment managers with a significant edge in the middle of a pandemic.
“Some buyside firms may already have staff with medical backgrounds, epidemiologists or virologists and you’re very lucky if you have that, but others you may need to work with experts in that space, academics, mathematicians, economists they can help you better understand the spread of the data,” Monfort said.
One example could be using SIR models, which are epidemiological models that compute the theoretical number of people infected with a contagious illness in a closed population over time. ‘S’ stands for the number of susceptible individuals, ‘I’ refers to the number of infected individuals and ‘R’ is the number of recovered individuals and while it can’t predict with certainty the spread of a pandemic, it can help to simplify the mathematical modelling of infectious diseases. For example, firms could map out variations in spread over time and the effects on different economies; how the spread compares with revenue generated in different countries or geographic reasons and the potential impact of COVID 19 on company revenue exposure to different geographic regions. “You can enhance the existing data that you have with these new {SIR model driven] overlays and it can give you a better lens to traverse the landscape right now,” Monfort said.
Technology
Having a technology framework that supports the ability to quickly and efficiently extract, transform and load (ETL) new data sets is also essential if funds are to make the most of investment data insights. However, this remains a stumbling block for many buyside firms, forcing data scientists to spend much valuable time cleaning and organising datasets and limiting the ability to quickly experiment and validate new investment ideas.
Monfort said that to some extent, firms are held back by the perception that implementing a solid investment data infrastructure is extremely complicated. “There’s a myth around getting started, that it is really complicated. It’s actually not, it’s much easier now to get access to cutting edge technology that is available in open source off the shelf tools and they are not as expensive as they used to be, but you do need to have a lean start up methodology approach [where] you can run small scale experiments to prove that something works before you build at a large scale,” Monfort said.
However, underlying all this, he said, is the need to automate as much as possible. “When there is so much more information [and] there is so much more volatility going on in the market, the effect that you are going to have from having automated systems is going to heighten your ability to find new sources of alpha and get that edge in the market,” Monfort said.
The ‘Leading Minds’ series will continue on July 29, at 11am AEST, with a panel discussion on optimising the structure and operation of dealing functions. To register go here:
*Wietske Blees is editor at Fund Business, a specialist events company for the funds management and super industry
