Generation of performance data and reviews for prospective clients
On the first working day of each month, performance data is downloaded over specific time periods. The data includes a range of factors such as launch dates, manager start dates, ratios, yield and so on. The report is run across the major universes in which Murdoch is interested: insurance funds, unit trusts, investment trusts, ETFs, offshore funds and pensions. “About 2.25 million data points are downloaded on that day,” says Austen. “The data goes into a spreadsheet that we have pre-populated and built. We use it for performance analysis and also in documentation for prospective clients.”
In the latter instance, FE Analytics underpins Murdoch’s offer of a ‘free fund review’. Prospective clients can provide a copy of their existing portfolio and Austen’s team will review and write a report on it with no charge and no obligation. The fund review sheets contain performance charts, data and tables showing how that fund has performed relative to its peer group and benchmarks. The data comes from FE Analytics but has Murdoch’s branding. “We have found this to be a useful tool and our clients seem to appreciate the effort that goes into it,” says Austen.
Researching funds for the buy or reserve list
Austen also uses FE Analytics when considering prospective funds to be added to the buy list or reserve list. He starts with an in-house built template FE Analytics report which provides scatter charts, asset allocation and performance data. He also uses the detailed information from fund factsheets to assess suitability for inclusion in the shortlist: start date of fund, sector, manager start date, yield, legal structure and so on. The end result is a document of around ten pages –four of which contain data generated solely from FE Analytics. One feature that Austen particularly appreciates is the ability to compare on a single chart the concentration of the sector average and the fund under consideration. “It allows me to see, for example, that the fund being considered has got 28% of the top five holdings whereas the sector average only has 18%,” he says.
Providing condensed, concise information to clients
“While some clients will want every piece of data they can get – all the statistics, all the analysis -more often than not, clients don’t want to be swamped by the detail. That’s why they choose us,” says Austen. He filters down the data from FE Analytics to provide a one-page document containing the condensed information from FE Analytics that is most relevant to the client. “We try to keep things as direct and concise as possible for clients,” he says. “The only time a client might be aware of FE Analytics is in the case of new clients, where we are doing a performance comparison with their existing portfolio. In that case, we would build a model in FE Analytics and compare it with the relevant sector and proposed Murdoch portfolio over a specific time period. That, along with a risk/return scatter chart would go into the report that is sent to client.”
Benefits of FE Analytics
Austen appreciates the breadth of functionality in FE Analytics – especially in comparison with the previous solution he was using. He and his team have access to daily pricing, to scatter charts and correlation analyses. They can build their own reports, incorporating filters to pull out fund or manager characteristics of particular interest: the length of time the fund manager has been there, for example. “We build a filter and run it on the unit trust to see how many managers have tenures longer than 3, 5, 7 and 10 years - with interesting results. Lipper Hindsight didn’t give this capability.”
He also finds it useful to be able to get a price on a particular date for a single-price fund: “you just plug in and get it.” He is able to look at dividend histories too: “We can see the dividend per share paid out over the last ‘x’ payment dates; so you can see how much income a client has been paid over a given time period.”
FE Analytics is also helpful in giving clients an indication of risk through scatter charts showing volatility.
And when it comes to fund analysis, Murdoch has built a proprietary risk model, which incorporates 48 different factors: quantitative (which come from FE Analytics); and qualitative factors, such as – what is the fund actually doing? Can it short? What is the borrowing requirement? Does it have insurance in place? Is there any business risk? What asset class is it invested in? What market cap is it looking at? The model also looks at the fund managers in detail – who are they? How long have they been there? What is their experience? What is their investment process and how robust is this? What would be the impact on the fund if the manager left?
The risk model ultimately provides a risk score for collectives including unit trusts, investment trusts, offshore funds, ETFs and VCTs.
“It is a very sophisticated model to monitor and assess risk. In complexity, range and approach, it surpasses what we have seen from our competitors – even some multi-asset funds,” says Austen.
To add to his armoury of research tools, Austen has also created the ‘AR ratio’ which works really well for the actively managed funds, on which Murdoch focuses. The AR ratio looks at the percentage of decisions that the fund manager needs to get right to achieve their investment objectives (taking into account charges) and compares this to the actual “skill rate”. “It’s really helped us in our analysis of fund managers as it helps us see through those who have performed well from a small number of good stock picks compared to those who consistently deliver from their stock-selection,” he says.
And Murdoch has certainly been successful in out-performing some of the biggest funds in the business in recent years, with FE Analytics being their comparative system of choice.
Says Austen: “We link it all in. FE Analytics is probably the best tool out there for what we do and the type of business we are. FE’s Model Portfolios have helped us greatly as we can show empiric past performance data, not just back-tested figures that didn’t actually happen (an issue many have with back-tested figures). FE Analytics has certainly been useful for us over the past eight years.”