Standard Life GARS Fund: MPI’s Factor X-ray
Using Standard Life Global Absolute Return Fund (SLI GARS) weekly performance data, we show how sophisticated factor analysis can provide valuable insights into this fund’s complex global “go anywhere” investment strategy.
The £27bn Standard Life Global Absolute Return Strategy Fund (GARS) has long been considered as one of the largest and most successful absolute return, UCITS, mutual funds in the market. Since its inception in 2008, it has had a superb track record in terms of performance production and risk control. However, it attracted investors’ attention recently as its performance turned lackluster since its peak in April 2015. Why has it underperformed? Have any poor decisions been made? As investors search for answers, they usually conclude that GARS is not an easy fund to comprehend.
GARS is not a traditional mutual fund by any means. According to its latest update on August 2016, the fund deploys between 20 and 35 different strategies across various asset classes, invests globally and often uses advanced derivatives techniques. Specifically, the fund follows strategies that are more similar to those of a global macro hedge fund than a traditional, balanced mutual fund. These features pose significant challenges for using traditional quantitative fund analysis methodologies to generate an illuminating analysis of the fund.
The objective of this case study is to present tools and techniques that can be used to provide insights into the fund’s investment strategy. We will demonstrate, in the sections below, how advanced dynamic returnbased modelling techniques can help investors understand the fund’s long-term investment strategy as well as identify the fund’s short term performance drivers and style/factor exposures.
Note that MPI does not claim to have a detailed knowledge of the fund’s actual strategy, positions or holdings beyond publicly available information; nor are we commenting on the quality or merits of GARS’ strategy. Instead, we use it as a case study to demonstrate how sophisticated factor analysis techniques can be used to better understand fund performance, and improve the overall selection and due diligence process when analyzing investment funds for client portfolios.
Model Selection: Factor Analysis using MPI’s DSA Model
Given its massive portfolio with thousands of holdings across various asset classes and in different geographic regions, analyzing and netting the fund’s positions would be extremely time-consuming and difficult to implement. On the other hand, factor analysis of the fund’s returns is a faster way to infer important exposures using readily-available historical return data. But which factor model is appropriate?
In common with typical global macro hedge funds, GARS can generate positive investment returns by implementing dynamic and opportunistic trading strategies to take advantage of shifts in macroeconomic trends. For this very reason, traditional returns-based approaches that use rolling static regressions to model dynamically changing exposures fail to provide meaningful results. In this study, we use MPI’s proprietary and patented Dynamic Style Analysis (DSA) technique to capture GARS’ time-varying factor exposures. DSA, being a truly dynamic regression model, is a powerful resource. It has a track record of accurately identifying short-term hedging trades; spikes in leverage caused by use of derivatives, and rapid strategy changes that could be caused, for example, by changes in management team.
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