If Alfred Jones and Benjamin Graham were alive today, I believe they would be surprised by the success of the “hedged” investment approach that they helped bring to life over 60 years ago. Even for those with direct experience of compound performance and the avoidance of permanent loss of capital, the growth of the hedge fund industry since then to $2.8 trillion today has been significant.

For investors as passionate about analysis as Jones and Graham, they might, nevertheless, be more surprised to see how little the conversation between investors and allocators has changed. Despite analytical advances made in other fields, both literature reviews and institutional investor surveys still overwhelmingly show that allocators rely on a combination of high-level manager conversations and the quantitative analysis of monthly performance since inception to help inform their decisions.

Despite the lack of change, institutional investors are, in truth, sophisticated relative to other investor types. Unlike retail investors – who are more sensitive to brand and trailing performance over one and two years – institutional investors focus on active risk and return, adjust for investment style, and prize consistency over a longer-term time horizon. On the qualitative side, institutional investors and their consultants prefer older funds with mid-sized, rather than very large, asset bases and give significant weight to onsite due diligence. Here discussions focus on the background and motivations of the manager, their drive, investment philosophy and ability to articulate an investment rationale for performance. Nevertheless, as with retail investors, once a fund gains traction within an allocator group it tends to receive subsequent inflows from that group.

Benefiting from big data
Survey data and anecdotal examples suggest that, up until now, manager due diligence has been broadly shielded from one far-reaching trend: “big data” – the ability to analyse large, often complex datasets quickly and efficiently, while extracting valuable insights not discernible at less granular levels. This trend, which has had a significant impact on the world around us – from finance, to medicine, marketing and sport – is now increasingly shaping manager due diligence. In our field, the wider adoption of separately managed accounts (SMAs) post-2008 has proved to be a key turning point, for alongside the traditional advantages of asset segregation, liquidity and control, SMAs provide the raw information on which innovative analytical techniques can be used, both on an individual account basis, but also across a wider portfolio of alternative investments.

By synthesizing and analysing the entire data set in aggregate, there are tangible benefits to the investor. These can include greater insight and accuracy regarding the drivers of a manager’s performance, the repeatability of their investment process, its strengths and potential weaknesses. By digging deeper into position-level data and evaluating it alongside market data, the characteristics of a firm, a manager, their team and a fund’s track record, for instance, let one identify style drift, behavioural responses to drawdowns and liquidity risks more efficiently and accurately. Critically, during manager reviews, the data sharpens the conversation around many of the standard questions institutional investors ask:

• How differentiated is your research? How contrarian?
• How crowded are your underlying positions? Are you early to ideas?
• What is the maximum capacity of your strategy? Have changes in market liquidity had an impact?
• To what extent does performance come from market timing, sector/style timing, short-term trading and stock selection?
• How stable is your investor base and your portfolio to macro shocks?
• How do you manage risk? How do you react to drawdowns?
• How do you manage the liquidity of your portfolio relative to the liquidity you offer investors?

Solid data analysis will always remain a supplement rather than a substitute for informed investment decisions. However, it can provide evidence-based answers to many of the standard questions institutional investors ask. It also provides a welcome tonic to promotional managers, or those resting on the laurels of successful outlier investment examples and brand name prior employers.

At the portfolio level, systematically tracking market opportunities across regions and sectors and comparing these to portfolios’ look-through position-level exposure allows gaps to be more readily identified. It provides a high-level framework that informs qualitative discussions with managers and analysts. Dynamic asset allocation becomes more robust when timely information is at hand regarding a portfolio’s precise underlying exposures and the developing opportunity set.

While the benefits of big data and advanced analytics are clear, getting there is not an easy matter. To give one illustration: more accurately estimating a firm’s asset stability under stress requires one to track fund flows over time, different share class terms, investor base characteristics, underlying position characteristics and market liquidity. How these factors interact with manager behaviour will determine where structural weaknesses will surface in times of stress.

Implementing a system that can track the required data and generate robust insights involves a diverse team of investment officers, analysts, technology developers, data scientists and data gatherers working seamlessly together across an organization. It requires an integrated data architecture that can track structured and unstructured information from multiple sources over time. It requires talented people with the creativity, practical investment experience and programming skills to know what type of relationships to look for, and to build the tools that point allocators in the right direction.

Customizing the components
The very success of the industry brings with it its own challenges, with crowding being one such issue. Back in 1949, Alfred Jones speculated at the end of the article that preceded the launch of his hedge fund that if enough “technicians” entered the market and their tools improved, they might “soon work themselves out of their present advantage”. While we are still far from this point, crowding in positions, themes, exposures and underlying return drivers is most certainly present. Informed manager selection, supported by deep data analysis, can help moderate this in hedge fund portfolios.

An additional tool to limit crowding is greater customization through managed accounts. Customization seeks to maximize a manager’s positive attributes, while avoiding the temptation to be overly prescriptive. We may take a view – backed by analytics – that the core strength of the team resides in the mid-cap segment of their portfolio, for example. As a result, performance may be enhanced by constraining the SMA’s remit to mid-cap equities and a maximum number of positions. Risk, in turn, may be mitigated by excluding less liquid security types. From a portfolio perspective, customization seeks to tailor the portfolio’s components to suit the objectives and constraints of that particular portfolio. At the portfolio level, customization also allows greater differentiation across underlying return drivers.

Conclusion
Asset allocation and manager selection will remain a combination of art and science. While we continue to strive to improve analysis, good research relies on a degree of humility about the true confidence bounds of results. Nevertheless, as our ability to narrow these bounds increases through improved data usage and analysis, the capacity to make evidence-based investment decisions increases.