Edhec-Risk
Features
Alternative Investments - May 15, 2013

An Analysis of the Convergence between Mainstream and Alternative Asset Management

This paper has two objectives. First, this paper provides an academic analysis of the main techniques that are currently used by hedge fund managers and that could be transported to the mutual fund and alternative UCITS space in a straightforward manner so as to provide better forms of risk management in a regulated environment.

We categorise techniques according to three groups: (1) risk management, (2) alpha generation and (3) leverage. Within the group of risk management techniques we review (1.1) techniques based on derivatives, (1.2) techniques based on dynamic trading strategies, (1.3) volatility scaling of positions, (1.4) risk measurement techniques, (1.5) currency overlay and (1.6) performance-enhancing compensation and incentive structures. In the context of alpha creation techniques, we discuss (2.1) advanced econometric techniques for asset allocation, (2.2) asset-specific betas and stock-picking, (2.3) shareholder activism, (2.4) order execution alpha and (2.5) currency alpha. Finally, we show how leverage can act as a performance driver and distinguish (3.1) financial leverage, (3.2) construction leverage, (3.3) instrument leverage, (3.4) risk-parity techniques and (3.5) VaR techniques and leverage.

Alternative investment fund managers are increasingly deciding to implement alternative strategies through traditional investment vehicles such as mutual funds in order to access assets from retail and institutional investors that, for various reasons (such as investment mandates, for example), cannot invest through less regulated structures. Packaging hedge fund strategies in a traditional format is not straightforward and it raises a lot of challenges for the managers as well as for the brand of the regulatory format. An important question is to know whether structuring hedge fund strategies through mutual funds will compromise these strategies and provide the same level of returns, considering the constraints under mutual fund regulations such as investment restrictions, liquidity requirements, operational requirements and risk management. We therefore carefully examine how dynamic trading and derivatives strategies can be transported to the mutual fund space.

Our second objective is to examine the convergence between the mainstream and the alternative asset management industry by studying UCITS and non-UCITS hedge funds. The latest amendment of the UCITS framework, referred to as UCITS III and IV, allows mainstream fund managers to supply regulated forms of hedge fund-type products to their traditional customer base, while also permitting hedge funds to reach out to the same customers. We refer to the latter as UCITS hedge funds (UHFs) to distinguish them from traditional non-UCITS hedge funds (HFs). A recent Pertrac study found that alternative UCITS Assets under Management (AuM) peaked at €178.82 billion in May 2011. Alternative UCITS or UCITS hedge funds are funds that follow a hedge fund type strategy aiming to generate absolute return or absolute performance. They are, in other words, simply UCITS that take advantage of certain investment techniques allowed by the UCITS regulations which enable them to pursue strategies that were previously more common in the alternative investment sector – in particular, the hedge fund sector.

Based on regulatory requirements that apply to UCITS, we economically motivate a range of hypotheses regarding differences in AuM growth, performance and risk between UHFs and HFs and empirically test them using one of the most comprehensive hedge fund databases constructed to date. We examine differences between UHFs and HFs based on a range of cross-sectional fund features such as investment objectives and other fund characteristics including compensation and redemption structures. Regulatory requirements that apply to UCITS imply that UHFs impose less binding share restrictions than HFs. Hence, UHF investors can exploit performance persistence, if any, more easily than HF investors. Our paper sheds light on the convergence of mainstream and alternative investment management as well as drivers of performance and risk for different types of UCITS funds. This study is timely since UCITS funds, and in particular the so-called retailisation of complex products and the use of total return swaps, recently attracted the attention of regulators in 2012.

UCITS funds differ from hedge funds in several ways which leads to testable hypotheses about differences in their performance and risk. First, the requirement of a (i) separate risk management function in UCITS funds as well as (ii) leverage limits and (iii) VaR (Value-at-Risk) limits leads to our first hypothesis that the risk of UHFs is lower than that of HFs. Measuring risk is a complex issue and therefore we apply a range of different risk metrics to capture tail-risk in addition to volatility (Patton (2009)). Second, UCITS funds face restrictions regarding the use of derivatives. This leads to two further hypotheses. Our second hypothesis is that restrictions in the use of derivatives reduce option-like payoff profiles and non-normal returns in UHF return distributions. Our third hypothesis is that reduced flexibility in the use of derivatives makes UHF returns less counter-cyclical than those of HFs. A fourth hypothesis is that the investment objective is crucial and that the extent to which UCITS restrictions affect risk and performance depends on the investment objective of the fund. We therefore carry out our hypotheses tests for all funds as well as by investment objective to distinguish, for example, Long/Short Equity funds, Global Macro funds as well as Event Driven funds. Previous studies of UHFs have typically focused on a smaller set of investment objectives (Darolles (2011)). Our fifth hypothesis is related to the fact that different countries have implemented the UCITS directive in different ways, which implies that geography and in particular domicile matters for UHFs.

One of the main strengths of our paper is that we examine the relationship between performance and risk of HFs and UHFs and a range of economically important fund characteristics related to fund manager incentives and fund liquidity which have not been previously examined in the context of UHFs. Liquidity is linked to fund performance in at least two important ways. First, liquidity, in terms of less binding redemption restrictions for UHFs investors, may allow them exploit performance persistence. On the one hand, Joenväärä, Kosowski and Tolonen (2012, hereafter JKT (2012)) show that HFs performance may hypothetically persist but investors’ ability to exploit it is limited by strict share restrictions. Hence, the UHF universe provides an interesting setting to test whether performance persists and whether it can be exploited in practice.

On the other hand, Teo (2011) provides evidence that capital outflows can be costly if HFs are exposed to liquidity risk. This suggests that liquidity may be harmful in certain circumstances. Thus, it is interesting to study the role of share restrictions and liquidity risk for UHFs.

Moreover, in terms of the time-series and number of funds in our data, this paper is to our knowledge one of the most comprehensive analyses of the performance and risks of HFs and UHFs. Previous studies have typically analyzed UHFs in isolation without comparing them to the HF universe or they have examined at most 460 UHFs and less than 2,800 HFs. First, we carry out a comprehensive aggregation process to construct an aggregate HF dataset that consists of more than 24,000 unique hedge funds that report at least 12 return observations. This database consists of active and inactive or defunct funds. This group of funds represents our HF control group. The number of hedge funds in our database is close to that reported by the UBS’ proprietary AIS database consisting of about 20,000 hedge funds and 45,000 share classes, while the PerTrac 2010 hedge fund database study finds that the hedge fund industry contains about 23,600 funds. Therefore, we believe that our aggregate database containing the union of five major databases is close to the true unobservable population of hedge funds.

Second, by merging data on UCITS funds from the EurekaHedge, BarclayHedge, and HFR databases on UCITS hedge funds we also construct an aggregate database on UHFs. In our study we thus carry out a comprehensive analysis and comparison of both UHFs and HFs. Hedge fund databases are also non-overlapping - we find that almost 70% of funds in our consolidated database report only to one of the used major databases. JKT (2012) recently documented that data biases are different between hedge fund databases, thus affecting stylized facts about performance and risk of HFs. This suggests that merging several databases will provide us with a more accurate view of the aggregate size of the UCITS hedge fund universe.

We document stylised facts about hedge fund performance, data biases, and fund-specific characteristics explaining cross-sectional differences in UHF and HF performance. To understand why the performance results differ between UHFs and HFs, we start by highlighting how the total return and AuM time-series differ on a value and equal-weight basis between UHFs and HFs. We then move to differences in fees and risk-adjusted performance and study how Sharpe ratios, the Fung and Hsieh (2004) seven-factor model alphas and risk exposures differ between HFs and UHFs.

Finally, we examine the cross-sectional relationship between fund characteristics and hedge fund performance. The existing literature on HFs has documented that managerial incentives, share restrictions and capacity constraints are associated with cross-sectional differences in hedge fund performance. Using portfolio sorts and the Fama and MacBeth (1973) regressions, JKT (2012) demonstrate that smaller and younger funds, and funds with greater capital flows deliver better future returns than their peers. This conclusion is in line with the previous literature (e.g., Teo, (2010) and Aggarwal and Jorion (2010)). In contrast to the existing literature, JKT (2012) find, however, that fund characteristics related to managerial discretion or illiquidity do not consistently explain hedge fund cross-sectional returns. In fact, they find very little evidence that share restrictions in the form of lockup, notice and redemption periods are related to higher risk-adjusted returns when they control for the role of other characteristics in multivariate regression. Given regulatory requirements on the liquidity of UHFs it is therefore of particular interest to study the relationship between UHF performance and fund liquidity.

In our empirical results, we find that UHFs underperform HFs on a total and risk-adjusted basis. However, UHFs have more favourable liquidity terms and when we compare liquidity matched groups of UHFs and HFs, we find that UHFs generate similar performance. Thus we uncovered an important liquidity-performance trade-off in the sample of UHFs. Our results also show that HFs have generally lower volatility and tail risk than UHFs, which is consistent with hurdles to the transportation of risk management techniques discussed. Finally we find important domicile effects related to firm and fund performance.



This research was produced as part of the Newedge "Advanced Modelling for Alternative Investments" research chair at EDHEC-Risk Institute.


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http://www.edhec-risk.com/features/RISKArticle.2013-05-15.0924

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