Hedge funds use a diverse set of techniques to improve performance and reduce volatility. Hedge funds have investment mandates that differ from those of traditional long-only managers. Hedge funds have an absolute return objective, i.e. achieving returns uncorrelated with the market (Ineichen (2002)). Absolute return managers therefore have different incentives regarding risk management. The portfolios of most long-only mutual fund managers closely track the benchmark (with significant risk or beta exposure) and, due to their investment mandate, they are mainly concerned about tracking error or active risk relative to a benchmark. When absolute return managers speak of risk, they normally mean total risk and its mandate is generating positive returns in excess of a hurdle such as LIBOR, for example. The absolute return objective implies that risk reduction techniques such as long/short strategies and derivatives positions are used to reduce benchmark exposures. Despite the fact that a hedge fund’s objective is to generate absolute returns, for purposes of performance and risk attribution it is instructive to decompose hedge fund returns into risk exposures (beta) and risk-adjusted exposure (alpha).1 It is well known2 that leverage can be used to scale and increase an existing alpha in standard performance regressions and therefore it is helpful to examine leverage separately as one of several potential performance drivers.
Given the large number of techniques it is useful to categorize the techniques into three main groups and several subgroups (see Table 1).
CLICK IMAGE TO ENLARGE
The three main groups that we use are (1) risk management techniques, (2) alpha creation techniques and (3) techniques related to leverage. Of course, some techniques may fall into more than one category. Risk management techniques which reduce beta exposures may not only change a fund’s return profile, for example, but may also increase the alpha of a fund relative to a given benchmark. Nevertheless, we believe that our choice of classification scheme facilitates the explanation of the mechanism underlying different techniques and their impact on fund performance and risk.
Risk Management Techniques
Risk management techniques encompass methods to reduce risk exposures and change the return distribution in such a way as to reduce the probability of (extreme) losses. Risk can be viewed from two perspectives. On the one hand, the distribution of returns can be described statistically using moments such as the mean, the volatility (standard deviation), skewness and kurtosis (lower probability of tail outcomes in general). Theoretically it can be shown that many common utility functions imply a preference for higher mean and skewness and lower volatility and kurtosis.3 In practice, many investors prefer funds with a higher mean return, lower volatility and lower drawdown. Drawdown is defined as the money lost since reaching the most recent high-water mark. One of the limitations of using the four statistical moments to select funds is that drawdown is often poorly approximated by skewness and kurtosis (Burghardt and Liu (2012)). The reason is that drawdown is affected by autocorrelation and that it is a multiperiod concept in contrast to the statistical distribution. On the other hand, the distribution of returns can be traced back to underlying risk exposures and risk factors such as equity, interest rate, credit, currency and volatility risk. Risk management techniques also include risk measurement tools which are used to monitor risk and inform risk management decisions.
We further distinguish the following five sub-groups of risk management techniques: (i) techniques to change the fund return payoff distribution by means of derivatives and tail risk hedges; (ii) techniques to change the fund return pay-off distribution by means of dynamic (option-like) trading strategies; (iii) risk measurement techniques;(iv) currency overlay and (v) performance-enhancing compensation structures and incentives (performance fees, clawbacks, staggered position reduction).
(i) Techniques to change the fund return distribution by means of derivatives (options, swaps, futures and other derivatives)
Futures and forwards are the simplest derivatives that hedge funds can use to manage risk. A futures contract is a standardized contract between two parties to buy or sell a specified asset of standardized quantity and quality for a price agreed today. The difference between forward contracts and futures contracts is that forward contracts are not standardized and not traded on the exchange (which leads to different margin and capital requirements). Futures and forward contracts exist globally for several hundred assets such as equities, interest rates, currencies and commodities. Another highly popular type of derivative is the swap. A swap is a derivative in which counterparties exchange cash flows of one party’s financial instrument for those of the other party’s financial instrument. In an interest rate swap, for example, two parties agree to exchange interest rate cash flows, based on a specified notional amount from a fixed rate to a floating rate (or vice versa) or from one floating rate to another. Swaps are commonly used for both hedging and speculating. Swaps exist on interest rates, currencies, commodities, variance, correlation, longevity indices, equities as well as credit default events.4 Some swaps, such as interest rate swaps, are relatively liquid while others such as correlation swaps are not. Total return swaps (TRS) have been used by UCITS fund structures to implement hedge fund-like strategies. In a total return swap, party A pays the total return of an asset and party B makes periodic interest payments. Globally there is a movement to move certain swap trading from OTC markets to central clearing and exchanges.
In addition to forwards and futures, hedge funds can use contracts for differences (CFDs) in some countries such as the United Kingdom, Hong Kong, the Netherlands, Poland, Portugal, Germany, Switzerland, Italy, Singapore, South Africa, Australia, Canada, New Zealand, Sweden, Norway, France, Ireland, Japan and Spain.5 A CFD is a contract between two parties that agree that the seller will pay the buyer the difference between the current value of an asset and its value at contract time. CFDs emerged in the 1990s in the UK where they were used by hedge funds and institutional investors as a cost-effective way to hedge equity exposure since they involved only small margin payments and avoided UK stamp duty tax. Similar to futures, CFDs also allow trading on margin.
Similar to futures contracts, options exist on a wide range of underlyings including equity indices, individual stocks, interest rates, futures, swaps (“swaption”), commodities, currencies and volatility indices. Options have asymmetric return profiles. A long put position on an equity index, for example, can be used by a fund to buy protection against large losses. The put option premium will reduce the mean return of a fund but is likely to reduce its volatility and increase its skewness (and thus reduce its tail risk). This is an example of a risk/return trade-off. The trade-off between mean return and volatility in financial markets is well known. In the last 10 years more evidence has accumulated of a trade-off between the mean return as well as skewness and kurtosis (Harvey and Siddique (2000)).
Following the financial crisis, practitioners including investors and hedge fund managers have become increasingly interested in what are loosely defined as ‘tail-risk hedges’ or ‘tail hedge funds’.
These vehicles are used to reduce tail risk by means of different derivatives or option-like techniques. As the simple option example above illustrated, a tail risk hedge, like any type of insurance, comes at a cost. One way to reduce the cost of the hedge is to implement it conditionally and vary the type of derivative used to implement it depending on where insurance is cheapest. Therefore, the challenge for ‘tail risk funds’ is to provide a given tail risk hedge (against a particular risk factor) at the cheapest possible costs. Different derivatives can be used to reduce extreme losses for a premium. These include put options, but also credit default swaps and variance or volatility swaps. Although the area of higher order moments and tail risk hedges has been studied in the academic literature for decades, the concept of tail risk was popularized significantly by Taleb (2007, 2008) who introduced such catchy labels as the ‘The Black Swan’ to explain the underpinnings of tail events and their impact on performance. Many hedge funds occasionally or regularly implement tail-risk hedges in their portfolios. Some tail hedge funds, however, claim to specialise in providing tail risk insurance. Tail hedge funds rose to prominence in 2008 when some of them delivered triple-digit returns for investors. Their success led to copycat funds that similarly aim to profit from rare extreme events. The first tail-protected ETF was launched in May 2012 and is traded on the TSX via Horizons ETFs.
Although tail hedge funds are gaining in popularity, the economic literature provides a sobering perspective on the potential returns that can be expected from them due to evidence of a return/tail-risk trade-off in hedge funds. Investors may find that reducing tail risk comes at a potentially high cost in terms of average absolute returns as well as risk-adjusted performance. There is evidence that some hedge funds buy tail risk insurance, while other hedge funds implicitly or explicitly sell tail risk insurance. In a recent paper, Kelly and Jiang (2012) document large persistent exposures of hedge funds to downside tail risk. Funds that lose value during high tail risk episodes earn average annual returns more than 6% higher than funds that are tail risk-hedged, controlling for commonly used hedge fund factors. Buraschi, Kosowski and Trojani (2012) further shed light on the drivers of tail risk by highlighting the role of correlation risk in driving drawdowns for long/short hedge funds, merger arbitrage funds, option trader funds and other funds that are susceptible to large losses when correlations unexpectedly change. These results are consistent with the notion that a significant component of hedge fund returns can be viewed as compensation for selling disaster insurance. Therefore, a fund that is buying (selling) tail risk insurance is likely to have lower (higher) returns, lower (higher) volatility and higher (lower) skewness. Several derivatives can be used to change the pay/off distribution by changing tail risk exposure.
Variance and correlation swaps are traded over-the-counter (OTC) and are less liquid than most put options or CDS index contracts.6 The relative price of tail risk insurance across these contracts can vary over time. Moreover, regulation can affect which of the derivative contracts offers the best tail risk hedge at a given point in time. Bans on naked short-selling or naked CDS positions,7 for example, can make CDS unattractive and instead make it preferable to search for approximate tail risk hedges using options or other derivatives for regulatory reasons. Buraschi, Kosowski and Trojani (2012) obtain data on OTC correlation swaps and show that option trader funds, for example, had a statistically significant loading on a correlation swap-based factor that explained part of their superior performance as well as their higher tail risk. Aragon, Kang and Martin (2012) collect a unique dataset of long equity option positions for hedge funds using SEC filings. They find that index option positions for hedge funds are associated with greater reductions in portfolio risk of poorly performing hedge funds.
(ii) Techniques to change the fund return distribution by means of dynamic (option-like) trading strategies (portfolio insurance, volatility scaling)
Apart from using derivatives to reduce risk as discussed above, hedge funds can also employ dynamic trading strategies to reduce risk. These strategies can be divided into two further subgroups: (a) dynamic replication strategies and (b) volatility scaling strategies. Dynamic trading strategies are rooted in dynamic replication and option pricing theory. A portfolio is said to replicate another portfolio if it generates at maturity identical cash flows (positive or negative). A put option on an equity index, for example, can be replicated using positions in equity index futures and borrowing/lending. Mathematically, concepts of replication involve the existence of a equivalent martingale measure.8 Dynamic trading strategies can serve the purpose of risk management, similar to derivatives. They can, however, also be used for arbitrage strategies. Replication is costly in practice and imperfect replication can be desirable if the objective is to take directional views on some risks while hedging out other risks. Such arbitrage strategies can be the source of alpha or risk-adjusted performance.
Portfolio insurance is one example of a dynamic trading strategy. It is a method of hedging a portfolio of stocks against market risk by short selling stock index futures. The method was invented by Leland and Rubinstein in 1976 and involves the replication of a put option by shorting the underlying in amounts determined by the constantly changing option delta. In principle portfolio insurance can be applied to other market risks such as interest rate risk, currency risk, volatility risk or commodity price risk. More advanced dynamic trading strategies are linked to volatility and correlation trading. Earlier we discussed variance and correlation swaps. A variance swap may be hedged and hence replicated using a portfolio of European call and put options with weights inversely proportional to the square of strike (Bossu, Strasser and Guichard, 2005). A correlation swap can also be synthetically replicated. This would require index and individual stock variance swaps. Another approach to create a correlation exposure is through a dispersion trade. This involves creating an exposure to correlation by shorting index options and going long individual options accompanied by positions in the underlying index components and the riskless rate in time-varying proportions. The discussion of dynamic replication strategies shows that hedge funds can dynamically trade in instruments such as futures and options to dynamically replicate option and swap pay-offs and thus hedge risk without having to outright purchase the instrument that the payoff replicates. One attraction of such dynamic trading strategies is that they may be cheaper than buying the corresponding derivative in the market which often involves paying a profit margin to the seller of the derivative.
(iii) Volatility scaling of positions and stop-loss rules
Another set of techniques that is not based on replication, but that also helps reduce portfolio volatility is related to volatility scaling and stop-loss rules. Commodity trading advisors and trend following funds are one group of investors that extensively employ volatility scaling of positions. Simply speaking, volatility scaling means that assets whose volatility goes up receive a lower weight in the portfolio; that is portfolio weights are inversely related to asset specific volatility. Volatility scaling is not predicated on an asset price model such as a Brownian motion and therefore, on a conceptual level, it significantly differs from dynamic replication strategies. Volatility scaling is employed by hedge funds both in the context of time-series momentum strategies (Baltas and Kosowski (2012b)) as well as cross-sectional momentum strategies. See Barroso and Santa-Clara (2012) for how the risk-adjusted return of equity momentum strategies can be improved by means of volatility scaling.
Although conceptually different from portfolio insurance and volatility scaling strategies, the use of stop-loss rules by hedge funds is another related technique. Stop-loss rules involve the reduction or closing of positions once losses above a certain level have been incurred. For example, a position may be cut by 50% (100%) if it experienced a loss of 2% (5%). Theoretically stop-loss rules should not improve performance for asset prices that exhibit mean reversion as positions are closed before asset prices recover. However, stop-loss rules can be very beneficial since they introduce discipline into the portfolio construction process and prevent behavioural biases from creeping in. One important bias is the disposition effect (Kahneman and Tversky (1979)) which states that investors have a tendency to sell winning investments more frequently than losing investments. Stop-loss rules can therefore prevent portfolio underperformance that is driven by a manager’s disposition effect. The empirical evidence on the performance of mechanical stop-loss rules applied in equity markets shows that the benefits of the technique are not constant and vary over time (Lei and Li (2009)).
(iv) Risk measurement techniques (VaR, EVT, advanced risk measures)
Risk measurement techniques are different from risk management techniques discussed above, but they may help risk management through the monitoring or risks and provide risk management signals. Hedge funds use techniques such as scenario analysis and Value-at-Risk (VaR) to monitor portfolio risk. Scenario analysis is a process of examining possible future events by considering alternative possible outcomes. It involves simulating portfolio returns under different extreme historical or hypothetical conditions. VaR is a simple but common risk measure of the risk of loss of a specific portfolio. For a given portfolio, probability and time horizon, VaR is defined as the threshold portfolio value such that the probability that the mark-to-market loss on the portfolio over the given time horizon exceeds this value is the given probability level.9 In principle, VaR can be calculated using forward looking or backward looking measures of volatility. Many hedge funds draw on the latest econometric research that documents the benefits of using high frequency data to calculate volatility estimates. Baltas and Kosowski (2012a) discuss the role of different volatility estimators in the implementation of trend following strategies. Blair, Poon and Taylor (2001) discuss the incremental information content of implied volatilities and high-frequency index returns, for example. A number of recent academic papers have studied volatility estimators that can improve on realised volatility estimators. The choice of volatility estimator is crucial for the successful implementation of VaR models. Extreme Value Theory10 and other recent innovations have been used to address the challenge of forecasting rare extreme events with a small number of observations. Hedge funds carry out research and innovate in the area of volatility estimation.
(v) Currency overlay
Currency overlay is a technique used to manage the currency exposure in a given portfolio (Record (2003)). Currency overlay can have two main objectives: (a) hedging exchange rate risk or (b) speculation and generation of alpha from tactical exchange rate bets.
Often one distinguishes passive and active currency overlay. Passive currency overlay involves the use of forward contracts to match the portfolio’s currency exposures in such a way as to insure against exchange rate fluctuations. The implementation of the overlay depends on parameters such as the maturity of the forward contracts, the frequency of the cash flows to be hedged, their currency as well as the rebalancing frequency.
(vi) Performance-enhancing compensation and incentive structures (high water marks, performance fees, clawbacks)
An important but often overlooked set of techniques used by hedge funds relates to their compensation structures. There is evidence that hedge funds that have higher performance fees achieve higher total and risk-adjusted performance (JKT (2012)). The use of high-water marks by hedge funds introduces option-like features into the compensation contract and affects risk taking and performance (Buraschi, Kosowski and Sritrakul (2012)). Another compensation feature used by many hedge funds are clawbacks. Clawbacks imply that portfolio managers do not receive their performance bonuses in full each year, but that some proportion is withheld until later years and their payout is conditional on future performance.11 See Light (2001) for an example of a clawback feature in the compensation scheme of Harvard Management Company.
Alpha Creation Techniques
The intercept in performance regressions is referred to as the alpha. The sign and magnitude of alpha is therefore affected by the choice of factors. The distinction between alpha and beta is often not clear and in practice alpha is often defined with respect to a simple value-weighted equity index such as the MSCI World index instead of a seven-factor model. It is well known that alpha in an unconditional performance regression can be generated by either stock-picking skill or market timing. Some of the techniques reviewed below implicitly fall into the category of market timing.
(i) Advanced econometric and forecasting techniques for Global Tactical Asset Allocation
One example of a technique that is fundamentally based on a form of successful market timing is Global Tactical Asset Allocation (or GTAA). GTAA generally refers to a top-down investment strategy that attempts to exploit short-term mispricings among a global set of assets. The implementation of GTAA in practice differs widely. Litterman (2003, Chapter 25) provides a detailed example of a GTAA application. GTAA can be further decomposed into a strategic rebalancing component and an overlay component. Strategic rebalancing is implemented to make sure that the portfolio stays close to the strategic benchmarks that the investment mandate prescribes. The overlay component aims at capturing excess return through tactical long and short positions in different asset classes and countries. Global macro funds, for example, invest across a wide range of securities such as global equities, bonds, commodities and currencies. To reduce transaction costs they often use futures and forwards to take positions. They use different signals including, but not limited to value and momentum. Asness, Moskowitz and Pedersen (2012) provide a good overview of the profitability of value and momentum strategies across different asset classes. Volatility and correlation can be viewed as separate asset classes. Implied volatility indices now exist not just for equity, bond and currency markets, but also for commodity markets such as oil. Therefore, tactical allocations to benefit from changes in implied or realized volatility or correlation can be viewed as part of the investment opportunity set for GTAA.
(ii) Asset-specific bets and stock-picking
In contrast to GTAA which focuses on general movements in the market, some techniques focus on the performance of individual securities. The signals used in practice for stock-picking are too numerous to list here and we can only provide a small number of unrepresentative examples. McLean and Pontiff (2012) provide one of the most comprehensive studies of 82 market anomalies identified in the academic literature. The techniques used by hedge funds to implement asset specific or discretionary bets can be classified into discretionary or systematic and quantitative approaches. Value and momentum strategies within asset classes are one example of systematic stock-picking strategies. Asness, Moskowitz and Pedersen (2012) not only provide evidence of value and momentum strategies across asset classes but also within asset classes. The academic literature has, for example, documented the information content of some analyst forecasts (Womack (1996)). In practice, some funds have developed sophisticated systems to capture the information in analyst recommendations. These systems are also referred to as alpha capture systems, and they allow investment banks and other organisations to submit ‘trading ideas’ or analyst recommendations in a written electronic format. Marshall Wace implemented one of the first alpha capture systems called TOPS (Trade Optimised Portfolio System) in 2001.12
(iii) Order-execution alpha
Some hedge fund strategies have grown out of techniques used by market makers and the returns earned as compensation for liquidity provision services. Order-execution alpha is sometimes used to describe alpha generated through the superior execution of orders and the provision of liquidity in the market (Easley, Lopez de Prado and O’Hara (2012)). The quantitative techniques used to implement it in practice are sometimes based on algorithmic trading. Algorithmic trading is the use of electronic platforms for entering trading orders with an algorithm determining the timing, price and quantity of an order. If algorithmic trading occurs at very high frequencies, it is often referred to as high frequency trading. Algorithmic trading can also take the form of scalping which is a method of arbitraging small price gaps created by the bid-ask spread.
(iv) Shareholder activism
Another technique used for stock specific bets that is however much more long-term than the strategies reviewed in the previous subsections, is shareholder activism. Generally speaking, activist shareholders use their ownership stakes in companies to monitor and influence the management of the company. Brav, Jiang and Kim (2009) provide a comprehensive review of shareholder activism by hedge funds. They note that hedge fund activism differs from that of other institutional investors in several ways. Hedge funds’ incentive fees and the prevalence of personal investments by their managers imply that they have strong incentives to generate performance. Moreover, hedge funds can use derivatives or leverage their stakes. Derivatives also allow them to potentially separate their cash flow and voting rights associated with certain investments (Black and Hu (2006)).
(v) Currency alpha
Although trading in foreign exchange markets could be viewed as only one type of asset specific bet or market timing with the objective of generating alpha, we review it separately since many managers specialize in currency markets. Currency alpha is related to currency overlay and the generation of alpha from currency markets is one type of active currency overlay strategy (see above). There are many different currency strategies, but they can be broadly classified as (a) carry, (b) trend-following, (c) value or (d) volatility based. Levich and Pojarliev (2008) provide a comprehensive overview and also use tradable proxies for (a)-(d). A carry trade strategy implies borrowing in a low interest rate currency and investing in a high interest rate currency. Trend-following currency strategies are based on time-series momentum. Value-based currency investing often depends on Purchasing Power Parity deviations. Volatility can also form the basis for currency managers who may use derivatives to benefit from volatility in currency markets.
Leverage as a Performance Driver
Hedge funds can use several techniques related to leverage to affect their performance and risk. Leverage can scale an existing alpha and can therefore be a performance or alpha driver (Buraschi, Kosowski and Sritrakul (2012)). We can distinguish three types of leverage: (a) financial leverage, (b) construction leverage and (c) instrument leverage.13
(i) Financial leverage (borrowing leverage and/or notional leverage; prime broker funding)
Financial leverage can be created through borrowing leverage and/or notional leverage. Hedge funds can trade on margin or borrow from a prime broker and use the funding to leverage existing positions.
(ii) Construction leverage
Construction leverage can be the result of how assets are combined in a portfolio and in particular whether long positions are hedged with short positions. Short selling involves the sale of a security not owned by the seller. A short seller must generally pledge to the lender other securities or cash as collateral for the shorted security in an amount at least equal to the market price of the borrowed securities. As an example, if a hedge fund has invested £100,000 in stock A, by shorting £100,000 worth of stock B, the fund uses the proceeds to increase its position in stock A (ignoring margin requirements and other transaction costs for simplicity).
(iii) Instrument Leverage
Instrument leverage measures the intrinsic risk of a certain security selected since different instruments have different levels of internal leverage. Futures positions are highly leveraged because the initial margins that are set by the exchanges are relatively small compared to the notional value of the contracts in question. Options and leveraged exchange traded funds are designed with embedded leverage. Recent research by Frazzini and Pedersen (2011) shows that embedded leverage lowers required returns.
(iv) Risk parity
Risk parity is one alternative investment approach followed by hedge funds. The premise of risk parity is that if asset allocations are adjusted (leveraged or deleveraged) to the same risk level, then the risk-adjusted or Sharpe Ratio performance of the portfolio can be increased. Note the analogy between volatility scaling in CTA strategies discussed above and risk parity: equating the volatility of different asset allocation components by levering the investments up and down is similar to volatility scaling futures positions. One famous example of a hedge fund that employs risk parity is the Bridgewater Associates All Weather fund launched in 1996. Bhansali et al.(2012) provide a critical examination of risk-parity techniques and benefits.
Thomas et al. (2012) examine risk parity in the context of momentum and trend following strategies.
(v) VaR and leverage
Earlier we discussed VaR as a risk measurement technique. The Sharpe ratio is not sensitive to leverage and therefore it may mask the very different risk/return combinations created by different levels of debt. VaR is sensitive to leverage levels and its use can therefore lead to better risk management decisions.
As described above, short-selling, leverage, financial derivatives, and alternative asset classes are the key tools at the disposal of hedgefund managers in their quest for alpha. Restrictions on techniques to achieve short exposure and prohibitions of certain asset types point to a serious potential downside of transporting hedge fund techniques to the mutual fund and UCITS space: potentially lower investment returns. An important question is to know whether structuring hedge fund strategies in the mutual fund/UCITS manner will compromise these strategies and provide the same level of returns, considering the constraints under UCITS regulations such as investment restrictions, liquidity requirements, operational requirements and
Financial markets exhibit risk/return trade-offs and therefore it is likely that transporting high return strategies from the hedge fund space to the more liquid and regulated mutual fund space will also involve trade-offs in the presence of restrictions on fees, liquidity and risk. In theory, the current UCITS framework (synthetic derivatives to get short exposure, leverage level, financial derivatives used for investment purposes) should allow many alternative strategies to be replicated. Nevertheless, UCITS won’t be the appropriate format for certain alternative strategies due to the liquidity of underlying strategies/holdings, some asset classes not being authorized under UCITS, investment limits and borrowing rules as well as rules governing the usage of financial derivatives. For example, shareholder activism strategies may be difficult to implement under UCITS rules due to very concentrated positions.
Hedge fund strategies
In sections 2.1-2.3 we have provided a comprehensive review of different techniques used by hedge funds to generate different performance and risk profiles. It is beyond the scope of this article to review the myriad strategy-specific investment styles employed by hedge funds, but below we provide a selective list of the main hedge fund strategies (Szylar (2012)). Some of them may be transported to the mutual fund space or structured within a UCITS.
(i) Long/short equity and short-selling
Long/short funds aim at identifying undervalued and overvalued securities. They then take long and short positions to benefit from the expected price corrections. Short positions can be used to exploit stock or asset-specific mispricing or to hedge the portfolio against risk factors or market movements, thus reducing portfolio volatility. As reviewed above, shorting is also a source of leverage and can drive performance.
(ii) Equity market neutral
The equity market neutral style generally aims at creating beta neutral portfolios relative to different risk factors. The neutral position can refer to beta, sector, country, currency, industry, market capitalization, style neutral, or any combination of these factors. In practice many equity market neutral funds are however not beta neutral (Patton (2008)).
(iii) Convertible arbitrage
Convertible bonds are bonds that give their holders the right to periodic coupon payments and, as of a fixed date, the right to convert the bonds into a fixed number of shares. Convertible bond arbitrage strategies typically involve a long position on the convertible bond, hedged with a concurrent short sale of the underlying common stock. Sometimes put options or credit default swaps may also be used to hedge the position depending on their relative prices. The objective is often to benefit from the underpriced option embedded in the convertible bond. Other potential sources of profit are the coupon return and short rebate as well as gamma trading. The coupon payments, however, are usually low compared to normal bond coupons, and the managers therefore often use leverage. Choi et al. (2010) examine the role of convertible bond arbitrageurs as suppliers of capital in the convertible bond market.
(iv) Global macro
Global macro managers typically pursue a top-down investment approach and their choices are oftentimes based on the analysis of macroeconomic variables associated with the different countries that they follow. The often make large directional bets and make extensive use of derivatives.
(v) Fixed income relative value
Fixed income relative value funds pursue diverse trading strategies. They include (a) issuance driven arbitrage (trades involving on-the-run and off-the-fun treasuries), (b) yield curve arbitrage, (c) inter-market spread trading, (d) futures basis trading, (e) swap spread trading, (f) capital structure arbitrage, (g) carry trades in fixed income markets, (h) long/short credit strategies, (i) break-even inflation trades (involving nominal and real bonds), (j) emerging markets fixed income, (k) treasuries over Eurodollar spread trades, (l) mortgage backed securities (MBS) trades.
(vi) CTA/Managed futures funds
Commodity trading advisors (CTA) or managed futures funds use futures, forwards and options to invest in commodities, interest rates, equity indices and currencies. They can be classified into discretionary funds, which resemble global macro funds in their trading approach, and systematic funds. Their trading strategies differ depending on the horizon that they exploit as well as whether they focus on trends or trend reversals and spread trades. Arnold (2012) investigates risk, performance and persistence of systematic and discretionary CTAs. Baltas and Kosowski (2012a, 2012b) create futures-based trends following benchmarks for CTAs and show that they explain more than 50% of the average CTA index performance.
(vii) Event-driven strategies
Event driven managers pursue a large number of different strategies. They include merger arbitrage funds that exploit merger events. Distressed securities funds within this group are active in companies that experience Chapter 11 filings, restructurings and bankruptcy reorganizations or recapitalizations. Other event driven strategies attempt to capitalize on company news events, such as earnings releases, spin-offs, carve-outs and share buybacks.
Potential restrictions regarding the transportation of alternatives strategies to the mutual fund and UCITS space
The transportation of alternative strategies to the mutual fund space under the UCITS format implies a certain number of changes such as the limitation of leverage, or the fact that physical shorting is not allowed under UCITS which means that short exposure can only be achieved using synthetic derivatives. Moreover, more parties are needed to manage an onshore fund (notably the custodian as well as the administrator), while there are also increased burdens on operations, compliance such as getting a pre-trade monitoring system, marketing and to some extent the investment staff of the investment advisor. Such restrictions are therefore likely to lead to higher costs for the UCITS version of a fund which contributes to cost differences between UCITS and non-UCITS funds.Three other constraints may also constitute an obstacle (Szylar (2012)). “The first one is the ineligibility of certain type of assets such as physical commodities, credit, distressed but it does not constitute an insurmountable obstacle for the majority of alternative strategies. No more than 10% of net assets (so called “trash-ratio”) may be invested, as non-core investments, in transferable securities and money market instruments which are not listed on a stock exchange or dealt in on another regulated market. The second constraint is the liquidity requirement under UCITS which has to be at minimum fortnightly. As most of hedge funds offer monthly or quarterly liquidity this may constitute a strong limitation for alternative managers. A UCITS fund must re-purchase or redeem its shares/units at the request of any unit-holder. UCITS funds can operate daily, weekly or bi-monthly dealing. UCITS funds have also to comply with investment diversification such as the 10% limit and 5/40 rule. Finally, the third one is linked with exposures to OTC counterparties. Exposures to OTC counterparties must be maintained within the UCITS limits. Risk exposure to an OTC counterparty may not exceed 5% NAV or 10% (in case of EU or equivalent credit institutions).
Another difference between UCITS hedge funds and hedge funds that is important for the returns that investors receive and that is not often mentioned is related to an administrative or contractual feature. The issue concerns equalization differences between the two groups (UCITS hedge funds and hedge funds). For hedge funds a new series is created for each share class at the dealing date, but the same process does not occur for UCITS hedge funds. This means that investors investing during a drawdown have an advantage.