ALT 4 – Hedge Fund Strategies
Hedge funds sit inside the alternative investments opportunity set, and the case for holding them is never settled in the abstract. It reduces to one question: does the extra alpha and the extra diversification a hedge fund delivers justify a fee load that no long-only manager would dare charge? That debate is live in the industry and it is the frame for everything in this reading.
The case in favour rests on two claims. The first is access to talent: the most capable investors gravitate to structures that let them roam across a wider universe of markets and instruments, and a hedge fund is that structure. The second is the shape of the returns rather than their level, because alpha earned while markets are falling is genuinely hard to source anywhere else.
The case against is not trivial either. Beyond the fees, the offering memorandum is a complex document that limited partners must actually understand. Investors also accept limited transparency into the underlying positions and into performance attribution, higher cost allocations for establishing and maintaining the fund structures, and long commitment periods with restricted redemption rights.
Each strategy imports its own risks
Every strategy family brings a different risk into a portfolio, and recognising which one is the analytical heart of this reading:
- Arbitrage-oriented strategies need heavy leverage to make small pricing differences worth harvesting. That leverage is dangerous to limited partners precisely when markets are stressed.
- Long/short equity and event-driven strategies carry less beta than a plain long-only allocation, but whatever beta remains is being bought at hedge fund fee levels, which makes it a very expensive form of embedded beta.
- Managed futures and global macro offer genuine asset class and approach diversification, but the return streams they deliver are naturally more volatile.
- Relative value volatility and long volatility strategies can manage extreme tail risk in a portfolio, at the cost of a return drag through ordinary market periods.
The practical consequence is that some hedge fund strategies are true portfolio diversifiers while others are simply return enhancers. Many of them use leverage to amplify the asset base, obtained through margin, through highly levered derivatives, or through other leveraged techniques.
Liquid alternatives and the shape of the industry
Over the past decade the traditional limited partnership has been supplemented by liquid alternatives, usually shortened to liquid alts. These are mutual funds, closed-end funds and ETF-type vehicles that run hedge fund-like strategies. They promise daily liquidity, transparency and lower fees, and they widen the investor base considerably. The empirical record is less flattering: liquid alts significantly underperform hedge funds running similar strategies, which suggests traditional funds are earning an illiquidity premium that simply cannot be carried across into a daily-dealing mutual fund wrapper. Regulatory criteria on these vehicles also restrict the use of highly risky and illiquid techniques.
The seven defining characteristics
The features below separate hedge funds from regulated investment vehicles. Learn them as a set, because they explain almost every strategy-level constraint that follows.
- Legal and regulatory position. Eligibility rules differ by country and are designed to restrict traditional hedge funds to sophisticated investors meeting income or net worth tests, with only a limited number of subscriptions accepted. Most traditional US hedge funds are sold as private placements. Registration with the regulator depends on the size of assets under management, but oversight against fraudulent conduct applies to every US fund whatever its size. Where a fund is domiciled in a tax-neutral location, and Bermuda, the British Virgin Islands and the Cayman Islands are the usual choices, it is normally presented as a stand-alone corporate entity under local rules. The single largest regulatory change of the past decade has been the liquid alt. Strategies liquid enough to qualify, generally managed futures and long/short equity, now appear as US mutual funds and as UCITs vehicles across Europe and Asia. Those vehicles may be marketed widely to retail buyers, they redeem daily rather than periodically, and an incentive fee is forbidden in most jurisdictions.
- Flexible mandates and few investment constraints. Low legal and regulatory pressure leaves managers largely unhindered across asset classes, securities, risk exposures and collateral. The offering memorandum states the mandate and objectives and sets out any constraints on asset classes, leverage, shorting and derivatives.
- A large investment universe. Managers can reach well beyond traditional instruments into private securities, non-investment-grade debt, distressed securities, derivatives, and contracts as unusual as life insurance policies or music and film royalties.
- Aggressive investment styles. Flexible mandates permit strategies that a traditional fund would refuse, including heavy shorting and concentrated domestic and foreign positions that harvest credit, volatility and liquidity risk premiums.
- Relatively liberal use of leverage. Two sources supply it: securities borrowed through a prime broker, and the implied leverage inside derivatives. Sometimes leverage is what makes a strategy return worth having. Sometimes the opposite is true, and derivatives bought to hedge away unwanted interest rate or credit risk generate large notional leverage while leaving a portfolio that is less risky than it was. Inside the equity strategies, leverage is applied mostly by quantitative managers, who find small statistical valuation aberrations over short windows and must magnify them before they matter.
- Liquidity constraints. A limited partnership fund can impose an initial lock-up, gates on redemption and defined exit windows. Those provisions let a manager take a position and hold it, which a daily-dealing vehicle cannot do. The evidence lines up with that: privately placed funds have beaten similar-strategy liquid alts products by roughly 100 bps to 200 bps per year on average.
- Relatively high fees. Historically 1% or more of assets under management as a management fee, plus 10% to 20% of annual returns as an incentive fee. The incentive fee exists to align the manager with the investors.
Single manager, multi-manager, and the taxonomy problem
The first split is between single manager and multi-manager funds. In a single-manager fund, one portfolio manager or one team runs a single strategy or style. Multi-manager funds take two forms. A multi-strategy fund houses several teams running different strategies inside one legal structure. A fund-of-hedge funds, almost always shortened to fund-of-funds, instead places capital with separate underlying managers, each of whom runs a strategy of their own.
At the single manager level, classification rests on three criteria in some mixture: which instruments are traded, so convertible bonds, foreign exchange, commodities or equities; which trading philosophy is applied, so discretionary or systematic; and which type of risk is assumed, so relative value, event driven or directional. Every commercial data vendor blends the three differently, which is why no two vendor taxonomies agree.
| Provider | Groupings | Detail |
|---|---|---|
| Hedge Fund Research (HFR) | Seven listed | Publishes manager statistics on more than 30 strategies. The single strategy groupings named are Blockchain, risk parity, macro, relative value, fund-of-funds, event driven and equity hedge. |
| Refinitiv Lipper | Ten | Multi-strategy, fund-of-funds, managed futures, global macro, fixed-income arbitrage, convertible arbitrage, event driven, long/short equity hedge, equity market neutral and dedicated short bias. |
| Morningstar CISDM | Finer still | Breaks out systematic futures and merger arbitrage among others, and divides the fund-of-funds category into relative value, multi-strategy, macro or systematic, event driven, equity and debt sleeves. |
| Eurekahedge | Nine | Relative value, multi-strategy, macro, long/short equities, fixed income, event driven, distressed debt, commodity trading adviser or managed futures, and arbitrage. |
| Credit Suisse | Nine | Multi-strategy, managed futures, long/short equity, global macro, fixed income, event driven, equity market neutral, emerging markets and convertible arbitrage. |
Note on the HFR row: the source text describes HFR as dividing funds into six single strategy groupings and then enumerates seven. The seven items above are exactly as listed; treat the count of six as an inconsistency in the text rather than a separate grouping scheme.
The Credit Suisse Hedge Fund Index is asset weighted and monitors approximately 9,000 funds. To qualify, a fund needs a minimum of US$50 million in assets under management, a 12-month track record and audited financial statements. Calculation and rebalancing happen monthly, and the figures are reported after every performance fee and expense has been taken out. Because the weighting follows fund size, the index tilts towards the larger managers, and in practice that means the multi-strategy houses.
Index methodology matters as much as index membership. HFR runs two families. The HFRX Index is equally weighted and admits funds whether or not they still accept new money. The HFRI series admits only those still open. Since a manager who closes a fund is usually a good one who has run out of capacity, HFRX tends to print higher numbers than HFRI year after year. That gap is not something an investor can capture, because the HFRX membership could never be assembled in real time, which limits how useful the series is. One further caution applies to all of this data. Fewer than 1% of hedge fund managers report themselves to every index provider named above, so overlap across the databases is much lower than one would guess and no single index covers the industry.
The six categories used in this reading
| Category | Sub-strategies covered | Primary risk |
|---|---|---|
| Equity | Long/short equity, dedicated short bias, equity market neutral | Equity-oriented risk |
| Event-driven | Merger arbitrage, distressed securities | Event risk |
| Relative value | Fixed-income arbitrage, convertible bond arbitrage | Credit and liquidity risk |
| Opportunistic | Global macro, managed futures | Varies with the opportunity set, across time and asset classes |
| Specialist | Volatility strategies, reinsurance strategies | Unique risks of niche sectors and esoteric instruments |
| Multi-manager | Multi-strategy funds, funds-of-funds | Combination and re-allocation risk across the above |
Event risk deserves a definition, because it drives the second category: it is the possibility that an unexpected event damages a company or a security. Unforeseen corporate reorganisations, failed mergers, credit rating downgrades and bankruptcies are all examples. Relative value strategies, by contrast, focus on the valuation of two or more securities against each other, and they take on credit and liquidity risk because the valuation gaps they exploit usually exist because of credit quality or liquidity differences. Opportunistic strategies work from the top down across an opportunity set that spans several asset classes and is usually macro in orientation. Specialist strategies need a specific skill or a specific market knowledge. Multi-manager strategies build a portfolio of diversified strategies and re-allocate among them dynamically.
Equity hedge fund strategies invest mainly in equity and equity-related instruments. The alpha comes from the sheer breadth of the global equity universe combined with skilful picking on both sides of the book. What usually determines how an equity hedge fund is classified is the size and the sign of its equity market exposure.
Four points on that spectrum matter. Long-only equity hedge funds hold long positions and sometimes use leverage. Long/short equity funds hold both long and short positions, which produces a more hedged and less volatile portfolio. Short-biased funds concentrate on strategic short selling of companies expected to lose value, occasionally with an activist inclination and occasionally with offsetting long positions. Equity market-neutral funds balance long and short exposures so that net exposure to the market, and to factors such as sector and size, is zero or close to it. They then look for pairs of securities whose relative prices have wandered away from their normal historical relationship, on the expectation that the relationship will reassert itself.
What the strategy actually does
Long/short equity managers buy the equities of companies they expect to rise, which means holding undervalued companies long, and sell short the equities of companies they expect to fall, which means holding overvalued companies short. The objective is flexibility: find attractive opportunities on either side and size them appropriately. Depending on the mandate, a manager can rotate between industry sectors, between factors such as value and growth, and between geographic regions. In practice most managers keep their philosophical biases and their areas of focus, and they lean heavily on fundamental research.
Market timing through beta tilts can contribute to performance, but the studies are not kind. Most fundamental long/short equity managers add little alpha through such adjustments, tending to be too net long at market highs and not net long enough at market lows. Stock selection therefore defines manager skill for most of them, with market timing an additive but secondary consideration. A manager who genuinely has timing skill is valuable from an allocation perspective precisely because the skill is rare. This is also the largest single strategy family in the industry, accounting for about 30% of all hedge funds.
Investment characteristics
Because skill comes mainly from stock selection, managers specialise, usually by geographic region, sector or investment style. Three characteristics define them: the strategy focus, the flexibility to hold long and short positions over time, and the use of leverage. Factor exposures follow directly from the mandate. A manager focused on small-cap growth stocks carries positive exposure to the size factor and negative exposure to the value factor. A manager focused on large-cap value carries the mirror image: negative size exposure and positive value exposure.
Equities drift upward over long horizons, so the typical book in this strategy carries a net long position. Some managers keep shorts on purely as protection against an unexpected downturn. Others are opportunistic and add shorts after uncovering problems with management, strategy or financial statements, or when valuation models flag selling opportunities in particular stocks or sectors. Performance during crisis periods is therefore an important differentiator between managers. Because fees are high, the goal must be idiosyncratic alpha, primarily from stock picking and secondarily from market timing, rather than embedded systematic beta that could be bought far more cheaply in a long-only fund.
| Dimension | Detail |
|---|---|
| Opportunity set | Diverse global opportunities create a wide universe from which to generate alpha through stock picking. Investment styles include value and growth, large cap and small cap, discretionary and quantitative, and industry specialisation. |
| Typical exposure | Average exposures of 40% to 60% net long, made up of gross exposures of 70% to 90% long against 20% to 50% short, although this varies widely. |
| Return objective | The target is a mean annual return in the region of what a long-only fund would deliver, achieved at roughly half the standard deviation of that long-only benchmark. |
| Short book | A minority hedge market risk with index-based shorts. The majority hunt single names on the short side, because that is where the alpha and the added absolute return sit. |
| Market timing | Some managers add alpha by timing the portfolio beta tilt, but the evidence suggests most do this poorly. |
| Vehicle | Handled by both limited partnership and mutual fund-type vehicles. |
| Attractiveness | Liquid and diverse, with transparent mark-to-market pricing from public quotes. The short side typically reduces beta risk and adds a second source of alpha and lower portfolio volatility. |
| Leverage | Variable. The closer a manager sits to market neutrality, or the more quantitative the process, the heavier the leverage needed to produce a meaningful return. |
| Benchmarks | Credit Suisse L/S Equity Index; Morningstar/CISDM Equity L/S Index; Lipper L/S Equity Hedge; and the Equity Hedge Indices published as HFRI and HFRX. |
Strategy implementation
When long and short positions are combined, what remains is the difference between the beta-adjusted long exposure and the beta-adjusted short exposure. A book with many strong sell ideas and a large short position can be net short for brief periods, but most managers settle at modest net long exposures averaging between 40% and 60% net long.
Many long/short equity managers are sector specialists, building the fund around their industry expertise and analysing situations they understand deeply, from the top down and from the bottom up at the same time. Natural areas of specialisation are the more complex sectors: telecom, media and technology, financials, consumer, health care and biotechnology, where sector knowledge adds real value. Generalists range more widely across industry groups and usually avoid the complex sectors. Biotechnology is the standard example, because corporate outcomes there can be binary depending on whether a drug trial succeeds. Generalists gain balance and flexibility but can miss industry subtleties that matter increasingly in a market where news arrives continuously and is heavily nuanced. In most cases the strategy ends up as a mix of alpha extracted from single-name selection on both sides plus some naturally net long embedded beta.
Dedicated short sellers hold short-only positions in equities they judge to be expensively priced against deteriorating fundamentals. They vary exposure only through portfolio sizing, at times by holding more cash. Short-biased managers run a less extreme version: they hunt the same expensive equities, but they may balance the short book with modest value-oriented or index-oriented long exposure, which helps them survive long bull markets.
Both types are trying to manufacture an uncorrelated or negatively correlated return stream. The raw material is the same in each case: a broken business model, accounting that turns out to be fraudulent, management that has lost control of the business, or any other development that will turn market opinion against a stock. The secular uptrend in global equities across recent decades has made this extremely hard, and there are fewer such managers today than in the 1990s. Volatility and economic disruption following the Covid-19 pandemic have widened the opportunity set again.
Activist short selling is a distinct practice: the manager takes a short position and then publishes the research behind the thesis. Where the manager has a solid reputation from earlier campaigns, publication tends to trigger a sharp price fall into which the activist can cover part of the position. In the United States, regulators have not treated this as market manipulation provided the activist does not publish erroneous information, does not charge for the research, which would create a conflict between subscribers and investors, and acts only in the interests of its limited partners.
Investment characteristics
Short sellers look for overvalued equities of companies whose fundamentals are deteriorating in ways the market has not yet perceived, and they aim to maximise returns during market declines. Where the approach works it supplies a genuinely negatively correlated return stream.
The mechanics are unforgiving. Short selling means borrowing securities, selling them high, then repurchasing them cheaply later and handing them back to whoever lent them. The manager must post collateral with the lender to cover potential losses, and must also service the securities loan with an interest payment that can become very expensive when the shares are hard to locate. A key risk is that the lender recalls the securities at an inopportune moment, before the expected decline has happened.
Risk management is also structurally harder than in long investing. A short position grows if prices advance against the seller and shrinks if prices decline, which is the opposite of the long-only case where losing positions naturally become smaller. Managers who become known as active short sellers can also find that company management stops taking their calls.
Regulation adds another layer. Many countries restrict short selling. The US alternative uptick rule works like this: once a share has dropped 10% or more against the previous close, any short sale must be filled above the highest standing bid, so the price has to be moving up before the order can go through. Many emerging markets have allowed short selling to enhance liquidity, and the Saudi Stock Exchange permitted short sales from 2016, but the risk of restriction in extreme conditions never disappears. During the global financial crisis the US SEC temporarily banned fresh short sales in a named group of financial shares, the stated purpose being to reduce systematic stress in the market.
Given the operational difficulty and the secular rise in equity markets, successful short sellers tend to strike quickly and then step back rather than holding a position indefinitely. The return profile of a good short-biased manager rises as the market falls and settles at roughly the risk-free rate when the market is going up. In an idealised world that would mean being short during down periods and holding low-risk government debt otherwise. The real objective is harder: pick short-sale stocks that still generate positive returns while the general trend is up. Targets are companies that are overvalued, that show revenues or earnings in decline, or that suffer from boardroom conflict, weak governance structures, or accounting that may prove fraudulent. Another category is companies whose future rests on a single product in development that the short seller believes will prove unsuccessful or non-repeatable.
| Dimension | Detail |
|---|---|
| Dedicated short sellers | Trade with short-side exposure only, although short beta may be moderated by holding cash. |
| Short-biased managers | Focused on good short-side stock picking, and willing to soften the short beta with cash and with a modest long book, either value-oriented or index-oriented. |
| Typical exposure | A dedicated short seller normally runs between 60% and 120% short at all times, while a short-biased manager runs nearer 30% to 60% net short. Both concentrate on single equity stock picking rather than index shorting. |
| Return profile | Return goals sit below those of most other hedge fund strategies, and the compensation is a negative correlation benefit. Short beta exposure also makes the return stream more volatile than that of an ordinary long/short equity fund. |
| Vehicle | Best handled in a limited partnership because of the difficult operational aspects of short selling. |
| Attractiveness | Liquid, and a source of alpha that is negatively correlated to almost everything else, priced from observable public quotes. History is less encouraging: realised returns have arrived in lumps and have generally disappointed. |
| Leverage | Low. There is enough natural volatility that short sellers do not need to add much leverage. |
| Benchmarks | The Lipper Dedicated Short-Bias Index and the Eurekahedge Equity Short Bias Hedge Fund Index. Some investors instead benchmark against the inverse of the returns on a related stock index. |
Index construction is a genuine problem in this category. With so few short-selling managers in existence, assembling an index that is acceptably diverse is difficult. To take one example, only four managers sit inside the Lipper Dedicated Short-Bias Index.
Strategy implementation
Stock selection dominates the process. Managers work bottom up, scanning the universe of potential sell targets for the shares most likely to fall substantially over the relevant horizon, looking for inherently flawed business models, unsustainable corporate leverage, and signs of poor governance or accounting gimmickry. Useful screening tools include the spreads on single-name credit default swaps, the yield spreads on corporate bonds, and the implied volatility embedded in exchange-traded put options. Technical analysis and pattern recognition can help with the timing of the sale. Accounting measures also feature: the Altman Z-score gives a read on how close a company is to bankruptcy, and the Beneish M-score is designed to flag financial statements that may have been manipulated. Because the practice is so dangerous, most successful short sellers do substantial forensic work on each candidate, and in doing so they make the market as a whole more efficiently priced.
A short-biased manager is researching Generic Inc. (GI). The company led the drug industry once, but research and development spending has fallen for 10 consecutive years and every one of its patents has now expired, leaving it inside the competitive generic market. Everything now rests on one new gastro-intestinal treatment. The research behind it was debt financed, so the leverage ratio is twice the industry average, and early clinical trials were inconclusive. Final trial results are due within one month. The market is constructive, but many medical experts doubt the efficacy of the drug.
| Metric | Generic Inc. | Industry average |
|---|---|---|
| PE (X) | 30 | 20 |
| PB (X) | 3.5 | 2.5 |
| T12M EPS growth | 3% | 18% |
Trading in GI shares is very thin, and the short-interest ratio stands at 60%. The broker reports that the shares are on special, meaning difficult to borrow, at a borrowing cost of 20% per year. Exchange-traded options on the shares trade actively, and the one-month prices look as though the market takes a positive view of the company.
The reason not to is supply and demand in the borrow. A 60% short-interest ratio and a 20% annual cost to borrow both point the same way: if the clinical results do show efficacy, a dangerous short squeeze could develop. On those negative demand and supply dynamics the manager decides not to add GI to the portfolio.
(a) Simply buy put options.
(b) Buy a long put calendar spread: buy a put expiring after the trial result date and sell a put expiring before it, so the premium received on the shorter tenor put partly finances the longer tenor put.
(c) Buy GI shares and lend them out at the attractive 20% rate, hedging the resulting long stock position by buying out-of-the-money puts, which creates a protective put position.
(d) If sentiment has made out-of-the-money calls expensive, sell them and use the premium received to fund out-of-the-money puts. That combination is a short risk reversal, and it delivers synthetic short exposure.
Equity market-neutral strategies, abbreviated EMN, take opposite positions in similar or related equities whose valuations have diverged, while holding net portfolio exposure to the market at or near zero. Managers neutralise market risk by constructing the portfolio so that expected portfolio beta is approximately zero. Many go further and set the betas for sectors and industries, and for common risk factors such as market size, the price to earnings ratio and the book to market ratio, equal to zero as well.
Because these portfolios take no beta risk and neutralise so many other factor risks, they normally have to apply leverage to both sides of the book to make the individual stock selections produce a meaningful expected return. Construction is often highly quantitative, holdings end up diverse, and positions are modified over relatively short horizons. Derivatives are an alternative route to the same place, with stock index futures and options used to drive the portfolio beta to zero. However the portfolio is built, the goal is the same: capture alpha while minimising beta exposure.
Four ways to build a market-neutral trade
Pairs trading is the most intuitive subset. A pair might be two similar companies where one looks cheap and the other expensive; it might be a holding company set against a subsidiary whose valuations have separated; or it might be two share classes of a single issuer, since multi-class stocks usually differ in voting rights. Whatever the pair, it becomes tradable when the prices fall out of alignment. Pairs are then monitored for their normal trading relationship, which conceptually is the degree of co-integration between the two price series. A position is established when unusually divergent spread pricing appears. The expectation is reversion to long-term mean values or to the fundamentally correct relationship, with the long leg rising and the short leg falling.
A caution attaches to purely quantitative pairs trading. Overall beta exposure is minimised, yet the book can still behave like a short volatility position when markets reach abnormal extremes of stress. That is less true where the trade rests on a fundamental pricing discrepancy that some identifiable event is expected to correct.
Stub trading pairs a parent company against its subsidiaries, with the weights set by how much of each subsidiary the parent owns. If parent company A owns 90% of subsidiary B and 75% of subsidiary C, and A looks expensive while B and C look cheap against their own historical mean valuations, the fund shorts one share of A and buys 0.90 shares of B together with 0.75 shares of C.
Multi-class trading works within a single issuer, trading one share class against another, most commonly voting against non-voting stock. The analysis mirrors pairs trading: establish how the returns of the two classes are co-integrated, examine the valuation metrics for each, and act when prices leave their normal ranges by shorting the expensive class and buying the cheap one. The gain comes from the relative pricing reverting to a more normal range.
Fundamental trade setups are not strictly equity market neutral but are still designed to be market neutral. A long or short equity position is hedged against an offsetting bond exposure where the relative pricing between the stock and the bond is out of alignment. This is capital structure arbitrage, and it is discussed again under event-driven strategies. Attractive expected outcomes here usually come from relative mispricings that exploit a potential event, such as a merger or a bankruptcy, that would change relative pricing. A market-neutral strategy can equally be built by positioning two bonds against each other, for instance to exploit a misunderstood difference in covenants or a differential asset recovery.
Quantitative market neutral and statistical arbitrage
When large numbers of securities are traded and positions are adjusted daily or hourly using algorithmic models, the manager is described as a quantitative market-neutral manager. The frequency is driven by the fact that market prices move faster than company fundamentals, and each move triggers a rebalancing back to neutrality. When the horizon shrinks further and the emphasis falls on mean reversion and relative momentum in market behaviour, the practice becomes statistical arbitrage trading.
There is a permanent tension here between holding a perfectly beta-neutral portfolio and paying the market impact and brokerage costs of near-continuous adjustment. Many EMN managers run trading-cost hurdle models to decide whether a rebalance is worth executing at all. Security selection remains the main source of skill, with market timing secondary. Sector exposure is usually constrained, although this varies by manager. A manager who is beta neutral overall but generates alpha by rotating between sectors goes by a different name: a market-neutral tactical asset allocator, or a macro-oriented market-neutral manager.
Investment characteristics
EMN managers insulate the portfolio from overall market movements and take advantage of divergent valuations in specific securities. The process is often quantitative and uses substantial leverage to make the return objective meaningful, although many discretionary EMN managers use significantly less.
These funds are most useful for allocation during non-trending or declining markets, because their returns are steadier and less volatile than most other hedge strategies. The conservative and constrained approach normally delivers less volatile returns than a manager who accepts beta exposure. The exception is when significant leverage forces portfolio downsizing. Prime brokers offer portfolio margining, and under it a market-neutral book can be run as far as 300% long against 300% short. Those margining rules hold only until losses reach a defined threshold, described as an excess drawdown, after which the broker can compel the manager to cut overall exposure. That is a key strategy risk, and it bites hardest on the quantitative managers.
Despite the leverage, the steady risk and return profile means EMN managers are often treated as preferred replacements for, or at least complements to, a fixed-income allocation at times when bond returns look unattractively low or the curve has flattened. The alpha is of a completely different kind, with completely different risks. EMN managers face leverage risk, which includes both the availability and the cost of leverage, and tail risk, which is the behaviour of a levered portfolio during market stress.
| Dimension | Detail |
|---|---|
| Return profile | Relatively modest, with portfolios aimed at market neutrality and differing constraints allowed on other factor and sector exposures. |
| Diversification and liquidity | Holdings are numerous and liquid, and across ordinary conditions the dispersion of returns is smaller than in most competing strategies. |
| Manager type | Many types exist, but many are purely quantitative rather than discretionary managers. |
| Horizon | The orientation is towards mean reversion, over horizons shorter than those of most competing strategies, which means more frequent trading. |
| Vehicle | Leverage this high sits outside what mutual fund rules permit, so the limited partnership is the preferred wrapper. |
| Attractiveness | The strategy harvests short-lived, idiosyncratic mispricings between securities that ought to be co-integrated. No beta risk needs to be accepted in order to earn the return, which is why the strategy comes into its own when markets look vulnerable or weak. |
| Leverage | High. With market and sector beta risks hedged away, higher leverage is generally deemed acceptable in striving for meaningful return targets. |
| Benchmarks | Credit Suisse Equity Market Neutral Index; Morningstar/CISDM Equity Market Neutral Index; Lipper Equity Market Neutral Index; and the Equity Market Neutral Indices published as HFRI and HFRX. |
Strategy implementation in four steps
- Evaluate the investment universe, keeping only securities that trade freely, carry enough liquidity, and can realistically be borrowed for shorting.
- Screen for buy and sell candidates, using fundamental models that take company, industry and economic data as valuation inputs alongside statistical or momentum-based models.
- Construct the portfolio under constraints that maintain market risk neutrality, so that the market value-weighted beta is approximately zero, often with money, sector or other factor neutrality imposed as well.
- Consider the availability and cost of leverage against the desired return profile and the acceptable drawdown risk, and introduce rebalancing execution costs as a filter on how often the portfolio should be rebalanced.
Markets are dynamic because volatility and leverage change constantly, so exposure to the market is always drifting. EMN managers must actively manage exposures to stay neutral over time, yet every rebalance costs money. The discipline is to avoid letting those costs overwhelm the security-selection alpha the strategy is designed to capture.
An EMN manager based in Hong Kong has been following PepsiCo Inc. (PEP) and Coca-Cola Co. (KO). Having studied how the new PEP drink is being marketed across Asia, she concludes the campaign is too controversial and the target market too narrow. PEP has relatively weak earnings prospects compared with KO, and three-month valuation metrics show PEP shares substantially overvalued against KO shares, with relative valuations beyond their historical ranges. She wants to allocate $1 million to the trade.
| Stock | Beta | S&P 500 Index weight |
|---|---|---|
| PEP | 0.65 | 0.663 |
| KO | 0.55 | 0.718 |
The $1 million allocation goes into the long KO leg in full, so the long position is $1,000,000. Assuming realised betas resemble historical betas, the short PEP position must be scaled down because PEP has the higher beta. The beta ratio is 0.65 ÷ 0.55 = 1.181818, so the short is
−$1,000,000 ÷ (0.65 ÷ 0.55) = −$846,154 of PEP shares.
Once that is set, only the difference in performance between PEP and KO affects the strategy, because it is insulated from market fluctuations. If PEP and KO valuations revert to within normal ranges over the next three months, the trade should be profitable.
One trap to note: the index weights given in the table play no part in the calculation. They are there to be ignored.
A caveat on that example. It is deliberately simplified. In reality most EMN managers would not hedge beta stock by stock but would hedge beta at the level of the overall portfolio, and they would also take account of other security factor attributes.
Event-driven managers hold corporate securities and derivatives with a view to profiting from how a specific corporate event resolves. The event list is long: a merger or acquisition, a bankruptcy, a share issuance or buyback, a capital restructuring, a reorganisation, an accounting change. The analytical work runs through financial statements and regulatory filings, examines corporate governance closely, covering management structure, board composition, matters put to shareholders and proxy voting, and assesses strategic objectives and competitive position.
There are two ways to time an event-driven position. A soft-catalyst approach commits capital ahead of an event, before it has happened at all. A hard-catalyst approach waits for the announcement and then invests where prices have not yet converged fully. Waiting for the hard catalyst produces a less volatile and less risky book. Merger arbitrage and distressed securities are the most common event-driven strategies.
The two deal structures
Mergers and acquisitions are classified by method of purchase. In a cash-for-stock acquisition the acquirer A offers the target T a cash price per share. If T trades at $30 and A offers $40 per share, A is offering a 33% premium, because $40 ÷ $30 − 1 = 0.333. A stock-for-stock acquisition instead exchanges a stated number of acquirer shares for each target share. With A at $20 and an offer of 2 acquirer shares for each T share, the holder of a T share ends up with $40 of value, provided the A price holds until completion.
Deal structures vary for tax reasons, for reasons of corporate structure, or because a defence has been built in to discourage a bid. The poison pill is the standard example. It is a pre-offer defence under which the bondholders of the target may put their bonds back to the company at a redemption price fixed in advance, usually at par or better, so the bidder must find more cash and the acquisition becomes more expensive. As a general pattern, acquirers offer cash when cash surpluses are high, and use stock when share prices are high and management regards its own shares as richly valued, treating overvalued shares as a currency.
Investment characteristics
In a cash-for-stock deal the manager may simply buy the target, expecting it to rise towards the offer price on completion. In a stock-for-stock deal the usual construction is long T against short A, sized on the exchange ratio itself, with the spread as the prize once the deal closes. If the deal fails, the manager loses on both legs, because the price of T has already risen and the price of A has already fallen in anticipation. Less often a manager takes the opposite view, that the deal will fail, usually because of anti-competition or other regulatory concerns, and then sells T and buys A.
On announcement, the target price generally rises towards the acquisition price and the acquirer price falls, either because of potential dilution of its shares or because cash is being used for something other than a dividend. The lag between announcement and closing means a deal can always fail: financing may not materialise, regulators may object, or financial due diligence may not pass. Hostile bids, where target management has not agreed the terms, are less likely to complete than friendly takeovers where management has already agreed.
Somewhere between 70% and 90% of mergers announced in the US market eventually complete. Allowing for the failures, for the costs of establishing the position such as borrowing the acquirer stock and commissions, and for the risk that terms are renegotiated in stressed markets, merger arbitrage typically offers a 3% to 7% return spread depending on deal-specific risks, with a particularly risky deal carrying more. If the average deal takes 3 to 4 months, the same capital can be turned over into fresh deals more than once a year, and modest leverage lifts net annualised returns into the 7% to 12% range, with returns driven by deal outcomes rather than by market factors. Spreading capital across many mergers, deal types and industries hedges the risk that any single one collapses.
The left tail is the thing to respect. When deals fail, the initial rise in the target and fall in the acquirer typically reverse. Arbitrageurs who entered after the announcement can lose heavily on the long target and short acquirer positions, often as much as negative 20% to 40%.
The payoff is a bond plus a short put
Corporate events are binary: an acquisition either succeeds or fails. That makes merger arbitrage economically equivalent to selling insurance on the deal. If it succeeds, no adverse event occurs and the manager collects the spread, exactly as an insurer collects a premium for bearing event risk. If it fails, the manager pays out on both legs, as an insurer pays a policy benefit. The payoff profile therefore resembles a riskless bond plus a short put option. The arbitrageur also holds something extra: a call option that becomes valuable if a White Knight, meaning another interested acquirer, bids higher for the target before the original proposal completes.
| Dimension | Detail |
|---|---|
| Liquidity and gains | The strategy is comparatively liquid. Gains are defined and come from single security takeover situations that have nothing to do with the market, punctuated by occasional shocks when a deal collapses without warning. |
| Market sensitivity | Deals fail more often when markets are stressed, so the strategy is not insulated from the market and carries left-tail risk. Economically the payoff is a bond combined with a written put. |
| Deal type | Cross-border M&A usually requires two sets of governmental approvals, and vertical integration deals often face anti-trust scrutiny, so both carry higher risk and offer wider merger spread returns. |
| Manager style | Some managers invest only in friendly deals at relatively tight spreads. Others take riskier hostile takeovers at wider spreads, where a higher bid from a White Knight may be expected. |
| Vehicle | The limited partnership is preferred, given the leverage involved, though a handful of merger arbitrage liquid alts funds run at low leverage and low volatility. |
| Attractiveness | Sharpe ratios are relatively high, built on returns in the low double digits and a standard deviation in the mid single digits, both of which shift with the leverage applied. The steady profile carries a left tail. |
| Leverage | Moderate to high. Leverage of 3 to 5 times is the usual range needed to lift the spread into a meaningful target return. |
| Benchmarks | Credit Suisse Merger Arbitrage Index; CISDM Hedge Fund Merger Arbitrage Index; and the Merger Arbitrage Index published by HFRI and HFRX. |
Strategy implementation
Positions are usually established in common equities, but the toolkit extends across the capital structure and into derivatives: junior and senior debt, preferred stock, convertibles and options are all used for positioning and for hedging. In a cash-for-stock deal the manager may use leverage to buy the target. In a stock-for-stock deal leverage is also common, but shorting the acquirer can be difficult because of liquidity or short-selling constraints, which is a particular problem in emerging markets.
Derivatives solve several of these problems. Out-of-the-money puts on the target, and out-of-the-money calls on the acquirer to cover the short, are the two standard overlays. Convertible securities give asymmetric payoffs: the convertible bonds of the target rise as the target shares rise on the acquisition, while their bond value cushions the fall if the deal fails. Where the acquirer credit is superior to the target credit, credit default swaps can be used. Protection is sold on the target, meaning the CDS is shorted, to benefit from its improving credit quality and the fall in the price of protection once the merger completes. If protection is cheap enough, buying it on the target instead can act as a partial hedge against deal failure. Broad market risk that might disrupt completion can be hedged with short equity index ETFs or futures, or with long index put positions.
The true source of alpha, however, is not the implementation. It sits in the first judgement of all: which deals to take on, and which to leave alone. Once in a deal, there are many ways to express the position depending on the deal-specific view.
An acquirer A trades at $45 per share and has bid for target firm T in a stock-for-stock structure, exchanging 1 A share for every 2 T shares. T was trading at $15 per share just before the announcement. Shortly afterwards T rises to $19 and A falls to $42 in anticipation of the required approvals and a successful closing. A hedge fund manager is confident the deal will complete, and therefore goes long 20,000 T shares against a short of 10,000 A shares.
Cost of the long leg: 20,000 × $19 = $380,000.
Proceeds from the short leg: 10,000 × $42 = $420,000.
Net spread if the merger completes: $420,000 − $380,000 = $40,000.
If the merger fails, prices should revert to pre-announcement levels. Closing the short requires repurchasing 10,000 A shares at $45, which costs 10,000 × $45 = $450,000. The long position of 20,000 shares of T falls to $15, worth 20,000 × $15 = $300,000.
Loss on A: $420,000 − $450,000 = −$30,000.
Loss on T: −$380,000 + $300,000 = −$80,000.
Total loss: −$30,000 + (−$80,000) = −$110,000.
In structural terms the position behaves like a default-free bond with $40,000 of face value, being the payoff when the deal completes, held together with a written binary put that expires worthless on success and costs $110,000 on failure.
Distressed securities strategies target companies already inside bankruptcy, heading towards it, or merely under financial stress. The routes into that condition are familiar: a business that has lost its edge, a balance sheet carrying too much debt, governance that has broken down, accounting irregularities, or fraud. Their securities have usually been cleared out of long-only portfolios by then, and can trade far below what they would be worth once the business is properly run again.
Hedge funds are natural buyers here for two structural reasons. They are not bound by institutional requirements on minimum credit quality, and they offer their own investors only periodic liquidity, typically quarterly and sometimes annually, which makes illiquid holdings far less problematic than they would be inside a mutual fund. Mispriced securities can be picked up ahead of a bankruptcy filing, in the middle of the proceedings, or after they conclude, and hedge funds generally aim to realise returns faster than a private equity firm would. That is not universal: managers in some distressed sovereign debt, in places such as Sri Lanka or Venezuela, face very long horizons before collecting.
Some distressed managers set out to control an entire class of securities within the capital structure, because that gives them creditor control over the bankruptcy or the reorganisation. Which class provides that control varies by country and by bankruptcy law. Active managers build concentrated positions and take board seats at the companies they are trying to turn around. Passive managers leave others to fund the legal bills of a capital structure reorganisation, which are often substantial and can include expensive proxy contests.
Distressed debt and other illiquid assets can take years to resolve and are hard to value, so managers running these portfolios require long initial lock-up periods, for example no redemptions for the first two years. Gates may also be set at the level of the fund or of the individual investor, capping what can be withdrawn in any one quarter. On valuation, external specialists may be needed to give an independent estimate of fair value. Where a security has little or no liquidity, and is classified as a Level 3 asset under the relevant accounting standards, its price is derived from a model, and that process smooths reported returns.
Liquidation against reorganisation
Bankruptcy typically produces one of two outcomes.
- Liquidation. The assets are sold off over some period and holders are paid sequentially by priority of claim: senior secured debt, then junior secured debt, then unsecured debt, then convertible debt, then preferred stock, and finally common stock.
- Reorganisation. The capital structure is rebuilt and the terms attached to existing claims are renegotiated. Holders of debt may agree to a longer maturity, or may swap their claims for shares in the restructured business. Existing equity is cancelled, leaving existing shareholders with nothing, and new equity is issued and sold to new investors to raise funds and improve the financial condition of the firm.
Investment characteristics
Many institutional investors, banks and insurance companies among them, cannot hold non-investment-grade securities under their mandates and must therefore sell as a company deteriorates. Forced selling of that kind dries up liquidity and forces heavy discounts into whatever trades do print, and that is where the opportunity comes from. The path from financial distress to bankruptcy can also unfold over long periods, and the complexity of the legal proceedings creates informational inefficiencies that leave securities improperly valued.
Doing this well needs specific skills: reading complicated legal proceedings, understanding bankruptcy processes and creditor committee discussions, modelling reorganisation scenarios, and anticipating how the market will react to each of them. Depending on relative pricing, managers may build capital structure arbitrage positions in the same distressed entity, going long the securities where recovery looks acceptable and short other securities, including the equity, where recovery prospects are poor.
Market conditions matter throughout. A liquidation that has to move fast will realise discounted prices and a lower total recovery rate. Once illiquid assets have to be shifted in a hurry, forced selling and liquidity spirals push prices down to fire-sale levels. In a reorganisation, current market conditions partly determine whether the firm can raise capital from asset sales or from issuing new equity, and how much.
| Dimension | Detail |
|---|---|
| Return profile | Among the event-driven strategies this one sits at the upper end for return, and also for variability. |
| Direction | Shorts and hedges are available, but the book normally leans long. Outcomes are driven by individual securities while remaining sensitive to the state of the wider economy. |
| Liquidity and pricing | Illiquidity is high, and highest of all where the manager takes a concentrated activist stake. Prices often come from a model, which smooths the reported series. Outcomes tend to be binary: very good or very bad. |
| Attractiveness | Returns arrive in lumps and follow the cycle. The best entry point is usually early in an economic recovery, once a period of dislocation has passed. |
| Leverage | Moderate to low. Given how volatile and how long-biased the strategy is, managers keep leverage modest, usually 1.2 to 1.7 times NAV invested, part of which is nominal leverage created by derivatives hedging. |
| Benchmarks | Credit Suisse Event Driven Distressed Hedge Fund Index; Lipper Event-Driven Index; CISDM Distressed Securities Index; and the Distressed Indices published by HFRI and HFRX. |
Alpha in distressed securities tends to be idiosyncratic. The strategy capitalises on information inefficiencies and on the structural inability of traditional managers to hold such securities at all.
Strategy implementation
In a liquidation, the work is estimating the recovery value for each class of claimant. If the manager estimate of recovery exceeds market expectations, perhaps because of illiquidity, the manager buys the undervalued debt and expects to realise the higher recovery rate. Suppose the senior secured debt of bankrupt company X is priced at 50% of par. By researching the quality of the collateral and estimating the potential cash flows in liquidation and their timing, the manager estimates a recovery rate of 75% and buys the debt expecting to capture the difference. Timing the legal process matters as much as valuing the claim, and specialist bankruptcy counsel is normally needed to judge it, because proceedings can run for several years. Even a correct recovery estimate may be only partly realised, or not realised at all, if the process drags on or market conditions deteriorate.
In a reorganisation, the questions are how the finances will be rebuilt, what the operating business is worth, and what each class of claim will eventually be worth. The manager evaluates the securities of the company and buys those that look undervalued given the likely outcome. The choice of security also depends on whether the manager wants a control position. If so, the manager will be active in negotiations and will seek to identify fulcrum securities. These are claims that sit partially in the money, will not be paid in full, and whose owners finish up holding the equity of the restructured company. Where too much debt rather than a broken business caused the trouble, the restructuring may convert the senior unsecured debt the manager bought into new shares, cancelling existing debt and equity, while new equity investors inject fresh capital. As the distress recedes and the restructured company is worth more, a flotation normally follows. The manager sells into it, and the profit is the gap between the price paid for the cheap senior unsecured debt and the proceeds of the new shares.
Fracking technology lifted US energy reserves suddenly and structurally, and oil fell from more than $60 per barrel in mid-2015 to less than $30 per barrel in early 2016. Debt investors became concerned about whether smaller, highly levered exploration and production companies could survive if low energy prices persisted, and junior unsecured paper from those issuers, already below investment grade, collapsed in price. Retail equity investors reacted far more calmly. The result was that the shares of several of these companies still carried significant implied enterprise value while their debt traded as if bankruptcy were imminent.
The position pays under several outcomes. If energy prices stayed low and bankruptcy arrived, the equities would become worthless while the unsecured debt might retain recovery value from asset sales, or might become the fulcrum securities converted into new equity in the reorganised business. If prices recovered, and they did, with oil closing 2017 at more than $60 per barrel, the unsecured debt would rebound far more than the equity would rise. Holding that debt against a short equity leg, or against long puts, therefore produced a position that could pay in more than one state of the world.
Equity market neutral is itself one equity-oriented form of relative value investing. The other common forms involve fixed-income securities and hybrid convertible debt, and like equity market neutral they use leverage heavily. Relative valuations move apart for three reasons: credit quality shifts, liquidity shifts, and, where an option is embedded in the security, implied volatility shifts. Under ordinary conditions the strategies collect a premium for bearing credit, liquidity or volatility risk. When a crisis arrives and leverage, credit deterioration, illiquidity and a volatility spike all land at once, the same strategies lose money.
Fixed-income arbitrage hunts mispricings by pairing long and short positions among debt instruments of every kind: government and corporate bonds, bank loans, and consumer paper such as student loans, credit card receivables and mortgage-backed securities. Opportunities arise wherever two instruments differ in duration, in credit quality, in liquidity or in embedded optionality.
Investment characteristics
In its simplest form the strategy buys the relatively undervalued security and sells short the relatively overvalued one, expecting the mispricing to resolve itself within the investment horizon. Four things push valuations outside their normal historical ranges: a difference in credit quality, meaning investment grade against non-investment grade; a difference in liquidity, meaning on-the-run against off-the-run; a difference in expected volatility, which matters most where an option is embedded; and even a difference in issue size. Stated more generally, the manager is trading today’s prices against where the relationship between them is expected to settle, and reversion to the mean is one important part of that. Positive net carry is frequently a goal in its own right, and capturing it may mean trading a kink in the curve or positioning for the curve to change shape.
Where the positioning involves accepting relative credit risks across different issuers, fixed-income arbitrage becomes what is more broadly called long/short credit trading. That version is naturally more volatile than exploiting small pricing differences within sovereign debt alone.
Unless the price discrepancy itself requires a yield curve exposure, arbitrageurs neutralise the duration of the two legs so that interest rate risk drops out. Duration neutrality only hedges small shifts in the curve. Larger yield moves, and non-parallel moves such as a steepening or a flattening, require derivatives: futures, forwards, swaps, and swaptions, which are simply options on a swap. Complexity varies by instrument. Plain government debt adds sovereign risk, and sometimes currency risk, to its interest rate risk, and neither is trivial in many countries, while securitised paper backed by assets or mortgages brings credit risk and pre-payment risk instead. Derivatives help hedge those too.
Pricing inefficiencies in fixed income are usually very small, particularly in developed markets, but the correlation across different securities is typically very high. That combination makes substantial leverage both necessary and acceptable. A ratio of 4 to 5 times assets to equity is typical. Inside some market-neutral multi-strategy funds, where fixed-income arbitrage is only a portion of total risk, the sleeve can be run at 12 to 15 times assets to equity. Leverage magnifies every risk the strategy carries, especially in stressed conditions.
A further complication is the enthusiasm of financial engineers for tranching structured products around fixed-income cash flows, residential mortgages above all, in order to separate out particular credit and prepayment risks. Inside a mortgage pool the cash flows may be sliced so that one set of credit tranche holders ranks ahead of another, or so that the interest payments and the principal payments flow to different owners. Relative value strategies built on that collateral carry a particular set of risks that only show up under stress: the negative convexity of many mortgage-backed securities and of the structures wrapped around them, default rates running ahead of assumptions once volatility rises, the balance sheet leverage of the funds themselves, and redemption pressure from their own investors.
Finally, scale and opacity. Measured by issuance, global debt markets dwarf equity markets, and the instrument types run into the thousands. Outside on-the-run government paper and other sovereign-backed issues, which trade freely in developed markets, liquidity is generally poor. Prices are naturally opaque and have to be worked out, which is hardest for off-the-run paper that trades only now and then, and depth in parts of the municipal and corporate markets can be very thin indeed. That is where the opportunity lives, and it is also where the positioning and liquidity risk lives.
| Dimension | Detail |
|---|---|
| Source of the profile | Three things drive it: securities that are highly correlated with one another, a yield spread that can be picked up, and an enormous variety of debt instruments spanning markets, credit qualities and convexity profiles. Structured products layer on complexity, and complexity is where mispricing hides. |
| Liquidity gradient | US government paper offers the deepest liquidity for curve and carry positions and the thinnest opportunity set. Move into other sovereign markets, then mortgage-related paper, then corporate credit, and liquidity falls at each step. |
| Attractiveness | A function of correlations between securities, the yield spread available, and the number and diversity of debt securities across markets. |
| Leverage | High, though what is available shrinks as the product gets more complicated. Prime brokers supply it through collateralised repurchase agreements, applying a haircut that varies with the security type. That haircut is the cushion protecting the broker against volatility and illiquidity should the collateral ever have to be sold. |
| Benchmarks | Credit Suisse Fixed Income Arbitrage Index; CISDM Debt Arbitrage Index; Lipper Fixed Income Arbitrage Index; and the Fixed Income Relative Value Indices published by HFRI and HFRX. |
More granular relative value indexes also exist within the same families, split by sovereign bonds trading, by credit trading and by asset-backed trading.
Strategy implementation
Yield curve trades. The workhorse is the calendar spread, which takes offsetting positions at two points on the curve where the relative mispricing is largest, for instance ahead of a flattening or a steepening. Macroeconomic forecasts form the backdrop. Both legs may come from a single issuer, in which case credit and liquidity risk largely cancel and interest rate risk is what remains, or from two issuers, normally operating in one industry, in which case credit quality, liquidity, volatility and issue-specific features are what drive the gap. Either way the manager profits as mean reversion occurs, with the longs rising and the shorts falling inside the target time frame.
Carry trades. Here the manager owns the higher yielding instrument and is short the lower yielding one, collecting positive carry while waiting for the temporary mispricing to close on both legs. The textbook version is long an illiquid off-the-run government bond against a short in the liquid on-the-run issue of matched duration. Duration and credit exposure are identical on both sides, so those risks wash out and liquidity risk is what the manager is really being paid for. In normal times the expensive on-the-run bond cheapens and the off-the-run bond richens, because a fresh auction produces a new on-the-run issue and pushes the old one into the off-the-run category.
The payoff profile of this carry trade resembles a short put option. When it works, the manager keeps the carry and adds a gain as the spread closes. When the spread widens instead, the payoff turns negative. Because mispricing of government securities is generally small, substantial leverage is applied, and once positions are that levered, even a brief adverse price move can set off margin calls that force liquidation at heavy losses. Curve trades, carry trades and relative credit trades are only the entry points, and the strategies built on top of them can be far more intricate.
A hedge fund watches government bond markets for valuation gaps between issues. The portfolio manager knows how Treasury Inflation-Protected Securities (TIPS) work: the coupon is a real yield, while realised inflation accrues into the principal and is repaid at maturity, so the holder carries no inflation risk. Since the Treasury issues both TIPS and nominal bonds at matching maturities, the two should yield the same once inflation is stripped out, and an over-the-counter inflation swap can strip it out, converting the TIPS cash flows into something that behaves like a nominal Treasury.
Once expected inflation has been allowed for, investors in normal periods still tend to favour nominal Treasuries over index-linked bonds, possibly because they doubt how faithfully inflation is measured over long horizons. The consequence is that a TIPS position packaged with its offsetting inflation swap has historically yielded roughly 25 bps to 35 bps above a nominal Treasury of the same maturity.
In November 2XXX, in the middle of severe market distress, the manager saw TIPS badly mispriced. Their inflation-adjusted yields sat well above nominal Treasuries, while the inflation swap market was pricing outright deflation as though it were already under way. He notes the following and considers whether to put on the trade.
| Leg | Fixed rate | Inflation rate | Cost |
|---|---|---|---|
| Buy 5-year TIPS | Receive 3.74% | Receive inflation | −1,000,000 |
| Short 5-year Treasuries | Pay 2.56% | not applicable | +1,000,000 |
| Inflation swap, receiving the fixed leg and paying the inflation index | Receive 1.36% | Pay inflation | 0 |
| Net of three trades | Receive 2.54% | not applicable | 0 |
The situation arose during extreme market stress, and such periods do occasionally produce near risk-free arbitrage of this size, typically driven by fear of deflation that pushes straight Treasuries into high demand for flight-to-quality reasons. The remaining risks are operational rather than market risks.
Selling the rich nominal bonds short while buying the cheap TIPS requires the fund to be an approved counterparty in the interbank repo market so that it can borrow the nominal bonds. It also needs bank credit approval to access the inflation swap market for the yield enhancement and the inflation hedge. Unfortunately, during extreme distress, credit lines to hedge funds shrink or are withdrawn rather than expanding. There is also the risk of losing the borrow on the short Treasuries, meaning the lender demands them back, which makes the trade hard to maintain.
Assuming those hurdles are cleared, speed matters, because the 2.54% is unlikely to survive long. Dislocations of that size close quickly once conditions normalise.
Convertible bonds are hybrid securities. The cleanest way to see one is as a plain bond bundled with a purchased call on the equity, where the exercise price is the strike multiplied by the conversion ratio. Three definitions do most of the work:
- The conversion ratio tells you how many shares one bond converts into.
- The conversion value is that ratio multiplied by the prevailing share price.
- The conversion price is the prevailing bond price divided by the same ratio.
Where conversion value sits well under the price of the bond, which is the same statement as the share price sitting well under the conversion price, the option is out of the money and the security trades like ordinary debt. Where conversion value sits well above the bond price, the option is in the money and the security starts tracking the shares.
Investment characteristics
Convertibles are complex and generally not well understood. Several inputs move their prices at once: the level of interest rates, the credit spread of the issuer, the coupon and redemption cash flows of the bond itself, and the value of the embedded option, which in turn depends on dividends, on the path of the share price and on how volatile the equity is. Issuance is sporadic and small relative to straight debt, which leaves most of these bonds thinly traded. Most are non-rated and carry fewer covenants than straight bonds.
That combination produces the structural opportunity. Because the equity option is buried inside a complex security that rarely trades, its implied volatility tends to be marked well below the volatility the underlying share actually realises. Supply drives the cycle too: the heavier the new issuance the market has to digest, the cheaper the paper gets and the better the arbitrage looks.
The central problem for the arbitrageur is that reaching the cheap embedded optionality means accepting or hedging away everything else in the security: interest rate risk, the credit risk of the issuer, and market risk, meaning the risk that the stock price falls and renders the embedded call less valuable. Each of these has a hedge available. Rates can be hedged with interest rate derivatives, issuer credit with a default swap, and equity exposure by shorting a delta-adjusted quantity of the shares, or by buying puts instead of selling stock. Every hedging tool used, however, erodes some of the attractiveness of the position it is protecting.
Managers therefore differentiate themselves by which risks they choose to leave open. Credit-oriented convertible managers decline to hedge the credit default risk of the issuer and take the position partly as a credit view. Others hedge the credit risk but take a long-biased directional view of the stock and deliberately underhedge the equity exposure. Volatility-oriented convertible managers go the other way, selling more stock than delta requires so the book leans bearish on the shares, which sharpens the exposure to a rise in volatility.
| Dimension | Detail |
|---|---|
| Core objective | Capture implied volatility that is structurally underpriced, by running a delta hedge in the shares against the long convertible and trading the gamma as the stock moves. |
| Liquidity issues | Two sources. First, the securities are naturally less liquid because of small issue sizes and inherent complexity. Second, the availability and cost of borrowing the underlying equity for short selling. |
| Attractiveness | The best conditions are heavy new issuance, volatility at moderate levels and a market that trades freely. The worst are a credit scare combined with a general drying up of liquidity, at which point supply and demand imbalances rather than fundamentals set convertible prices. |
| Leverage | High. The trade needs several legs at once, a short sale, a credit default swap and a rate hedge among them, and the gain from delta hedging is modest, so leverage has to do the work. A typical book runs 300% long against 200% short, the shortfall on the short side reflecting the delta-adjusted equity exposure required to offset the convertible. |
| Benchmarks | Credit Suisse Convertible Arbitrage Index; CISDM Convertible Arbitrage Index; Lipper Convertible Arbitrage Index; and the FI-Convertible Arbitrage Indices published by HFRI and HFRX. |
This is one of the core strategy areas in the industry, and multi-strategy houses commonly run it in the same building as their long/short equity, merger arbitrage and distressed books.
Strategy implementation
The classic trade buys the cheap convertible and sells the expensive shares against it. How many shares to short in order to sit delta neutral is set by the delta of the convertible itself. When the conversion price is low against the current share price, so the embedded call sits deep in the money, delta approaches 1. When the conversion price is high against the share price, so the call is out of the money, delta approaches 0.
Combining a long convertible with a short equity delta exposure leaves the portfolio essentially balanced for small changes in the equity price. As the stock moves further, the delta hedge changes, because the convertible carries the natural positive convexity of positive gamma while stock gamma is always zero. The consequence is that the arbitrageur drifts synthetically longer in equity as the share rises, and synthetically shorter as it falls. That drift is then hedged at favourable prices by resizing the short: more stock sold as prices rise, stock bought back as prices fall. Large enough swings in the share price, together with disciplined rebalancing and everything else held equal, make the strategy profitable. Put precisely: the arbitrageur gains whenever the volatility the equity actually realises exceeds the implied volatility paid for inside the convertible, after hedging costs.
Three circumstances create trouble.
- The borrow. Shares must be located and borrowed, and the owner may want them back at an inopportune moment, such as during a price run-up or whenever supply is low and demand is high. A short squeeze can produce substantial losses and a suddenly unbalanced exposure. Fixing the borrow for a defined term reduces the danger and adds to the cost.
- Credit. The bond carries issuer credit risk, so any move in spreads, in either direction, throws the value of the convertible out of line with the value of the equity hedge, and the manager may or may not have hedged that separately.
- Time decay. The embedded call decays, and that decay costs money whenever realised equity volatility falls away or implied volatility compresses across the market as a whole.
Extreme market volatility is not the friend of this strategy that it might appear to be. Extreme volatility usually implies heightened credit risk, and because convertibles are naturally less liquid, convertible managers generally do not fare well in such periods. Worse, hedge funds are now the effective market makers in convertibles, and a crisis is exactly when their own investors ask for money back, which sharpens the left-tail risk in the strategy just when it hurts most.
A convertible arbitrage fund based in Dubai is examining a position in QXR Corporation, using the euro-denominated convertible and the underlying shares.
| Item | Value |
|---|---|
| Price (% of par) | 120 |
| Coupon (%) | 5.0 |
| Remaining maturity (years) | 1.0 |
| Conversion ratio | 50 |
| S&P Rating | BBB |
| Metric | QXR Inc. | Industry average |
|---|---|---|
| Price (per share) | 30 | |
| P/E (x) | 30 | 20 |
| P/BV (x) | 2.25 | 1.5 |
| P/CF (x) | 15 | 10 |
Additional information: carrying the short for a year costs €2 per share, payable to whoever lends the stock, and the shares pay a €1 dividend.
Now the equity valuation. Every metric is 50% above the industry average: P/E 30 against 20, P/BV 2.25 against 1.5, and P/CF 15 against 10, since 30 ÷ 20 = 2.25 ÷ 1.5 = 15 ÷ 10 = 1.50.
In relative terms the shares are expensive and the convertible is cheap, so the position to put on is long the bonds and short the stock.
| QXR share price | Long stock via convertible bond | Short stock | Total profit |
|---|---|---|---|
| 24 | 0 | 6 | 6 |
| 36 | 12 | −6 | 6 |
| 30 | 6 | 0 | 6 |
Outflows: the €2 per share borrowing cost plus the €1 dividend payable to the lender, which is €2 + €1 = €3 per share.
Inflow: the convertible pays a 5% coupon, which on €1,000 par is €50, and with 50 shares per bond that equals €50 ÷ 50 = €1 per share equivalent.
Net drag: €3 − €1 = €2 per share, so every outcome in the table falls by €2. The total profit becomes €6 − €2 = €4 per QXR share.
Opportunistic managers hunt returns anywhere they can find them, across many markets, many instruments and many techniques. Their unit of analysis is not the individual security but the asset class, the sector, the region, the macro theme and the relationship between one asset class and another, worldwide. Returns are consequently driven by big themes, by global linkages, and by where markets sit in a trend or a cycle.
These funds resist neat categorisation, but they can generally be divided on three dimensions: the type of analysis driving the strategy, which is technical or fundamental; how decisions are implemented, which is discretionary or systematic; and the instruments and markets traded.
- Fundamental strategies take economic data as their raw input and concentrate on what a security, a sector, a market or a relationship between markets ought to be worth.
- Technical analysis uses statistical methods to predict relative price movements from past price trends.
- Discretionary implementation relies on manager skill to interpret new information and make decisions, and it can be subject to behavioural biases such as overconfidence and loss aversion.
- Systematic implementation follows fixed rules, coded into algorithms that trade with minimal human involvement, but it can struggle with new and complex situations that have no historical precedent.
Scale has created a new problem for the systematic side. As systematic trend-following funds have grown, so has negative execution slippage caused by multiple trend-following models reversing at once, producing a herding effect. That can temporarily overwhelm normal market liquidity and distort fundamental pricing, giving the trend-following overshoots that momentum-signal triggers create.
What global macro managers do
Global macro managers trade relationships between markets worldwide, across almost every instrument available. The derivative toolkit alone spans futures, forwards, swaps and options written on metals both precious and base, on other commodities, on currencies, and on bond and equity indexes. Cash instruments sit alongside them: government paper, corporate credit and single stocks. Because the possibilities are so wide, managers tend to concentrate on themes, for example trading undervalued emerging market currencies against an overvalued US dollar using OTC currency swaps; on regions, for example trading stock index futures on the Italian FTSE MIB against the German DAX to capture differences in eurozone equity valuations; or on styles, for example systematic against discretionary spread trading in energy futures. The views behind these trades concern how healthy each economy is, what its central bank is likely to do, how the yield curves relate to one another, where inflation and relative purchasing power are heading, and how capital and trade flows are moving. Most of that gets expressed as a currency position or a position on a rate curve.
Macro managers are anticipatory and sometimes contrarian. Some try to extract carry gains or ride momentum, but most tend to be early and then benefit when rationality returns to relative pricing. That makes an allocation to global macro particularly useful when a sudden reversal in markets is feared. The canonical illustration is the subprime mortgage crisis. A number of these managers saw the American housing problem forming as early as 2006 and bought credit default swap protection, sometimes on the mortgage bonds themselves, sometimes on tranches of the structures built from them, and sometimes just on whichever broad credit index looked most exposed. They had to wait until 2007 and 2008 for those positions to pay off, but some performed spectacularly as conditions turned into the global financial crisis, and holding such a manager inside a larger portfolio proved very valuable.
One caution: because global macro managers trade a wide variety of instruments and markets by different methods, they are a heterogeneous group. They are therefore not as consistently dependable a source of short alpha as pure systematic trend-following managed futures funds, which attempt to capture any significant market trend. Their compensating advantage is that they are more anticipatory.
Investment characteristics
Global macro managers draw on fundamental and technical analysis alike when valuing a market, and they implement both discretionarily and systematically. The view taken can be directional, for example buying the bonds of banks expected to benefit from a normalisation of US interest rates, or thematic, for example buying the winners and shorting the losers from Brexit. Because of the heterogeneity, an allocator needs extra due diligence and close attention to the current portfolio in order to anticipate correctly what factor risks a given manager will deliver.
Leverage is a common feature, usually obtained through derivatives. Posting 15% to 25% of equity as margin against futures or forward positions gives control of a face amount running to 6 or 7 times the assets of the fund. That embedded leverage gives the manager ease and flexibility in both relative value and directional positioning.
Returns come from spotting a trend in a global market and being positioned for it, which is why a quiet, mean-reverting market is the natural enemy of the strategy. Sharp falls in equities, a change of regime in interest rates, a currency devaluation, a jump in volatility, or a geopolitical shock from a trade war or an act of terrorism are the risks the strategy carries and, at the same time, the events it exists to exploit. Positions may fail to react as expected because of unforeseen contrary factors or because the anticipated global risk simply does not materialise. Macro managers therefore produce lumpier and more uneven return streams than most other hedge fund strategies, with generally higher volatility.
The environment matters enormously. Quantitative easing after the global financial crisis kept conditions benign for most of the following decade, and benign conditions are close to the worst possible setting for this strategy. Equity and fixed-income markets trended higher, but volatility across those and many other markets, including currencies and commodities, was relatively low, and in some cases central bankers intervened to curtail undesirable outcomes and thereby prevented macro trends from fully forming. Because intervention moderates both the trendiness and the volatility that are the lifeblood of the strategy, some allocators began avoiding it altogether. That may be shortsighted, since opportunistic strategies can be very useful across a full market cycle for diversification and alpha, and the sharp reversal of monetary policy after the Covid-19 pandemic demonstrated the opportunity set returning.
Strategy implementation
The process runs from the top down, using macroeconomic and fundamental models to form a view on where an asset or an asset class is heading, or on what it is worth relative to something else. The instruments used to express it can be anything: single securities, baskets, index futures, currency futures and forwards, metals contracts, agricultural contracts, cash bonds or bond futures, and options written on any of the above.
A directional bet is driven by fundamental data on one market or asset, judged against its own history and against the macro trend expected ahead, to decide whether it is cheap or expensive. Relative value positioning asks a different question: which of two assets is cheap against the other, given historical and expected macro conditions. Take the ASEAN block countries, meaning Indonesia, Malaysia, the Philippines, Singapore and Thailand. Suppose their currencies are weakening against the dollar. A directional model may read that as making the shares of the big exporters cheap, and recommend buying them. Dig further and the public bonds of those same exporters may look cheaper still against the shares, in which case the trade becomes long the bonds and short the equity. Such a gap is likely for a simple reason: equity prices absorb a currency move almost immediately, while bond prices take longer to catch up.
Success needs two things at once: a correct fundamental view of the market, and the proper method and timing to express it tactically. Managers who repeatedly enter too early, exit too late, or choose an inappropriate implementation will face redemptions. Given the natural leverage available, managers can be tempted to carry too many positions at once, but the diversification benefit of doing so is typically smaller than in more idiosyncratic long/short equity strategies. The reason is the nature of risk-on and risk-off conditions, often driven by central bank policy, which move a variety of asset classes in a correlated way.
Consider the following hypothetical macroeconomic scenario. A group of emerging economies has been expanding fast, arguably too fast, and has run up budget and trade deficits of a size not seen before, because growing populations want more public services and more imported goods. Their central banks have been defending their currencies in the market for a long time now, and voters in many of these countries are turning in large numbers to candidates offering punitive business taxation and expansive welfare programmes. These trends are expected to continue.
A global macro portfolio manager works through the published accounts of the central bank of one leading emerging economy and concludes that its foreign exchange reserves may soon be exhausted, at which point the currency defence has to stop.
Buying put options is the sensible route, because if the expectations fail to materialise the loss is capped at the premiums paid. The puts should cover a variety of emerging market currencies, emerging market government bond futures, and emerging market equity indexes. Sizing the moneyness to conviction matters: buy puts already in the money for the high conviction trades, and cheaper out-of-the-money puts where confidence is lower.
To capture a possible flight to safety on the other side, the manager should also own calls on the reserve currencies of the developed world, and calls on bond futures for the highest rated developed market issuers.
The academic case for managed futures was first made by John Lintner, in a 1983 paper that is still cited. The strategy trades futures and options on futures above all, with forwards and swaps used occasionally, and the underlyings are typically equity and bond indexes together with commodities and currencies. As futures markets have grown in open interest and liquidity across different countries, some managers have added sector and industry index futures and more exotic contracts, including futures on weather variables such as temperature and rainfall, and derivatives on carbon emissions.
Investment characteristics
Because the returns are largely uncorrelated with stocks and bonds, adding managed futures to a traditional portfolio improves the risk-adjusted profile and pushes the efficient frontier outward in mean-variance terms. The value added has typically shown up during market stress. Through 2007 to 2009, these managers were short equity index futures and long bond futures at precisely the point when equities were selling off and bonds were rallying. That pattern is what positive skewness looks like in practice, and it is useful for offsetting strategies whose skew runs the other way.
The return profile is nonetheless very cyclical. From 2011 to 2018 currency and bond markets stopped trending, volatility drained out of many markets, and the acute stress episodes that this strategy feeds on simply stopped appearing. Outside equities in a handful of developed countries, most markets either went sideways or reverted, and performance suffered accordingly. There is also a subtler point: a trend in equities is, by construction, the least diversifying trend a portfolio of stocks and bonds could be given.
The correlation benefit has also changed now that sovereign yields have run down towards zero. Trend following fixed-income markets higher is unlikely to be as repeatable going forward. If managers begin trend following fixed-income markets lower, as developed market interest rates normalise, positive returns may still be realised, but with a very different and less valuable correlation behaviour against equity markets. Given the upward sloping shape of most global yield curves, there is also less natural fixed-income carry contribution from trend following those markets to the downside, meaning towards higher rates and lower prices.
Operationally, three features define the strategy: deep liquidity, activity spanning many asset classes, and the freedom to switch between long and short at will. Futures markets are among the most actively traded in the world. To take one measure, the daily dollar turnover in the CME E-mini equity index contract runs at 3 to 4 times that of SPY, the largest equity index fund on the planet. Futures also give highly liquid exposure to a wide range of asset classes traded across the globe 24 hours a day.
Because central clearinghouse management of margin and risk means futures contracts require relatively little collateral, long and short positions can be taken with higher leverage than with traditional instruments. Margin on a futures position, long or short, runs anywhere from 0.1% to 10% of the notional amount, against the 50% required on US equities. That is inherent leverage, and it is what makes managed futures managers able to be dynamic on both sides.
The financing structure differs fundamentally from a levered long-only portfolio. A long-only fund borrows cash and buys more assets with it. A futures fund owns nothing; what it holds is exposure equal to the notional value of its contracts. Most of the capital in the account, ordinarily 85% to 90% of it, sits in short-dated government paper or in whatever other liquid collateral the clearing house will accept, and the residual 10% to 15% collateralises the long and short futures positions.
Strategy implementation
Highly liquid contracts let these funds run a wide range of strategies. Nearly all of them rest on a pattern recognition trigger, driven either by momentum and trend or by a volatility signal, applied across several horizons at once, with a short-horizon mean reversion filter layered over the longer-horizon core.
A concrete illustration: a manager running a long-horizon model concludes that gold will trend lower, perhaps because the short moving average has crossed beneath the long one, and puts on a short in gold futures. Some time later a second model, running on a shorter horizon, reports that the downward momentum has faded and a bounce back towards the mean looks likely. The outputs of the two models are then blended into a single net position, with the long-horizon model carrying the greater weight and the short-horizon filter the lesser.
Carry relationships and volatility measures are frequently bolted onto the core momentum and breakout signals, and they earn their keep mainly in position sizing. Managers commonly set relative sizes by looking at two things together: how volatile each contract is, and how its returns move with the other contracts in the book. Both push the same way. A more volatile asset gets a smaller allocation, and so does an asset that moves closely with what is already held. Correlation analysis therefore becomes a second step, applied as a portfolio sizing risk constraint.
Beyond sizing, every managed futures manager needs an exit rule, and will use a price target exit, a momentum reversal exit, a time-based exit, a trailing stop-loss exit, or some combination of these. The key to running the strategy well is consistency of approach and the avoidance of overfitting when backtesting across different markets and time periods, since the goal is a model that performs in a future out-of-sample period. Trading models naturally degrade as more managers use similar signals and the opportunity diminishes, so managers constantly search for new and differentiated signals, increasingly built from nontraditional, unstructured data and other big data analysis.
Time-series against cross-sectional momentum
Away from the search for non-price signals, the most common managed futures approach is time-series momentum trend following, abbreviated TSM. Momentum strategies are driven by the past returns of the individual assets: managers go long assets rising in price and short assets falling in price. What matters in TSM is the absolute sign of the trend, so the book can sit net long or net short according to which way each asset is currently moving. It works best when the past returns of an asset are a good predictor of its own future returns.
A second, less common approach is cross-sectional momentum, abbreviated CSM. It ranks a group of assets, normally within one asset class, and buys the strongest while shorting the weakest. CSM strategies generally result in a net zero or market-neutral position. They work well when how a market has performed against its peers predicts how it will perform next. The practical limit is availability: at the asset class level there may simply not be enough listed contracts to form a proper cross-section.
| Dimension | Detail |
|---|---|
| Liquidity and crowding | Both are highly liquid, but managed futures shows some crowding and execution slippage as assets under management have grown rapidly. Global macro, being more heterogeneous in approach, faces less significant execution crowding. |
| Implementation | Managed futures managers tend to be more systematic. Global macro managers lean towards discretion in how they apply their models and their tools. |
| Stress behaviour | When markets are under stress, managed futures returns tend to show a fat right tail, and that is precisely what makes them diversifying. Global macro has delivered comparable protection in the same episodes, though the outcomes vary far more from manager to manager. |
| Volatility | Despite the positive skewness, both are somewhat cyclical and sit at the more volatile end of the hedge fund spectrum, with volatility positively related to the time horizon of the strategy. Being early, and staying early, is a recurring failing on the macro side. |
| Leverage | High, and embedded in the contracts themselves. Initial margin of only 10% to 20% of equity supports notional exposure of 6 to 7 times fund assets, with the margin required on any single contract set by how volatile its underlying is. Options, used heavily by many macro managers, layer on more leverage and add positive convexity. |
| Managed futures benchmarks | Credit Suisse Managed Futures Index; Lipper Managed Futures Index; CISDM CTA Equal-Weighted Index; and the Macro Systematic Indices published by HFRI and HFRX. |
| Global macro benchmarks | Credit Suisse Global Macro Index; Lipper Global Macro Index; CISDM Hedge Fund Global Macro Index; and the Macro Discretionary Indices published by HFRI and HFRX. |
An institutional investor is considering an allocation to a managed futures strategy focused on medium-term momentum trading in precious metals. Two funds are under evaluation, and both trade gold, silver, platinum and palladium futures. One runs a cross-sectional momentum strategy and the other a time-series momentum strategy. Both use trailing 6-month returns to generate buy and sell signals, and both volatility-weight their futures positions so that each has equal impact on the overall portfolio.
The TSM fund. Every day the manager buys whichever metals show a positive trailing 6-month return and shorts whichever show a negative one. The bet is that contracts with positive returns will continue to rise in absolute value and those with negative returns will continue to fall, generating an expected profit on both sides. The consequence is that the fund could end up long all four contracts, or short all four, at the same time.
Comparison. In ordinary conditions CSM nets out to no market exposure at all, because two longs sit against two shorts. TSM has no such property: whether it is net long or net short depends purely on how many of the metals happen to be trending up and how many down. The TSM fund is therefore likely to be more volatile than the CSM fund, and much more exposed to any period in which precious metals move strongly in one direction.
Specialist strategies require highly specialised skill sets for trading in niche markets. The two covered here are volatility trading and reinsurance or life settlements.
Volatility trading
Volatility has become an asset class in its own right over the past several decades, and niche managers specialise in relative volatility strategies across geographies and asset classes. The reason those relative opportunities exist is structural. Asian markets produce a large volume of structured products carrying cheap embedded options, and investment banks routinely strip those options out and sell them on, which keeps volatility priced low there. In North America and Europe the habit runs the other way, with investors buying options that are out of the money as protection, and implied volatility priced higher as a result. Relative value volatility arbitrage therefore means finding volatility where it is cheap, selling it where it is dear, and neutralising the time decay that any options book carries. What the manager can also harvest depends on the instruments chosen. Where puts and calls or variance swaps are used, actively adjusting the gamma as markets move adds a second source of value.
Two forms of relative value volatility trading
Time-zone arbitrage captures the volatility spread on a single underlying fungible global asset traded in different sessions. Yen options are the classic case: for two decades from the early 1980s, implied volatility on them was persistently marked lower in the Asian sessions than on the same options quoted in London, New York or Chicago, which is to say the IMM futures market.
Cross-asset volatility trading captures the spread between different underlyings. A manager may find Nikkei 225 volatility available in Asia below the level at which volatility on the US large-cap index is trading in New York, notwithstanding that the Japanese index realises more volatility than the American one. This form often involves idiosyncratic, macro-oriented risks.
A simpler version pits outright buyers of volatility against the traders who habitually sell it. The correlation between equity volatility and equity returns runs at roughly minus 80%, which is another way of saying that volatility rises when markets fall, with the options pricing skew reflecting that tendency. Long volatility is therefore a useful diversifier for long equity investments, at the cost of the premium the buyer pays. Selling volatility earns a volatility risk premium, which is compensation for providing crisis insurance to holders of equities and other securities.
Instruments and their trade-offs
The deepest volatility market in the US is the short-dated VIX futures contract, and the VIX itself measures 30-day implied volatility on options on the large-cap index, as computed by the Chicago Board Options Exchange. Volatility is non-constant, and high levels are difficult to sustain, because a market that has jumped will usually settle down again, which leaves VIX futures prone to mean reversion. Add to that the tendency of a VIX future to roll down an upward sloping implied volatility curve as it approaches expiry, and many practitioners end up preferring simple exchange-traded options, over-the-counter options, variance swaps and volatility swaps. Mean reversion still affects these products, but less explicitly than it affects the futures.
- Exchange-traded options. Maturities typically extend to no more than about two years. Longer-dated options carry more absolute exposure to volatility levels, meaning vega, while shorter-dated options carry more delta sensitivity to price changes, meaning gamma. Three things need watching. The volatility term structure normally slopes up, but a crisis can invert it. The smile across strikes means options away from the money usually price at higher implied volatility than those at the money. The skew means out-of-the-money puts may be marked above out-of-the-money calls. Traders capture the relative timing and strike pricing opportunities with structures such as straddles, calendar spreads and bull or bear spreads. Expressing an outright long view means buying options and then delta hedging the gamma that comes with them. Managing that embedded gamma matters: a manager can hold a correct view on a volatility expansion and still fail to capture the gain in a spike by managing gamma poorly. Some managers instead use options to express an intermediate-term, directional insurance view on both price and volatility and do not delta hedge actively at all.
- OTC options. Tenor and strike prices can be customised and expiry can extend beyond what the exchange offers, at the cost of counterparty credit risk and added illiquidity risk.
- VIX Index futures and options on them. These express a pure volatility view without the constant delta hedging that isolating volatility from an equity put or call requires. The offsetting problems are the strong mean reversion in volatility pricing and the abundant supply of traders looking to sell volatility in order to capture the volatility premium and the roll down. Roll down is a consequence of the upward slope in the volatility term structure: as time passes, an option decays faster than it otherwise would. Theta on a long option is negative in every case, and where the shorter maturity carries lower implied volatility, the passage of time speeds that decay up.
- Volatility swaps and variance swaps. Each is a forward contract written on what the underlying actually realises: volatility in the first case, variance, which is volatility squared, in the second. The strike is normally set at inception so that neither side pays anything, and that level is then described as fair volatility or fair variance. When the contract matures, whoever receives the floating leg settles the gap between realised volatility, or variance, and the strike, scaled by a notional that never changes hands. The exposure is to volatility alone, which is not true of a listed option, where the volatility exposure is entangled with the price of the underlying and has to be separated out by delta hedging. Uses are threefold: expressing a view on future realised volatility, trading realised against implied, and hedging volatility risk carried elsewhere in the book. Longer-dated and tailored maturities and strikes are also available.
Running a long volatility strategy with OTC swaps, options or swaptions means finding undervalued instruments, and that means staying in constant dialogue with options dealers across the world and across asset classes. A position, once established, is either exercised, sold into a volatility event, delta hedged along the way if it is a long options position, or simply left to expire. A long volatility strategy is a convex strategy, because volatility pricing moves asymmetrically and is skewed to the right, and because strike prices can be set so that the option cost is small while the potential payoff is many multiples of the premium.
The challenges are practical. None of these OTC instruments trades on an exchange, so every contract has to be negotiated. They are typically structured under ISDA documentation and subject to bilateral margin agreements negotiated within an ISDA Credit Support Annex, but counterparty risk and liquidity risk remain higher than for a listed instrument, both when opening a position and when closing one. Smaller hedge funds may not be able to open ISDA lines with bank counterparties at all until they pass a minimum assets under management threshold, generally $100 million. Above everything else, buying volatility gives positively convex outcomes, but it is paid for twice over, in premium and in the roll down along the volatility curve.
| Dimension | Detail |
|---|---|
| Risk profile | A long volatility position is positively convex, which is what makes it useful as a hedge. Sellers of option premium take the other side and collect a steadier return so long as conditions stay ordinary. |
| Alpha source | Trading volatility on a relative basis, between one region and another or one asset class and another, can generate genuine alpha for a portfolio. |
| Liquidity | Varies by instrument. The VIX futures and options complex is very liquid. Listed index options trade well out to roughly two years, and the further out the tenor the thinner the market. OTC contracts can be tailored to longer maturities but they are harder to trade and cannot easily be transferred between counterparties. |
| Leverage | The natural convexity of volatility instruments means outsized gains can sometimes be earned with very little up-front risk. Notional values appear nominally levered, but the asymmetry of long optionality is an attractive feature of the strategy. |
| Benchmarks | Benchmarking is genuinely hard in a niche this small. CBOE Eurekahedge publishes four series: a Tail Risk Index built from 11 managers, a Relative Value Volatility Index also built from 11, a Short Volatility Index built from 5, and a Long Volatility Index built from 15. |
Growth has been solid, equities have been climbing and rates have stayed low. Consumer borrowing, across subprime mortgages, credit cards and personal loans, has been expanding quickly and now exceeds anything seen historically. In mid-January a long volatility manager bought a basket of index puts struck 10% out of the money with one year to run, paying $100 per contract, which corresponded to an implied volatility of 12%.
By mid-April consumer borrowing still looks dangerous and the market still looks ripe for a correction, yet the index has added a further 20% on top of where it stood in January and volatility is low, so the same options are now marked at only $50 per contract.
By mid-July a crisis few participants saw coming has arrived. Volatility has jumped, and the index has given up 25% from its April level, leaving it 10% under where it started in January. The puts are now marked at an implied volatility of 30%.
Despite an initial 50% mark-to-market loss by mid-April, when the contracts fell from $100 to $50, the position is likely to show substantial unrealised profits by mid-July. Six months have passed, so other things equal there is some time decay loss on the long puts. Working strongly the other way, the options have gone from 10% out of the money to at the money, and implied volatility has increased 2.5 times, since 30% ÷ 12% = 2.5. For a put with six months left and a strike at the current index level, that repricing lifts the value substantially, because the sensitivity of an option to implied volatility peaks when the option sits at the money. By mid-July the position should therefore show a large unrealised profit.
Reinsurance and life settlements
Hedge funds have also entered insurance, reinsurance, life settlements and catastrophe reinsurance. Under an insurance contract the insurer pays out to the policyholder, or to whoever is named, if a defined insured event happens, and receives periodic premiums in return. Motor and household cover, life cover, and catastrophe cover against floods, hurricanes or earthquakes are the familiar categories. Contracts in this market are drafted individually and in detail, far less standardised than other financial contracts, so they are generally illiquid and difficult to buy or sell after initiation.
The primary market has existed for centuries, but the secondary market has grown substantially in recent decades. Individuals holding whole or universal life policies they no longer want can surrender them to the original issuer, but increasingly they find that third-party brokers will pay cash values significantly above the surrender value. Those brokers then offer the policies as investments to hedge funds. A fund that can form a differentiated view of individual or group life expectancy, and is right, earns attractive uncorrelated returns. Catastrophe reinsurance has drawn hedge fund capital for the same reason. These newer secondary markets have made insurance contracts easier to trade and worth more to hold. The insurer gets risk transfer, capital management and solvency management out of the reinsurance market; the hedge fund gets uncorrelated alpha.
How a life settlement strategy works
Life insurance protects the dependants of the policyholder in the event of death. The secondary market involves selling that contract to a third party, which is the life settlement. Pricing one calls for close biometric work on the individual insured and a command of actuarial technique, so the manager either builds that expertise in house or buys it from an actuarial adviser who can be trusted.
The strategy involves analysing pools of life insurance contracts offered for sale, typically by a third-party broker who bought them from the original policyholders. The manager looks for three characteristics together:
- the amount the insurer would pay on surrender is low relative to what the policy is worth;
- the premiums required to keep it in force are also low; and
- the odds are good that death will come sooner than standard actuarial tables predict.
Having identified a suitable policy, or far more commonly a pool of them, the manager pays a lump sum through the broker to the policyholders, who transfer the right to the eventual policy benefit. From then on the fund pays the premiums and waits to collect the death benefit. The economics work when the discounted value of the benefit exceeds the discounted value of the premiums paid in the meantime. The two key inputs are the expected policy cash flows, meaning the up-front lump sum, the ongoing premiums and the eventual death benefit, and the time to mortality. Neither has anything to do with the behaviour of financial markets, which is precisely why this strategy area is uncorrelated with other hedge fund strategies.
Catastrophe reinsurance
Catastrophe insurance protects the policyholder against floods, hurricanes and earthquakes, events that are idiosyncratic and have nothing to do with how financial markets behave. An insurer will cede part of that exposure, usually everything above a stated attachment point and up to a capped amount, to a reinsurer, and the reinsurer in turn looks to hedge funds for capital. Three conditions have to hold for the fund to earn an attractive uncorrelated return: enough diversity of policies by geography and by type of cover; an adequate cushion of loan loss reserves provided by the insurer; and premium income sufficient to pay for the risk.
Valuation may require the manager to consider global weather patterns and to forecast with sophisticated prediction models using a wide range of geophysical inputs. More commonly, though, the manager assumes a typical weather pattern, derives worst-case losses under each reinsurance structure, and sets those against the premium income on offer. When a catastrophe does strike, enough geographic spread should stop any single event doing real damage, and the fund then benefits as premiums rise, which they always do after a loss year. Trading venues for catastrophe bonds and catastrophe risk futures are still developing. Either instrument allows an investor to go long the risk, or to lay off catastrophe exposure already sitting in a book of insurance contracts. Both issuance and performance follow the calendar, with much of the supply of catastrophe bonds arriving ahead of the North American hurricane season in May and June, and those bonds do well when the season turns out to be quiet.
A Singapore specialty fund concentrates on life settlements. Its staff includes biometric and actuarial specialists whose job is to value blocks of life policies put up for sale by insurance brokers. Those brokers acquire the policies from individuals who have no further need for the cover and who would rather take cash now than accept what their insurer offers on surrender.
The manager knows that one broker has been buying heavily from a single insurer, a company with a reputation among practitioners for lax underwriting, charging thin premiums to insure a large number of people in poor health, and for settling surrenders on ungenerous terms. That broker is now marketing a pool weighted heavily towards policies from that insurer, and bidders will receive data covering a random sample of the policies in the pool.
The analysts report that a large share of the insured have since been diagnosed with early-onset Alzheimer’s disease or other seriously debilitating conditions, which is why they needed cash quickly to pay for assisted living and specialist care. Their further finding is that a diagnosis of early-onset Alzheimer’s disease cuts average life expectancy by around 10 years relative to someone without it.
Ongoing premium payments the fund would have to make to the originating insurance companies to keep the policies active. Two factors shape this forecast: the low premiums that insurer is known to charge, and the reduced life expectancy across much of the pool, which shortens the period over which premiums must be paid.
Timing of the death benefits the fund will eventually receive. How widespread early-onset Alzheimer’s disease and the other serious conditions are within the pool, and by how much they shorten expected lifespans, is what drives this estimate.
A discount rate has then to be selected that properly compensates for the risks being taken, and the present value follows from it. What the fund bids depends on how that present value compares with any reserve price the broker has set, and on how much competition is expected in the auction. If the fund buys the pool and the biometric, actuarial and financial forecasts are met or exceeded, the investment should yield attractive returns that are uncorrelated to other financial markets.
In practice most investors hold a range of hedge fund strategies rather than one. Three approaches combine them into a portfolio:
- assembling the mix yourself, placing money directly with a set of individual funds that each run a different strategy;
- hiring a fund-of-funds manager, who takes the allocation decision and spreads it across a roster of individual managers; and
- buying a single multi-strategy fund, inside which several in-house teams each run a different strategy.
Nothing about the first two approaches is peculiar to a mix of strategies. Either can be used to buy a single strategy just as easily.
Funds-of-funds
A fund-of-funds manager pools investor capital and spreads it over separate individual funds whose strategies are, ideally, only loosely correlated with one another. Five jobs come with the role: diversifying across strategies; making occasional tactical, sector-based reallocation decisions; selecting the underlying managers and conducting due diligence on them; running the portfolio and monitoring risk on an ongoing basis; and producing consolidated reporting. Other benefits follow from scale and relationships: entry into funds that have closed, cheaper monitoring per dollar invested, currency hedging, leverage arranged and managed at the level of the whole portfolio, and redemption terms better than a single manager would grant.
The disadvantages are equally concrete: a double layer of fees, a lack of transparency into the processes and returns of the individual managers, the inability to net performance fees across managers, and an additional principal–agent relationship. Take the fees. Underlying managers charge on historical norms of 1% to 2% and 10% to 20%, and on top of that the fund-of-funds itself historically levied 1% and 10% on the performance of the whole portfolio. Weak performance has made those terms negotiable, and a 50 bps management fee with a 5% incentive fee, or a single flat 1% charge and nothing else, is now common.
Liquidity management deserves attention because it can produce squeezes. Most funds-of-funds require an initial one-year lock-up and then offer monthly or quarterly liquidity, typically with a 30-day to 60-day redemption notice. The underlying investments may not fit those terms at all: some underlying managers, and newer investments in particular, have their own lock-ups or redemption gates. The manager therefore has to stagger commitments so that the aggregate liquidity profile stays conservative, while forming a view on how likely redemptions are and how large they might be. A reserve line of credit is often arranged as a liquidity backstop against the mismatch between cash available from underlying investments and cash needed to meet redemptions.
Why investors use them
For a smaller institution, or a wealthy family, the fund-of-funds is above all a way in. Minimum tickets at individual hedge funds run from $500,000 to $5,000,000, and $1,000,000 is the usual figure. Building a reasonably diversified portfolio of 15 to 20 managers would therefore require $15 million to $20 million, which is a large amount even for wealthy families and small institutions, and selecting those 15 to 20 managers would demand time and resources most such investors lack. There is also the tax reporting burden attached to each separate hedge fund investment. Set against that, a single fund-of-funds ticket of $100,000 buys the same diversified roster of managers. Scale and relationships also open doors to funds that no longer accept new money.
The appeal is not limited to smaller investors. Endowments, foundations and pension plans often use funds-of-funds as their preferred first path into the hedge fund space, because the manager supplies more than manager research: allocation expertise at the strategic, tactical and style level comes with it. The strategic allocation is the long-run split across hedge fund styles. A typical one might read 20% long/short equity, 30% event-driven, 30% relative value and 20% global macro. Tactical allocation means periodically overweighting and underweighting styles across different market environments according to conviction, and total capital or total risk can also be dialled up and down as the opportunity set changes.
Leverage and negotiating power add two further advantages. Commercial banks lend against the portfolio through their prime brokerage arms, secured on the fund assets they already hold in custody. Redemptions from hedge funds are often paid out slowly, and it is normal for 10% of the amount to be withheld until the audit is complete, so borrowed money bridges the gap and puts capital back to work for the investors who remain. Separately, by pooling smaller investors into a single larger commitment, a large fund-of-funds may extract cheaper fees, better liquidity terms, rights to add capital later, or extra transparency, and may secure a commitment to receive the best terms offered to any future investor. These are valuable concessions a smaller investor could not obtain directly, and some funds-of-funds argue that they are worth more than the extra fee layer costs.
Taken together, a portfolio assembled from strategies that behave differently should show broader diversification, fewer extreme exposures, lower realised volatility, and less exposure to the failure of any one manager than a set of direct holdings would, alongside economies of scale, manager access, research expertise, liquidity efficiencies, portfolio leverage opportunities and negotiated concessions.
Implementation of a fund-of-funds portfolio
Implementation is a multi-step process running over several months. The first task is simply meeting managers, through commercial databases and at the capital introduction events prime brokers host, where funds pitch their opportunity set and their credentials to prospective allocators. The strategic allocation across strategy groupings is then decided.
Formal manager selection follows, using quantitative and qualitative methods and both top-down and bottom-up approaches. For each strategy grouping the universe is screened down to a peer group of candidates. Direct interviews follow, alongside the paperwork: pitchbooks, the standard industry due diligence questionnaire published by the Alternative Investment Management Association, recent quarterly letters and risk reports, and past audits. Managers typically meet each candidate on several occasions, including at least one onsite visit, and the focus becomes increasingly granular, moving from investment philosophy and portfolio construction to personnel, operational and risk management processes.
Once a fund becomes a true candidate, the Offering Memorandum and the Limited Partnership Agreement are fully reviewed, service providers such as the auditor, legal adviser, custodian bank and prime broker are verified, and background checks and references obtained. At larger firms a dedicated team of specialists performs this operational due diligence, either validating the conclusions of the investment team or raising concerns to be addressed before allocation. At this point the fund-of-funds may seek concessions agreed in side letters, covering reduced fees, added transparency, capacity rights to build the investment in future, or improved redemption liquidity. Bargaining power here scales with the size of the cheque.
After approval and inclusion, the process moves to ongoing monitoring and review. The concerns there are performance consistency with the stated objectives and any sign of style drift, personnel changes, regulatory issues, or shifts in correlation and return behaviour relative to other managers in the portfolio and to similar peers.
Multi-strategy hedge funds
A multi-strategy fund houses several strategies inside one hedge fund vehicle. Each is run by its own team, and all of them draw on a single set of operational and risk systems.
The key advantage is speed. Capital can be moved between strategy areas far faster and more cheaply than any fund-of-funds could manage, because the manager sees every position and understands how the risks taken by one team interact with those taken by another. That allows faster reaction to real-time market events, for instance by dialling leverage up or down inside one strategy as the risk in its opportunity set changes. Teams can also focus entirely on their portfolios, because the business, operational and regulatory work is handled by administrative professionals, which is a common reason talented portfolio managers join such firms.
Fee structures can be considerably more attractive than the fee layering of a fund-of-funds, and the reason is netting risk. A fund-of-funds investor always bears netting risk: the investor pays performance fees to the winning underlying funds while absorbing the return drag from the losing ones. Even if the aggregate performance across all funds is flat or negative, incentive fees are still owed to the winners.
At a multi-strategy fund the general partner normally takes that netting risk onto their own account when the teams perform differently. Two things make this attractive to the investor. The netting risk sits with the general partner rather than the client, and the incentive fee charged at investor level is calculated on the fund result after the good and bad teams have been offset against one another. The structure can create internal discord, however. Because the general partner bears the netting risk, the overall bonus pool may shrink, and high-performing teams that do not receive their full incentive amounts become disaffected, which ultimately causes personnel losses.
Some multi-strategy firms instead operate a pass-through fee model. There may be no management fee at all. What the investor pays instead is the actual cost of running each team, salaries and team-level incentive compensation included, after which a manager-level incentive fee is charged on the result of the whole fund. Under that arrangement the investor picks up part of the netting risk, and pays for it in place of a management fee, while the general partner keeps the rest of it, since the fund-level incentive fee may fall short of what the individual teams are contractually owed.
The dominant risk in these vehicles is leverage. Positions are watched closely internally, and because the infrastructure costs so much, the fee model leans towards performance rather than management fees. Leverage sitting on top of disciplined risk management is normally harmless; in a stress period, a miscalibration of that risk management is anything but, and the industry remembers the blow-ups, Ritchie Capital in 2005 among them, precisely because they came from the left tail. Operational risk is also poorly diversified by definition, since all operational processes run inside the same fund structure. Finally, the breadth of strategies on offer is capped by the talent available in house, and firms of this kind frequently recruit people who think alike.
On implementation, multi-strategy funds engage in both strategic and tactical allocation just as a fund-of-funds does. Because the teams manage each strategy directly under one roof, they usually know sooner when a tactical shift is warranted, and they can execute it faster. The mirror-image weakness is that they may be less willing to exit strategies where the core expertise sits in house. Shared risk systems have a further advantage: interactions and correlations between the strategies run by different teams become visible, which they rarely are inside a fund-of-funds structure.
| Dimension | Detail |
|---|---|
| Return objective | Both aim at a steady, low-volatility return achieved through diversification across strategies. Multi-strategy funds have generally posted the better numbers, at the price of wider dispersion and the occasional heavy loss, usually traceable to the leverage they run. |
| Trade-offs | The multi-strategy vehicle reallocates faster and charges more sensibly, since netting is handled at strategy level, but concentrates operational risk in one manager. The fund-of-funds spreads strategies more widely at the cost of seeing less and reacting more slowly. |
| Liquidity terms | Lock-ups and redemption cycles look much the same across the two. What differs is that multi-strategy vehicles frequently add a quarterly cap on redemptions, applied at the level of the fund or of the individual investor. |
| Leverage | Leverage at a multi-strategy fund is materially higher than at a typical fund-of-funds, which keeps it modest, and that raises the odds of a blow-up when markets seize. The counterweight is that seeing the strategies clearly, and being able to move quickly, has historically left multi-strategy funds better at protecting capital. |
| Fund-of-funds benchmarks | CISDM Fund-of-Funds Multi-Strategy Index; Lipper Fund-of-Funds Index; the Fund of Funds Composite Indices from HFRI and HFRX; and, as a broad proxy for a diversified manager pool, the Credit Suisse Hedge Fund Index. |
| Multi-strategy benchmarks | CISDM Multi-Strategy Index; Lipper Multi-Strategy Index; the Multi-Strategy Indices from HFRI and HFRX; and the Multi-Strategy Hedge Fund Index. |
Since 2008 the economics of the fund-of-funds business have deteriorated, squeezed by falling fees, by investors moving towards cheap passive long-only exposure, and by the arrival of liquid alternatives aimed at retail buyers. Multi-strategy vehicles have gained ground over the same period, as institutions increasingly choose to allocate straight to a single manager and skip the extra fee layer.
A fund-of-funds charges 1% and 10%, and splits its capital evenly between two underlying managers, Pyrenees Fund (PF) and Ural Fund (UF), each of which charges 2% and 20%. Ignore fee compounding and assume all fees are paid at year-end. An incentive fee applies only to the gain remaining after management fees and expenses have been taken out.
At the underlying funds: gross 20% less the 2% management fee leaves 18%, and the incentive fee is 20% × 18% = 3.6%. So
Net return for a PF or UF investor = 20% − 2% − 3.6% = 14.4%.
At the fund-of-funds: the 14.4% arrives, the 1% management fee is deducted leaving 13.4%, and the incentive fee is 10% × 13.4% = 1.34%. So
Net return for the fund-of-funds investor = 14.4% − 1% − 1.34% = 12.06%.
Fees have absorbed (2% + 3.6% + 1% + 1.34%) ÷ 20% = 7.94% ÷ 20% = 39.7% of the total gross investment return, with the remainder going to the investor.
Net return for a UF investor = −5% − 2% − 0% = −7.0%. No incentive fee is due because there is no gain.
Gross return arriving at the fund-of-funds = (0.5 × 14.4%) + (0.5 × −7.0%) = 7.2% − 3.5% = 3.7%.
Fund-of-funds incentive fee = 10% × (3.7% − 1%) = 0.27%.
Net return for the fund-of-funds investor = 3.7% − 1% − 0.27% = 2.43%.
Now look at what happened to the money. The original gross return across the two funds was 20% × 0.50 + (−5% × 0.50) = 7.5%, and of that, fees consumed
{[0.50 × (2% + 3.6% + 2% + 0%)] + (1% + 0.27%)} ÷ 7.5% = (3.8% + 1.27%) ÷ 7.5% = 67.6%.
In the first case the investor kept 12.06% out of 20%. In the second the investor kept a meagre 2.43% out of 7.5%, because incentive fees were still paid to the winning fund while the losing fund dragged the aggregate down. That is fee netting risk, and it is the single strongest argument against the fund-of-funds structure.
Everything covered so far points to a single conclusion about risk. Long/short equity and event-driven managers carry natural equity market beta risk. Arbitrage managers are exposed to credit spread risk and to market volatility tail risk. Opportunistic managers are exposed to the trendiness, or directionality, of markets. Relative value managers do not expect trendiness at all; they are counting on mean reversion. Each strategy has its own factor exposures and its own vulnerabilities, and in many strategies those exposures arise simply from holding financial instruments whose prices respond to the factor. Long and short exposures to a given factor across different securities are not equal, which leaves a non-zero net exposure.
The tool applied here is a conditional linear factor model, used to bring those exposures into view and analyse them. Other approaches exist, but this one stands in fairly for the family of risk factor methods that practitioners actually use.
Why the model has to be conditional
A linear factor model gives insight into the intrinsic characteristics and risks of a hedge fund investment. Strategies shift with conditions, and a conditional specification accommodates that by letting the analysis be run separately for a defined market environment, which is how the analyst finds out whether a strategy carries particular risks when conditions turn abnormal. A conditional model can show that a risk exposure, to credit or to volatility, which is insignificant during calm periods, becomes significant during turbulent ones.
The importance of this is not theoretical. Industry assets compounded at better than 25% a year from 2000 through 2007, peaking above $2.6 trillion, and then shrank at a compound rate of 17% a year across the crisis years of 2007 to 2009. Global assets under management did not surpass the 2007 high until 2014. Thousands of hedge funds were shuttered as performance plunged, and a great many of those managers had been caught off guard by what their actual risk exposures turned out to be once the crisis arrived.
The model
The conditional linear factor model applied to the returns of a hedge fund strategy takes the following form, where the second row of terms exists only during the defined crisis period:
Reading the parts:
- The return on hedge fund i in period t is the dependent variable.
- Each normal-period term is the loading on one of the K risk factors for fund i in period t, measured over ordinary conditions.
- Each crisis-period term represents the incremental exposure to that risk factor during financial crisis periods, where the dummy variable equals 1 during the crisis, defined here as June 2007 to February 2009, and 0 otherwise.
- The intercept is the alpha for hedge fund i.
- The error term is a random error with zero mean and a standard deviation specific to the fund.
A factor beta answers a single question: by how much do fund returns move when that factor rises by one unit and every other factor is held still. Whatever is not explained by the risk factors comes from three places: alpha, meaning the unique investment skill of the manager; omitted factors; and random errors. Note the crucial reading rule for the crisis coefficients. They are incremental, so the total crisis exposure is the normal-period beta plus the crisis-period beta, not the crisis coefficient alone.
The six candidate factors, reduced to four
The starting point is a broad set of macro-oriented, market-based risks spanning the major asset classes: equities, bonds, currencies, commodities, credit spreads and volatility. Six factors are used at the outset, following the specification in Hasanhodzic and Lo (2007) and the way practitioners build these models.
| Factor | Label | Definition |
|---|---|---|
| Equity risk | SNP500 | The monthly total return, dividends included, on the S&P 500. |
| Interest rate risk | BOND | The monthly return on the Bloomberg Barclays index of intermediate corporate bonds rated AA. |
| Currency risk | USD | The monthly return on the US Dollar Index. |
| Commodity risk | CMDTY | The monthly total return on the GSCI, the Goldman Sachs commodity benchmark. |
| Credit risk | CREDIT | The gap between the seasoned Baa and Aaa corporate bond yields published monthly by Moody’s. |
| Volatility risk | VIX | The month-on-month change in the closing level of the CBOE Volatility Index. |
Highly correlated factors would create multi-collinearity, so a four-step stepwise regression is used to build a model less likely to contain them.
- Identify the potentially important risk factors.
- Compute the correlation of every factor with every other factor. Where the model has two states, run that calculation separately for ordinary conditions and for crisis conditions. As an illustrative threshold, treat a pair as highly correlated once the coefficient passes 60%.
- Take a highly correlated pair, A and B. Run the return series on every factor except A, then run it again on every factor except B. Compare the adjusted R-squared of the two regressions and keep whichever factor delivered the higher one.
- Repeat step three for every other highly correlated pair, eliminating the least useful factors in terms of explanatory power and thereby avoiding multi-collinearity.
Applied to the two databases used here, that procedure dropped both BOND and CMDTY from the final model, because retaining CREDIT and SNP500 produced higher adjusted R-squared values than retaining BOND and CMDTY. Four factors remain: SNP500, CREDIT, USD and VIX, each with a crisis counterpart prefixed by the letter D.
The two databases
The analysis uses Lipper TASS and Morningstar Hedge/CISDM, two of the most widely used hedge fund databases, over the period 2000 to 2016. Each splits its universe three ways: live funds, still operating and open; defunct funds, which covers those shut down, those still trading but closed to new money, and those delisted and re-registered elsewhere; and all funds, meaning the two groups combined. Five filters are applied, removing any fund that reports gross rather than net-of-fee returns, that reports in a currency other than the dollar, that reports less often than monthly, that supplies no assets under management figure or estimate, or that has fewer than 36 months of history.
TASS and CISDM contain 6,352 and 7,756 funds respectively. Of those, 82% of TASS funds and 80% of CISDM funds are defunct, leaving 18% and 20% live. Those proportions match what is known about how frequently hedge funds close and how short the average life of one is. Including defunct funds is useful to allocators, because the historical track record of a manager starting a new fund may include a defunct one, and further analysis can then establish whether the fund closed because of poor performance and excessive redemptions or, in the opposite case, because initial success brought an overabundance of inflows and created capacity issues. From a data analysis perspective, including defunct funds also corrects for the survivorship bias that would otherwise distort every conclusion drawn.
Reading the factor exposures
The table below is the interpretive key. For each factor it gives the typical market trend, the position a manager would want, and the factor exposure that follows from holding it.
| Period and factor | Typical market trend | Desired position | Desired factor exposure | Comment |
|---|---|---|---|---|
| Normal: SNP500 | Equities rising | Long | Positive | Adds risk in order to lift return |
| Normal: CREDIT | Spreads flat or narrowing | Long | Positive | Aims to add risk and increase return |
| Normal: USD | USD flat or depreciating | Short | Negative | A short dollar position adds to return |
| Normal: VIX | Volatility falling | Short | Negative | Selling volatility adds to return |
| Crisis: DSNP500 | Equities falling sharply | Short | Negative | Cuts risk |
| Crisis: DCREDIT | Spreads widening | Short | Negative | Cuts risk |
| Crisis: DUSD | USD appreciating | Long | Positive | USD is a haven in crisis periods |
| Crisis: DVIX | Volatility rising | Long | Positive | Negative correlation with equities |
Application to equity hedge fund strategies
The return characteristics below cover live funds in the equity-related categories over 2000 to 2016. Two ratios need defining first. The Sortino ratio swaps downside deviation in for standard deviation inside the Sharpe calculation, so only returns falling short of a stated threshold count as risk; set that threshold at zero and the measure is built purely from losses. Rho captures first order serial autocorrelation, which is how strongly a fund return relates to its own previous return. Because hedge funds may hold illiquid securities, which artificially smooths returns and lowers measured standard deviation, autocorrelation must be investigated alongside risk and return. A high Rho is a warning that the reported series has been smoothed, and therefore that the underlying holdings may trade rarely and be hard to sell.
| Database | Category | Sample size | Annualized mean (%) | SD of mean (%) | Sharpe ratio | Sortino ratio | Rho (%) |
|---|---|---|---|---|---|---|---|
| TASS | Long/short equity hedge | 350 | 11.30 | 22.86 | 0.62 | 1.33 | 11.0 |
| CISDM | US small cap long/short equity | 67 | 9.88 | 19.60 | 0.65 | 1.14 | 11.71 |
| CISDM | US long/short equity | 218 | 9.41 | 17.50 | 0.62 | 0.60 | 12.76 |
| CISDM | Asia/Pacific long/short equity | 31 | 8.87 | 20.27 | 0.45 | 0.73 | 16.72 |
| CISDM | Global long/short equity | 86 | 8.83 | 16.93 | 0.44 | 0.76 | 17.43 |
| TASS | Equity market neutral | 38 | 7.81 | 10.20 | 0.83 | 0.80 | 9.3 |
| CISDM | Equity market neutral | 40 | 7.48 | 8.82 | 0.79 | 0.65 | 16.29 |
| CISDM | Europe long/short equity | 47 | 7.05 | 11.59 | 0.56 | 0.69 | 13.92 |
| TASS | Dedicated short bias | 4 | 2.91 | 14.75 | 2.27 | 1.35 | 20.0 |
| CISDM | Bear market equity | 2 | 2.04 | 7.37 | 0.29 | 0.70 | 9.15 |
Read the table carefully before drawing conclusions. Long/short equity hedge in TASS has the highest mean return at 11.30% and also the highest standard deviation at 22.86%. Among categories with more than four funds, equity market neutral in TASS has the highest Sharpe ratio at 0.83. The same long/short equity hedge category has a Sortino ratio of 1.33, the highest of any category with more than four funds, with only the four-fund dedicated short bias category higher at 1.35. Global long/short equity in CISDM shows a Rho of 17.43%, the largest of the categories with more than four funds, again with only dedicated short bias higher at 20.0%.
The interpretation follows naturally. By accepting some beta and some illiquidity exposure, long/short equity managers generally outperform equity market-neutral managers in total returns delivered. Their returns are also more volatile, which is why they produce lower Sharpe ratios. Both results are what intuition would predict.
Turning from returns to factor exposures, funds running equity market-neutral strategies carry only a small equity market loading, an average beta of 0.11 that is significant at the 10% level, and sit close to neutral on the remaining factors in both states of the world. Long/short equity funds show equity loadings in ordinary periods that clear significance at the 5% level. The range runs from 0.24 at the low end, for Europe long/short equity, up to 0.58 for the US and US small cap categories alike. There are no significant incremental exposures to equity risk during crisis periods, but total crisis exposures, meaning normal plus crisis, are positive and significant for all long/short equity strategies. The US long/short equity example is the one drawn in Figure 4: total equity exposure in crisis times is 0.58 + 0.03 = 0.61.
Average exposures conceal a great deal. Looking instead at the percentage of individual funds with significant exposures at the 10% level or better, the heterogeneity becomes visible. Dedicated short-biased funds aside, the great majority of equity-related funds show a significant positive equity loading in ordinary periods: more than 30% of market-neutral funds and more than 70% of long/short equity funds. Once the crisis window opens, under 40% of long/short equity funds register any incremental equity loading at all, and among those that do the sign goes both ways. That suggests managers were able to reduce the adverse crisis effect on their returns, most likely by deleveraging, by outright selling of stock including short sales and equity index futures, or by buying index put options. It also indicates that while they did not reduce their long beta tilt by much, on average they did not make matters worse by aggressively trying to pick the bottom. All of this is consistent with an average incremental crisis equity exposure of approximately zero.
On the other factors, most long/short equity managers have no significant CREDIT exposure. Only about one-third do, mainly negative, which means an improvement in credit conditions, whether narrower spreads or upgrades, does little for them. More awkwardly, among the quarter of funds that pick up an incremental CREDIT loading in the crisis, the shift is towards positive, and a positive loading is precisely what hurts when spreads blow out and downgrades come through in a sell-off. Dollar and volatility loadings barely register in ordinary times, and hardly any funds show a significant one. Once the crisis window opens, however, whatever additional loadings do become significant are usually negative. About 40% of Europe long/short equity funds show significant negative USD exposure, which may reflect a bet that a crisis would send money into the euro or the yen rather than into the dollar. Nearly 40% of those funds also show negative added VIX exposure during crisis times, which is to say they are short volatility. Returns at some high-profile hedge funds have been damaged by exactly that, being unexpectedly short volatility during a crisis, which is why heterogeneity of factor exposures matters as much as the averages.
Bearish Asset Management runs a fund with a short tilt that is varied according to how attractive the opportunities look. A conditional risk factor model has been fitted to 10 years of monthly returns, a span that takes in more than one episode of market crisis. Shaded coefficients in the original output are significant at the 5% or 10% level.
| Coefficient | Estimate | t-statistic |
|---|---|---|
| Intercept | 0.005 | 1.10 |
| USD | 0.072 | 0.72 |
| CREDIT | −0.017 | −0.07 |
| SNP500 | −0.572 | −9.65 |
| VIX | −0.164 | −2.19 |
| DUSD | 0.456 | 1.31 |
| DCREDIT | −0.099 | −0.40 |
| DSNP500 | 0.236 | 1.74 |
| DVIX | 0.105 | 1.03 |
The negative equity risk exposure is exactly what a short-biased strategy should produce, so it needs no further explanation. The negative VIX loading is the interesting one, because it is consistent with short volatility exposure. The likely explanation is that puts are being written against part of the short book, so the fund is harvesting a volatility premium alongside its equity view.
For the crisis period, add the incremental coefficient to the normal one:
−0.572 + 0.236 = −0.336.
The exposure remains negative and significant, so the fund stayed short-biased, but it was less negatively exposed to equity risk during crisis periods than in normal times. The natural reading is that the manager was purposefully harvesting some of the short exposure into market weakness, which is to say covering shorts as prices fell.
Application to multi-manager strategies
The same apparatus applied to multi-manager funds produces a clear ranking on returns and a more troubling picture on crisis behaviour.
| Database | Category | Sample size | Annualized mean (%) | SD of mean (%) | Sharpe ratio | Sortino ratio | Rho (%) |
|---|---|---|---|---|---|---|---|
| CISDM | Multi-strategy | 111 | 8.52 | 11.01 | 0.89 | 1.32 | 20.09 |
| TASS | Multi-strategy | 100 | 7.85 | 11.51 | 0.86 | 1.00 | 22.7 |
| CISDM | Fund of funds, debt | 20 | 6.52 | 7.94 | 0.89 | 0.68 | 13.89 |
| TASS | Fund of funds | 454 | 5.73 | 10.03 | 0.38 | 0.52 | 19.9 |
| CISDM | Fund of funds, relative value | 12 | 5.31 | 8.58 | 0.70 | 1.31 | 15.86 |
| CISDM | Fund of funds, macro/systematic | 30 | 5.09 | 10.16 | 0.39 | 0.57 | 8.15 |
| CISDM | Fund of funds, equity | 104 | 4.69 | 9.15 | 0.41 | 0.44 | 12.27 |
| CISDM | Fund of funds, event | 10 | 4.59 | 4.99 | 0.75 | 0.56 | 13.76 |
| CISDM | Fund of funds, multi-strategy | 164 | 4.47 | 7.18 | 0.54 | 1.34 | 12.43 |
On these numbers the multi-strategy vehicle beats the fund-of-funds. Mean returns of 7.85% in TASS and 8.52% in CISDM lead the group, and the Sharpe and Sortino figures rank among the best as well. Rho above 20% in both databases is also the highest of the group, which points to strongly autocorrelated returns. That is reasonable, given that such a fund may be running several less liquid books at once, convertible arbitrage, fixed-income arbitrage and other relative value strategies among them. It also explains the quarterly redemption caps, imposed at investor or fund level, that these vehicles use and funds-of-funds generally do not.
On factor exposures, every fund-of-funds category except macro/systematic shows a significant positive equity loading across the full period, running from 0.14 to 0.33. The macro/systematic exception, with a coefficient of −0.02, is consistent with the earlier finding that opportunistic hedge funds tend not to be exposed to equity risk in aggregate. The multi-strategy vehicles are significantly exposed to equity too, but the sign flips between databases, −0.14 in CISDM against 0.22 in TASS, which is a blunt reminder of how much the choice of database can matter.
The critical finding concerns crisis behaviour. Taken as a group, these funds do not appear to hedge anything through diversification once a crisis begins. Had they done so, the crisis equity coefficients would come through significantly negative, and they do not. This is consistent with research findings that in the 2007 to 2009 crisis, spreading capital across hedge fund strategies failed to reduce total portfolio risk. The conclusion drawn there was that during crises simple diversification is insufficient, and that liquidity, volatility and credit risks matter more, particularly because leverage magnifies them.
Looking at individual funds rather than averages tells a partly different story. Most of these funds carry a significant positive equity loading, yet roughly 30% register incremental crisis equity loadings that go both ways. The funds with negative loadings were evidently able to shield investors from substantial market declines by deleveraging, by selling equity before the crisis, or by short selling. Roughly 40% of the group carries a significant CREDIT loading, mostly negative, so improving credit spreads were unlikely to help them, and the crisis added further loading in the same negative direction. Around 50% of fund-of-funds debt and fund-of-funds relative value funds picked up a negative incremental credit loading in the turbulence, which protected them as credit conditions worsened.
Across the whole period these funds carry almost no dollar or volatility exposure. In a crisis both jump, and both jump negative. Take fund-of-funds equity: only 2% show a negative VIX loading over the full sample, yet 60% pick one up as an additional crisis exposure, and the dollar shows the same pattern. Negative exposures of that kind are exactly what an investor would not want at a time when volatility is spiking and the US dollar is likely appreciating. Natural embedded leverage may partly explain them. The general lesson is the one the whole section is built on: because crisis periods generate unexpected exposures to systematic risks, a conditional factor model is essential rather than optional.
The final question is the practical one: what happens to a traditional portfolio when a hedge fund strategy is added to it? The exercise takes one strategy category at a time, builds an equally weighted basket of every fund inside it, and drops a 20% slice of that basket into a 60% equity and 40% bond portfolio. Equities are proxied by the total return index for US large caps and bonds by the Bloomberg Barclays intermediate AA corporate index. The resulting combined allocation is 48% stocks, 32% bonds and 20% in the hedge fund strategy portfolio.
Treat this as an illustration rather than a recommendation. In practice it is unlikely that any investor would hold a 20% allocation consisting of an equal weighting of every live fund in a single strategy category.
| Category | Type | Database | Mean return (%) | SD (%) | Sharpe ratio | Sortino ratio | Maximum drawdown (%) |
|---|---|---|---|---|---|---|---|
| 60% stocks / 40% bonds | Traditional portfolio | not applicable | 6.96 | 8.66 | 0.62 | 1.13 | 14.42 |
| Systematic futures | Opportunistic | CISDM | 7.34 | 6.94 | 0.83 | 1.68 | 8.04 |
| Dedicated short bias | Equity | TASS | 6.02 | 5.59 | 0.79 | 1.02 | 16.06 |
| Bear market equity | Equity | CISDM | 5.97 | 5.68 | 0.77 | 1.43 | 16.62 |
| Distressed securities | Event driven | CISDM | 7.40 | 7.67 | 0.75 | 1.38 | 20.00 |
| Fixed-income arbitrage | Relative value | TASS | 7.50 | 7.82 | 0.75 | 1.39 | 12.68 |
| Global macro | Opportunistic | CISDM | 6.97 | 7.29 | 0.74 | 1.38 | 5.19 |
| Equity market neutral | Equity | TASS | 6.81 | 7.17 | 0.73 | 1.80 | 10.72 |
| Equity market neutral | Equity | CISDM | 6.79 | 7.13 | 0.73 | 1.36 | 4.99 |
| Merger arbitrage | Event driven | CISDM | 6.85 | 7.22 | 0.73 | 1.35 | 5.60 |
| Multi-strategy | Multi-manager | CISDM | 7.00 | 7.47 | 0.72 | 1.34 | 13.83 |
| Event driven | Event driven | TASS | 7.13 | 7.76 | 0.71 | 1.44 | 20.96 |
| Fund of funds, macro/systematic | Multi-manager | CISDM | 6.47 | 7.05 | 0.69 | 1.31 | 10.65 |
| Long/short equity hedge | Equity | TASS | 7.22 | 8.29 | 0.68 | 1.45 | 21.34 |
| US long/short equity | Equity | CISDM | 7.17 | 8.22 | 0.68 | 1.24 | 16.77 |
| US small cap long/short equity | Equity | CISDM | 7.53 | 8.75 | 0.68 | 1.23 | 27.02 |
| Convertible arbitrage | Relative value | TASS | 6.76 | 7.75 | 0.66 | 1.27 | 31.81 |
| Fund of funds | Multi-manager | TASS | 6.43 | 7.53 | 0.64 | 1.23 | 18.92 |
| Fund of funds, equity | Multi-manager | CISDM | 6.39 | 7.76 | 0.62 | 1.11 | 21.63 |
This is a selection from the full set of 29 strategy portfolios examined. The baseline row is the traditional 60/40 portfolio against which each combined portfolio should be compared.
Return contribution
Against a traditional portfolio mean return of 6.96%, the best of the combined portfolios is the one holding US small cap long/short equity, at 7.53%, which is 57 bps better than the baseline. Three other allocations produce average annual returns above 7.30%: fixed-income arbitrage at 7.50%, distressed securities at 7.40% and systematic futures at 7.34%.
The broader pattern matters more than any single winner. A 20% slice of almost any of these strategies pulls the standard deviation of the whole portfolio down while lifting both risk-adjusted ratios, and roughly one combined portfolio in three also ends up with a smaller maximum drawdown. So hedge funds do two jobs at once in a stock and bond portfolio: they improve risk-adjusted return and they diversify.
Risk-adjusted contribution
The two ratios answer different questions. The Sharpe ratio defines risk as standard deviation, so it penalises upside and downside variability alike. The Sortino ratio counts only the variability that falls short of a minimum target, so upside movement is not penalised. Where a strategy is prone to large negative events, Sortino is the more informative of the two.
The best Sharpe ratio in the whole set, 0.83, belongs to the portfolio holding systematic futures. Close behind sit the portfolios holding distressed securities, fixed-income arbitrage, global macro and equity market neutral. On the Sortino ratio, the best combined portfolios by a wide margin come from allocations to equity market neutral in TASS, systematic futures, long/short equity hedge and event driven in TASS. Reading across both ratios, the strategies that improve risk-adjusted performance most are systematic futures, equity market neutral, global macro and the event-driven group. The implication for the multi-manager categories is uncomfortable: for all the breadth they can access, funds-of-funds and multi-manager vehicles add very little to risk-adjusted performance.
Risk reduction and drawdown
Many investors hold hedge fund strategies for risk reduction rather than return. Measured against a traditional portfolio standard deviation of 8.66%, the strategies producing the lowest combined portfolio standard deviations are systematic futures at 6.94%, and fund-of-funds macro/systematic and equity market neutral at a little more than 7.0%. Dedicated short bias and bear market equity are excluded from that comparison, because between them the two categories contain just 6 live funds and the statistics rest on a very thin sample. Those categories provide significant risk-reducing diversification, and they are the same categories that enhance risk-adjusted returns.
The other end is instructive too. Combined portfolios built around event-driven and distressed securities, or around relative value and convertible arbitrage, show relatively high standard deviations and therefore deliver little risk reduction. The explanations are structural. Most event-driven and distressed positions are long-biased with binary outcomes, and relative value and convertible arbitrage suffer forced deleveraging and liquidity problems whenever markets come under stress.
A drawdown is the gap between the peak value a portfolio has reached, its high-water mark, and the lowest point it touches before setting a new peak. Maximum drawdown is the largest such gap. Against a baseline maximum drawdown of 14.42%, the smallest figures belong to the opportunistic group, global macro and systematic futures in particular, joined by merger arbitrage and equity market neutral.
These findings knit back into the factor model. Those same strategies showed little equity or credit loading in the crisis window, and they also carry the lowest serial autocorrelation, which is the signature of genuinely liquid holdings. Risk mitigation for traditional assets therefore comes from three properties together: the risks are different ones, the approach is opportunistic, and the positions can be sold even when markets are under stress. At the opposite end, the long/short equity, event-driven and distressed, and relative value and convertible arbitrage groups all produce large maximum drawdowns once combined with the baseline portfolio. That is no surprise, given that the conditional model found equity risk sitting inside the event-driven and relative value books and credit risk turning significant in them once the crisis began.
An advisory firm serves small institutions and local college endowments. One client is a private four-year college whose endowment stands at $150 million and whose student body numbers 3,000. The portfolio supports 5% of current annual spending needs and holds a traditional 60% stocks and 40% bonds allocation. Over the coming 5 years the college intends to grow the student body to 4,000.
The principal wants to recommend a 20% allocation to a hedge fund strategy. The investment committee has stated three considerations for the combined portfolio: the allocation should maximise risk-adjusted returns, limit downside risk, and not impair portfolio liquidity. Fees are a further concern, and the committee is firm that stacked layers of fees must be avoided. Historical returns are assumed to be good proxies for future returns.
| Category | Type | Mean return (%) | SD (%) | Sharpe ratio | Sortino ratio | Maximum drawdown (%) |
|---|---|---|---|---|---|---|
| 60% stocks / 40% bonds | Traditional portfolio | 6.96 | 8.66 | 0.62 | 1.13 | 14.42 |
| US small-cap long/short equity | Equity | 7.53 | 8.75 | 0.68 | 1.23 | 27.02 |
| Event driven | Event driven | 7.19 | 7.83 | 0.71 | 1.31 | 20.57 |
| Sovereign debt fixed-income arbitrage | Relative value | 7.50 | 7.82 | 0.75 | 1.39 | 12.68 |
| Fund-of-funds, equity | Multi-manager | 6.39 | 7.76 | 0.62 | 1.11 | 21.63 |
Risk-adjusted returns: the combined Sharpe ratio of 0.62 is identical to the 60/40 baseline of 0.62, and the Sortino ratio of 1.11 is below the baseline of 1.13, so there is no improvement at all.
Downside risk: the maximum drawdown of 21.63% against the baseline of 14.42% is 50% higher, since 21.63 ÷ 14.42 = 1.50, so the combined portfolio would carry substantially more downside risk.
Liquidity: redemption terms are stacked, with a lock-up and gate at the fund-of-funds and another at each underlying manager, so portfolio liquidity is likely to suffer.
Fees: given the fund-of-funds structure, the endowment would pay two layers of fees and would face fee netting risk.
Risk-adjusted returns: it produces the highest Sharpe ratio at 0.75 and the highest Sortino ratio at 1.39 of the strategies presented, both well above the baseline of 0.62 and 1.13.
Downside risk: the maximum drawdown of 12.68% is the lowest of the shortlist, and it is below the 14.42% of the traditional portfolio, so downside risk actually falls.
Liquidity: sovereign debt in most developed markets trades freely, so portfolio liquidity should be unaffected.
Fees: like the other non-layered choices on the list, this one costs a single set of fees and carries no netting risk.
Pulling the reading together
Three threads run through everything above. First, classification is a means to an end: the point of grouping strategies is to predict which risks a manager will import into a portfolio. Second, the fee structure is not a detail but a determinant of net outcomes, which is why netting risk and fee layering weigh so heavily against the fund-of-funds structure. Third, the risk you can see in normal conditions is not the risk you will face in a crisis, which is the entire argument for the conditional factor model. Strategies that stay liquid, avoid equity and credit exposure in a crisis, and carry low serial autocorrelation are the ones that genuinely mitigate risk in a traditional portfolio, and the historical evidence lands consistently on the opportunistic and market-neutral categories.