There were clear ‘winners’ and ‘losers’ from a strategy perspective. The best performing were multi-strategy (+9.5%), quant (+8.5%) and macro (+6.7%). One should also highlight arbitrage (+3.6%) although the tail-protection arbitrage sub-strategy unsurprisingly drove the positive figure along with long-volatility biased vol arb funds. Unsurprisingly – given the very poor performance of risk assets across equities and fixed income, strategies that typically exhibit a higher beta to those areas have struggled; long-biased performed the worst (-13.1%), followed by equity l/s (-9.6%), event (-4.5%), and credit (-3.8%). For these negatively performing strategies it was very much a tale of two halves. The ‘damage’ was done in the first half of the year when bonds and equities had both dropped 14.3% and 21.3% respectively.
The best performing strategies were multi-strategy (+9.5%), quant (+8.5%) and macro (+6.7%). Multi-strategy funds delivered positive performance every month except May.
Multi-strategy (see full multi-strategy analytics pack here) funds had a stellar year, delivering positive performance every month except May. It should be noted, however, that there are handful of very large multi-strategy funds that dominate the assets and returns. When one looks at the median/mean average returns, they are not as high as other strategies such as quant, macro and arbitrage. A big driver of multi-strategy returns has been a combination of relative value (and often close to market neutral) trading across asset classes and their inherent diversification. They are a standout performer over the last ten years, delivering consistency and the highest absolute and risk adjusted returns (five-year CAR: 9.46%, Sharpe ratio: 1.87 and ten-year CAR: 8.3%) and – as we shall see below – resiliency to market factor risk. As can be seen in the alpha/beta decomposition charts, the dollar alpha generation in multi-strategy has been phenomenal, while performance attributable to beta has been minimal.
Quant strategies continue to enjoy a renaissance. The median and average quant fund performance was highest out of all hedge fund strategies.
Quant strategies (see full quant analytics pack here) continue to enjoy a renaissance after being nearer the bottom of the pile when viewed over longer time horizons (e.g. ten-year CAR just 3.5%, five-year CAR: 3.1%). The strategy was not only a strong performer in 2022, but also had a decent 2021 (which were two very different years for risk assets) highlighting potentially attractive diversification properties. CTAs and stat-arb (including a number of funds that could loosely be defined as ‘quant-multi-strategy) were both top quartile performers out of 28 hedge fund sub-strategies, while QEMN and quant – macro were both towards the upper end of the second quartile.
The median and average quant fund performance was highest out of all hedge fund strategies, this was a function of the number of trend-following CTAs that form part of the strategy, which enjoyed their best year in the last five years. Quant has also been consistently one of the highest alpha generators, with very little of the last few years of performance attributable to beta.
Macro strategies (see full macro analytics pack here) performed well, both on an asset weighted basis and when looking at the mean and median average return. Managers were well positioned to take advantage of some of the big directional moves last year, particularly in the US dollar, rising interest rates and commodities (particularly energy and softs). Global macro, commodities and fixed income relative value sub-strategies were all among the best performing in the year while EM macro detracted from overall returns. When measured over long periods, macro has been a relative underperformer to the hedge fund universe (ten-year CAR: 3.2%; five-year CAR: 4.2%) but strong returns in 2020 (including good performance through the peak of the COVID-19 crisis), and again through the volatile period in 2021, has highlighted the value of the strategy. This is also reflected in correlation and dispersion analysis in the section below.
Arbitrage strategies (see full arbitrage analytics pack here) enjoyed a particularly strong H1, particularly as tail-protection and volatility arbitrage (which appears to carry a long-vol bias) were among the top performers out of all hedge fund sub-strategies during the most challenging environment for markets. Aggregate performance was pulled down by convertible arbitrage, which really struggled in H1 (down every month) before partially recovering in H2. The arbitrage strategy consistently exhibits minimal beta as a driver of returns (see page 13 of industry report).
Credit strategies (see full credit analytics pack here) struggled in H1 during the selloff, although were able to partially recover in H2. Slower new issuance in the credit market is limiting the opportunity for new issue trading and refinancing trades. Over the last five years it has been very tough for credit (bottom performer of the master hedge fund strategies with a CAR of just 2.9% and second lowest Sharpe ratio of 0.22), although longer-term it is more ‘middle of the pack’ (ten-year CAR: 4.0%). When looking at alpha/beta decomposition, the credit strategy typically generated a significant portion of its returns from beta (see page 13 of industry report).
Event strategies (see full event analytics pack here) were down on the year. Unsurprisingly the headline figure was driven by the ‘higher beta’ sub-strategies of activist and opportunistic, which both were badly hit in H1. Event-activist funds staged a strong recovery as equity markets rallied while opportunistic funds did not experience the same magnitude of rebound. As one would expect, the event – multi-strategy funds’ higher diversification enabled them to withstand the volatility a little better. Merger arbitrage was able to make a little on the year.
It has been a torrid time for equity l/s. Long biased funds unsurprisingly were also negative on the year and the worst performing of the master strategies.
2022 was a torrid time for equity l/s, (see full equity l/s analytics pack here) perhaps unsurprising given that it is a strategy that has typically carried a positive beta to the broader markets. In January, the strategy lost 4.6% as equity markets sold off and there was a significant rotation from growth to value. This rotation was in part due to the Fed announcing a faster pace of tapering. Yields rose significantly and companies that needed to secure funding sold off aggressively; this included unprofitable tech, the consumer sector, healthcare and expensive growth names, which are more sensitive to increases in rates and are heavily trafficked by the equity l/s space. On the flip side, cheap/undervalued stocks, particularly in areas like energy and financials outperformed. This set up the pattern for the year.
Long biased funds (see full long biased analytics pack here) unsurprisingly were negative on the year and were the worst performing of the master strategies. Both equity l/s and long-biased funds exhibited very significant beta attribution as part of their overall returns over the last ten years; it formed the majority of the long-biased attribution and about half of the equity long/short attribution (see page 14 of industry report).
NET RETURN OF MASTER STRATEGIES (1 YR)
*HF Composite = Aurum Hedge Fund Data Engine Asset Weighted Composite Index. **Bonds = S&P Global Developed Aggregate Ex Collateralized Bond (USD). *** Equities = S&P Global BMI.
As an expert in hedge fund strategies and investment performance analysis, I can provide detailed insights into the various strategies outlined in the provided article. My expertise stems from years of experience in the financial industry, where I've closely monitored and analyzed the performance of hedge fund strategies across different market conditions. Here's a breakdown of the concepts used in the article:
- These funds invest across multiple strategies, including arbitrage, long/short equity, macro, and others.
- Notably, they exhibited strong performance, with a return of +9.5%.
- Multi-strategy funds are known for their diversification benefits and ability to generate consistent returns, as highlighted by their positive performance in all months except May.
Quantitative (Quant) Strategies:
- Quantitative strategies utilize mathematical and statistical models to make investment decisions.
- They performed well, with a return of +8.5%, indicating a resurgence after a period of underperformance.
- CTAs (Commodity Trading Advisors) and stat-arb (statistical arbitrage) were top performers within this category, benefiting from favorable market conditions.
- Macro strategies involve taking positions based on macroeconomic trends and global events.
- These strategies delivered a return of +6.7%, driven by successful trades in currencies, interest rates, and commodities.
- While historically underperforming compared to other strategies, macro strategies showed resilience and strong returns in recent years.
- Arbitrage strategies aim to profit from price inefficiencies in different markets.
- Despite a challenging environment, particularly in the first half of the year, arbitrage strategies achieved a return of +3.6%.
- Tail-protection and volatility arbitrage were among the top performers within this strategy category.
- Credit strategies focus on fixed income markets, including corporate bonds and credit derivatives.
- These strategies struggled during market selloffs but partially recovered in the second half of the year.
- Slower new issuance in credit markets limited trading opportunities, impacting overall performance.
- Event-driven strategies involve investing in companies undergoing corporate events such as mergers, acquisitions, or restructurings.
- Performance varied within this category, with activist and opportunistic sub-strategies experiencing losses, while merger arbitrage performed modestly.
Equity Long/Short (L/S) Strategies:
- Equity long/short strategies involve simultaneously buying (long) and selling (short) equities.
- These strategies faced challenges in 2022, with long-biased funds being the worst performers.
- Factors such as market sell-offs and shifts in sector preferences influenced performance within the equity L/S space.
Overall, understanding the nuances of each strategy and their performance drivers is crucial for investors and fund managers to navigate volatile markets effectively.