• Prospects look brighter for long-short hedge funds on increased dispersion in equity markets.
• Dispersion is a core driver of active stock- picking performance (particularly for quantitative approaches) but until recently had been compressed under the weight of easy money.
• Historical data shows that the performance of equity-based quantitative funds has improved relative to a classic 60/40 portfolio when access to capital tightens.
Many hedge fund strategies have faced challenging market conditions for several years. Ongoing quantitative easing since the Global Financial Crisis (GFC) has kept money cheap, limiting equity market dispersion, which is a key driver of fund performance.1 (Dispersion is a measure of how much the return of the average stock differs from the overall market’s return.) This is particularly true for quantitatively driven approaches, which tend to be highly active and diversified.2
However, as the era of easy money appears to be ending, with multiple rate hikes imminent, conditions may be turning in favor of quantitative funds (typically referred to as quants).3 Rate increases make it likely that equity performance dispersion will increase, which should allow greater room for these active strategies to find opportunities and inefficiencies in the market.
THE TIDE TURNS, FINALLY
Since the GFC, excess liquidity and low interest rates created a “rising tide lifts all boats” scenario, as access to cheap capital has helped keep even the weakest companies a oat.4 This resulted in limited distinction between stocks based on fundamental quality. But, as it must, the tide is finally turning.
Annual consumer price index inflation in the United States hit 7.9% in February of this year—the highest rate since February 1982.5 The U.S. Federal Reserve has already clearly signaled its intention to raise rates multiple times in 2022, with major banks forecasting hikes ranging between 125 and 175 basis points.6 Quantitative easing measures are also expected to cease in the first half of the year.
This will, in aggregate, tighten liquidity and create a greater separation between fundamentally strong and weak businesses, resulting in greater dispersion within and across multiple sectors globally. Historical performance data shows that the performance of directional quant funds7 relative to a traditional 60/40 equity-to-bond portfolio has improved during periods such as these, when rates rise (see Exhibit 1).
WHY A MORE DISPERSED ENVIRONMENT SUITS QUANTS
Quantitative equity investing is based largely on long and short stock picking.8 Fund managers generally select stocks based on statistical analysis, often through the lens of a factor with historically proven performance, and aim to find and exploit market opportunities and inefficiencies.
Quant funds can analyze a broader range of stocks than a traditional, fundamental approach and at greater speed.9 They can exploit their technology and statistical algorithms to process multiple inputs across a more diverse range of stocks, sectors, markets, and geographies, which improves consistency and predictability.10 The strategy generally hinges on capturing a large number of small, profitable trades.
A more dispersed market environment therefore broadens the opportunity set and improves the odds of success of a long-short construct. However, in a market where financial fundamentals or specific “factors” are of diminished importance, when correlations increase and the deviation of individual stock performance from the overall sector or market trajectory is very slight, a directional quant strategy may underperform the broader market.11
In the bull market that has stretched across much of the post-GFC era, short exposure has served to dilute returns as much as hedge risk.
As the prospect of higher rates started to be priced into markets over the last year, a largely uni-directional market—and therefore a challenging one for quants—has fractured into more differentiated trajectories (see Exhibit 2).
THE HUMAN CHALLENGE OF AN INFLATIONARY ENVIRONMENT
It is important to point out that few portfolio managers and stock investors have operated in a market environment like the one in which we now find ourselves. Particularly novel is the high level of inflation—as mentioned above, the highest reading since February 1982. While most managers are highly capable and are likely to have studied best practices for investing through inflationary environments, maintaining that discipline in practice is much more difficult. Human emotions and cognitive and behavioral biases can easily come into play.
Unfortunately, it is not unusual for fundamentally oriented investors to become anchored to their beliefs during macro shocks, such as a spike in inflation. Algorithm-led quantitative strategies do not face this issue, with a clearly defined rules-enforced discipline.12 Furthermore, many of these models have been backtested many years.13 While back tests have limitations, model-driven analysis incorporating robust statistical processing may better arm funds to invest in a high-inflation environment than the fundamentally driven strategies managed by practitioners that have only invested in an era of low inflation.
RISKS TO OUR THESIS
Directional quant strategies certainly have value as part of a balanced portfolio in any environment, but there are risks to our positive thesis for quantitative funds, centered around dispersion reverting to more subdued levels.
First, there is the chance that the market has already fully, or even excessively, priced in rate increases, which might limit further yield rises from here. As discussed earlier, yield rises have correlated with improved quantitative directional fund performance, so a yield decline might prove a headwind.
Second, an exogenous shock, such as the negative economic impacts rippling out from the conflict in Ukraine, could lead to a change in policy implementation that slows or curtails rate hikes by the U.S. Federal Reserve and other central banks.
More generally, an ongoing challenge for quant models is that history is not circular. Quant models are generally derived from extensive backtesting of investment strategies across an array of environments.14 However, this backward-facing analysis only categorically proves that a strategy would have worked historically. Quant approaches may find it challenging to quickly recognize paradigm shifts and adapt to new market regimes.
APPENDIX: WHAT IS QUANTITATIVE INVESTING?
Quantitative investing refers to any investment approach that relies upon mathematical models and algorithms to decide what to invest in and when. Almost all investment professionals now integrate quantitative analysis into their investment decisions, but quant strategies are entirely led by algorithms in terms of both analysis and their active trading process and will not incorporate any information that cannot be systematically aggregated and analyzed.17
Quant strategies are typically crafted through extensive empirical research that identifies factors that have historically generated excess returns. This process, known as backtesting, assesses the viability of a strategy by modeling how it would have performed across various periods. This analysis will consider economic, accounting, and financial data, but may also leverage governmental data, demographic information, and even non-standard data sources such as website traffic or credit card activity. As the volume of data digitally collected and made available continues to expand globally, quant models have a larger dataset through which to fine tune their algorithms (see Exhibit 3).
Once these factors have been identified and the algorithms developed, the human element is removed from the process, with investment decisions made and executed by computers. This systematic approach can result in a consistent and predictable execution of the selected strategy. The algorithm constantly monitors the market to identify mispricing opportunities that meet the defined criteria for the fund, be that based upon value, growth, quality, momentum, or some other statistical measure.
(1) Source: Connor, Gregory, and Li, Sheng, “Market Dispersion and the Pro tability of Hedge Funds,” 2009.
(2) Source: Bloomberg, “Wall Street Quant Strategies Have Proved Their Worth,” January 31, 2022.
(3) Source: Federal Open Market Committee, “Summary of Economic Projections,” March 16, 2022.
(4) Source: FRED Economic Data, ICE BofA BB US High Yield Index Effective Yield.
(5) Source: U.S. Bureau of Labor Statistics.
(6) Source: Reuters, “Factbox: What global banks forecast for Fed rate hikes in 2022,” February 18, 2022.
(7) Directional quantitative funds are long-short funds that typically assume a net exposure to the market one way or the other, in contrast to market neutral approaches, which aim to fully balance exposures.
(8) Source: Bloomberg, “Wall Street Quant Strategies Have Proved Their Worth,” January 31, 2022.
(10) Source: Coresignal, “Quantitative Investing Strategies: A Quick Guide,” February 18, 2022.
(11) Source: Connor, Gregory, and Li, Sheng, “Market Dispersion and the Profitability of Hedge Funds,” 2009.
(12) Source: Coresignal,“Quantitative Investing Strategies: A Quick Guide,” February 18, 2022.
(13) Source: Empirica,“Guide to Algorithmic Trading and Quant Funds ’Profitability.”
(14) Source: Empirica, “Guide to Algorithmic Trading and Quant Funds ’Profitability.”
(15) Source: Hedge Fund Research Inc. Quantitative directional equity funds had a beta of 0.57 against the MSCI World Index and 0.56 against the S&P 500 from January 2000 to December 2021.
(16) Source: Hedge Fund Research Inc., as of March 3. Data from January 2000 to December 2021.
(17) Source: Bloomberg, “Wall Street Quant Strategies Have Proved Their Worth,” January 31, 2022.
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