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Market-Neutral Funds: Watch These Fast-Growing Quantitative Equity Strategies

And a short primer on the strategy.

Morningstar, 06/19/2017

Months of near-consistent outflows have taken a toll on the market-neutral Morningstar Category: Total assets for these funds have dropped from a peak of $34 billion in August 2014 to $21 billion at the end of May 2017.

However, two of the top five largest funds in the market-neutral category—Vanguard Market Neutral VMNIX and AQR Equity Market Neutral I QMNIX—have grown their total assets substantially during this period. Below, we provide an overview of equity market-neutral investing and the associated capacity concerns with the strategy and these funds.

Equity Market-Neutral Strategies’ Distinguishing Features
Systematic market-neutral equity long/short strategies typically deploy capital both in developed and leading emerging markets. They’re also usually based on fundamentally and technically driven substrategies, with dollar and beta neutrality generally maintained at the fund/strategy level.

The funds use leverage to varying degrees to amplify their returns, which are driven primarily by pure alpha (for example, company-specific diversifiable risk) or style factors (such as value, momentum, quality factors, and so on). In its purest form, a market-neutral fund should not only target maximum factor diversification, but also factor neutrality. In other words, a single factor should not drive, say, only the long side of the portfolio without management also shorting the weak stocks in that specific factor.

These funds primarily invest in plain-vanilla equity securities, but they also trade synthetic instruments like equity swaps or "contracts-for-difference," where it is difficult to borrow stocks for shorting purposes (particularly in emerging markets). Instead of looking for the home-run picks that drive returns for many fundamental stock-pickers, market-neutral managers look for relatively smaller anomalies in the equity markets. These anomalies can exist due to investor preferences, indexation, demand and supply anomalies, and other factors. Since the expected returns for individual trades are relatively small, it is not uncommon for these funds to use leverage and hold several hundred stocks, if not thousands, in their portfolio (counting long and short positions), and have relatively high turnover.

This type of investing requires sophisticated technological and investment infrastructures. The investment processes often consist of risk-modeling and optimization protocols, stock-selection models, and transaction-cost modeling. These components generally work in tandem to ensure that the portfolios are properly diversified, and that dollar, beta, and factor neutralities are maintained as market conditions change (assuming the manager targets such neutrality). Automated trade-execution systems need to be integrated with middle-office risk-management systems to ensure smooth execution.

A large number of mostly fundamental and technical factors combined in different configurations comprise the building blocks of market-neutral investing from a bottom-up perspective. Managers combine the factors—style or otherwise—into separate substrategies that are clustered into themes such as valuation, profitability, quality, investor sentiment, and liquidity, and sometimes more-complex themes like catalyst-driven trades.

Identifying these factors and developing them into alpha-generating modules requires large sets of data collected from not only major data sources like Bloomberg and Morningstar but also using other means, including proprietary data sets. Managers have devoted much research to this latter area in order to further diversify the building blocks of quantitative investing, emphasizing newer techniques like machine learning in addition to traditional statistical techniques. Managers have experimented with artificial intelligence, neural networks, random forests, natural language processing, and other methods to enhance the alpha-generation process. However, it is too early to say if these conceptual methods can add consistent value in the real world.

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