What's Trending - Allocator Perspectives on Investment Strategies - Q1:2017

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In an era of computerized markets and big data, many investors believe that quantitative strategies provide a significant edge over discretionary strategies. However, not all investors are so enthusiastic. Why are investors increasingly interested in quantitative strategies? Why do others still prefer discretionary strategies? Which one will become their preferred choice?

Quant or Not: The New Battle For Capital

Over the past year, hedge funds have received negative press about strategy underperformance and large investor redemptions. However, during this same period, there have been articles about large inflows into quantitative hedge funds and articles about discretionary managers hiring teams of data scientists to develop quantitative capabilities. In the recent words of hedge fund manager Paul Tudor Jones, "No man is better than a machine, and no machine is better than a man with a machine."1

The views expressed herein were shared by different investment consultants, fund of funds, and wealth management firms.

Discretionary vs. Quantitative

Discretionary and quantitative strategies are both fundamentally based. The big difference between them is implementation. Discretionary strategies rely on human judgment when executing trades. Quantitative strategies rely on algorithms (or a coded set of rules) that trade automatically on behalf of humans.

In the opinion of investors that are bullish on the quant space (the Quant Bulls), markets have changed, and access to information is much greater. It can be harder for discretionary managers to exploit inefficiencies. Quant Bulls believe that quantitative methods are data-driven ways of improving a strategy’s effectiveness. They are based in logic and economics, and they can also eliminate behavioral-biases that we have as humans. Quant Bulls also believe that algorithms can provide investors with better risk-adjusted returns and better liquidity for a fraction of the cost.

Increased investor interest in quantitative strategies has stemmed from frustration over consistent underperformance of discretionary strategies. Investors are now starting to view quantitative strategies as possible:

  • Diversifiers to a portfolio of discretionary hedge funds
  • A complete replacement for a portfolio of discretionary hedge funds
  • Diversifiers in a multi-asset class portfolio
  • An overlay to a multi-asset class portfolio

In the opinion of Quant Bulls, investors becoming more comfortable with quantitative strategies may be akin to people becoming comfortable with driverless cars or digital medical examinations. Although we intellectually accept that computers may be better and more precise at forecasting, we, as humans, may still feel uneasy with this approach. However, as these technologies become more mainstream, younger generations may become more accustomed and reliant on them.

Quant Methodology and Crowding

Quantitative strategies rely heavily on big data. As a result, managers dedicate significant time collecting and cleaning data. Common data used when developing algorithms include:

  • Price/Book
  • Operating Cash Flow/Sales
  • Free Cash Flow
  • Earnings
  • Value
  • Growth
  • Market Capitalization
  • Volatility
  • Momentum
  • Tail Risk

As the space has become more crowded over time, the use of these factors has caused increased correlations between quantitative strategies. To develop an information edge, many managers have dedicated time toward finding newer less-collected data. Examples include satellite images of store parking lots and other similar data from government satellites. As per the Quant Bulls, these new types of data just became available over the last couple of years and can be used to track real-time consumer trends.

Providers of Strategies

There are two big providers of algorithmic strategies:

  • Capital Markets Businesses: Quant bulls have indicated that capital markets businesses of large banking enterprises are now the largest provider of quantitative strategies in the market. In the last couple of years, capital markets businesses have doubled the number of strategies offered to clients. The largest users of these algorithms include:
    • Discretionary hedge funds that want to incorporate quantitative strategies
    • Discretionary multi-strategy funds that want a quantitative sleeve
    • Institutional investors that use a fiduciary to select quantitative strategies

    Generally, these algorithms are specific to a single factor (usually an index), and clients are provided with the full set of rule of the algorithms. The strategies are generally accessed by clients via total return swaps. As a result, they often provided clients with daily liquidity, usually subject to three-day’s notice. Since these strategies are tied to indices and are low-cost, their increased popularity can also be seen as part of the wider adoption of passively managed strategies.

  • Quantitative Asset Managers: Quantitative strategies are available to investors via both hedge funds and mutual funds. The space can be divided into two categories:
    • Single-Factor Strategies: Single-factor strategies are viewed as less proprietary. As a result, managers usually charge lower fees and provide more transparency.
    • Multi-Factor Strategies: Multi-factor strategies are viewed as highly proprietary. As a result, managers usually charge higher fees and provide little transparency to investors.

    Strategies are further categorized by the use of securities and derivatives, the use of leverage, net exposure ranges, and trading frequencies. Sub-strategies available to investors include statistical arbitrage, quantitative market neutral, and managed futures.

Hesitations With Quant

In the opinion of investors that are bearish on the quant space (the Quant Bears), there are a number of items that keep them from investing in quantitative strategies. These items include:

  • Risk and Transparency Differences: As per the Quant Bears, data scientists and discretionary analysts think differently about risk. As a result, Quant Bears struggle when trying to conduct apples-to-apples comparisons between strategies.

    With discretionary strategies, many investors have an easier time understanding stories about how companies are doing and the reasons for their performance. Additionally, discretionary managers often provide investors with risk reports, which breakdown fundamental exposures.

    With quantitative strategies, models are fundamentally-based. However, in order to maintain the proprietary nature of their multi-factor strategies, quantitative managers generally provide little, if any, transparency about their models. With that said, quantitative managers do provide investors with exposure reports, which breakdown factor exposures.
Discretionary StrategiesQuant Strategies
View of Risk Sectors, Geographies Factors
Return Attribution Beta vs. Alpha Risk Premia
Leading Market Benchmarks Can Easily Compare Cannot Easily Compare (Multi-Factor); Can Easily Compare (Single Factor)
Position-Level Changes Impacting Performance Transparency Little Transparency
Risk Reports Comparable With Other Discretionary Strategies Not Easily Comparable With Discretionary Strategies (Multi-Factor); Easily Comparable with Discretionary Strategies (Single Factor)

Quant Bulls are comfortable selecting multi-factor managers. Although little transparency is provided about the models, Quant Bulls find comfort in managers that can clearly describe their process for model development. Quant Bulls also rely heavily on manager pedigree and reputation, and they often select blue chip managers.

Quant Bears, on the other hand, are not comfortable selecting multi-factor managers. They are uneasy with the lack of transparency into the models and having to rely so heavily on manager pedigree and reputation.

  • Performance Unpredictability: Quant Bears believe that multi-factor strategies may be less correlated to the market. Although they may provide better downside protection, Quant Bears are unsure about what kind of upside they can expect given the lack of transparency.

    Another concern of Quant Bears is that quantitative managers cannot out-predict their own models. Models are developed with historical data. As a result, many models are unable to forecast unpredictable, disruptive market events.

  • Risk of Sharp Drawdowns: Models are a set of instructions, which can be highly effective when they work. However, when they do not, they can result in sharp drawdowns. As per the Quant Bears, when drawdowns occur, they happen quickly and can be much larger than the drawdowns predicted by the models of the manager.

    The information edge of discretionary managers includes their ability to talk with company management and to have discussions within their network of other company investors. Factor models do not always incorporate these types of information. As per the Quant Bears, excluding this data from models can cause them to fail and result in performance drawdowns.

    Also, quantitative strategies can experience high correlations to each other, especially on the downside. As per Quant Bears, algorithms are designed to identify specific market environments and then execute trades. The trading by one algorithm can trigger the trading of other algorithms. This mirroring, or domino effect, within an extremely short time frame can cause extreme volatility and lead to sharp performance drawdowns.

    Lastly, the use of leverage to augment returns can magnify sharp performance drawdowns.

  • Difficulty With Position Sizing: When determining position sizing (the size a new fund within in a larger portfolio of funds), Quant Bulls are comfortable with relying on a multi-factor strategy’s historical metrics. These metrics include volatility and correlation. Quant Bears, on the other hand, struggle with position sizing, because of the lack of transparency into the models.

    Given that single-factor strategies are often transparent, position sizing can be easier.

Views on the Future

As per the Quant Bulls, we are living in an era of computerized markets and big data, and quantitative strategies can provide an edge that discretionary strategies do not. This has resulted in increased adoption of quantitative strategies by investment and wealth management businesses. Quant Bulls believe that these strategies will become more popular in client portfolios, and that quant allocations may grow to between 3% and 15% of portfolio allocations.

Quant Bears, on the other hand, may never become comfortable with multi-factor strategies. They may remain uncomfortable with their limited transparency and their heavy reliance on historical data, which makes it challenging for models to forecast disruptive events. As a result, Quant Bears will likely maintain their focus on discretionary strategies.

]Laurence Fletcher and Gregory Zukerman, "Withdrawals Plague Once-Mighty Hedge-Fund Firms Brevan Howard and Tudor," http://www.wsj.com, (August 16, 2016).

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