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?
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 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:
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.
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:
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.
There are two big providers of algorithmic 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.
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.
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:
|Discretionary Strategies||Quant 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.
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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