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Renaissance Technologies quietly generated 66% annual returns, before fees, between 1988 and 2018, making it the most successful hedge fund in the world. While Warren Buffett is known for handpicking the best stocks, James Simon used quantitative statistical analysis to drive the performance of Renaissance Technologies’ Medallion Fund.
These days, many exchange-traded funds (ETFs) take a similar approach to picking stocks. While smart-beta ETFs target specific factors, a growing number of actively managed quant funds use statistical analysis to identify correlations and generate attractive risk-adjusted returns. That said, there are some critical drawbacks to keep in mind.
Let’s take a look at how quant ETFs work, the differences between quant and smart beta, and five ETFs to consider for your portfolio.
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Quant funds rely on algorithmic investment strategies to select and weigh securities in a portfolio. For example, Renaissance Technologies LLC computed statistical probabilities from petabytes of proprietary data to predict asset price movements. In particular, its most successful fund identified correlations between securities to exploit for profit.
Of course, there are many different types of quant strategies. Most funds use a sophisticated statistical model that incorporates momentum, quality, value, and financial strength. In addition, they may control risk through comparison to a benchmark, enabling the funds to remain diversified without compromising the model.
Quant funds tend to have lower expense ratios because they don’t require as many traditional analysts. However, they may have higher trading costs since there’s more turnover. The other major drawback is that quant funds are ‘black boxes’ that might struggle with so-called ‘black swan’ events of unexpected volatility.
The most famous example of the risk of quant funds was the fall of Long-term Capital Management in 1998. After generating 21%, 43%, and 41% returns during its first three years, the fund lost $4.6 billion in less than four months due to two ‘black swan’ events—the 1997 Asian financial crisis and the 1998 Russian financial crisis—and was liquidated.
The terms ‘quant’ and ‘smart beta’ are often used interchangeably, but there are some key differences to keep in mind. The most significant difference is that smart-beta funds tend to optimize for specific factors based on screening criteria. In contrast, quant funds focus on optimizing risk-adjusted returns using statistical models.
Smart-beta funds rely on factors to select and weigh securities in an index. For example, the Vanguard Value ETF (VTV) offers exposure to large-cap companies that exhibit value characteristics, such as dividends or a solid balance sheet. Other funds may target things like low volatility, growth, dividend appreciation, or other factors.
Quant funds rely on statistical analysis more than factors. For example, the AdvisorShares Q Dynamic Growth ETF uses a proprietary Q Methodology to guide portfolio design and risk management, including the generation of tens of thousands of portfolio simulations to optimize asset allocation for a given level of risk.
As of October 18, 2021, these are the five best actively managed quant ETFs in terms of year-to-date (YTD) returns.
|ETF Name||Ticker||Expense Ratio||YTD Performance|
|Hull Tactical US ETF||HTUS||0.97%||18.72%|
|Cambria Global Momentum ETF||GMOM||0.93%||15.80%|
|AdvisorShares Q Dynamic Growth ETF||QPX||1.46%||12.51%|
|AdvisorShares Q Portfolio Blended Allocation ETF||QPT||1.17%||5.50%|
|SPDR FMS Systematic Growth Equity ETF||SYG||0.60%||1.42%|
Quant funds leverage complex statistical models to maximize risk-adjusted returns. While most quant funds are structured like hedge funds, a growing number of actively managed ETFs employ these strategies, bringing them to everyday retail investors. However, investors should keep the drawbacks in mind to avoid unexpected losses.
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