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Investing basics: what is a quantitative fund?

Michael Schramm and Emma Rapaport  |  11 Oct 2019Text size  Decrease  Increase  |  
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The idea of computer algorithms deciding where your money should be invested was once the stuff of science fiction. But it's here and it’s delivering attractive returns to individual investors.

Fund managers such as BlackRock, State Street and Arrowstreet are building “proprietary quantitative models”, which identify and use statistical trends to seize on opportunities in all kinds of asset classes.

If that leaves you a little perplexed, fear not. Morningstar Inc data journalist Michael Schramm breaks it down. Then we'll explore two quantitative strategies that Morningstar Australia fund analysts have given a silver-rating.

What is quantitative investing?

Quantitative investing is an investment process in which securities are chosen based on defined rules.

Conventional active management involves a team doing security-specific research: modelling company financials, comparing industry peers, and assessing competitive advantages in picking the best stocks. While these approaches surely contain some rigid elements, humans make the final call, which embeds a qualitative flexibility in the decision-making process.

Quantitative investing has some creativity and flexibility, but it’s just in choosing, arranging, and replacing data and inputs, not in choosing the actual stocks. A team hunts for the best inputs so a statistical model can spit out the most attractive ideas with these criteria. The aim is to minimize human judgment (and potential bias) when choosing stocks.

How does a quant fund choose its data and criteria?

Managers sift through a forest of data, seeking the slivers that best indicate outperformance. These slivers then become criteria in the model that screens securities.

They identify the best criteria through back-testing data. In back-testing, they isolate a criterion they believe could outperform, such as stocks with low price/earnings ratios, over set a time period, like five years. They’ll then simulate how a portfolio with this criterion fares against the broader market over the five-year period.

If the back-test indicates the criterion helped returns, the team will test the variable in other market environments and data sets to ensure the relationship isn’t an isolated incident.

What types of data and criteria can quant managers use?

Most quantitative strategies will incorporate a company’s financial statement data. This can include metrics like net income and ratios like net margin or price/earnings.

They can also gauge sentiment about the economy or a business. Potential data points include gross domestic product growth or the dispersion of analysts’ earnings estimates for next quarter.

Finally, these strategies might use unstructured data, or variables outside of conventional financial analysis. Satellite imaging or credit card data might contain consumer trend insights and help with estimating company sales, for instance.

What are the ways quant funds differentiate themselves?

Quantitative strategies might choose different metrics within similar data. One team might use price/book, another might use price/earnings, and a third could use both.

Two strategies could use identical criteria but differ in how they use it. Do they compare a stock’s price/earnings to sector peers, industry peers, both, or neither? Are chosen criteria emphasized in sectors or industries that show stronger relationships? The criteria can also differ by time period: One team might gauge a stock’s price/earnings over three months while another looks at six months.

Plus, a criterion’s value can deteriorate over time, so managers regularly replace decaying ones for promising ones.

How does strategic beta fit into this?

Strategic beta replicates a benchmark. But the benchmark is weighted by a factor other than market cap; usually it’s tilted toward stocks whose financial metrics are associated with an investing style such as value (low price/book), momentum (price movements), or growth (increasing revenue).

Strategic-beta funds seek to earn returns you would expect from a specific investing style. Each style has common and well-known factors, so tilting a portfolio toward one is easy and inexpensive. However, this also makes strategic-beta funds simpler in construction.

Pure active quantitative strategies, contrarily, can choose investments outside their benchmark. This oftentimes results in more complex (and expensive) models that choose stocks through sophisticated algorithms or unique information like unstructured data.

Related article: Investing basics: what's so 'smart' about smart beta ETFs?

How do quant strategies manage risk?

Quant funds often use an optimizer: a separate model component that keeps sector and position sizes in check.

Imagine if a model spit out 10 technology stocks as the highest-ranking choices. Buying them all would spike the fund’s technology weighting, exposing it to sector risk if the sector dipped. An optimizer creates constraints--such as maximum sector weightings--to limit risks like these.

Quantitative strategies are often less concentrated. Because the model has defined rules, it’s easy to evaluate a wide security universe and buy the ones that look attractive, unlike a human research team that might have resources to deeply cover only a fraction of the market.

What are unique challenges to quant funds?

Most quantitative funds struggle in rapidly shifting markets, as past relationships might be less meaningful in different, future environments. For instance, many US-fund fared poorly in the fourth quarter of 2018 and the first quarter of 2019 when stocks plummeted and subsequently rose.

There are also some strategic risks and challenges quant funds can face, including:

  • Survivorship Bias: past data not accounting for businesses that no longer exist. Some trends might look different if the data included bankrupt companies.
  • Data Mining: identifying a criterion because you actively sought it out rather than it being a legitimate excess-return source. You can often find a relationship in data just by looking hard enough, even if it’s not truly there.
  • Overfitting: when a statistical model anchors its assumptions too heavily on past data, to the point that these trends might not hold in the future.

Quantitative strategies also trade frequently as the model’s rankings constantly shift. This means the portfolio is constantly changing to reflect the best ideas, which results in above-average trading costs. These costs make it more difficult for funds to add value after fees.

How do I invest in quantitative strategies?

Below are two quantitative strategies that Morningstar Australasia analysts like – one investing in Australian equities and the other in global equities.

BlackRock Advantage Australian Equity (Silver-rated)

Morningstar Australia senior analyst Simon Scott says BlackRock’s Advantage Australian Equity fund is an outstanding choice for investors seeking low-tracking-error quantitative equities exposure at a very low price.

"Systematic investing may not be for everyone, but this strategy makes a compelling core investment," he says.

"BlackRock harnesses technology to identify economically sound investment ideas using five model groupings of earnings direction, relative valuation, market/management actions, earnings quality, and timing signals.

"The signals within these groupings utilise a decision-tree framework to define a return forecast for each stock on a daily basis."

Scott says BlackRock stands out because it uses alternative data to verify and piece together various signals.

"For example, using geospatial data at the macro level or neural networks to determine sentiment. While not unique, to do it well requires size, scale, and immense computing power--attributes BlackRock possesses in spades," he says.

BlackRock Advantage Australian Equity charges a fee of 0.45 per cent a year, which is well below the average of its wholesale peer group.

The fund boasts a strong history of outperforming the index and the category average. In 2018, the fund returned -3.27 per cent, and year to date has returned 24.60 per cent.

Arrowstreet Global Equity (Silver-rated)

Another fund to consider is the Arrowstreet Global Equity. Its strengths lie in a focused and innovative investment team, and a robust approach that continues to evolve.

"Arrowstreet's approach is unique among quantitative offerings. While it employs classic indicators such as value, momentum, and quality, its real value proposition lies in what Arrowstreet calls expanded linkages: a search for underappreciated relationships that are predictive of stock prices, including across countries, industries and the supply chain," notes former Morningstar analyst Sarah Fox.

"Determining these linkages requires an enormous amount of data, infrastructure and analysis.

"There are high barriers to entry here and it’s clear Arrowstreet have fashioned an unrivalled, sustainable competitive advantage."

Fox adds that heavy resources and time are spent on improving execution, allowing the group to react quicker to the fast flow of market information, while maintaining control of implementation costs.

However, at 1.28 per cent, the fee is relatively high.

Arrowstreet Global Equity has outperformed the benchmark and most competitors over most time periods. The fund returned 3.17 per cent in 2018, and year-to-date has returned 17.79 per cent.

. Michael Schramm is a is a data journalist, investor education for Morningstar Inc. Emma Rapaport is an editor for Morningstar Australia.

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