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How quantitative funds are stacking up with buyers

Five investors give their thoughts on quant funds and the relationship between asset managers and the machine

Ernest Lim, Julius Baer

Director, managed solutions advisory Asia

Quant strategies mostly rely on pattern recognition where models look to correlate past periods of superior returns with specific factors including value, size, volatility, yield, quality and momentum.

Such approaches have several fundamental weaknesses, such as hindsight bias, where one believes that understanding the past allows for prediction of the future.

While such a strategy might not have adapted well to the recent unprecedented market gyrations, when invested over the mid- to long-term with the right allocation size, we believe it can help produce good risk-adjusted returns for an overall long-biased investment portfolio.

 

Ernest Lim, Julius Baer

Director, managed solutions advisory Asia

Quant strategies mostly rely on pattern recognition where models look to correlate past periods of superior returns with specific factors including value, size, volatility, yield, quality and momentum.

Such approaches have several fundamental weaknesses, such as hindsight bias, where one believes that understanding the past allows for prediction of the future.

While such a strategy might not have adapted well to the recent unprecedented market gyrations, when invested over the mid- to long-term with the right allocation size, we believe it can help produce good risk-adjusted returns for an overall long-biased investment portfolio.

 

Bryan Goh, Tsao Family Office (formerly Karuna)

CEO and CIO

The success of quant techniques is highly data-dependent. An algorithm trained in one neighbourhood of data cannot be informed about events too far away from that area. The more finely tuned an algorithm, the less robust it is when markets stray.

The explosion in the availability of data and computing power improves the performance of algorithms, but as complexity increases and humans start to lose touch with their creations, the ability to understand algorithms diminishes.

Call me old-fashioned, but I want to know how and why decisions are made. We monitor quant funds to understand what they look for and their potential market impact, but we don’t invest in them.

Sameer Deshpande, Citibank

Regional head of investments, Asia & EMEA

In the past, many asset managers invested in data science in terms of both talent and infrastructure in an effort to harness alternative data sets and machine learning. It is too early to judge the success of such an approach because it has a short track record and is not exclusively used in the investment process.

Separately, the more traditional quantitative strategies, such as factor investing and CTA strategies, once the domain of hedge funds, have increasingly found expression in the traditional mutual fund formats.

They have potential diversification benefits but are clearly dependent on each investor’s actual portfolio. Since 2018, however, such strategies have experienced outflows and performance has been impacted.

We monitor a range of managers undertaking such strategies on an ongoing basis and selectively onboard managers who have a strong track record and research capabilities.

 

Arjan de Boer, Indosuez Wealth Management

Head of markets, investments and structuring, Asia

The performance of pure quant funds in general has been disappointing, and the picture is increasingly worsening. This is fuelling doubt about whether such strategies are actually effective. We have seen many big names in the asset management industry closing funds or quant funds bleeding cash.

Apart from performance, the strategy behind quant funds is often difficult to understand, let alone explain to private investors. As such, we prefer a fundamental investment process over a quantitative factor-based one, which is reflected in our recommended funds.

We do have funds such as the Amundi European Equity Conservative fund on our recommended list, which uses quantitative methods (smart beta and factoring investment) to supplement the human decision-making process.

 

 

 

 

Gary Dugan, Purple Asset Management

CEO

Historically, money managers have often relied on macroeconomic models based on economic data to gauge the outlook of asset classes. However, economic data can be imprecise, subject to revision or too filled with emotion.

Big data aggregates information that is virtually real-time and measures actual economic activity. Models based on measures of real economic activity rather than perception or estimation is much more valuable. 

We have a reasonable amount of belief in quant funds, particularly with the extra analytical tools they have available from the growth of big data and AI. We believe good quant funds should have a more predictable performance relative to one that relies solely on the human touch. 

The downside of quant funds is that some can be too confusing and random in their performance. At the end of the day, if we don’t understand it, we suspect the end client won’t either.