As we contemplate the prospect of increased volatility for late 2020 and into 2021, we sat down with Alex Pire to talk about factor investing and how the Seeyond International Minimum Volatility ETF (MVIN) can help investors stay invested in international markets while navigating uncertain times.

Before we discuss factor investing, can you give us some perspective on your economic outlook and the state of global equity markets?
I believe investors have officially entered an environment that can only be referred to as "The Era of Great Uncertainty," and for the foreseeable future, this is likely to be our new normal. As we survey the direction of stock markets, various economic data, and investor sentiment, one thing seems clear – uncertainty is here to stay. Over the past few months, we have wrestled with cognitive dissonance between positive stock market returns and negative news related to the public health and economic consequences of COVID-19. Without question, market uncertainty has been significantly mitigated by massive government intervention. However, I would caution that much of the “good news” has already been priced in and that markets and equities may become more vulnerable to volatility in the absence of “better news.” This leads me to believe that investors may want to consider built-in diversification and the benefit of liquid diversifiers and alternatives as we enter the consolidation phase of the post-crisis rally.

You mentioned liquid diversifiers – Seeyond is focused on factor investing and considers factors like minimum volatility to be a liquid diversifier. Is this a new concept?
No, that’s a misconception. Smart beta has helped to popularize factor-based investing and growth has been tremendous since the mid-’90s. Part of it is that beyond the flurry of buzz words – smart beta, strategic beta, factor investing – lies a more recent structural change in the marketplace. Investors are increasingly evaluating their investments in terms of sources of risk and returns, as opposed to the traditional country/sector framework.

Big Data has helped to revolutionize factor investing, but Seeyond believes that it’s important to view factor investing from the perspective of a long tradition of market practice and academic research that has always been used in investing. To give a few examples, the low volatility anomaly – seeking higher returns by investing in low volatility stocks – has been publicized since the early 1970s. Value investing – buying cheap, selling expensive – can be traced back to the early 1930s. Momentum investing – buying high, selling even higher – goes back even further.

When you put it all together in a portfolio that combines long equity investments and minimally correlated risk factors relative to the broader market, it can make factor investing an appealing liquid diversifier.

We hear the term smart beta used extensively in conjunction with factor-based investing, and some consider these terms to be interchangeable. Can you help us define smart beta?
Smart beta is a term that has been used to loosely group all passively managed products that seek to track indexes that are not weighted by market capitalization. Smart beta can therefore be thought of as a passive implementation of factor-based investing. The underlying indexes rely exclusively on rules-based quantitative models to drive their portfolio construction process, and the products – typically exchange-traded funds – seek to track these indexes in a similar fashion to traditional passive management.

But it is important to use care with these products. The team at Seeyond believes that quantitative models should be treated for what they are: powerful tools to understand the market and foster discipline in the investment process. When it comes to actual investment strategies, we believe it all starts with clear investment goals, and ends with an understanding of how what is selected for the portfolio can achieve those goals. Seeyond’s approach to factor investing is built on these two pillars: a repeatable quantitative model and active oversight.

MVIN combines a quantitative investment process with a “human” or active management element. How does the strategy define which factors to use in your investment process?
We believe one of the problems with strictly quantitative factor investing is the risk of data mining. While big data and power computers can enhance consistency in investment strategies, the overwhelming quantity of data and back-testing possibilities is a potential difficulty – it can create unwanted noise and misleading signals. That is where clear investment goals come into play. At Seeyond, to define factors, we want to be convinced by both statistical evidence and financial theory that an equity factor has the potential to generate persistent alpha over time, display a singular risk/reward profile, and have the ability to be implemented consistently.

With the help of this analysis framework, we narrow our factor analysis to four clearly defined equity factors for the purpose of building portfolios: low volatility, value, momentum, and small capitalization.

Can you talk a little bit about why Seeyond takes an active approach?
In our view, the difference between theoretical factors and actual portfolios cannot be stressed enough. The high active share and tracking error displayed by different low volatility indexes and strategies are a good illustration of how choices made when implementing factors can have a significant impact on the investment outcome. Unlike capitalization-weighted strategies, all factor-based strategies are not created equal. In particular, we believe that passive and benchmark-aware implementations have the potential to water down the characteristics of certain factors and introduce unwanted sources of risk in the portfolio.

The team at Seeyond acknowledges the active nature of equity factors by opting for an active implementation. The general expectation is that an active manager would take active discretionary bets. That is not what active means to Seeyond. The team feels a need to redefine active in the context of quantitative investing – active oversight and active implementation – which in turn means we want to be accountable for every stock in the portfolio and find the right balance between risk management, adaptability, and trading costs.
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