The capitulation in the Nasdaq 100 and technology or growth stocks more generally in 2021 and 2022, has reiterated the important role that style bias can play within portfolios, but particularly in one's equity exposure. Put simply, there are three broad styles that are the most prevalent, being passive, value and growth, with the latter being replaced by the former as interest rates continued to rise.
Another round of quarterly reviews and performance round-ups in April, and incoming in June, is bringing performance surprises to many investors and advisers who after years of strong returns may have accumulated ‘unintended style biases’ within portfolios. And you could be forgiven for doing so, the technology, mid and small-cap sectors delivered several years of strong returns and hence, attracted more than their share of capital.
In my experience, identifying and measuring style bias isn't as straight forward as it would seem, yet it is incredibly important as it can have a significant impact on long-term returns. Styles perform vastly differently in varying parts of the business cycle which makes the role of portfolio construction an integral role of providing advice.
At a consultant level, supporting large financial advisory group to construct high-quality portfolios, we tend to recommend a blend or combination of styles to ensure portfolios can navigate most conditions well and ultimately assist advisers in keeping their clients invested during periods of volatility.
Style is generally expressed in a fund manager's investment philosophy and process. However, a statement on their website doesn’t mean they will stick to it when everything is going against them. Simply taking a managers advertised style as fact is the biggest contributor to unintended style bias, the issue being that investors may end up on the wrong side of the bias at a, or multiple, points in time.
Among the most important roles of an asset consultant is to ‘look under the hood’ of every existing and potential investment an adviser may recommend. This includes an exercise of regressing the returns against multiple and varying benchmarks to understand those which they most closely emulate. Being a data-driven process, this can be taken to multiple levels of complexity. For instance, the second level involves analysing the individual investment selection decisions managers have made over multiple cycles to understand if they do truly stick to their knitting.
Obviously, this is only the beginning, and on the micro level of individual funds and stocks. The more valuable part of this analysis is looking at the exposures from a total portfolio risk level, using data that in general is only available to those in the research industry. The most important issues can be assessed including whether there is duplication of investments across managers, sector biases to say technology or healthcare, and size biases across the market cap spectrum.
In an environment where long-term returns are expected to be lower than in recent history, understanding and identifying potential sources of alpha will only grow in importance.
Jake Jodlowski, principal, Atchison Consultants