Gregg S. Fisher founded Gerstein Fisher in 1993 based on a vision of offering a quantitative investment management approach grounded in sound economic theory and more efficiently implemented through technology. Today the firm embodies that vision and continues to reflect Gregg’s commitment to ongoing research and quantitative, factor-based investing.
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Throughout his career, Gregg has worked to bridge the gap between academic theory and real-world investment practice. He has spearheaded research projects on areas of study including the efficacy of momentum and valuation models, tax-efficient investing, the impact of investor behavior on investment returns, and the persistence of certain investment factors across global equity markets.
I interviewed Gregg last week.
You were one of the first to focus exclusively on multi-factor investing, an approach that is now much more widely used and at the center of the active/passive/smart beta discussion. What is multi-factor investing and how is it implemented in your funds (Multi-Factor Growth Equity, Multi-Factor International Growth and Multi-Factor Global Real Estate)?
Multi-factor investing works by identifying characteristics, or “factors,” of stocks or other securities that research shows explain differences in historical and expected returns. The multi-factor model is actually a straightforward idea: The portfolio return is equal to the risk-free rate, plus factor premiums and exposures, plus what’s left, the residual (or “alpha”).
There are two main theories that explain factor premiums: The efficient market theory offers a risk-based explanation, and behavioral-based theory links some factors to the actions of investors. The risk-based story suggests that equity investors should be rewarded for taking risk in the form of enhanced returns. So as risk ebbs and flows, security returns will fluctuate. The behavioral explanation suggests that investors’ greed and panic causes prices to change due to buying and selling. We don’t take a position on which explanation is right, risk-based or behavioral-based; what we care about is that we have factors that hold up over time based on empirical data.
Which factors and asset classes are providing opportunity in this environment? How do you estimate the expected long-term returns from factors and asset classes?
We recently wrote a short paper on the relative decline in investor interest (measured by returns and market-cap growth) in profitability in recent years. There is a case that (much as in other forms of traditional value-based investing) being comfortable maintaining or shifting exposures into recently out-of-favor factors (small-cap and value stocks in 2017, for example) may be beneficial over the long term.
In estimating long-term expected returns, we use as much historical data as possible without assuming that history will repeat. The recent strong performance of momentum, for example (outperforming by between 5% - 10% in 2017 versus low-momentum stocks), and the long-term ~6% annualized premium momentum has demonstrated over the last 50+ years has to be weighed against its significant downside, where in 2008-2009 for example, it underperformed the general market by 60% or more. In addition, some of the momentum premium can be eaten up by high turnover and trading costs, since it is one of the so-called fast-moving factors.
Read the full article here by Robert Huebscher, Advisor Perspective