Systematic Investment Strategies

Abstract

Systematic, rules-based investment strategies are where academia and practice are currently interacting strongly. My objective in this editorial is to offer some thoughts on research on systematic investing, including three articles in this issue, that can provide significant practical benefits for academics, practitioners, and investors alike.

Author’s note:

The views expressed in this editorial are my own and do not necessarily reflect the views of Bank of America Merrill Lynch.

Notes

¹ I use the term factor in a generic sense. A factor is a characteristic of a firm or asset class that is believed to drive its returns and that is typically used to create a rules-based portfolio. For example, a firm’s P/B can be used to construct a portfolio of low-P/B firms.

² See, for example, Andrew Ang, Asset Management: A Systematic Approach to Factor Investing (New York: Oxford University Press, 2014); Lasse Pedersen, Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined (Princeton, NJ: Princeton University Press, 2015); and Antti Ilmanen, Expected Returns: An Investor’s Guide to Harvesting Market Rewards (Chichester, UK: John Wiley & Sons, 2011).

³ The focus of this editorial is equities, because systematic strategies were first developed in that asset class. But the majority of my assertions also apply to other asset classes to the extent that systematic strategies are widely implemented. For example, the fixed-income space has seen tremendous growth in ETF assets in the last couple of years, factor-based strategies have become popular in all asset classes, and numerous investment products offer access to cross-asset risk premiums.

⁴ I define coordinated investing as buying stocks in “baskets.” For example, when an investment in an ETF is made, the ETF sponsor buys the entire basket of constituent stocks at the same time.

⁵ For a detailed discussion of ETFs as products and how they are used in investment strategies, see Hill, Nadig, and Hougan (2015).

⁶ This particular interpretation of factor returns explains why returns to some factors have been very persistent. An alternative interpretation is that some factor returns are either behavioral or friction-induced market anomalies and so timing them should be associated with the ebbs and flows of the underlying behavioral tendency or market friction. A third interpretation is that some factor returns can be the result of extensive data mining. When I refer to factor returns here, I am referring only to the former categories. A separate stream of research is focused on establishing criteria for factor return significance and determining a sensible set of factors (see, e.g., Harvey, Liu, and Zhu 2016).

⁷ Here, I am referring only to factor returns in the two categories I mentioned earlier (i.e., rewards for risk taking or for a behavioral tendency) and not those that are the result of data mining.

⁸ On Google Trends, for example, interest in “machine learning” is stable over 2004–2012 and then increases monotonically to its current peak level.

⁹ Standard techniques, including CART, LASSO (least absolute shrinkage and selection operator), principal component analysis (PCA), and countless variants, are found in widely used machine-learning libraries (e.g., Python’s scikit and MATLAB^®’s Statistics and Machine Learning Toolbox).

¹⁰ A trading algorithm determines and implements a schedule of trade execution—that is, the volume and timing, during a day or multiple days, of a stock’s purchase or sale.

¹¹ “Quant training” can mean many things, but skills in basic empirical asset pricing, risk management and portfolio construction, market microstructure, quantitative methods, and programming are essential.