Systematic Investment Strategies
- Daniel Giamouridis (Co-editor of FAJ)
- Financial Analysts Journal
- A version of this paper can be found here
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What are the research questions?
- How will systematic (“coordinated”) investing affect prices?
- What is the risk of increasingly coordinated holdings (crowding)?
- How does coordinated investing affect market microstructure and optimal execution?
- Is factor timing possible?
- What are the issues in the design of factor-based strategies?
- What is the role of data science and machine learning?
What are the Academic Insights?
- The author defines “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. Theoretical predictions have yet to be extensively tested with data, while the empirical evidence that is published on this question is mixed. While ETFs facilitate efficient pricing, it has been established that market prices have become noisier. Higher levels of systematic trading risk in ETF constituent stocks compared to non-constituent stocks have been documented. These observations have implications for diversification, trading and cross-asset dependence of impact cost and stock selection (alpha).
- There are too many investors and strategies chasing too few factors (remember August 2007?). An accurate measure of crowding is needed. The measure should include bottom-up measures of constituent liquidity, market impact, and should take into account the tendency that investors have to “time” factors.
- Systematic strategies, by design, have a natural order flow, leading to coordinated portfolio trade lists. The execution of these trade lists increases covariances and correlations of intraday returns and volume, both contributing to variability of observed execution costs.
- Academic evidence suggests that reducing tracking error (active risk) during times of high volatility in the market is an effective strategy for avoiding “bad” times in factor investing. Increasing interest from practitioners on this topic should spur research on the market positioning of factor portfolios, identifying factor exposures to macro risks, and factor risk concentration in portfolios.
- Questions remain as to how a specific factor product performs relative to its stated objective. What is the specific factor strategy’s return performance relative to the actual factor premium? How is the actual factor premium to be measured? As an example, do all Value products perform equally at capturing the Value premium? How is the Value premium defined in an applied setting?
- The issue of whether or not “machine learning” is actually a new paradigm for systematic strategies has not been determined. It is undeniable that quants have always used “big data” and statistical methods which are the commonly accepted hallmarks of machine learning. Can other machine learning approaches such as pattern recognition, outperform the traditional application of linear regression to factor investing and trading? The empirical evidence with respect to the application of machine learning to all aspects of investing: alpha, beta, risk management, trading and execution is yet to be explored.
Why does it matter?
In this editorial published by the CFA, the author discusses key research topics that will directly impact investment practice. The discussion is based on conversations with investors and active managers, both on the quantitative side and of the fundamental/macro persuasion, who recognize their performance is affected by the growth in quantitative/systematic strategies.
The application of systematic investment strategies in the context of current practices in asset management presents an abundance of questions that can benefit from the rigorous approach of empirical research.
The most important definitions from the paper (there was no chart!)
- The author ” uses 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.
- Factor returns are rewards for risk-taking. 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).
- For more references, 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).
- The focus of this editorial is equities because systematic strategies were first developed in that asset class. But
the majority of the author’s 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.”
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.
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