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How AI May Help Investors Tackle Cyclicality of Equity Factors
AI-driven dynamic factor timing may help investors adjust to the natural cycles of factor performance, potentially enhancing responsiveness to changing market conditions.
KEY POINTS
What it is
We explore how an artificial intelligence-based framework could improve equity-factor performance by dynamically adjusting to market conditions over time.
Why it matters
Traditional, static methods may not pick up these market changes, while artificial intelligence approaches are well-suited to identify them and adjust.
Where it's going
We anticipate several important implications of this methodology, providing investors with enhanced tools for navigating increasingly complex financial markets.
Equity investors have long embraced equity factor-based investing — using factors such as quality, value and momentum — with the aim of improving portfolio performance. However, just like many other assets, factors exhibit cyclicality, meaning they can each go through periods of underperformance. In recent research, we collaborated with University of Chicago Professor Stefan Nagel to uncover ways to apply artificial intelligence to potentially mitigate the risks of cyclicality through what we call dynamic factor timing. Let’s take a closer look.
Factor timing involves identifying relationships between variables such as sentiment, valuation, or the business cycle and how they impact factor returns. Most prevailing factor timing techniques are static in nature, following the model output to overweight the factors more likely to outperform. However, this static approach ignores the changing, or dynamic, relationship between predictor variables and factor performance over time. Recognizing that, in reality, factor relationships are not constant, we aimed in our research to build upon static approaches and create a dynamic approach to capture changing relationships. We call this dynamic factor timing.
With dynamic factor timing, we used artificial intelligence to determine whether the output from a factor timing approach has a sufficient chance of success. In other words, artificial intelligence can insert skepticism into the mix of whether to apply a factoring timing approach and to what degree. Should the approach deem factor timing temporarily ineffective, then the use of factor timing could be dialed down, or even turned off. This can happen when the degree of skepticism is high, as determined by artificial intelligence. Alternatively, when the model is confident and the degree of skepticism is low, the portfolio tilts toward the dynamic factor-timing portfolio.
We have found through this research with Professor Nagel that merely identifying and exploiting relationships that are economically meaningful and showing positive long-run average results can ignore how those relationships change over time. Traditional, static methods may not pick up these changes, while artificial intelligence approaches are well-suited to identify them and adjust. We anticipate several important implications of this research, providing investors with enhanced tools for navigating increasingly complex financial markets.
Meet Your Expert
Michael Hunstad, Ph.D.
Deputy Chief Investment Officer & Chief Investment Officer of Global Equities
Michael Hunstad is Deputy Chief Investment Officer and Chief Investment Officer of Global Equities for Northern Trust Asset Management. Michael is a member of the Asset Management Executive Group and has oversight of all equity portfolio management, research and trading activities including quantitative, index and tax-advantaged strategies. Additionally, he assists with the development of investment vision, strategy portfolio construction and risk management framework for the firm’s broad investment platform.
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