Equity factor strategies have faced challenges since the COVID-19-induced market crash of 2020. These challenges have led to underperformance compared to cap-weighted indexes. While there are various explanations for this, our focus is on the question of whether it is possible to align a factor portfolio’s performance more closely with a cap-weighted benchmark while retaining the benefits of factor investing.
To answer this question, let’s start by reviewing the drawbacks of cap-weighted indexes. In these indexes, companies with higher market caps receive a higher weighting, while smaller companies receive a lower weighting. The risk with cap-weighted strategies is threefold. First, companies with larger weights may experience losses as they revert to lower price levels. Second, underweighting smaller companies may prevent investors from benefiting from potential growth. Finally, cap-weighted indexes are concentrated in a small subset of large stocks, which lacks diversification and exposes investors to significant downside risk if these stocks experience large drawdowns.
In contrast, a well-constructed equity factor strategy is driven by risk factors that have been shown to reward investors over the long term. These factors, including Value, Momentum, Size, Profitability, Investment, and Low Volatility, have been empirically validated and have a clear economic rationale. Multi-factor portfolios that include exposure to these factors are typically more diversified and have lower volatility compared to cap-weighted indexes.
What Can Be Done?
While tilting towards cap-weighted benchmarks may not be beneficial in the long run, there is a middle ground. Investors can continue investing in a factor strategy while applying tracking error constraints to reduce the performance gap between factor portfolios and cap-weighted indexes over a given period. This approach has both pros and cons, both in the short and long term.
How Do Tracking Error Constrained Factor Portfolios Behave?
The performance differences between a standard six-factor portfolio and tracking error constrained variants can be seen in the chart below. Applying tracking error constraints reduces the performance gap between factor portfolios and cap-weighted indexes. However, these constrained portfolios also have higher volatility and a deterioration in downside protection compared to the standard portfolio.
Factor Portfolios with Tracking Error Constraints,
31 December 2022 to 30 June 2023
| Cap Weighted | Six Factor Equal Weight | Six Factor Equal Weight 1% TE Target | Six Factor Equal Weight 2% TE Target | |
| Return | 17.13% | 6.04% | 14.70% | 12.38% |
| Volatility | 14.44% | 13.10% | 14.05% | 13.72% |
| Sharpe Ratio | 1.01 | 0.27 | 0.87 | 0.72 |
| Max. Drawdown | 7.43% | 7.90% | 7.51% | 7.61% |
| Relative Return | – | -11.09% | -2.43% | -4.75% |
| Tracking Error | – | 4.65% | 0.98% | 1.95% |
| Information Ratio | – | n/r | n/r | n/r |
| Max. Relative Drawdown | – | 10.04% | 2.19% | 4.29% |
The sector composition of the TE-controlled portfolios in the following table shows a reduced underexposure to the Technology sector relative to the standard multi-factor portfolio. This is not surprising, as larger technology companies have been a major driver of the outperformance of cap-weighted indexes.
Sector Allocations as of 30 June 2023
| Cap Weight-ed | Six Factor Equal Weight | Six Factor Equal Weight 1% TE Target | Six Factor Equal Weight 2% TE Target | ||||
| AbsoluteWeight | Relative Weight | Absolute Weight | Relative Weight | Absolute Weight | Relative Weight | ||
| Energy | 4.7% | 6.3% | 2.0% | 5.3% | 0.6% | 5.9% | 1.2% |
| Basic Materials | 2.3% | 2.6% | 0.3% | 2.4% | 0.0% | 2.4% | 0.1% |
| Industrials | 8.8% | 7.4% | -1.4% | 8.3% | -0.4% | 7.9% | -0.9% |
| Cyclical Consumer | 12.4% | 11.7% | -1.0% | 12.0% | -0.3% | 11.7% | -0.7% |
| Non- Cyclical Consumer | 6.5% | 11.2% | 5.1% | 7.4% | 0.9% | 8.3% | 1.8% |
| Financials | 12.7% | 13.1% | 1.5% | 12.9% | 0.2% | 13.1% | 0.4% |
| Health Care | 14.2% | 17.7% | 4.2% | 14.8% | 0.6% | 15.4% | 1.2% |
| Tech | 34.5% | 21.5% | -15.7% | 31.7% | -2.8% | 28.9% | -5.7% |
| Telecoms | 1.1% | 2.0% | 0.9% | 1.3% | 0.2% | 1.6% | 0.4% |
| Utilities | 2.7% | 6.6% | 4.1% | 3.8% | 1.0% | 4.8% | 2.1% |
Over a longer period, controlling for tracking error reduces risk-adjusted performance by increasing volatility and reducing returns. The information ratios and the probability of outperforming the cap-weighted index also deteriorate slightly.
Long-Term Risk Adjusted Performance,
30 June 1971 to 31 December 2022
| Cap Weighted | Six Factor Equal Weight | |||
| Standard Portfolio | Standard Portfolio TE 1% | Standard Portfolio TE 2% | ||
| Annual Returns | 10.22% | 13.10% | 10.95% | 11.63% |
| Annual Volatility | 17.33% | 15.53% | 16.82% | 16.38% |
| Sharpe Ratio | 0.33 | 0.55 | 0.38 | 0.43 |
| Max. Drawdown | 55.5% | 50.9% | 54.0% | 53.5% |
| Annual Relative Returns | – | 2.88% | 0.72% | 1.41% |
| Annual Tracking Error | – | 4.20% | 1.14% | 2.21% |
| Information Ratio | – | 0.69 | 0.63 | 0.64 |
| Max. Relative Drawdown | – | 20.1% | 5.8% | 10.7% |
| Outperformance Probability (One Year) | – | 66.89% | 67.71% | 67.38% |
| Outperformance Probability (Three Years) | – | 79.42% | 75.81% | 75.30% |
| Outperformance Probability (Five Years) | – | 86.94% | 84.62% | 84.44% |
Conclusion
Using tracking error constraints can help manage the tracking error of multi-factor indices and reduce sector deviations. However, aligning a factor portfolio’s performance with a cap-weighted index may be detrimental to both absolute and risk-adjusted returns in the long term. Simple cap-weighted approaches to equity investing lack the conceptual foundations for superior long-term risk-adjusted performance.
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All posts are the opinion of the author and should not be considered investment advice. The opinions expressed do not necessarily reflect the views of CFA Institute or the author’s employer.
Image credit: ©Getty Images/ Wengen Ling
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