Factor Performance Review
Of the six factors we track (growth, quality, low-volatility, momentum, size, and value) in our multi-factor strategy, the low-volatility factor enjoyed the best performance over the past month, turning in 4.7% relative to its benchmark. The small-cap factor also had a good month, earning 3.8% relative to its benchmark; the strong performance for small-caps followed a weak January, when the small-cap factor underperformed by 4.5%. Value also outperformed over the past month, contributing 2.2%. Performance for each of the six factors is shown as the gray bars in Fig. 1.
Despite its strong performance last month, the size factor (small-caps over large-caps) still lags the other five factors, as it has underperformed by 19.9% on a trailing 12-month basis. Also, despite its recent outperformance, value continues to lag on a trailing 12-month basis.
Fig. 1 – Recent Performance of Factors
Multi-Factor Portfolio Performance Review
We apply a dynamic tilting mechanism to a multi-factor portfolio that adjusts weight toward the factors with the best recent performance, and away from the factors with the worst recent performance. Fig. 2 shows the cumulative performance of this dynamic multi-factor strategy relative to the S&P 500 since 1997.
Fig. 2 – Dynamic Multi-Factor Strategy Relative Performance
From the start of 2020 through March 4, 2022, the dynamic multi-factor strategy returned 38.9%. Over that same period, the S&P 500 gained 34.0%, for 4.9 percentage points of outperformance for our strategy. Fig. 3 below shows the monthly performance of the dynamic strategy vs. the S&P 500 since the start of 2020. The dynamic strategy turned in a positive return for February, outperforming the S&P 500 by 0.5%. An overweight toward the low-volatility factor in February contributed to the outperformance.
Fig. 3 – Dynamic Strategy Recent Relative Performance
Dynamic Model: Factor Weights for March
Fig. 4 below indicates the latest weights assigned to each of the six factors in the dynamic multi-factor strategy. The dynamic strategy is currently overweight the value and quality factors while being underweight size (small-cap) and growth.
Fig. 4 – Updated Factor Weights in Dynamic vs. Static Multi-Factor Portfolio
Baseline Stock Selection Model: Performance and Discussion
We recently launched a stock selection framework that uses factors across five dimensions (value, quality, momentum, estimates and investment) to predict stock performance. The model produces a list of 100 favored investments from across the S&P 500 constituents. As an ongoing part of these monthly updates, we will report on the performance of the stock selection framework. Fig. 5 below shows the historical performance of the basket of 100 favored stocks, rebalanced monthly.
Fig. 5 – Performance of Long Basket of Stock Selection Model (Relative to S&P 500)
Fig. 6 below shows the performance of the favored baskets for each of the 5 composite factors (value, quality, momentum, estimates and investment) that make up the stock selection model, along with the performance of the overall model for February. The overall model contributed 0.66% of outperformance relative to the S&P 500 during the past month (yellow bar at right). At the factor level, value and investment enjoyed the best performance, while the quality basket underperformed its benchmark.
Fig. 6 – Performance of Factors and Overall Model for February
