Growing stronger: Looking Beneath Growth's Outperformance

GROWING STRONGER: LOOKING BENEATH GROWTH'S OUTPERFORMANCE

Growth stocks have put up an impressive performance in 2020, with many emerging from the initial Covid crisis's depths to end the year higher. With their heavy emphasis on future expected earnings and sales, it is not unreasonable to think of Growth stocks as the opposite of Value stocks, which brings back the age-old debate of Growth vs. Value.

Value staged a brief reversal in September 2019 as part of the broader “reflation trade” narrative but was episodic and short-lived; attention once more returned to Growth. Growth is not one of the traditional risk factors, so what does Growth encompass, and what makes certain Growth funds better performing than the others?

To understand this effect, we first extracted all 317 funds labeled as ‘US Equity Large Cap Growth’ within Morningstar. We then create a subset universe of funds with a long track record and a large AUM to run against PremiaLab Pure Factor Model. This subset universe contains 196 funds (avg AUM of 9.1B, total 1.8T AUM) and covers 99% of the entire Growth fund AUM.

The objective of the analysis is to decompose the return tracks into the more familiar risk factors through the prism of our factor model to better understand the factor composition of Growth funds. For this study, we used the US Equity Quality, Momentum, Value, Low Vol, and Size factors.

 

 

US EQUITY PURE FACTORS

The 5Y cumulative performance of the Pure Factors shows a growing divergence between Quality & Momentum vs. Size & Value (see Figure 2). A high degree of polarization is also observed between Value vs. other US Equity Factors (See Figure 3).

 

 

 

PremiaLab Pure Factors^® are constructed by harvesting over 2000 liquid systematic strategies from leading index providers to capture the general market consensus while eliminating model-specific interference. The model covers Equities, FI, Credit, FX, and Commodities with a total of 47 factors.

 

US GROWTH FUNDS FACTOR DECOMPOSITION

Over the past 5 year period, an astounding 194/196 of the Growth funds from our sample outperformed SPX, with average performance +19.67% p.a (median: +19.12% p.a.) vs. SPX +12.93% p.a.. This significant outperformance wasn’t without variability, with Growth fund returns ranging from +11.7% p.a. (min) to +38.1% p.a. (max) over the period.

Correlation analysis suggests a high level of homogeneity across funds with an average correlation of 0.98 over the same period. However, large dispersion of returns is a testament to the notion that there are different ways to implement Growth.

A static 5Y weekly return regression performed on the Growth funds reveals significant implementation differences. Overall, an average R² of 95.4% is achieved through this analysis. 92.6% of it is attributable to benchmark risk, while 2.8% is driven by factors. It is also interesting to note that more than 50% of the funds have a significant (p-value < 5%) factor tilt in all 5 factors.

 

 

There is a general perception that the Growth factor is the inverse of Value factor but is that always the case? We take a deeper look to gain insight into the most common factors used in Growth funds.

Arranging the beta exposure by fund performance, the top quartile funds deployed factors more significantly with a wider variety of factor tilts such as Momentum, Size, and Low Vol in addition to the traditional long Quality & short Value pair. More importantly, there is an absence of Quality tilt within the top performers. Every fund in the top quartile was negative on Low Vol. As we move down the quartiles, there is a falling frequency of usage in Momentum, Quality, and Size. There is a minimum factor tilt in the lowest quartile, and we highlight a noticeable drop in Value & Low Vol presence.

 

 

ROLLING PERFORMANCE ATTRIBUTION

While the factor risk measured might seem limited in a single period regression, Growth funds factor risk exposure can change dynamically over time, suggesting Growth funds are not static and constantly evolving around different market regimes/environments. This motivated us to deploy a more dynamic approach in this section to drill deeper and investigate the relationship between Growth funds and factor risks.

To capture the dynamic behavior, we performed rolling regression (52W, Monthly rolling window) on individual fund against SPX and 5 other US Equity Factors (Low Vol, Momentum, Size, Value, Quality) and plot the AUM weighted rolling regression metrics to generalize the typical Growth fund behavior. The objective is to understand the factor risk exposure of these funds and how it changed over time. Therefore we removed the benchmark from Figure 6 below to really enable us to focus on the factor tilt.

The Rolling Beta graph (Figure 5) plots the average beta coefficients over time and confirms the factor positioning is stable. Betas of Value and Low Vol are consistently negative, while Quality, Size, and Momentum are typically positive. Since 2018, Quality slowly became less relevant as its beta gradually dropped close to 0, while Momentum slowly picked up importance.

 

 

The rolling Return Attribution graph (Figure 6) demonstrates how factors could explain the variation of returns versus the market benchmark and gives us insights into the factors responsible for the overperformance or underperformance over specific periods. Rolling Return Attribution reveals Growth funds’ performance is primarily driven by three factors: Value, Low Volatility, and Size.

 

 

Value performance was consistently poor over this period, with clustered periods of positive performance during the second half of 2016, Q3 2019, and Q1 2021 (ongoing) due to rising interest rates on the back of improved growth prospects for global growth.

After adjusting for the benchmark risk, short Low Vol has shown to be contributing negatively towards the funds’ performance for most of the time, as mutual funds typically invest in high volatility stocks to track their fully invested benchmarks while keeping liquid cash available to meet potential investor redemptions.

Size has not been performing well in the recent 5Y, and the combined effect of positive Size exposure and its poor performance has resulted in a negative return attribution across the sample period, as the move to passive investment vehicles has benefited large-cap companies.

 

CONCLUSION

We have shown through the use of our Pure Factors that growth funds’ outperformance over the market comes mostly from exposures to Value, Low Vol, and Size. Value played a key role and has received much attention recently. It will be worthwhile to do a similar exercise for Large Cap Value Funds.

Growth funds have had a stellar year capitalizing on the divergence of equity factors performance, making them especially vulnerable to a potential factor rotation. Accompanied with increased sensitivity towards Momentum, a change in factor dynamics could easily destabilize their performance and lead to heightened market volatility.