There seems to be something like a distant relationship between low
volatility and the quality investment approach, but nothing more: this
was the verdict of the first two parts of this series of articles, where
we subjected the two investment strategies to a descriptive (Part 1) and qualitative (Part 2) comparison. However, the value
portfolio also examined seems to have almost nothing in common with the
two approaches mentioned above. In this article, we will use statistical
indicators to analyze whether the findings so far also stand up to
quantitative evaluation. For this purpose, the sensitivities of the
three factor portfolios to the Fama-French factors known in the
financial literature are determined.[1]
Explanatory power times five
The three portfolios examined all rely on factors that empirically
demonstrate better risk/return characteristics than the market index
over the long term. The selection of these factors is no coincidence.
Theory and practice have been dealing with the question of which
systematic factors explain the excess return of investments for decades.
From a multitude of answers, different convictions regarding the best
investment strategy have emerged. Eugene Fama and Kenneth French's
"five-factor model", an updated version of their famous "three-factor
model" from 1993, provides a sound comparison of the return composition
of the three investment approaches shown here.[2] While Fama and French
in the 3-factor model still assume that the return on a share can
essentially be explained by the "market risk", the "size of the company"
(size) and the "valuation" (value), the two renowned scientists have
expanded their universe of yield-driving factors in the 5-factor model
by the - not undisputed [3] - dimensions "profitability" and "investment
activity". While the market risk factor reflects the risk premium of a
market portfolio diversified across the entire equity market compared to
a risk-free investment, the size factor takes into account the fact
that shares with low market capitalization (so-called small caps) have
historically achieved a higher return than large-capitalized securities
(so-called blue chips). In contrast, the value factor is based on the
observation that shares with a low price/book ratio (P/B) yield a higher
average return than shares with a high P/B ratio. The two newly added
factors target different aspects of "quality". While the profitability
factor is based on an increased return on equities with good operating
profitability, the investment factor assumes that high investment
activity, measured as a strongly growing balance sheet total, leads to
declining profitability and increases risk.[4] The "five-factor model"
is of interest to us because it measures how strongly an investment
strategy is exposed to the major return drivers identified by Fama and
French. Accordingly, the five-factor model is well suited to locate the
differences and similarities between the investment returns of the three
portfolios we examined.
Volatility is a part of it
Since we examine a value, a quality and a low volatility portfolio,
it makes sense to include a volatility factor in the statistical
analysis in addition to the five-factor model, which already includes
value and quality. The volatility factor is based on the empirical
effect known as the "low volatility puzzle", according to which
securities with low volatility often perform better in the long term
than those with high volatility. In line with Fama-French's methodology,
we construct the volatility factor as a long-short portfolio, with
equities with historically low volatility receiving a positive weight
(long) and equities with high volatility a negative weight (short).
When explaining the returns of the three portfolios using the Fama
French factors and the additional volatility factor, clear patterns can
be seen (see table and chart). As expected, the portfolios have, in
addition to the market factor, the highest exposure to the factor that
should be included in it according to the construction criteria. Thus,
the value portfolio is mainly driven by the value factor, the quality
portfolio by profitability and the low-volatility portfolio by the
volatility factor.
The analysis of the coefficients and their statistical significance
helps us to better understand the similarities and differences of the
three portfolios examined. The low-volatility and quality portfolios
show that both are influenced by risk and profitability factors.
Although the returns of the two portfolios are positively driven by
lower risk and high profitability equities, there are clear differences
in the level of coefficients. The low-volatility portfolio is strongly
driven by the risk factor, while the quality portfolio has a
statistically significant but much smaller exposure than the volatility
factor. In addition, there are other differentiating features. The
investment and value factors show that low volatility and quality are
not equivalent substitutes for each other. While the low-volatility
portfolio is mainly made up of companies with low investment activity
that make a positive contribution to returns, no comparable statement
can be made for the quality portfolio. The corresponding regression
coefficient here is even slightly negative, but not statistically
significant. With the value factor, the difference between the two
investment paradigms becomes even clearer: while the low volatility
portfolio has no significant exposure to the value factor, the quality
approach has a strong negative impact on the value factor. This negative
sensitivity proves to be critical for the quality portfolio, as
securities with a high valuation have a negative impact on the return.
This is a fact that must be taken into account in the construction of
the portfolio.
Finally, the analysis of the regression coefficients of the value
portfolio provides an important insight into stock selection. As is to
be expected from the construction, the value portfolio shows a strongly
positive sensitivity to the value factor. Among the securities selected
by the value portfolio, however, there is also a large number of
securities with high volatility, which is reflected in a negative
coefficient relative to the volatility factor and is an important reason
for the comparatively weak performance of the value portfolio. When
constructing a value portfolio, the risk component of the individual
securities must therefore be explicitly taken into account. This is
important in order to avoid a so-called "value trap". This is the only
way to prevent a value portfolio from investing in equities that look
cheap in valuation terms but are also very risky or even on the verge of
bankruptcy. In short, we conclude that when constructing a value
portfolio the risk should be taken into account and when structuring a
quality portfolio the valuation should be taken into account.
The coefficients of the three portfolios compared to the other two
factors market and size do not provide any differentiating features. As
expected, the market factor proves to be the most important and
comparably strong driver of returns for all portfolios. The fact that
the size factor has a negative sign for all three portfolios is due to
their composition, as only equities of medium-sized and large companies
were used to construct the portfolios.
Volatility and quality: two independent factors
According to our analysis, the returns on the low-volatility and
quality portfolios are thus both influenced by risk and profitability,
but to varying degrees. The same picture emerges with regard to the
sensitivities to the value and investment factor: here, too, there are
considerable differences. Paired with the findings from the first two
articles, this demonstrates that there are commonalities between quality
investing and the low-volatility approach, while the value approach
focuses differently. However, if one looks at it in a more
differentiated way, it becomes clear that there are fundamental
differences between all the above-mentioned investment paradigms. Low
volatility and quality have certain points of contact, but they are
definitely independent investment approaches.
The question remains as to how the three portfolios will behave
during different market cycles. We will examine this in more detail in
the coming 4th part of the analysis.
[1] The sensitivities correspond to the coefficients of a multiple
linear regression of the portfolio returns to the Fama-French factors. A
statistical test (t-test) can be used to check the significance of the
results.
[2] Fama and French 2015
[3] Blitz et al. 2018
[4] See Novy-Marx 2013 and Fama and French 2006.
