Growth vs. Value
May 4, 2015
Entering an Exclusive Dimension
In one of the previous posts, Alpholio™ made the case for increasing the mid-cap stock holdings in the portfolio. As promised, in this follow-on post, we will examine the performance of growth vs. value equities.
A recent article on this topic in The Wall Street Journal states that
Over the past year, the average U.S. large-cap growth fund has risen 18.2%, while the average U.S. large-cap value fund is up 10.4%… from 2003 through 2013, the average gap between the two styles of stock-picking for large-cap stocks was 0.75 percentage point… it’s a similar story among small-company stocks, where growth-stock funds […] are up 16% over the past year. Funds investing in small-cap value stocks […] are up 7.7%.
The trend of growth equities outperforming value equities is hardly a past-year phenomenon. Contrary to what might be expected, this trend is also not confined to the last seven years since the market’s trough during the financial crisis. The trend is best examined using specific ETFs as opposed to hypothetical and unspecified “average U.S. [mutual] funds.”
To start with, let’s use the Total Return service of the Alpholio™ App for Android to review the long-term performance of a couple of long-lived large-cap ETFs, the iShares S&P 500 Growth ETF (IVW) and iShares S&P 500 Value ETF (IVE), from their inception through March 2015, using monthly total returns:
In that period, the large-cap value ETF handily outperformed its growth counterpart, albeit with a slightly higher standard deviation (a measure of volatility of returns). However, this only paints a part of the picture: in 2000, growth stocks significantly underperformed, following the deflation of the dot-com bubble. If the start of the analysis period is advanced to the beginning of 2001, growth slightly outperformed value:
Through the market peak in October 2007, growth stocks did not advance as much as value ones did, but they suffered a much smaller drawdown (45.4% for growth vs. 56.7% for value, as calculated by the Portfolio service).
The growth outperformance becomes even more pronounced when the beginning of the analysis is moved to April 2005 for a 10-year evaluation period:
Large-cap growth stocks returned about 2% more than their value counterparts, and did so with much smaller volatility. As shown by the Rolling Returns service, in the same period growth outperformed value in about 90% of all rolling 36-month intervals, 67% of 24-month intervals, and 63% of 12-month intervals:
The median difference of rolling 12-month returns over the last 10 years was over 2.6% in favor of growth.
For mid-cap stocks, let’s use the iShares S&P Mid-Cap 400 Growth ETF (IJK) and iShares S&P Mid-Cap 400 Value ETF (IJJ). As with large-caps, the 10-year performance of growth mid-caps was better than that of their value peers:
Finally, a similar chart for the iShares S&P Small-Cap 600 Growth ETF (IJT) and iShares S&P Small-Cap 600 Value ETF (IJS) also demonstrates the growth superiority over value:
It is worth noting that the outperformance of growth stocks over value ones in this analysis period appears to directly contradict the value effect in the classic three-factor model. However, the latest research from Fama-French indicates that this factor is less important in the presence of the beta, size, profitability and investment factors.
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January 7, 2014
Correlations of Factor ETFs
A cover story in Barron’s provides lots of interesting details about the history and operations of Dimensional Fund Advisors (DFA). Founded in 1981, DFA has recently reached $332 billion in assets under management (AUM).
About 78% of these AUM are in stocks, and about 85% in low-cost mutual funds with an average expense ratio of 0.39%. The funds have a small-cap and value tilt, based on the Fama-French three-factor model. Lately, the firm started to augment its funds with a profitability factor.
The article states that
More than 75% of its funds have beaten their category benchmarks over the past 15 years, and 80% over five years, according to Morningstar — remarkable for what some investors wrongly dismiss as index investing.
To substantiate this, the article compares two similar funds from DFA and Vanguard:
For example, take the Vanguard Small Cap Value index fund (VISVX), which is based on the S&P 600 Small Cap Value index and is the counterpart to Dimensional’s DFA US Small Cap Value (DFSVX). The DFA fund has a much smaller tilt — its average market value is $1.1 billion, versus Vanguard’s $2.7 billion — and on all measures is much more value-oriented. So the Dimensional fund better captures the market-beating advantage of small and value stocks. In fact, a lot better: The DFA fund returned 42% in 2013, beating 88% of its peers in Morningstar’s small-cap value category, versus the Vanguard fund’s 36% return, which beat just 53%. Over 15 years, which includes periods that were less favorable to small and/or value stocks, DFA’s fund returned an average of 12% a year, beating 80% of peers. The Vanguard fund returned 10% on average, beating just 37% of peers. The Dimensional fund costs twice as much as Vanguard’s — 0.52% versus 0.24% — but the significant outperformance more than makes up for that difference.
That only tells a part of the story. According to Morningstar data, DFSVX had a lower Sharpe Ratio than VISVX in the 3-year (0.96 vs. 1.01) and 10-year (0.47 vs. 0.48) periods through 2013. This is also reflected in the generally higher volatility and upside and downside capture ratios for the DFA fund. As a result, the DFA fund produced lower returns than the Vanguard fund did in the down years of 2007, 2008 and 2011.
The article says that a deliberately paced trading as well as market making in the 14,000 stocks DFA owns both add to its outperformance. However, DFA faces an ongoing criticism: since its funds are sold exclusively through 1,900 rigorously screened and trained financial advisors, they are not easily accessible to individual investors, especially those with a small amount of investable assets, not willing to pay advisory fees or already having an unaffiliated advisor. This is what creates an “exclusive dimension” of DFA, which Alpholio™ can help investors enter. Following up on one of the previous posts, let’s analyze DFSVX in more detail.
The following chart shows the relative performance of the fund vs. its reference portfolio of ETFs:
An investor who committed to the fund in early 2005 would have gained only a modest amount of cumulative RealAlpha™ by late 2013. This was mostly caused by the fund’s underperformance in the three years mentioned above. In addition, at about 22.7% the annualized volatility of the fund was 2% higher than that of its reference portfolio in the overall analysis period.
The next chart illustrates ETF weights in the reference portfolio in the same period:
The fund could effectively be emulated by a collection of just four related ETFs: iShares Russell 2000 Value (IWN; average weight of 34.9%), iShares S&P Small-Cap 600 Value (IJS; 30.1%), iShares Morningstar Small-Cap (JKJ; 18.5%), and iShares Morningstar Small-Cap Value (JKL; 13.7%). (The remaining two ETFs accounted for only 2.8% of the reference portfolio on average.)
The weighted expense ratio of these four ETFs is currently only 0.33% compared to the fund’s 0.52%. In addition, while an investor trading these ETFs might incur some commission, spread and premium/discount costs, he/she would not have to pay a recurring advisory fee of about 1% (or be forced to switch advisors) to gain benefits similar to those offered by DFA funds. Over time, dedicated factor ETFs will likely make such fund substitution even easier. Thus, entering an exclusive dimension of factor investing is no longer as hard as it has been.
To get a unique perspective on the DFA and other funds, please register on our website.
August 28, 2013
On Factor Investing
BlackRock has recently introduced a set of four iShares ETFs that follow factor indices. They are:
The first three of these ETFs debuted on April 16, 2013, while the fourth one three months later. Therefore, as of this writing, there are only 91 and 28 trading day data available for these ETFs, respectively. Traditionally, at least three years worth of data (a minimum of 36 monthly data points) are required to calculate a return correlation between two investments. However, it may be helpful to take an early look on how the return correlations among these ETFs and the iShares Core S&P 500 ETF (IVV) are shaping up so far:
Since daily returns are assumed to contain a substantial amount of “noise,” and the observation period is very limited, the above figures certainly cannot be considered very reliable. However, there is an early indication that the majority of correlations are lower than 0.6, which should aid in portfolio diversification. A research paper from BlackRock shows that idealized zero-net-investment factor portfolios constructed using Fama-French approach* can have much lower long-term correlations:
*MktRf = market, SMB = size, HML = value, CME = quality.
Only time will tell whether these new factor ETFs provide low inter-correlations and sufficient returns to truly benefit an investment portfolio. However, early signs are encouraging.
August 22, 2013
Quite a few of recent industry articles focus on factor investing.
Rick Ferri has a two-part article on the topic, with the first part covering the history of multi-factor models, and the second part delving into more practical considerations. According to the author, factor investing has the following benefits:
- Outsized performance (returns) compared to a single-factor (market) portfolio, e.g. as historically observed for small-cap stocks
- Combination of uncorrelated factors leads to a higher risk-adjusted performance of the portfolio
- Intellectual enrichment and academic stimulation that stems from studying of multi-factor models.
The author also points out the disadvantages of factor investing:
- Cost of factor vehicles [although the actual expense ratio of VTI is 0.05% and not the cited 0.15%]
- Historical lack of risk premium persistence of factors such as size.
The author comes to the same conclusions about factor-based products from DFA as Alpholio™ already did in one of the previous posts. However, in his zeal to defend pure market-based factor investing, the author confuses terms:
Finally, tracking error is the name give [sic] to a strategy that falls short of a market benchmark. It could mean the downfall for many multifactor investors.
A tracking error of a factor investment vehicle is in reference to the theoretical index of this vehicle and not to the market benchmark. For example, a momentum factor does not purport to track the S&P 500® index.
The author goes on to combat the term “smart beta” in his post on InvestmentNews. Despite a more pragmatic approach from Arnott, the author quotes noted academics (Sharpe, Fama, and French) to instill the message of beta purity, :
I believe the original definitions are best left unchanged. Beta is non-diversifiable market risk, other return dimensions are defined as additional risk factors, and putting these risks together in a portfolio is multifactor investing.
In the end, does it really matter if factor coefficients in a multiple regression are labeled beta1, beta2, …, or beta, gamma, delta, …? Sure, “smart beta” may sound like a marketing gimmick from fund providers to peddle their latest products, but it is a simple way of conveying the difference of these factors from plain market-cap based ones.
Finally, an article in Morningstar focuses on the scientific background of multifactor investing. It also presents two points of view on factors: from the perspectives of efficient and not perfectly rational market. The author leans toward the second interpretation, which is supposedly supported by the following “evidence:”
It’s also hard to reconcile them all [factors] as representing risk because if you lump them all together, you get an eerily smooth return stream.
The reason for this smoothing is that excess returns of factors are generally un- or low-correlated, and thus tend to cancel “bumps” in portfolio returns. This does not mean that each factor does not represent risk.
The author then proceeds to cover the problem of redefinition of alpha, which results from the introduction of multiple factors, and concludes that from his perspective such an adjustment is inappropriate because it “redefines outperformance:”
From my perspective, the mountains of studies purporting to show that active equity managers can’t beat the market are really showing that much of their excess returns can be replicated by a handful of factor strategies.
Regardless of semantics and opinions, if a manager’s excess returns can be replicated by cheaper and readily accessible instruments (such as factor ETFs), then there is no need to pay the excess management fee.
From Alpholio™’s perspective, all of the above discussions are academic. Whether or not factors have persistent risk premia, market is efficient or not perfectly rational, what truly matters is whether or not active management adds value over a reference portfolio after all fees have been taken into account. Factor ETFs provide yet another set of potential explanatory variables that squeeze the alpha to its essence, the RealAlpha™.