About 90% of American drivers consider themselves safer and more skilled than average. Obviously, these perceptions do not reflect reality. After all, 9 out of 10 people can’t be above average. Nevertheless, the results are compelling: they illustrate an innate human tendency to overestimate our own talents and abilities and underestimate those of others.
Equity mutual fund managers are likely to have a similarly skewed view of their ability to generate alpha by outperforming the stock market. Otherwise, how would they justify their work?
But maybe we’re missing the point. Perhaps most drivers drive safely and most fund managers outperform, with only a few responsible for a disproportionate share of traffic violations and accidents and major capital losses, respectively. Unfortunately no. Most fund managers underperform their benchmarks: Only 17% of US large-cap mutual fund managers outperformed the S&P 500 over the past 10 years, according to the latest S&P SPIVA Scorecard. Also, there is no consistency among those few who outperformed. All this implies that the successful selection of the manager is almost impossible.
But research shows that factors more than ability explain both outperformance and underperformance. So, outperformance and alpha are not exactly the same thing. So how do we explain the difference?
While fund managers highlight their ability to create alpha for clients, fund benchmarks compare their performance against a benchmark. For example, the Invesco S&P 500 Pure Value Exchange Traded Fund (ETF, RPV) returned 0.7% over the past 12 months, while its benchmark, the S&P 500, returned -10.2 %. The S&P 500 Value Index might be a better benchmark for RPV, but relative to the broad index, the ETF has provided significant value, pun intended, to its investors.
RPV Smart Beta ETF Outperformance = Alpha?
Factor exposure analysis
Because the RPV ETF selects approximately the 100 cheapest S&P 500 stocks, it is a value-focused strategy. A regression analysis looking back one year validates this. RPV has high betas relative to the S&P 500 (it’s a long-only strategy), as well as value and quality factors.
The exposure to the value factor and the negative beta of the quality factor are intuitive because cheap firms tend to rank poorly on quality metrics. Stocks that trade at low valuations tend not to be highly profitable and often have excessive leverage or other problems.
Factor Exposure Analysis — RPV Smart Beta ETF: Betas, last 12 months
Analysis of the contribution
With the betas factor, we can create a contribution analysis. RPV had a high beta compared to the S&P 500 (0.90), which was down 10.2% over the past 12 months. Therefore, the broad market contributed to RPV’s return by -9.1%. Except for the value factor, which contributed 12.5%, other patrimonial factors had a marginal impact.
Factor Contribution Analysis: RPV Smart Beta ETF, last 12 months
Since we know how much equity and stock market factors have contributed to RPV’s performance, we can also calculate the residual. Theoretically, this represents the skill of the manager, or any market beta and factors that are not responsible. In other words, it’s the alpha.
For RPV, the alpha was negative. But how can alpha be negative when the ETF outperformed its benchmark? The implication is that the value-focused strategy was poorly implemented. Management fees, market impact and transaction costs must also be considered. While there will always be slippage, this only explains part of the -5.7% result.
Based on this analysis, investors would have been better off avoiding RPV and buying S&P 500 and factor exposures through a zero-cost ETF and risk premium indexes, respectively.
Alpha calculation: RPV Smart Beta ETF, last 12 months
The alpha calculation can be a bit confusing as RPV is a smart beta ETF that provides exposure to the value factor and we are using a factor exposure analysis to measure the contributions. But we can replicate this approach with Fidelity Contrafund (FCNTX), one of the best-known mutual funds. FCNTX has a long history going back more than 40 years and manages close to $100 billion. The fund has a concentrated stock portfolio dominated by Amazon, Microsoft, Apple and other growth stocks.
But over the past 12 months, that strategy hasn’t worked well either: FCNTX is down more than 20% due to beta and factor exposure. According to the contribution analysis, the S&P 500 and equity factors cannot fully explain the negative performance, meaning the alpha was negative. As such, the fund manager must bear responsibility for at least some of the losses.
Alpha calculation: Fidelity funds (FCNTX), last 12 months
Superior performance to Alpha
By analyzing the contribution of 13 mutual funds and ETFs in the US stock market, we can demonstrate the significant difference between outperformance and alpha. In just one case, the Davis Select US Equity ETF (DUSA), it outperformed and alpha was nearly identical at -0.5%. The ETF has factor exposure, but the contributions were offset. This means that the loss can only be attributed to fees or lack of skill.
When it comes to the ARK Innovation ETF (ARKK), much of the recent criticism may be overblown. According to our calculations, Cathie Wood, ARKK’s fund manager, has created alpha. The ETF is down 61.8% over the last 12 months, but the market accounted for -17.7% of that and another -53.0%. Thus, there was 8.9% alpha. ARKK is very concentrated with a few growth names – Tesla, for example. This results in betas on the S&P 500 of 1.7 and value factor of –1.35. As factor exposure analysis reveals all of this, investors only have themselves to blame if these bets go south.
Active Fund Managers: Outperform Alpha
Different input, different output
Although contribution analysis is the most meaningful alpha calculation methodology, the data used is important. So far, we have used factors from FactorResearch. Standard industry definitions for stock selection and market capitalization restrictions apply to define the stock universe. They also include transaction costs and are beta-neutral.
With the Dow Jones and Fama and French data, the alphas vary somewhat. Fama and French’s three-factor model makes the biggest difference because only market, size, and value factors come into play.
Factor definitions are important and should be as practical as possible. For example, the universe of Fama shares and French factors include small illiquid caps that many investors do not have access to, have no transaction costs and are built to be dollar neutral. Comparing a product to these factors sets unrealistic expectations.
Equity fund manager alphas by data source
Capital allocators have more and more data and better technology to inform their allocation decisions. But the same goes for fund managers.
This evolution has made markets more efficient and harder to achieve outperformance. Even in emerging markets or private markets like private equity, managers’ returns over the past decade indicate little in terms of value creation and nothing of consistency.
Given this, it raises the question of whether alpha is worth measuring.
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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.
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