Indexology^® Blog

Sectors and factors are different ways of viewing the world, but they are not mutually exclusive.  We find an example of such a close, symbiotic relationship between the technology sector and the quality factor.  Exhibit 1 shows that the S&P 500 Information Technology index has a strong tilt towards quality, while the biggest overweight in the S&P 500 Quality Index is towards information technology.

Both technology and quality have outperformed during both calendar 2020 and the 12 months ending April 30.   This raises an obvious question: Has technology done well because of its exposure to quality, or has quality done well because of its exposure to technology?

We begin our inquiries by calculating quality scores for all the constituents of the S&P 500, based on the following metrics: return on equity, financial leverage and accruals ratio.  We then rank the constituents by their quality score, and slice the S&P 500 into quality quintiles.  Finally, we analyze how much of S&P 500 Info Tech’s weight is in each of these quintiles.

Exhibit 2 illustrates that an astonishing 70% of S&P 500 Info Tech’s weight is in the top quality quintile.

Quality is clearly important to the technology sector, but how important is technology to the quality factor? To answer, we turn the tables and look at technology’s weight in the S&P 500’s top quality quintile.

We observe in Exhibit 3 that 53% of the capitalization of the top quality quintile in the S&P 500 is in technology, dominating the other sectors.  But an even greater share – 70% – of large-cap technology comes from the top quality quintile. Therefore, we can conclude that quality’s influence on technology is greater than technology’s influence on quality.

Both S&P 500 Info Tech and the top quality quintile of the S&P 500 are highly concentrated, as we see in Exhibit 4. While the five largest names in Info Tech are all in the top quality quintile, only three out of the five largest names in the top quality quintile are in Technology, further confirming our conclusion that quality’s influence on technology outweighs technology’s influence on quality.

While we cannot fully disentangle the overlap between the technology sector and the quality factor, the data argue that quality has greater significance to technology than the other way around. Understanding both perspectives is not only helpful, but also important to recognizing the intertwined relationship between sectors and factors.

Is Healthcare providing a potential opportunity for Indian investors? Explore recent sector performance with S&P DJI’s Koel Ghosh and see how India’s Healthcare sector stacks up to broader markets.

Read more here: https://www.indexologyblog.com/2020/05/11/covid-19-revelations-health-care-is-wealth/

The U.S. equities market had a wild start in 2020. Following the March 2020 sell-off, the S&P 500® posted its largest monthly gain (12.8%) since 1987. Meanwhile, VIX® went from its long-term median to an all-time high within a month before it settled around 30. One thing that has been debated lately is whether VIX, often referred to as the “fear gauge,” has gone back to normal and indicates that the market has hit the bottom. To answer this question, we need to investigate several aspects of VIX and its related trading activities.

VIX Futures Curve Not Completely Back to Contango

As pointed out in a recent VIX paper from the CFA Institute Research Foundation, the VIX futures curve is in contango about 80% of the time and usually goes into backwardation in distressed markets. On Feb. 24, 2020, the VIX futures curve flipped into backwardation, and it kept this downward sloping shape until May 6, 2020—the shaded area in Exhibit 1 shows the backwardation period of the VIX futures curve. However, the current price difference between the first- and second-month VIX futures is small, and the curve is more flat than upward sloping. In fact, the futures curve went back to mild backwardation on May 12 and May 13, 2020. This is unlike typical contango in VIX term structure.

VIX Level Remains Elevated

On Feb. 21, 2020, VIX closed at 17.08, near its long-term median of 17.27. In less than a month, it skyrocketed to an all-time high of 82.69. As of May 22, 2020, it was hovering around 30. Although this most recent level seems rather tame compared with its March peak, investors should understand that the current VIX readings are around its 90th percentile level of 28.7.

History has demonstrated that although volatility may rise rapidly, it often declines slowly. In other words, the market tends to remain volatile for a while after a shock occurs. A VIX level of 30 implies annualized volatility of 30%, or daily moves of 1.9% in the market during the next 30 days. Given that the S&P 500 has moved about 0.76% daily on average since 1990, the current VIX level implies that investors collectively anticipate outsized daily moves, at least in the short term.

VIX Derivatives Trading Volume

Derivatives trading on VIX dropped from its March peak, but it picked up again during the week of May 15, 2020. VIX options’ average daily volume (ADV) during the week of May 15 was only a quarter of the ADV during the week of March 13; however, it almost doubled the ADV from the week of April 24. The pickup in trading volume was accompanied by a jump in VIX levels and futures prices, indicating that investors were pricing elevated risk into June 2020 contracts. This is not surprising given that many states were positioned to reopen their economies after May 15, 2020.

Conclusion

It is yet to be seen whether reopening the U.S. economy will fuel a resurgence of COVID-19 cases. On top of that, President Trump’s statement to cut ties with the second-largest economy in the world cast an additional shadow over the market outlook. The VIX levels, futures curve, and trading activities seem to tell us that, despite the improved optimism in the market, elevated risk is likely to be the new norm, at least for a short while.

What do presidential debates and an argument between passive and active investors have in common? They are both thrilling, demand the highest levels of rhetorical skills, don’t change audience opinions, and everyone goes home entertained. While presidential debates remain as exciting as ever, the shrillness of conversation between active and passive investors seems to have lessened. One could argue that this change is due to (1) a smaller degree of outperformance of star managers, (2) a convergence of fee differentials, and (3) the investment industry widening its focus to offer more solutions.

Performance

One of the drivers behind the shift in capital from active to passive is the question of active managers’ ability to outperform their benchmarks. Here, the burden of proof has fallen squarely on active managers. While there are, indeed, a few active managers who outperform, S&P DJI’s SPIVA® U.S. Scorecard[1] highlights that the last time the majority of all active U.S. managers outperformed was in 2013. In 2019, a full 70% of all active U.S. investors underperformed.

Based on the SPIVA Scorecard, some areas of the market are relatively easier for alpha generation than others. Over the past 10 years, on a net-of-fees basis, 63% of international small-cap (ISC) managers underperformed their benchmark, while the number for the U.S. large-cap core (LCC) was 97%. While there are periods of outperformance for a median manager, alpha is volatile and mostly negative (see Exhibit 1).

While the alpha generation of median managers is underwhelming, what about the best-performing investors? Let’s look at the top-quartile managers (see Exhibit 2). Identifying top managers a priori is, of course, unrealistic, and many strategies don’t survive—only 50% of LCC and 60% of ISC funds that existed 10 years ago are still live.[2] However, even with the benefit of perfect hindsight, what we want to analyze is whether we can discern any change in the magnitude of alpha for the best managers.

Top investors are still able to generate excess returns, although the downtrend is apparent, and persistence of this alpha is fleeting.[3]

Convergence of Fee Differentials

Many of us are aware of the pressure that active and, more recently, passive investors have been under with respect to the structural decline in fee levels. Technological and analytical advances contributed to increasing economies of scale and brought up a heightened investor focus on sources of performance. As fees continue to march lower (with the differential dropping by about one-third since 2000), the contrast between active and passive has become less pronounced (see Exhibit 3).SPIV

Both Passive and Active Are Focusing on Solutions

As healthcare and college costs continue to march higher, and as the burden of saving for retirement has shifted from employers to employees, the investment industry has repositioned itself. Managers are evolving beyond long-only, asset-class-specific strategies. Instead, outcome-based solutions such as target date funds, 529 plans, and income guarantee products have proliferated.

One can argue that the investment industry has morphed into a “manufacturing” industry: various solutions come to market that try to target a specific outcome. Contrast this with 10+ years ago, when many value propositions had to do with attempting to outperform a benchmark while maintaining a certain level of risk. Here at S&P DJI, we are actively engaging in providing index-based solutions such as our U.S. equity franchise, volatility indices, multi-factor and multi-asset approaches, thematic funds, ESG offerings, real assets, and custom indices.

Active and passive investors are opinionated groups. However, as the industry continues to shift and investment approaches evolve, we suspect that the two camps will continue to converge, heated discussions will become less so, and the focus will center more around providing appropriate solutions to investors regardless of the investment approaches.

[1] SPIVA U.S. Scorecard, Year-End 2019

[2] SPIVA Institutional Scorecard, Year-End 2018

[3] Soe, A., Can You Beat the Market Consistently?, 2019

Globally, as markets have matured, we have seen the institutional share of public equity increasing.[1] Professional managers are continuing to enter the investment management industry, resulting in market research becoming more institutionalized. Hence, more and more industry research analysts are competing against each other and chasing the same set of stocks to generate alpha (or excess returns over the benchmark). This has been one of the factors impeding active fund manager’s ability to exploit information asymmetry to generate alpha. A similar scenario seems to be unfolding in the Indian Equity Large-Cap category, where generating alpha is becoming increasingly difficult for active fund managers, as seen in the SPIVA India Scorecards over the years.

Each box in Exhibits 1a and 1b showcases the spread in alpha[2] generated by Indian Large-Cap Equity active funds in a calendar year. Exhibit 1a calculates alpha based on gross returns minus the S&P BSE 100 (TR) returns (benchmark for large-Cap funds in India), whereas Exhibit 1b is based on excess net-of-fees returns over the benchmark. The exhibits show that there is a wide spread in alpha generated by funds. Exhibit 1a shows that in each calendar year, a fairly large proportion of active fund managers failed to beat the S&P BSE 100 benchmark on a gross-returns basis, i.e., even without deducting management fees. The story looks even more dismal when we look at net alpha (Exhibit 1b), where the median active fund beats the benchmark in only 5 of 10 years.

Each observation in Exhibit 2a shows the distribution of yearly gross alpha for the large-cap category funds for each calendar year from 2010 to 2019,[3] and the gray line is the cumulative distribution curve of all the observations. Exhibit 2b shows the same based on net alpha. According to the gross return distribution in Exhibit 2a, almost 35% of the observations lie to the left of the vertical line that represents the “0% gross alpha.” In other words, 35% of the observations had negative gross returns. When we generate the same chart for net alpha, the distribution curve shifts to the left, with nearly 52% of the observations that lie to the left of the vertical line representing “0% net alpha.” Both charts show that alpha distribution is skewed to the right with a few outlier observations pulling the return alpha average values to the right, while the median values lie to the left of the mean values.

We also conducted a t-test (one-tail) to investigate if the gross alpha and net alpha are significantly greater than 0. Exhibit 3 shows that at a 95% confidence level, gross alpha is significantly greater than 0%. However, the same does not hold true for net alpha. In addition, the mean values exceed the median values for both the gross and net alpha, indicating the gross and net alphas are skewed positively by a few high return funds in the large-cap fund category. These results show the challenges of fishing for outperforming active large-cap funds over the long term.

The short answer to the question in the title of the blog is: quite nearly. With vanishing alpha, it may have long lasting implications for the active management industry where the most skilled managers survive and the funds that lie consistently on the left tail of the curve in Exhibit 2 would face strong challenge to grow or survive for long periods. Based on our SPIVA India Year-End 2019 Scorecard, 68.8% of funds in this category did not survive for the 10-year period ending in December 2019. With the competition that the increasing number of passive investment products brings, active fund managers may find themselves under pressure to generate higher gross alpha over the benchmark or to moderate costs and fees in an attempt to offer enhanced (net) alpha to investors.[4]

[1] https://www.oecd.org/corporate/Owners-of-the-Worlds-Listed-Companies.pdf

[2] Gross alpha = gross returns – benchmark returns; net alpha = net returns – benchmark returns; net returns = gross returns – management fees

[3] Therefore each large-cap fund (that survives and reports during the period from 2010 to 2019) will contribute 10 observations to the histogram. Only funds that survived and reported returns during the entire calendar year were considered for alpha calculations.

[4] This is analysis is inspired from an earlier research carried out by Vanguard for mutual funds domiciled in the U.S. https://personal.vanguard.com/pdf/ISGZSG.pdf