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Exploring VIX® in Volatile Markets

How the S&P Managed Risk 2.0 Indices Dynamically Respond to Risk

Low Volatility Response in Brazil

Performance Trickery, part 3

Dissecting the Asset- and Equal-Weighted Fund Performance from the SPIVA® Japan Year-End 2019 Scorecard

Exploring VIX® in Volatile Markets

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How can VIX data help us understand the current market environment? S&P DJI’s Tim Edwards explores what recent historical highs for VIX could mean for equity and commodity markets moving forward.

Get the latest Risk & Volatility dashboard on Indexology: https://spdji.com/indexology/risk-management/risk-volatility-dashboard

The posts on this blog are opinions, not advice. Please read our Disclaimers.

How the S&P Managed Risk 2.0 Indices Dynamically Respond to Risk

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Explore how a transparent, rules-based approach to risk management is designed to offer participation and downside protection with S&P DJI’s Tianyin Cheng.

To learn more, read Tianyin’s latest blog, “The Trade-Off between Upside Participation and Downside Protection.”

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Low Volatility Response in Brazil

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Maria Sanchez

Associate Director, Global Research & Design

S&P Dow Jones Indices

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The S&P/B3 Low Volatility Index Celebrates Five Years of Outperforming Its Benchmark

The COVID-19 pandemic hit the Brazilian equity market hard, causing the worst monthly performance since September 1999.[1] As measured by the S&P Brazil BMI, the Brazilian equity market lost 29.80% in March 2020. The S&P/B3 Low Volatility Index exceeded its benchmark by 640 bps during the same period. Moreover, the low volatility strategy presented superior cumulative and risk-adjusted returns over the 20-, 10-, and 5-year periods (see Exhibit 1).

S&P/B3 Low Volatility Index: Hit Ratio

To highlight the behavior of the S&P/B3 Low Volatility Index in different market circumstances, we analyzed the monthly return data in up and down markets.[2] The results shown in Exhibit 2 are asymmetric, displaying some participation in the up markets and evident downside protection.

The S&P/B3 Low Volatility Index outperformed the benchmark in down markets, providing an average monthly excess return of 2.12%; the strategy exceeded the average by more than 400 bps.

Conclusion

Over the long term, the S&P/B3 Low Volatility Index presented relatively attractive performance in comparison with its benchmark, providing more evidence for the existence of the Low Volatility anomaly, and suggesting that volatility reductions achieved during declining markets do in fact potentiate the benefit of the S&P/B3 Low Volatility Index in terms of risk-adjusted returns.

Happy anniversary, S&P/B3 Low Volatility Index, and keep doing what you do!

[1]   Observed period from September 1999 to March 2020.

[2]   Up and down markets are as measured by the S&P Brazil BMI.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Performance Trickery, part 3

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Craig Lazzara

Managing Director and Global Head of Index Investment Strategy

S&P Dow Jones Indices

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Success is hard to come by for active managers, as readers of our SPIVA reports know well.  Sometimes what appears to be stock selection skill is in fact simply a byproduct of style drift across the capitalization scale.

A majority of large-cap active managers outperformed the S&P 500 only 3 times in 19 years of SPIVA data.  It’s surprising, therefore, to see that 68% of midcap managers outperformed the S&P MidCap 400 Index last year (and 62% of small cap managers outperformed the S&P SmallCap 600).  In fact, a majority of midcap managers outperformed in each of the past three years.  In those same years, an average of 66% of large cap managers underperformed the S&P 500.

Why are the results so good for mid-cap managers?  It’s possible that smaller stocks are less efficiently priced than the largest companies, so that stock selection in the mid- and small-cap universe is easier; the higher dispersion of midcaps and small caps would support that view.  Or perhaps mid- and small-cap managers are simply more skillful than their large-cap counterparts.

These explanations would be more plausible were it not for the long-term record.  For the 10 years ended December 31, 2019, for instance, 84% of mid-cap and 89% of small-cap managers lagged their index benchmarks.  If smaller companies were an easier game with more astute players, the score would be better than this.

We argue instead that style drift – for example, the ability of a midcap manager to buy large cap stocks – can shed light on mid- and small-cap managers’ success.  In 2019, e.g., the S&P 500 rose by 31.5%, well ahead of the 26.2% gain of the S&P MidCap 400.  A mid-cap manager who tilted his portfolio slightly up the capitalization scale might have been rewarded for doing so.  If our conjecture is correct, we’d expect midcap manager performance to improve whenever the S&P 500 outperforms the S&P 400.  Exhibit 1 shows that to be the case.

Exhibit 1.  Style Drift Helps to Drive Midcap Performance

Source: S&P Dow Jones Indices. Data from Dec. 31, 2000 through Dec. 31, 2019. Table is provided for illustrative purposes. Past performance is no guarantee of future results.

A majority of midcap managers outperformed the S&P 400 six times, for an overall success rate of 32%.  When the S&P 500 beat the S&P 400, however, the managers’ success rate rose to 57% (4/7), vs. a success rate of only 17% (2/12) when the S&P 500 lagged.  Alternatively viewed, if the majority of midcap managers outperformed, the 500 usually beat the 400; if the majority underperformed, the 400 was typically in the lead.

Logically, if midcap underperformance creates an opportunity for midcap managers, midcap outperformance might create an opportunity for large cap managers.  Exhibit 2 shows that it does.

Exhibit 2.  Style Drift Helps to Drive Large Cap Performance

Source: S&P Dow Jones Indices. Data from Dec. 31, 2000 through Dec. 31, 2019. Table is provided for illustrative purposes. Past performance is no guarantee of future results.

Midcaps beat large caps in each of the three years when the majority of large cap managers outperformed, suggesting that large-cap managers might have augmented their performance by tilting down the cap scale.  There were no years when the S&P 500 beat the MidCap 400 and the majority of large cap managers outperformed.

Whenever there are significant differences between the performance of capitalization-specific indices, there are opportunities for managers to add value by moving up or down the cap scale.  So far in 2020, of course, the S&P 500 is well ahead of its smaller counterparts; mid- or small-cap managers might well benefit in next year’s SPIVA data.  Such opportunistic moves may be commendable, but they are not evidence of skill at stock selection.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Dissecting the Asset- and Equal-Weighted Fund Performance from the SPIVA® Japan Year-End 2019 Scorecard

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Arpit Gupta

Senior Analyst, Global Research & Design

S&P Dow Jones Indices

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In the SPIVA® Japan Year-End 2019 Scorecard, we evaluated the percentage of Japanese active funds that underperformed their respective benchmark indices, as well as the average fund returns on an equal- and asset-weighted basis. Equal-weighted returns are a measure of average fund performance, while asset-weighted returns are a measure of the performance of the total money invested in that category. In certain fund categories, such as the Japanese equity mid/small-cap funds, U.S. equity funds, and emerging equity funds, we noticed significant divergence in the equal-weighted and asset-weighted returns.

Exhibit 1 shows the cumulative equal-weighted performance relative to the asset-weighted performance across these three categories over the 10-year period ending in December 2019. The asset-weighted performance consistently lagged the equal-weighted performance in the Japanese equity mid/small-cap funds category. We also observed similar trends in the U.S. and emerging market equity fund categories between December 2009 and December 2015, though the trend reversed in the past three to four years. When equal-weighted returns outperformed asset-weighted returns, it implies smaller active funds outperformed their peers with larger sizes during the period.

To further examine performance difference between larger and smaller funds for the Japanese equity mid/small-cap fund, U.S. equity fund, and emerging equity fund categories, we bucketed the funds into three tertiles based on each fund’s assets and tracked the tertiles’ performance for each category over the past five years.[1] The top tertile in the Japanese equity mid/small-cap fund category had five-year average total assets[2] of JPY 1,355 billion, accounting for 83% of average assets in the category, while the top tertile in the U.S. and emerging equity fund categories had five-year average total assets of JPY 1,175 billion and JPY 718 billion, accounting for 83% and 95% of average assets in their respective categories (see Exhibit 2).

In the Japanese mid/small-cap fund category, the third tertile (comprising funds with the smallest assets) outperformed the first and second tertiles, which further confirmed smaller funds outperformed larger funds in this category, and all three tertiles outperformed the benchmark over the past five years. In contrast, the top tertile (comprising funds with the largest assets) outperformed the second and bottom tertiles in the U.S. and emerging equity funds categories, showing larger funds outperformed their smaller peers in these two categories, though all three tertiles underperformed their respective benchmarks in the past five years.

These observations indicate pronounced small-cap premia was consistently captured by smaller funds in the Japanese mid-/small cap equity funds. For larger-sized funds, fund managers’ investment proposition may involuntary dip toward stocks with lower return potential due to the selection constraints to pick stocks with larger market capitalization and sufficient trading liquidity, aiming to construct lower-turnover strategies.[3] In contrast, smaller-sized fund categories had much more flexibility to chase investment pools of stocks that offered higher return premia in lieu of lower liquidity or float market capitalization. In addition, this also implies the economies of scale advantage played a less-prominent role in the outperformance of the Japanese equity mid/small-cap fund category.

1 Funds are dissected in tertiles based on assets for each month over the period from Dec. 31, 2014, to Nov. 30, 2019. The top 1/3 of funds with the highest assets are included in the first tertile, while the bottom 1/3 of funds with the smallest assets are included in the third tertile.

2 Five-year average total assets are the average monthly figures of total fund assets in each tertile within each respective SPIVA category for the period from Dec. 31, 2014, to Nov. 30, 2019.

3 Jeffrey, A., Busse, Tarun, Chordia, Lei, Jiang, and Yuehua, Tang (2014). “How Does Size Affect Mutual Fund Performance? Evidence from Mutual Fund Trades.” Research Collection Lee Kong Chian School of Business

The posts on this blog are opinions, not advice. Please read our Disclaimers.