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Limiting Risk Exposure with S&P Risk Control Indices

A Dynamic Multi-Asset Approach to Inflation Hedging

Profiling Minimum Volatility

ETFs in Insurance General Accounts – 2021

Fleeting Alpha Scorecard: Year-End 2020

Limiting Risk Exposure with S&P Risk Control Indices

INTRODUCTION

The volatility seen during the Global Financial Crisis (GFC) in 2008 broke the calm that was present in financial markets from 2004 to early 2007.  Most asset classes experienced significant pullbacks, markets became volatile, and the correlation between asset classes increased significantly.  Portfolio construction based on the backward-looking correlation model failed, as the expected diversification benefit was eliminated precisely when it was needed the most.

In the aftermath of the GFC, institutional market participants with long-term investment horizons have responded with aversion to this volatility by considering a number of risk control strategies.  The risk control strategies adjust market exposure in inverse relation to risk to target a stable level of volatility in all market environments.  For institutional market participants with long-standing liabilities, which can range from defined benefit plans to variable annuities offered at insurance companies, a risk control strategy may provide a smoother path of asset returns (see Exhibit 1) and could more closely align the performance of the institution’s assets to the characteristics of its liabilities.

Limiting Risk Exposure with S&P Risk Control Indices: Exhibit 1

S&P Dow Jones Indices has developed a risk control framework through a series of risk control indices, which seek to measure various underlying equity- or futures-based indices at set risk levels.  S&P Dow Jones Indices’ risk control indices feature:

  • Globally accepted, independent underlying indices like the S&P 500, S&P 500 Low Volatility, and S&P 500 Dividend Aristocrats®;
  • Transparent methodology based on the underlying index’s historical volatility;
  • Measurements of risk, based on volatility, to help market participants control risk at a predefined level; and
  • Utilization of the same constituents as the underlying index.

S&P Dow Jones Indices has created a suite of risk control indices based on a large number of equity and thematic indices, along with the S&P GSCI® and the other commodity indices in its series (see the Appendix for a complete list).

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A Dynamic Multi-Asset Approach to Inflation Hedging

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Lalit Ponnala

Director, Global Research & Design

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Fiona Boal

Managing Director, Global Head of Equities

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Jason Ye

Director, Strategy Indices

EXECUTIVE SUMMARY

Inflation is one of the most significant risks to investment returns over the long term. Core equities and conventional bonds tend to deliver below-average returns in rising inflation environments, which can encourage investors to seek out inflation-sensitive assets, such as commodities, inflation-linked bonds, REITs, natural resource stocks, and gold, to protect their portfolios from inflation shocks.

In this paper, we construct a multi-asset index for inflation protection.  First, we look into forecasting inflation.  Next, we analyze the inflation sensitivity of various asset classes.  Then, we identify strategies for different inflation regimes.  Finally, we present portfolios that adjust their allocation dynamically to changes in the inflation regime.

INTRODUCTION

As record levels of monetary and fiscal stimulus are pumped into the recovering global economy, inflation has returned to the discussion.  The low-inflation environment of the past few decades has penalized inflation-sensitive assets.  Given that inflation can be notoriously difficult to forecast, and market participants may experience unexpected inflation shocks, it is worthwhile to revisit the concept of inflation protection.

For many investors, the unprecedented and coordinated fiscal stimulus in the wake of the COVID-19 pandemic has justified concerns over inflation.  Neville et al. summarized four factors that suggest heightened inflation risk: (1) unprecedented increase in money creation, (2) historically high fiscal deficit level, (3) recent increase in long-term yields, and (4) the inflation derivatives market pricing in a 31% probability that the average inflation rate will exceed 3% over the next five years.

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Profiling Minimum Volatility

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Craig Lazzara

Managing Director, Index Investment Strategy

EXECUTIVE SUMMARY

  • Minimum volatility is part of a broader group of defensive strategies that have been in existence for decades. They are based on the low volatility anomaly, the phenomenon that lower-risk stocks outperform over time, contradicting the conventional wisdom that risk and reward go hand in hand.
  • Low volatility strategy indices attempt to exploit this anomaly systematically. The typical behavior patterns of low volatility strategies are that they go up less when the market is up and go down less when the market is down. They offer protection in down markets and participation in up markets.
  • More than with most factor strategies, the potential value added of low volatility strategies is largely dependent on market dynamics. Dispersion of returns tends to be higher in times of crisis; this disparity gives defensive strategies such as low volatility a leg up.

Profiling Minimum Volatility: Exhibit 1

INTRODUCTION

Following a few years of significant market gains, enthusiasm for low volatility strategies has waned, particularly compared with the period after the 2008 Global Financial Crisis. This is understandable since protection is probably not top of mind when things are going well and are seemingly on an upward trajectory.

THE LOW VOLATILITY ANOMALY

Low volatility strategies explicitly aim to deliver a pattern of returns relative to the market. Their goal is to reduce risk (volatility), and that goal is constant in both good times and bad.

Low volatility is a characteristic. Low volatility accompanied by outperformance is an anomaly. The phenomenon of lower-risk assets also outperforming higher-risk assets over time was noted by academics almost half a century ago.  Flouting the conventional wisdom that risk and return
go hand in hand, this phenomenon was dubbed the low volatility anomaly. Outperformance does not occur at all times (particularly in strong market performance cycles), but the anomaly has been observed universally across different markets and asset classes.

When it comes to low volatility portfolios, there are different approaches to index construction that yield different characteristics and results. In the U.S., the S&P 500 Minimum Volatility Index is one way to pursue lower risk in a systematic way.

The methodology underlying the S&P 500 Minimum Volatility Index relies on optimization, minimizing volatility subject to stock- and sector-level exposure constraints. Compared with a rankings-based methodology such as the one used for the S&P 500 Low Volatility Index, the optimized approach has typically resulted in less performance divergence from the benchmark.

Profiling Minimum Volatility: Exhibit 2

In the period from January 1991 through May 2021, the minimum volatility index delivered nearly the same return as the benchmark S&P 500, but at substantially lower risk—a 16% reduction. On a 10-year rolling basis, the S&P 500 Minimum Volatility Index’s volatility was consistently lower than the S&P 500 throughout the entire period (see Exhibit 3).

Profiling Minimum Volatility: Exhibit 3

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ETFs in Insurance General Accounts – 2021

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Raghu Ramachandran

Head of Insurance Asset Channel

After a chaotic start to the year, U.S. insurance companies added USD 4 billion to exchange-traded funds (ETFs) to their general account portfolios in 2020. By year-end 2020, U.S. insurers increased their ETF AUM by 18% from 2019. Life companies, in particular, returned to the market and purchased large amounts of ETFs. In spite of, or because of, the volatility in the bond market, insurance companies had strong flows into Fixed Income ETFs, adding USD 5 billon in 2020.

In our sixth annual study of ETF usage in U.S. insurance general accounts, for the first time we analyzed the trading of ETFs by insurance companies (see page 37) in addition to the holding analysis. In 2020, insurance companies traded USD 63 billion in ETFs, representing a 10% growth over 2019’s trade volume. On average, insurance companies traded twice as many ETFs during the year as they held at the beginning of the year. Certain categories have substantially higher trade ratios. We also noted interesting observations about the size of insurance company trades.

HOLDING ANALYSIS

Overview

As of year-end 2020, U.S. insurance companies invested USD 36.9 billion in ETFs. This represented only a tiny fraction of the USD 5.5 trillion in U.S. ETF AUM and an even smaller portion of the USD 7.2 trillion in invested assets of U.S. insurance companies. Exhibit 1 shows the use of ETFs by U.S. insurance companies over the past 17 years.

ETFs in Insurance General Accounts – 2021: Exhibit 1

In 2020, ETF usage by insurance companies increased 18.4%; this is a slightly higher rate than the 16.0% increase in 2019. The growth rate has remained consistent since 2004, when insurance companies began investing in ETFs (see Exhibit 2). This growth rate implies a doubling of ETF AUM roughly every four to five years (see Exhibit 3).

ETFs in Insurance General Accounts – 2021: Exhibit 2-3

In 2019, the number of ETF shares held by insurance companies declined for the first time in 12 years, but in 2020, the number of shares held increased by 8.5% (see Exhibit 4).

ETFs in Insurance General Accounts – 2021: Exhibit 4

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Fleeting Alpha Scorecard: Year-End 2020

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Berlinda Liu

Director, Multi-Asset Indices

SUMMARY

The Fleeting Alpha Scorecard combines elements of the SPIVA® U.S. Scorecard and the Persistence Scorecard to show how outperforming mutual funds from one three-year period continue to perform thereafter. The former report compares actively managed funds against their passive benchmarks, while the latter compares funds against their peers.

For the Fleeting Alpha Scorecard, we first identify funds that beat their benchmarks, based on three-year annualized returns, net-of-fees. We then examine whether these funds continue to outperform during each of the next three one-year periods.

There was significant dispersion in the likelihood of funds outperforming by category, with the most notable split occuring between growth and value funds. For example, as of Dec. 31, 2017, 84 of the 261 large-cap growth funds had outperformed the S&P 500® Growth in the previous three years. Of those winners, 21 (or 25%) outperformed for the subsequent three years. But on the value side, while 78 out of 338 funds had outperformed the S&P 500® Value as of Dec. 31, 2017, only 1 of those winners managed to continue outperforming annually through 2020 (see Exhibit 1 and Report 1).

Fleeting Alpha Scorecard: Year-End 2020: Exhibit 1

In 4 of the 18 domestic equity categories tracked, no funds managed to repeat their outperformance, and fewer than 10% did so in an additional four categories (see Report 1).

Echoing a point from the SPIVA U.S. Year-End 2020 Scorecard, prior to the evaluation of alpha persistence, few funds beat the benchmark for the initial three years (2015-2017). In 13 of the 18 domestic equity categories, fewer than 20% surpassed the benchmark, significantly reducing the original universe into the pool of "winners" for subsequent tracking.

International equity funds had slightly higher rates of outperformance in the initial period and were more stable in their alpha maintenance across categories and time. The conspicuous exception was emerging market funds where no active manager managed to repeat their positive alpha through 2020.

We take into consideration that cyclical market conditions can unduly influence a snapshot of the performance persistence figure. The figures in Report 2 are calculated by: 1) creating a version of Report 1 for each quarter between December 2011 and December 2020, and 2) taking simple averages of the persistence figures for each of the categories.

This analysis showed that the average outperformance persistence in each of the subsequent three years fell rapidly. Across all funds in the tracking universe, the average outperformance persistence by year was 33.8%, 13.7%, and 6.7%, respectively.

The growth/value split was visible in this longer timeframe as well. As Exhibit 2 shows, while the percentage of outperforming value funds was reasonably similar to their growth counterparts in year one, their alpha proved substantially less durable, suffering a harsher decline by year three.

Fleeting Alpha Scorecard: Year-End 2020: Exhibit 2

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