the corona quarter...the us witnessed the biggest quarterly increase in short-horizon risk since at...

April 2020 | QONTIGO APPLIED RESEARCH Q1 2020 INSIGHT REPORT

Confidential – Not for Redistribution – Copyright © 2020 Qontigo GmbH.

The Corona Quarter Applied Research Team: Melissa R. Brown, CFA; Diana R. Baechle, PhD; Olivier d’Assier; Natan Borshansky; Christoph Schon, CFA, CIPM

April 2020 | QONTIGO APPLIED RESEARCH Q1 2020 INSIGHT REPORT 1


The Corona Quarter

Introduction

The first quarter of 2020 came in roaring like a lion and

went out like a (slaughtered) lamb. In the first half of

the quarter, stock indices around the world were

pushing new records week after week up to the Feb. 19

market peak. In the global selloff that followed—

propelled by the economic fallout of the coronavirus

pandemic—most markets suffered a 23-day loss of

between 30% and 40%, wiping out earlier gains entirely.

The bloodbath in equities not only ended the longest-

running bull market in the US history (2009-2020), but

also threw indices worldwide into a bear market. The

small rebound in the last week of March, as

governments ramped up stimulus plans, did little to

offset prior losses. The result? Massively negative Q1

returns for all markets.

Small capitalization stocks and emerging market stocks

fared the worst, while China emerged from Q1 with the

smallest market loss.

Driven by historic swings in markets, risk tripled or

quadrupled for most benchmarks, style factors,

industries, countries and currencies. While North

America and Europe were among the most volatile

markets at the end of the quarter, China was the least

risky market, relative to the other geographies Axioma

models track closely.

Both emerging and developed country currencies saw

risk soar, particularly in March, despite coordinated

efforts by central banks to blunt the impact of the crisis

and to bolster their currencies.

No sector was spared the market decline, with Energy

and Financials ending Q1 as the biggest losers in the

US. Not surprisingly, Health Care was among the sectors seeing the smallest quarterly decline.

Key Takeaways…

Indices around the globe saw risk rise to twice

to six times their beginning-of-quarter levels.

The increase in risk in the US was by far the

biggest for a quarter since 1982.

The risk gap between emerging and developed

markets and between small- and large-cap

stocks narrowed.

Almost no factor blocks, countries, currencies

or industries were spared the sharp and

sudden jump in risk

Countries and sectors that used to be leaders

in risk turned into some of the least risky, such

as China and Information Technology.

Asset correlations reached or exceeded eight-

year highs, contributing to the increase in

overall risk.

Most style factors in most regions produced

returns far larger in magnitude than expected

with some up to nine standard deviations away

from the long-term average.

In another shift from the norm, several style

factors fared better among larger stocks

(versus the usual higher performance in small

cap), and in a long-only context (rather than on

the short side).



Benchmark Risk and Return

Stocks worldwide suffered one of the worst quarters since the

global financial crisis, a development virtually no one saw

coming at the beginning of the year. Most markets continued

their decade-long rally into the first month and a half of 2020,

boosted by strong economic data, progress in the US-China

trade negotiations, and central banks keeping interest rates at

low levels.

But things changed for the worse, as the coronavirus spread

rapidly around the globe. What initially seemed to be an issue affecting primarily the Chinese market, ended up

pummeling markets everywhere. The Chinese market fell 12% as its exchanges opened on Monday, Feb. 3 after

the Lunar New Year holiday, which was extended by the onset of the coronavirus outbreak. Although many

Chinese businesses were closed, with travel suspended and borders shut, China rebounded in February before

falling again in March. The seesawing of the Chinese market, as represented by the STOXX China A 900 index,

resulted in a cumulative quarterly loss of 9%—the smallest among major indices (Figure 1).

Most other indices around the world reached their peaks on Feb. 19, and then crashed 30% to 40% in the

following 23 days, as the coronavirus spread across the world, paralyzing entire economies. By quarter end, all

regions (except China) recorded losses greater than 20%, with the Russell 2000 and STOXX Emerging Markets

being the hardest hit, posting losses of 29% and 31%, respectively.

Not surprisingly, as markets fell precipitously, expected volatility across the globe soared, with benchmarks

seeing short-horizon risk rise from two- to six-times their beginning-of-quarter levels (Figure 2). Canada started

the year with the lowest volatility (of the 15 regions in Figure 2), but saw a six-fold increase, joining in the top

five riskiest geographies. The US stood out as having higher volatility than almost any other major region. The

Russell 2000 went from being in the middle-of-the-pack to having the highest volatility. China saw the smallest

increase in risk and shifted from being the riskiest benchmark at the end of 2019 to the least risky at the end of

Q1.

The relative volatility among these 15 indices was the same at the medium-horizon, as the forecasted risk for

each region was proportionally lower than its short-horizon counterpart (Figure 3).

For the US, the volatility increase in Q1 was by far the biggest quarterly increase since at least 1982. The

monthly increases in March and February have been exceeded only once—in October 1987—as measured by

Axioma’s US short-horizon fundamental model (Figure 4). US short-horizon risk began the year near the all-

time low of 5% reached in Nov. 2015, and started rising even before the market peak in February, finishing the

quarter at levels only seen during the global financial crisis of 2008-2009 and the 1987 market crash.

Please note that the increases in total

risk also drove many changes in the

components of active portfolio risk.

Later in this report, we delve into

style factor returns and

corresponding changes in volatilities

and correlations.



Figure 1. Risk and Return for Major Benchmarks

Benchmark predicted risk rose dramatically as global markets plummeted in Q1.

Note: The risk models used are the most local available. For example, the Russell 2000 forecast is based on Axioma’s Small Cap (USSC4)

model, STOXX Emerging 1500 is based on the Emerging Markets model, etc.

Source: FTSE Russell, Qontigo

-45%

-35%

-25%

-15%

-5%

5%

15%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

STOXX USA 900

-45%

-35%

-25%

-15%

-5%

5%

15%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

Russell 2000

-45%

-35%

-25%

-15%

-5%

5%

15%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

STOXX Canada 240

-40%

-30%

-20%

-10%

0%

10%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

STOXX UK 180

-45%

-35%

-25%

-15%

-5%

5%

15%

5%10%15%20%25%30%35%40%45%50%

Jan Feb Mar

STOXX Europe 600

-45%

-35%

-25%

-15%

-5%

5%

15%

5%

10%

15%

20%

25%

30%

35%

Jan Feb Mar

STOXX-Japan-600

-35%

-25%

-15%

-5%

5%

15%

10%

15%

20%

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30%

35%

40%

Jan Feb Mar

STOXX Australia 150

-40%

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-10%

0%

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10%

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45%

Jan Feb Mar

STOXX AP 600 xJP

-15%

-10%

-5%

0%

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10%

5%

10%

15%

20%

25%

30%

35%

Jan Feb Mar

STOXX China A 900

-40%

-30%

-20%

-10%

0%

10%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

STOXX Global 1800

-40%

-30%

-20%

-10%

0%

10%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Jan Feb Mar

STOXX Emerging 1500



Figure 2. Short-Horizon Predicted Volatility, Selected Benchmarks

Benchmark risk rose twice to six times their beginning-of-quarter levels.

Note: Most benchmark risks are calculated in home currencies, with the STOXX Global Total Market, STOXX Global 1800, STOXX

Emerging 1500, STOXX Asia Pacific, STOXX Asia Pacific ex-Japan in USD.


0%

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North America Europe Asia-Pacific Global

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020



Figure 3. Medium-Horizon Predicted Volatility, Selected Benchmarks

China shifted from being the riskiest at the start of Q1 to the least risky at the end of it.


Figure 4. Historical Level of Short-Horizon Fundamental Risk for the Axioma Market Portfolio US-LMS (broad US universe)

The US witnessed the biggest quarterly increase in short-horizon risk since at least 1982.

Source: Qontigo

0%

5%

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SA 9

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ex-

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STO

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STO

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18

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North America Europe Asia-Pacific Global

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020

0%

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1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

200

2

2004

2006

200

8

2010

2012

2014

2016

2018

2020



The US weight in the STOXX Global 1800 index rose about a percentage point this quarter, but its contribution

to the benchmark risk rose more than two percentage points. The US’s contribution to the index risk exceeded

65% at the end of the quarter. Q1 2020 is now the 11th quarter of the past 12 in which the US accounted for

more benchmark risk than would have been expected given its weight (Figure 5).

In comparison, with 10% index weight, Japan came next, but it only accounted for 6.8% of the risk. These

contributions may help inform country-allocation decisions in a global portfolio.

Figure 5. US Weight and Contribution to Short-Horizon Risk in the STOXX Global 1800 index

US’s contribution to benchmark risk exceeded 65% by quarter end.

Source: Qontigo

40%

45%

50%

55%

60%

65%

70%

Weight Pct of Risk



Short-horizon risk for both US small-cap and US large-cap stocks skyrocketed in Q1, following similar paths.

While risk initially rose more for large-cap names, risk for the two categories of stocks approached parity at the

beginning of March (Figure 6). That reversed quickly, but still, by the end of the quarter, the level of the ratio

between the US Small Cap and US Large Cap risk—as measured by the Axioma’s US All Cap and US Small Cap

models, respectively—remained well below peak levels, suggesting that at this point in time expectations for

small-cap stocks do not need to significantly exceed those for large caps to justify an investment. The risk for US

small caps was only 16% higher than for their larger cap counterparts at the end of Q1.

Similar to the ratio of US small-cap vs. US large-cap risk, the ratio of emerging markets to developed markets

short-horizon risk also fell in Q1. After dropping below 1 briefly in mid-March, the ratio between STOXX

Emerging Markets 1500 and STOXX Global 1800 ended the quarter slightly above 1, indicating that emerging

markets stocks had higher predicted volatility than developed markets, but the difference was relatively small.

Figure 6. Ratio of Axioma Market Portfolios US-LMS-xTop 1000 (US Small Cap) and US-Top 1000 (US Large Cap) Short-Horizon Risk* and STOXX Emerging 1500 to STOXX Global 1800 Short-Horizon Risk

The difference in small-cap versus large-cap risk and between developed and emerging risk narrowed in Q1.

*US Small Cap risk is calculated using Axioma’s USSC4 model, and US Large Cap risk uses US4 Model. For Emerging and Developed risk,

we are using the Worldwide and Emerging Markets Models, respectively. All use Axioma’s fundamental model variants.

Source: Qontigo

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

US Small Cap/US Large Cap Risk

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Emerging Markets/Developed Markets Risk



Country and Currency Risk

Risk of developed markets, as represented by the STOXX Global 1800 index, was five times higher at the end of

the quarter, compared with the beginning of Q1. Market risk started to rise immediately when the market fell in

February, while other components took a bit longer, but in the end all components of risk shot up (Figure 7).

Over the course of Q1, market risk more than quadrupled, industry and country risk tripled, currency risk rose

by one-and-a-half times, and style risk doubled (although was up more in mid-March), as measured by Axioma’s

Worldwide fundamental short-horizon model. The increase in specific risk would have been quite high in more

normal times, but its increase of 78% seemed relatively low in comparison to other factors.

Country extra-market1 risk increased substantially in the past quarter for every major developed country

(Figure 8). Italy took the top risk spot, after its risk almost quadrupled during Q1. Japan, where volatility also

increased fourfold, was a close second. The UK went from being one of the riskiest countries at the end of Q4

2019 to the second-least risky after the US, even as its extra-market risk tripled in Q1. The relative risk of most

continental European countries has increased compared with other regions.

Developed market currency risk shot up across the board in Q1, with risk for most currencies doubling (Figure

9). The Norwegian krone and Australian dollar saw their risk triple over the quarter. The Norwegian krone

became by far the riskiest of the developed market currencies, its volatility exceeding even that of most major

emerging market currencies.

1 Risk related to the country over and above that of other risk-model factors, which can be interpreted as how similar to, or different from, the country is from the overall market. This factor is also known as country risk.



Figure 7. STOXX Global 1800 Short-Horizon Risk by Factor Block

All components of risk shot up for developed markets in Q1.

Source: Qontigo

Figure 8. Developed Countries Extra-Market Risk Levels

Italy has taken the top risk spot, after its risk almost tripled during Q1.

Source: Qontigo

0.8%

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0%

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Jan Feb Mar

Total (Left)

Market-SH (Left Scale)

Country-SH (Right Scale)

Currency-SH (Right Scale)

0.30%

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0.90%

1.10%

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0.5%

0.7%

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1.1%

1.3%

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1.9%

Jan Feb Mar

Style (Left)Specific (Left)Industry (Right)

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NORTH AMERICA EUROPE ASIA PACIFIC

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020



Figure 9. Developed Currencies Risk Levels

Developed market currency risk shot up across the board in Q1, with risk doubling or tripling.

Source: Qontigo

Whereas developed market risk rose more than four-fold, emerging market risk “only” tripled. All components

of risk rose for the STOXX Emerging 1500, with style risk seeing the biggest relative increase, followed by market

risk (Figure 10). Currency risk doubled, and industry and specific risk rose less than 100%, as measured by

Axioma’s Emerging Markets short-horizon fundamental model.

As we saw in developed markets, extra-market risk for all emerging countries saw substantial increases during

Q1 (Figure 11). Poland led in the way, with its risk more than quintupling from the beginning to the end of the

quarter. Colombia replaced Chile as the riskiest among emerging countries, but Greece was not far behind.

Czech Republic remained the least risky country.

The increase in risk for some emerging market currencies was less than that in developed markets during Q1.

However, the Mexican peso and Indonesian rupiah tripled in volatility, and the Russian ruble was not far

behind. The Turkish lira, which was the riskiest emerging currency for much of 2019, saw no increase in risk in

Q1, and was no longer the riskiest emerging market currency, having switched places with the Mexican peso

(Figure 12).

Aggregate currency correlations fell slightly in January, but rose sharply in February and March (Figure 13). As a

result of a flight-to-quality rush into dollars that began a couple weeks after the market peak, all developed

market currency pairs saw their correlation increase in Q1, as did all but one major emerging market currency

pairs. Every developed market currency became far more correlated with the global market, and, again, all but

one emerging market currency (Chinese yuan) did as well. Among developed currency pairs, the Japanese yen

saw the highest individual-pair increase (with the British pound), while the Brazilian real recorded the biggest

increase (with the South African rand) among emerging markets. Style factor correlations dropped, with the

median correlation turning slightly negative by March end.

0%

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20%

NOK GBP NZD AUD SEK JPY DKK CHF EUR CAD SGD

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020



Figure 10. STOXX Emerging Markets 1500 Short-Horizon Risk by Factor Block

All components of risk rose for emerging markets, with style risk seeing the biggest relative increase.

Source: Qontigo

Figure 11. Emerging Countries Extra-Market Risk Levels

Colombia replaced Chile as the riskiest among emerging countries.

Source: Qontigo

1.0%

3.0%

5.0%

7.0%

9.0%

8%

13%

18%

23%

28%

33%

38%

Jan Feb Mar

Total Risk (Left)

Market (Left)

Country (Right)

Currency (Right)

0.4%

0.6%

0.8%

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1.2%

1.4%

1.6%

1.8%

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Jan Feb Mar

Style (Left)

Specific (Left)

Industry (Right)

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ASIA PACIFIC EMEA LATIN AMERICA

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020



Figure 12. Major Emerging Currencies Risk Levels

The Mexican peso became the most volatile emerging market currency in Q1.

Source: Qontigo

Figure 13. Worldwide Model Median Factor Correlations

Aggregate currency correlations fell slightly in January, but rose sharply in February and March.

Source: Qontigo

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MXN ZAR RUB BRL TRY IDR KRW INR THB CNY TWD

3/29/2019 6/28/2019 9/30/2019 12/31/2019 3/31/2020

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Style Country Industry Currency



US Sector Risk

No US sector was spared in the overall market decline, with all 11 sectors in the STOXX US 900 recording losses

for the quarter. Not surprisingly, Health Care posted the smallest losses (of 12%), followed by Information

Technology, which had been the best performer in 2019. The Energy sector was additionally battered by the oil

crisis, its losses exceeding 50% for Q1 (Figure 14).

Along with the massive increases in benchmark risk observed globally, US sectors saw substantial increases in

their predicted volatility since year-end 2019. Info Tech’s risk almost doubled, but that was a much smaller

increase than for any other sector, and it is now one of the least volatile sectors in the STOXX US 900 index. On

the other hand, risk for Real Estate and Utilities more than tripled. Real Estate—at the low end of the sector risk

range a quarter ago—is now second only to Energy in its volatility. By quarter end, Energy’s volatility surpassed

50%.

Figure 14. US Large-Cap Sector Risk and Return, and Medium-Horizon Risk2

All sectors posted losses in Q1, with Energy remaining the worst performing and riskiest sector.

CV Comm Serv FI Financials MA Materials

CD Consumer Disc HC Health Care RE Real Estate

CS Consumer Staples IN Industrials UT Utilities

EN Energy IT Info Tech US-900 STOXX US 900

Source: Qontigo

2 Sector risk is calculated by creating a portfolio of all stocks in that particular GICS 2018 sector in the US STOXX 900. Weights, based on US STOXX 900 weights, are then rescaled to add to 100%. The risk of the sector portfolio is then calculated using Axioma’s US4 Medium-Horizon Fundamental model.

CVCD

CS

EN

FI

HC

IN

IT

MA

RE

UT

US-900

-60%

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Q1

20

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urn

Beginning of Quarter Risk0%

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60%12/31/2018 6/28/2019 9/30/2019

12/31/2019 3/31/2020



Info Tech’s weight in the STOXX USA 900 index increased almost 2.5% during Q1, but its risk contribution

dropped more than 4%, as its volatility fell relative to other sectors. Still, Info Tech remained the largest

contributor to benchmark risk for the STOXX USA 900. Health Care, Consumer Staples and Communication

Services all contributed less to the benchmark risk than would be expected given their weights in the US index

(Figure 15). The volatility increase for Real Estate and Utilities meant they are no longer contributing less risk

than implied by their (albeit small) weights.

Figure 15. Sector Weights and Risk Contribution to STOXX USA 900

Info Tech’s contribution to benchmark risk declined in Q1.

Source: Qontigo

0%

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40%31 Dec 2019Weight Pct of Risk

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30%31 Mar 2020



Asset Correlations, Dispersion and Decomposition of the Change in Risk

Asset correlations started the quarter low versus historical levels, in a few cases very close to all-time lows, but

spiked during Q1 (Figure 16). Correlations at least doubled for all indices by mid-March (some much more than

doubled) and exceeded prior high levels since at least 2000. Asset correlations did ease off a bit by quarter end,

although remained high relative to what we have seen in recent years. Higher correlations mean higher overall

risk.

We look at the decomposition of the change in risk from the point of view of the factor model and a full

covariance matrix to determine what drove the changes in short-horizon risk in the quarter. 3 Factor volatilities

soared in Q1 (as detailed in the section below), as did individual stock volatilities, driving the total increase in

risk for Single-Country and Multi-Country indices (Figures 17 and 18). Factor correlations for single-country

indices did not have a big impact, and even offset some of the increase from volatility in the STOXX UK 180.

Factor volatilities boosted the total increase in risk for STOXX Asia Pacific ex-Japan and STOXX Emerging Markets

1500. When we look at the decomposition in risk from the standpoint of the asset correlation matrix, risk was

driven higher by both higher volatility in individual stocks and higher correlations among those stocks, in both

single- and multi-country indices.

Asset dispersion is a function of correlations (the lower the correlations the higher the dispersion) and volatility

(the lower the volatility the lower the dispersion). Not surprisingly, dispersion increased dramatically in Q1,

driven by the huge increase in volatility and despite higher correlations (Figure 19). March had the highest

monthly dispersion of at least the past eight years, with levels roughly two times the average over that period.

This meant that although stocks were down substantially, there was a big spread between winners and losers, a

potential advantage for managers who could successfully distinguish between the two.

3 The components of a risk forecast are the portfolio holdings, its factor exposures, the covariance matrix, and stock-specific

risk. In order to decompose the change in risk of a benchmark or portfolio, we employ the following methodology. We first look

at the impact of the change in holdings, so we use last period’s risk model with the current portfolio to calculate a risk forecast,

and the difference is attributable solely to the change in holdings. Second, to evaluate the change in stocks’ characteristics, we

update the factor exposures and, again, use last period’s risk model, but current holdings and exposures. Third, we look at the

impact of specific risk changes by using current specific risk estimates, but the prior period’s covariance matrix. We calculate

the impact of changes in correlation by using last period’s correlations with all the other data as of the current period. Finally,

the residual is the impact of the change in factor volatility. While the ordering described above will affect the results, we find

that the results do not change substantially when we change the order. Intuitively we believe that the biggest impact comes

from change in factor volatility, and our results bear out our intuition. The overall impact of asset-level correlations is not seen

until we combine these components, so to see the impact of changes in stock correlations we must also decompose the full

asset covariance matrix. In this case we calculate the change attributable to changes in portfolio composition, individual stock

volatility, and, importantly right now, correlations among assets.



Figure 16. Median Pairwise 60-Day Rolling Asset Correlations

Correlations started Q1 at low levels vs. history, but spiked during the quarter.


0

0.2

0.4

0.6

0.8

1

20

00

20

03

20

06

20

09

20

12

20

15

20

18

STOXX-USA-900

Russell 2000

0

0.2

0.4

0.6

0.8

1 STOXX-Canada-240

STOXX-UK-180

0

0.2

0.4

0.6

0.8

1

20

00

20

03

20

06

20

09

20

12

20

15

20

18

STOXX-Europe-600

STOXX-Asia-Pacific-600-ex-Japan

0

0.2

0.4

0.6

0.8

1 STOXX-Japan-600

STOXX-Australia-150

0

0.2

0.4

0.6

0.8

1 STOXX-Global-1800

STOXX-Emerging-Markets-1500



Figure 17. Decomposition of the Quarter Change in Short-Horizon Risk, Single Country Indices

Factor volatilities soared in Q1, as did individual stock volatilities.


Figure 18. Decomposition of the Quarter Change in Short-Horizon Risk, Multi-Country Indices

Higher asset correlations added significantly to the increase driven by higher stock volatility.

-5%

0%

5%

10%

15%

20%

25%

30%

35%

PortfolioComposition

Stock Characteristics

StockSpecific

Volatility

FactorVolatility

FactorCorrelations

Factor Matrix

-5%

0%

5%

10%

15%

20%

25%

30%

35%


StockVolatility

StockCorrelations

Asset-Asset (Dense) Matrix

-5%

0%

5%

10%

15%

20%

25%

30%

35%


Stock Characteristics

StockSpecific

Volatility

FactorVolatility

FactorCorrelations

Factor Matrix

-5%

0%

5%

10%

15%

20%

25%

30%

35%


StockVolatility

StockCorrelations

Asset-Asset (Dense) Matrix



Source: Qontigo

Figure 19. Average Monthly Asset Dispersion

Dispersion increased dramatically in Q1, driven by the huge increase in volatility and despite higher correlations.

Source: Qontigo

0%

2%

4%

6%

8%

10%

12%

14%Q1 2019 Q2 2019 Q3 2019 Q4 2019 Q1 2020



Style Factor Returns and Volatilities

We present three ways of looking at factor returns: 1) returns for traditional factor-mimicking portfolios; 2)

returns for alternative factor portfolios; 3) returns for sample factor portfolios, with various constraints.

First, the traditional “Factor-Mimicking Portfolio” (FMP) is the factor definition we use to build our risk models.

The FMP is long/short and built on a very broad universe of stocks, rebalanced daily to have unit exposure to the

factor and no exposure to any other factor. The return from this portfolio is the “ideal” an investor can expect

from the factor.

Second, for a few factors in three of our most-used models (US, Worldwide and Emerging Markets), we then

present alternative ways of defining the factor portfolio. As we have noted before, the “Factor-Mimicking

Portfolios” are meant to represent a pure exposure to the given factor and no exposure to any other risk factor.

They are long-short and recomputed daily, meaning managers may not be able to achieve the appropriate

exposure to the factor. Using the same factor definition as for the FMP, we vary one of the characteristics at a

time, so we look at a portfolio constructed daily (but without the outlier handling in the base model, and built

on a slightly different universe), one with a monthly rather than daily rebalance frequency, one using a narrower,

large-cap universe, and one in which the portfolio can only be short up to the benchmark weight (“long-only”). All of

these portfolios have no exposure to any other model factor. We also look at a couple other popular factor

constructions, namely the top quintile of stocks from the factor and the top minus the bottom quintile. These

portfolios will have significant exposures to other style, sector, country and currency factors.4

Finally, we have our US “sample factor portfolios.” We have created a series of portfolios that are meant to

proxy the processes of style-based managers (although with fewer constraints). These portfolios are optimized

monthly, with a Russell 1000 benchmark and selection universe. We used six factors as the starting point for our

Sample Factor Portfolios (Value, Earnings Yield, Momentum [Medium-Term], Growth, Dividend Yield and

Profitability) and aimed to maximize exposure to the factor, while maintaining a 3% tracking error. For each

factor, we created three versions:

I. ”Constrained”, with no net exposure to any of the other style factors or any industries;

II. “Lightly Constrained”, with more realistic constraints of +/- 2% for sectors and +/-0.2 for other style

factors; and,

III. ”Unconstrained”, with exposure to the factor in question, but no other factor constraints.

The portfolios had no turnover or trading constraints. We also produce a multi-factor portfolio with alpha

defined as an equal-weighted combination of Value, Earnings Yield, Momentum, Growth, Profitability, [negative]

Size and [negative] Volatility. The Minimum Variance portfolios sought to create the portfolio that had the lowest

variance, while maintaining an effective number of names that was about one-third that of the Russell 1000

(enough to be meaningful, but not so much that variance would be too high), but 1) constraining most other

style factors, and 2) with no other constraints (such as style or industry). Finally, we created Low Risk portfolios,

which minimized exposure to stocks’ total risk, while maintaining 3% tracking error with the same two versions

as our Minimum Variance portfolio. These portfolios can provide long-only factor-based managers another view

into their performance.

In all cases, the returns we examine are active returns.

4 See our papers “What, Exactly Is a Factor” and its follow-up, “What Is a Factor: The Impact of the Long-Only Constraint” for more detail.

https://go.axioma.com/WP20190405-WhatExactlyIsaFactor_Registration.html?utm_source=insights-page

https://go.axioma.com/WP20190627-WhatIsaFactorPart2_Registration.html?utm_source=insights-page

https://go.axioma.com/WP20190627-WhatIsaFactorPart2_Registration.html?utm_source=insights-page



Table 1 shows the factor-mimicking portfolio returns for the first quarter region by region. An asterisk next

to the factor return indicates that the factor return was at least two standard deviations away from its long-

term average—what we would consider an “outsized” return. We also highlight the regions where the factor

returns were highest (green) and lowest (pink) for the quarter.

Throughout the course of the first quarter, and particularly after the market peaked, we wrote several blog

posts detailing factor performance.5 While some factors behaved as expected, others produced unexpectedly

high-magnitude returns when expectations were likely to be that returns would be close to zero, while others

produced big returns in the “wrong” direction. Most notable was the number of factors for which returns fell

well outside of a two standard deviation range around the average return, which was far more than we would

expect to see.

For some factors, such as Momentum, Growth, Profitability, Volatility and Market Sensitivity, the return was in

the expected direction in most regions—certainly a positive for managers who tilt on those factors. Value and

Earnings Yield, in contrast, produced returns that were far lower than average, and likely produced big drags on

portfolio returns.

Other factors also stood out. For example, Leverage’s return fell between three and nine standard deviations

below the long-term average of around zero (the nine was in US small cap). In many regions, Liquidity was three

or four standard deviations below zero (most notably in Canada). Exchange-rate sensitivity was eight standard

deviations below average in the US, but almost seven above in Japan. And as companies either announced

dividend cuts or saw heightened expectations they would do so, dividend yield had a tough quarter, with

returns as high as 4.5 standard deviations below average in the Worldwide model. While lower-liquidity names

fared better than their more-liquid counterparts (quite possibly because investors tend to sell the easiest stocks

to sell in a big selloff), large-cap stocks trounced their smaller-cap counterparts in just about every model.

Table 1. Factor-Mimicking Portfolio Quarter Returns by Region

A majority of factors produced outsized returns in Q1.

Source: Qontigo

5 See, for example, “Markets – and Factor Returns – Run Wild: Time to Check Your Bets”, “Quant Quake 2020? As Factor Volatility Mirrors Market Volatility, Most Returns Head in the Wrong Direction” and “Quant Quake Comparison? This Looks Worse”.

https://www.axioma.com/blog/post/markets-and-factor-returns-run-wild-time-to-check-your-bets/

https://www.axioma.com/blog/post/quant-quake-2020-as-factor-volatility-mirrors-market-volatility-most-returns-head-in-the-wrong-direction/

https://www.axioma.com/blog/post/quant-quake-2020-as-factor-volatility-mirrors-market-volatility-most-returns-head-in-the-wrong-direction/

https://www.axioma.com/blog/post/quant-quake-comparison-this-looks-worse/

https://www.axioma.com/blog/post/quant-quake-comparison-this-looks-worse/



Our alternative factor portfolios also produced some unusual results in the first quarter (Table 2). Over the

long term, the portfolios created that are confined to a large-cap universe of stocks have typically lagged those

based on the broader universe, suggesting the factor worked better among smaller names. Similarly, when the

ability to short was limited to only benchmark weight, the portfolio also fared worse, suggesting the factors

produced better performance on the short side.

In the first quarter, for most factors across the three regions the long-only portfolio’s returns were substantially

better than for the equivalent long-short portfolio (although typically with the same sign, so if one was positive

the other was, too). The only exception was Profitability in the US, where the long-only portfolio still produced a

highly positive return. In the US and Emerging Markets, several factors produced higher returns in the large-cap

universe, especially Profitability in the US and Earnings Yield in Emerging Markets. This was not the case in the

Worldwide model, however. Finally, when we changed the rebalance frequency to monthly from daily,

Momentum saw better returns in the US and Emerging Markets models, perhaps by avoiding some of the

churning that went on as markets seesawed in the quarter’s second half.

We also observed very high magnitude returns in the top quintile and top-bottom quintile portfolios, much

higher than for our optimized alternatives (although in the same direction). While the underlying factor was a

contributor to those returns, other factor exposures also played a major part and explained the size of the

return. For example, the top quintile Value portfolio in the US was clearly dragged down by its high Value

exposure (which accounted for almost -5% of the active return of -19.48%), but negative exposures to Size and

Profitability and positive exposures to Dividend Yield and Market Sensitivity contributed about 1%-2% each.

Sector bets in aggregate detracted more than 4.5%, notably owing to a small positive Energy exposure, a large

positive Financials’ bet (almost 50% overweight!), and a large negative bet on Information Technology. Users of

this type of factor-return construction, whether in ETFs or attribution, should be aware of the impact of all these

unintended bets.



Table 2. Alternative Factor Returns

In an unusual twist this quarter, large-cap and long-only portfolios generally fared better than long-short all-cap.

US

Worldwide

Emerging Markets

Source: Qontigo

Active Return Differences

Q1 2020 Daily Monthly

Russell

1000

Top

Quintile

Top-

Bottom

Long

Only

Monthly-

Daily

R1000-

3000

Daily LO-Daily

Momentum 0.85% 1.94% 1.55% 11.74% 16.69% 4.84% 1.09% 0.70% 3.99%

Value -4.86% -4.78% -5.26% -19.48% -20.06% -0.31% 0.08% -0.40% 4.55%

Earnings Yield -6.56% -6.70% -6.23% -34.71% -2.54% -3.27% -0.14% 0.33% 3.29%

Profitability 4.92% 4.44% 7.56% 20.77% 9.21% 3.23% -0.48% 2.65% -1.68%

Low Volatility 2.54% 2.69% 3.52% 6.52% 5.13% 11.43% 0.15% 0.98% 8.89%



Large

Cap

Top

Quintile

Top-

Bottom

Long

Only

Monthly-

Daily

Large

Cap – All

Cap LO-Daily

Momentum 2.23% 2.15% 0.99% 10.71% 16.56% 6.09% -0.07% -1.24% 3.86%

Value -1.36% -1.02% -3.91% -14.83% -15.02% -0.27% 0.34% -2.55% 1.09%

Earnings Yield -0.66% -0.98% -3.01% -17.74% -7.61% 0.51% -0.32% -2.35% 1.18%

Profitability 1.49% 1.67% 1.60% 10.71% 10.41% 3.74% 0.18% 0.11% 2.25%

Low Volatility 4.93% 4.94% 4.31% 4.82% 4.03% 9.71% 0.01% -0.62% 4.78%



Large

Cap

Top

Quintile

Top-

Bottom

Long

Only

Monthly-

Daily

Large

Cap – All

Cap

LO-

Daily

Momentum 5.88% 6.89% 7.07% 4.85% 9.38% 6.88% 1.01% 1.18% 1.00%

Value -1.94% -1.96% -6.44% -3.63% -8.88% -1.38% -0.02% -4.50% 0.56%

Earnings Yield -0.34% 0.12% 6.79% -1.66% -2.28% 6.76% 0.46% 7.13% 7.10%

Profitability 0.67% 0.49% 1.11% 0.67% 3.20% 2.37% -0.18% 0.44% 1.70%



Finally, we turn to our sample factor portfolios, which experienced considerable turmoil during the quarter.

This was a good quarter to have imposed constraints. In all the single-factor and multi-factor portfolios, the

unconstrained versions fared substantially worse than the variants that had strict or loose constraints (Figure

20). The difference in returns was striking, particularly for Profitability, Value, the Multi-Factor portfolio and

especially Dividend Yield.

The performance differential largely came after the market peaked in February (post-peak returns are included

in the Figure 20 legend). It is also of note that just tilting on low-risk stocks (as opposed to minimizing overall

portfolio variance) was not a particularly helpful strategy in the quarter, especially the unconstrained version

that led to some damaging industry allocations (most notably an overweight in Mortgage REITs). In addition, the

inability to rebalance and retighten constraints intra-month meant that some other factors had a larger-than-

expected impact.

In a continuation of the trend from last year, the multi-factor portfolio fared worse than any of the single-factor

portfolios (most of which are included as factors in the alpha that drives the multi-factor portfolios). High

Earnings Yield, Small Size and high Value were all significant detractors, and industry exposures (again, mainly

Mortgage REITs) also had a large impact on the unconstrained version.

This discrepancy between using a multi-factor alpha versus combining single-factor portfolios is a topic we

wrote about early in the quarter with the release of the new STOXX factor indices6 and concluded that multi-

factor retained the edge, although the case could be made for single-factor as well.

In addition to lower returns, unconstrained portfolios also experienced substantially higher levels of tracking

error, well above the 3% target. Even with constraints tracking error was higher than expected, but it was much

closer to 3% and well below that of the unconstrained portfolio. The tightest constraints meant the smallest

deviation of tracking error from its target. Where performance was poor for these portfolios it was really poor,

as evidenced by their realized information ratios (Table 3).

6 See “To Combine Factors or To Combine Portfolios? That is the Question for the Smart Beta Investor…”

https://www.axioma.com/blog/post/to-combine-factors-or-to-combine-portfolios-that-is-the-question-for-the-smart-beta-investor/



Figure 20. Sample Factor Portfolios and Corresponding Factors Cumulative Active Returns

Constraints were a major help for returns and realized active risk in Q1.

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

Jan Feb Mar

Momentum

Uncons 2/20-3/31: -3.24%

Lightly 2/20-3/31: -1.27%

Constrained 2/20-3/31: -1.14%

MT Momentum 2/20-3/31: 0.14%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Jan Feb Mar

Profitability

Uncons 2/20-3/31: 2.24%

Lightly 2/20-3/31: 2.81%

Constrained 2/20-3/31: 2.56%

Profitability 2/20-3/31: 5.12%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

Jan Feb Mar

Earnings Yield

Uncons 2/20-3/31: -5.34%

Lightly 2/20-3/31: -2.50%

Constrained 2/20-3/31: -2.79%

Earnings Yield 2/20-3/31: -1.77%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

Jan Feb Mar

Value

Uncons 2/20-3/31: -5.81%

Lightly 2/20-3/31: 0.07%

Constrained 2/20-3/31: -0.15%

Value 2/20-3/31: -2.49%



-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Jan Feb Mar

Low Total Risk

Uncons 2/20-3/31: -1.87%

Lightly 2/20-3/31: -0.73%

-Mkt Sens 2/20-3/31: 3.44%

-Volatility 2/20-3/31: 3.38%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Jan Feb Mar

Minimum Variance

Uncons 2/20-3/31: 5.87%

Lightly 2/20-3/31: 9.09%

-Mkt Sens 2/20-3/31: 3.44%

-Volatility 2/20-3/31: 3.38%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

Jan Feb Mar

Multi Factor

Uncons 2/20-3/31: -7.71%

Lightly 2/20-3/31: -5.18%

Constrained 2/20-3/31: -2.85%

-16.0%-14.0%-12.0%-10.0%

-8.0%-6.0%-4.0%-2.0%0.0%2.0%4.0%6.0%8.0%

Jan Feb Mar

Multi Factor

Earnings Yield 2/20-3/31: -1.77%

MT Momentum 2/20-3/31: 0.14%

Profitability 2/20-3/31: 5.12%

-Size 2/20-3/31: -3.25%

Value 2/20-3/31: -2.49%

-Volatility 2/20-3/31: 3.38%



Table 3. Sample Factor Portfolios: YTD Information Ratios

Again, the Multi-Factor portfolios equaled or lagged the single factors.

The impact of the market rout on factor returns also had a significant impact on the underlying

components of active portfolio risk, not just for style-based managers but for all investors trying to

manage the active risk of their portfolios. While factor volatilities were mainly down a small amount

from the end of 2019 to Feb. 19, 2020, they subsequently soared—doubling, tripling, or even more for a

few factors—after that point (Table 4). Although the changes seemed biggest in the US models, almost

every factor in every geography experienced an increase, so even with no changes in the portfolio

holdings there would have been a substantial change in risk.

And, of course, it was not only factor volatilities that changed. Large returns, some positive and some

negative, also led to many substantial changes in factor correlations (Table 5). As we saw for volatilities,

correlations in most cases were little-changed in the first half of the quarter (here we are using just the

Worldwide model for illustration, although results were similar in other regions), but quite a few saw

huge changes after that. We use a rule of thumb that a change of about 0.3 is significant. In the

worldwide model the correlation change of 38 of the 132 factor pairs met or exceeded this threshold,

and others were quite close. And significant or not, these correlations combined with the volatilities we

discussed above, would have materially changed active portfolio risk, and in quite a short time.

Finally, Table 6 shows the current level of volatility, both short- and medium-horizon, for the factors in

the US and Worldwide models. For almost all factors the volatility is in the top quintile relative to where

it has been historically, with the majority in the top decile. A few factors (US SH Earnings Yield, US SH

Exchange Rate Sensitivity, US MH Profitability and Short-Term Momentum (which is only in the SH

model) for both US and Worldwide, were at all-time highs at quarter end.

All in all, on top of highly negative benchmark and numerous outsized factor and industry returns, it has

been an extremely challenging time for risk management that seems unlikely to abate any time soon.

Constrained Lightly Unconstrained

Dividend Yield -1.3 -1.4 -1.2 Earnings Yield -1.3 -1.1 -1.2 Growth 0.6 0.3 -0.1 Momentum -0.3 -0.4 -0.7 Profitability 1.3 1.2 1.0 Value 0.2 0.3 -1.2 Minimum Variance 0.9 — 0.5 Low Total Risk 0.1 — -0.3 Multi Factor -1.3 -1.2 -1.1



Table 4. Short-Horizon Predicted Style Factor Volatility Changes

The US models in general saw the biggest increases in factor volatility.

Source: Qontigo



Table 5. Short-Horizon Predicted Style Factor Correlation Changes, Worldwide Model

The correlations between factors shifted dramatically after February 19.

Source: Qontigo



Table 6. Short-Horizon and Medium-Horizon Predicted Volatility Levels, Changes and Rankings, WW4 and US4 Models

Factor volatilities rose to historic highs.

Note: Highlighted green cells fall in the bottom quartile, while purple cells in the top quartile relative to historical levels.

Source: Qontigo

Pred.

Current

Rank Pred.

Current

Rank

Vol Quarter Year vs. Vol Quarter Year vs.

Mar-20 Ago Ago History Mar-20 Ago Ago History

Dividend Yield

Medium Horizon 2.17% 0.59% 0.93% 93 1.05% 0.33% 0.52% 74

Short Horizon 3.01% 1.61% 1.85% 98 1.34% 0.69% 0.89% 85

Earnings Yield

Medium Horizon 3.93% 1.25% 1.71% 99 1.57% 0.51% 0.70% 86

Short Horizon 4.70% 2.41% 2.62% 100 1.94% 0.92% 1.17% 95

Exchange Rate Sensitivity

Medium Horizon 4.19% 3.18% 3.16% 99 1.75% 1.15% 1.10% 98

Short Horizon 5.35% 4.41% 4.42% 100 1.71% 1.06% 1.04% 96

Growth

Medium Horizon 2.54% 0.88% 1.28% 98 1.12% 0.38% 0.47% 86

Short Horizon 2.70% 1.22% 1.69% 97 1.21% 0.52% 0.69% 90

Leverage

Medium Horizon 3.20% 2.22% 2.16% 98 1.43% 0.96% 0.81% 96

Short Horizon 3.69% 2.80% 2.73% 99 1.74% 1.29% 1.14% 99

Liquidity

Medium Horizon 3.44% 1.72% 1.38% 85 2.82% 1.74% 1.22% 92

Short Horizon 5.00% 3.28% 3.32% 98 3.14% 2.12% 1.80% 97

Market Sensitivity

Medium Horizon 11.05% 6.16% 5.86% 93 7.60% 3.81% 3.39% 85

Short Horizon 8.14% 4.12% 3.26% 87 6.02% 2.18% 2.29% 78

Medium-Term Momentum

Medium Horizon 4.67% -0.50% 1.73% 86 2.95% -0.05% 1.27% 75

Short Horizon 5.17% 0.67% 2.66% 91 3.74% 1.23% 1.94% 89

Profitability

Medium Horizon 5.40% 3.48% 3.38% 100 1.49% 0.70% 0.76% 92

Short Horizon 5.71% 3.87% 3.92% 99 1.67% 0.82% 1.00% 95

Short-Term Momentum

Short Horizon 23.29% 21.20% 21.12% 100 16.63% 15.40% 14.84% 100

Size

Medium Horizon 11.39% 6.70% 6.06% 96 5.73% 2.83% 2.70% 79

Short Horizon 12.12% 7.81% 7.13% 96 7.12% 4.45% 4.25% 95

Value

Medium Horizon 3.47% 1.08% 1.47% 93 1.94% 0.77% 0.86% 94

Short Horizon 4.31% 1.77% 2.27% 97 2.38% 1.28% 1.36% 97

Volatility

Medium Horizon 6.85% 2.50% 1.75% 94 4.65% 2.17% 1.71% 81

Short Horizon 7.65% 3.67% 3.67% 95 5.00% 2.90% 2.54% 87

US4 Model

Current Vs.

WW4 Model

Current Vs.



Conclusion

The first quarter of 2020 was an unprecedented period, as the effects of the coronavirus pandemic crippled the

global economy. Markets worldwide fell dramatically, despite enormous government policy responses, finishing

the quarter with abysmal losses. Volatility skyrocketed with major benchmarks seeing their risk increase two- to

six-fold, with all components of risk contributing to the climb.

The coronavirus crisis reallocated the relative risk of countries, with the US reaching risk levels not seen since

the global financial crisis and the 1987 market crash, while China became the least volatile among the

geographies Axioma models track closely. Small-cap companies and emerging markets had the worst returns,

but not the biggest volatility increases, so the risk gap between small-cap vs. large-cap stocks and emerging vs.

developed markets narrowed. Currency risk spiked, too, despite central banks’ efforts to support their

currencies.

All US sectors finished the quarter in the red, with Energy recording the largest losses, while Health Care

(unsurprisingly) the smallest losses for the quarter. Energy remained the most volatile US sector.

Asset correlations rose across regions, in some cases reaching levels not seen since 2000, and boosting the

increase in risk. At the same time, asset dispersion widened, the big spread between winning and losing stocks

becoming a potential advantage for investors with the ability to distinguish between the two.

Most style factors in most regions produced returns far larger in magnitude than expected, with some up to

nine standard deviations away from the long-term average. In another shift from the norm, several style factors

fared better among larger stocks (versus the usual higher performance in small cap), and in a long-only context

(rather than on the short side).

The impact of the market rout on factor returns also had a significant impact on the underlying components of

active portfolio risk, not just for style-based managers but for all investors trying to manage the active risk of

their portfolios. Every style factor in every geography experienced large increases in volatility, so even with no

changes in the portfolio holdings there would have been a substantial change in risk. The large moves in factor

returns also led to many substantial changes in factor correlations.

All in all, on top of highly negative benchmark and numerous outsized factor returns, it has been an extremely

challenging time for risk management that seems unlikely to abate any time soon. However, at this writing,

market sentiment seems to be improving in developed markets7, as pandemic containment measures are

showing signs of success. Perhaps investors are starting to see the light at the end of the tunnel.

Russell 2000 is a registered trademark of FTSE Russell. Copyright© FTSE Russell 2020. All rights reserved. [Marketing

please add a similar statement for STOXX indices, if necessary].

7 See ROOF score commentary on market sentiment here: https://axioma.com/ROOF

https://axioma.com/ROOF

the corona quarter...the us witnessed the biggest quarterly increase in short-horizon risk since at...

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