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Value Spreads Are Back to Tech Bubble Highs: Is Everyone Out There Cray-Cray?

Source: AQR. January 1, 1990 – August 3, 2022.

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Is (Systematic) Value Investing Dead?

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August 5, 2022
-
Cliff Asness

Topics - Factor/Style Investing
Value

Global Value Spreads
Hypothetical AQR Industry-and-Dollar-Neutral All-Country Value Portfolio
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Spreads are constructed using a hypothetical AQR value composite that includes five value measures: book-to-price, earnings-to-price, forecast earnings-to-price, sales-to-enterprise value, and cash flow-to-enterprise value. Spreads are measured based on ratios and are adjusted to be dollar-neutral, but not necessarily beta-neutral through time. To construct industry-neutrality, the value spreads are constructed by comparing the value measures within each industry. The all-country universe is based on roughly 85% developed / 15% emerging weights, derived based on proprietary ex-ante risk targets as of 7/31/2022. The developed data starts January 1990, while the emerging universe is included starting December 1994. The risk models used are the Barra Developed Equity Risk Model and Barra Emerging Equity Risk Model. Hypothetical data has inherent limitations, some of which are listed in the Disclosures. For illustrative purposes only and not representative of an actual portfolio AQR currently manages. Please read the Disclosures for important information.

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Over the last few years, we’ve calculated the value spread various ways in these blogs. Sometimes just in the USA. Sometimes using only one measure like P/B when we want to go really far back in time. What we present here is the closest yet to how we actually view value and represents the value spread we look at most often in making decisions about tilts and the like. Other variants may differ somewhat.

Perspective
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July 12, 2022

Perspective
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May 9, 2022

Perspective
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February 4, 2022

Perspective
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December 16, 2021

Perspective
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May 8, 2020

Disclosures
Spreads are constructed using a hypothetical AQR value composite that includes five value measures: book-to-price, earnings-to-price, forecast earnings-to-price, sales-to-enterprise value, and cash flow-to-enterprise value. Spreads are measured based on ratios and are adjusted to be dollar-neutral, but not necessarily beta-neutral through time. To construct industry-neutrality, the value spreads are constructed by comparing the value measures within each industry. The all-country universe is based on roughly 85% developed / 15% emerging weights, derived based on proprietary ex-ante risk targets as of 7/31/2022. The developed data starts January 1990, while the emerging universe is included starting December 1994. The risk models used are the Barra Developed Equity Risk Model and Barra Emerging Equity Risk Model. Hypothetical data has inherent limitations, some of which are listed in the Disclosures. For illustrative purposes only and not representative of an actual portfolio AQR currently manages. Please read the Disclosures for important information.

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of AQR Capital Management, LLC, its affiliates or its employees.

Past performance is no guarantee of future results.

Diversification does not eliminate the risk of experiencing investment loss.

This document has been provided to you solely for information purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such.

There can be no assurance that an investment strategy will be successful. Historic market trends are not reliable indicators of actual future market behavior or future performance of any particular investment which may differ materially and should not be relied upon as such. This material should not be viewed as a current or past recommendation or a solicitation of an offer to buy or sell any securities or to adopt any investment strategy.

HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH, BUT NOT ALL, ARE DESCRIBED HEREIN. NO REPRESENTATION IS BEING MADE THAT ANY FUND OR ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN HEREIN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY REALIZED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK IN ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS THAT CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS, ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.

This document is not research and should not be treated as research. This document does not represent valuation judgments with respect to any financial instrument, issuer, security or sector that may be described or referenced herein and does not represent a formal or official view of AQR. This document has been prepared solely for informational purposes. The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons. Nothing contained herein constitutes investment, legal, tax or other advice nor is it to be relied on in making an investment or other decision.
AQR Capital Management, LLC, (“AQR”) provides links to third-party websites only as a convenience, and the inclusion of such links does not imply any endorsement, approval, investigation, verification or monitoring by us of any content or information contained within or accessible from the linked sites. If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which AQR.com has no control. In no event will AQR be responsible for any information or content within the linked sites or your use of the linked sites.
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spread ( data , key , value , fill = NA , convert = FALSE , drop = TRUE , sep = NULL )
library ( dplyr )
stocks <- data.frame (
time = as.Date ( '2009-01-01' ) + 0 : 9 ,
X = rnorm ( 10 , 0 , 1 ) ,
Y = rnorm ( 10 , 0 , 2 ) ,
Z = rnorm ( 10 , 0 , 4 )
)
stocksm <- stocks %>% gather ( stock , price , - time )
stocksm %>% spread ( stock , price )
#> time X Y Z
#> 1 2009-01-01 -1.1754938 0.6336561 -5.9265184
#> 2 2009-01-02 -1.0627343 -0.7106185 -0.2012541
#> 3 2009-01-03 -1.0705238 -3.5392503 -3.1825495
#> 4 2009-01-04 2.5011843 -1.7321029 1.2650120
#> 5 2009-01-05 -1.5898217 0.6577330 -0.3982349
#> 6 2009-01-06 0.3959386 -4.6752217 -2.5660559
#> 7 2009-01-07 1.0122784 1.2544913 -0.4143983
#> 8 2009-01-08 0.7512999 -2.3759464 2.4637004
#> 9 2009-01-09 0.6575822 -0.9047347 1.2471138
#> 10 2009-01-10 -0.4650852 -0.5436360 -3.1626294
stocksm %>% spread ( time , price )
#> stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06
#> 1 X -1.1754938 -1.0627343 -1.070524 2.501184 -1.5898217 0.3959386
#> 2 Y 0.6336561 -0.7106185 -3.539250 -1.732103 0.6577330 -4.6752217
#> 3 Z -5.9265184 -0.2012541 -3.182550 1.265012 -0.3982349 -2.5660559
#> 2009-01-07 2009-01-08 2009-01-09 2009-01-10
#> 1 1.0122784 0.7512999 0.6575822 -0.4650852
#> 2 1.2544913 -2.3759464 -0.9047347 -0.5436360
#> 3 -0.4143983 2.4637004 1.2471138 -3.1626294

# Spread and gather are complements
df <- data.frame ( x = c ( "a" , "b" ) , y = c ( 3 , 4 ) , z = c ( 5 , 6 ) )
df %>% spread ( x , y ) %>% gather ( "x" , "y" , a : b , na.rm = TRUE )
#> z x y
#> 1 5 a 3
#> 4 6 b 4

# Use 'convert = TRUE' to produce variables of mixed type
df <- data.frame ( row = rep ( c ( 1 , 51 ) , each = 3 ) ,
var = c ( "Sepal.Length" , "Species" , "Species_num" ) ,
value = c ( 5.1 , "setosa" , 1 , 7.0 , "versicolor" , 2 ) )
df %>% spread ( var , value ) %>% str
#> 'data.frame': 2 obs. of 4 variables:
#> $ row : num 1 51
#> $ Sepal.Length: chr "5.1" "7"
#> $ Species : chr "setosa" "versicolor"
#> $ Species_num : chr "1" "2"
df %>% spread ( var , value , convert = TRUE ) %>% str
#> 'data.frame': 2 obs. of 4 variables:
#> $ row : num 1 51
#> $ Sepal.Length: num 5.1 7
#> $ Species : chr "setosa" "versicolor"
#> $ Species_num : int 1 2

Development on spread() is complete, and for new code we recommend
switching to pivot_wider() , which is easier to use, more featureful, and
still under active development.
df %>% spread(key, value) is equivalent to
df %>% pivot_wider(names_from = key, values_from = value)
See more details in vignette("pivot") .
Column names or positions. This is passed to
tidyselect::vars_pull() .
These arguments are passed by expression and support
quasiquotation (you can unquote column
names or column positions).
If set, missing values will be replaced with this value. Note
that there are two types of missingness in the input: explicit missing
values (i.e. NA ), and implicit missings, rows that simply aren't
present. Both types of missing value will be replaced by fill .
If TRUE , type.convert() with asis =
TRUE will be run on each of the new columns. This is useful if the value
column was a mix of variables that was coerced to a string. If the class of
the value column was factor or date, note that will not be true of the new
columns that are produced, which are coerced to character before type
conversion.
If FALSE , will keep factor levels that don't appear in the
data, filling in missing combinations with fill .
If NULL , the column names will be taken from the values of
key variable. If non- NULL , the column names will be given
by "" .
Developed by Hadley Wickham , Maximilian Girlich.

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