Price Innovation, Risk Taking and Artistic

Creativity∗

Jianping Mei†, Michael Moses‡, and Yi Zhou§

This Version: February 1, 2014

Abstract

In this paper, we use the residual variance of art prices to examine risk taking by

contemporary artists. The objective is to use asset pricing to obtain a better under-

standing of the creative process. Our empirical work shows a few interesting results.

First, we discover that residual risk is significantly and positively related to the aver-

age price level achieved by an artist. Second, residual risk has additional explanatory

power in terms of how often the artist’s works are cited and exhibited, even after we

control for artist fixed (reputation) effects. Third, we find that the artists who have

more residual risks are highly valued by collectors. Moreover, artworks by those artists

with high residual risk tend to outperform the market within and out of sample. Our

conclusion is that residual risk is a good proxy for creative risk taking by contempo-

rary artists. The most creative artists dare to take more risks, which results in higher

residual price volatility of their artworks.

JEL Classification: Asset Pricing & Cultural Economics.

Keywords: Creativity, Innovation, Asset Pricing

∗We appreciate the comments from seminar participants at Brandeis University, Tilburg University, Che-ung Kong Graduate School of Business (CKGSB). We thank the research assistants at CKGSB: Yao Xiaocui,Wang Lu and Zhang Li for excellent data collection work. We deeply appreciate Adam Jolles and Depart-ment of Art History at Florida State University for tremendous encouragement and support. All remainingerrors are ours.†Contact Author, Professor of Finance, Department of Finance, CKGSB, 3F, Tower E3, Oriental Plaza,

1 East Chang An Avenue, Beijing 100738, China, Email: jpmei@ckgsb.edu.cn, Office: 010-85188858 Ext.3322, Fax: 010-85186800.‡Co-founder, Beautiful Asset Advisors LLC, Email: mmoses@stern.nyu.edu.§Assistant Professor of Finance, Department of Finance, College of Business, Florida State University,

Rovetta Business Bldg, 353, 821 Academic Way, P.O. Box 3061110, Tallahassee, Florida 32306-1110, Email:yizhou@cob.fsu.edu, Office: 850-644-7865, Fax: 850-644-4225.

Price Innovation, Risk Taking and Artistic Creativity

Abstract

In this paper, we use the residual variance of art prices to examine risk taking by con-

temporary artists. The objective is to use asset pricing to obtain a better understanding of

the creative process. Our empirical work shows a few interesting results. First, we discover

that residual risk is significantly and positively related to the average price level achieved

by an artist. Second, residual risk has additional explanatory power in terms of how often

the artist’s works are cited and exhibited, even after we control for artist fixed (reputation)

effects. Third, we find that the artists who have more residual risks are highly valued by

collectors. Moreover, artworks by those artists with high residual risk tend to outperform

the market within and out of sample. Our conclusion is that residual risk is a good proxy

for creative risk taking by contemporary artists. The most creative artists dare to take more

risks, which results in higher residual price volatility of their artworks.

JEL Classification: Asset Pricing & Cultural Economics.

Keywords: Creativity, Innovation, Asset Pricing

1 Introduction

This paper uses the residual variance of art prices to examine risk taking by contemporary

artists. The objective is to use an asset pricing approach to obtain a better understanding of

the creative process. Our work is motivated by two influential papers by Galenson and Wein-

berg ((2000) and (2001), GW afterwards).1 These studies address the difference in the ca-

reer peaks of 19th-century artists and 20th-century artists. They discover that 20th-century

artists peaked significantly younger, in their late 30s and early 40s, while 19th-century artists

peaked around their 50s. GW uses the market price of art to measure creativity; they are

most interested in at what age the artist produces the most expensive work. However, their

measure of creativity uses only the information contained in the first moment of art prices.

It is natural to ask whether information based on the second moment of art prices would

generate additional insights into the art creation process. There is a large body of literature

in finance that examines the volatility of asset price. Researchers such as Hirshleifer, Low,

and Teoh (2012) have used the volatility of stock prices to examine risk taking by firms and

the creativity of CEOs. As noted in Campbell, Lettau, Malkiel, and Xu (2001), one may

gain additional insight about individual stock risk by decomposing stock returns into market

return and idiosyncratic return and by examining the time variation of the residual variance

after adjusting for the market risk component. They note that this approach of examining the

second moment of residuals brings us insights into the time varying nature of firm volatility.

Bartram, Brown, and Stulz (2012) further find that the higher idiosyncratic volatility of U.S.

firms is driven by higher firm innovation–higher R&D spending than comparable firms in

foreign countries.

We build on a large body of literature on measuring art market returns to compute

idiosyncratic risk for artworks. Baumol (1986) analyzes 640 repeated sales records from

1652 to 1961 from Reitlinger’s book and reaches the conclusion that the average annual rate

1Galenson et al (Galenson (2001), Galenson (2006a), Galenson (2006b), Galenson (2009b), Galenson andKotin (2005), Galenson (2007), Galenson and Kotin (2008), Galenson (2009a) and Galenson (2010)) also dida series of studies on the career cycles of creative people in various other fields, such as visual art, music,literature, film, and so forth.

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of return for art is 0.55%. Goetzmann (1993) extends Baumol (1986)’s data, and he finds

that the return of the art market depends on the time period; the return in the second half

of the 20th century rivals the stock market. Mei and Moses (2002) search catalogues for

all American, 19th-century and Old Master, Impressionist, and Modern paintings sold at

Sotheby’s and Christie’s, and collected close to 5,000 pieces of repeated sales data covering

the period from 1875 to 2000. They find that the annualized art market return was about

10.1%, comparable to the U.S. equity return. Goetzmann, Renneboog, and Spaenjers (2011)

finds that art-market returns are determined by economic growth and distribution of wealth.

This paper will combine the hedonic models of GW and the repeated sales model of

Goetzmann (1993) to compute the residuals of art prices. This approach has the advantage

of being able to use the greatest amount of transaction data from the auction market. Our

objective is to isolate those price innovations that reflect deviation from the regular art

market and career-price path of an artist. And as we will argue in a later section of the

paper, the average squared residuals (or residual risk) reflect price diversity as a result of

risk taking by artists. We further employ the GARCH (1,1) model of Engle and Bollerslev

(1986) to investigate the persistence of risk taking by artists.

Our empirical work has yielded a few interesting results. First, we discover that the

residual risk is significantly and positively related to the average price level achieved by an

artist. Second, residual risk has additional explanatory power in terms of how often the

artist’s works are cited and exhibited, even after we control for artist fixed (reputation)

effects. Third, we find that those artists who have more residual risks are highly valued by

collectors. Moreover, artworks by those artists with high residual risk tend to outperform

the market within and out of sample.

More importantly, our analysis enriches our understanding of the creative process. It

captures risk taking by contemporary artists, which is an important part of the creative

process. It shows that art creation is a process full of uncertainties. Ex ante, the most

creative artists need to take the considerable risks that their newly created art could be

misunderstood or hated by the market. The most creative, relatively speaking, are also

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likely to have the most to lose. But they must continue to innovate despite this risk, even

though are they are not always rewarded for their efforts.

The paper proceeds as follows. We describe the models in Section 2 and our data in

Section 3. In Section 4, we first compute the residual risk of art prices and then examine the

relationship between the residual risk and the importance of artists as perceived by art critics

and historians. In Section 5, we use an asset pricing approach to see if the art market values

those artists whose artworks have high residual price risks. We provide several extensions

and consider alternative explanations for our findings in Section 6. We then conclude the

paper.

2 Methodology

Our first model is a hedonic regression of the logarithm of price:

Ln(Pijt) = α1sij +α2s2ij +α3s

3ij +α4s

4ij +

∑δ(t) + θ(i) +XB+ εijt, εijt ∼ N(0, σ2

i ) (1)

In this model, i denotes the artist i, j denotes the jth painting, and t is the time of the

transaction. sij denotes the the age of the artist when the jth painting is created by the ith

artist. This model says that the logarithm of the price is a polynomial function of the age of

the artist when the painting is created. The time dummy δ(t) stands for the return of the art

market index, and the artist dummy θ(i) denotes the fixed effect of the artist. X represents

the vector of the hedonic variables, such as the height, width, medium (oil or watercolor,

etc.), shape (rectangular, oval or other shapes), whether or not the painting is signed, or

whether the transaction took place in one of the three major auction houses–Christie’s,

Sotheby’s, or Phillips. We use nominal prices, as the time dummies would automatically

pick up inflation. We use log prices to mitigate the artificial mean effects since high prices

also tend to have a high variance.

Model 1 is quite similar to that used in Galenson and Weinberg ((2000) and (2001)). It

says that the log price of an artwork is a function of the age of the artist, the art market

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movement, the artist fixed effect, and some characteristics related to the artwork. GW

discover that the value of an artwork is related to the age of the artists when it was produced.

They find the quality of the artwork declines precipitously for successful modern American

artists as their age increases.

Goetzmann, Renneboog, and Spaenjers (2011) find that the prices of individual artworks

are largely influenced by art market returns. Thus, εijt measures the residual value of the

artwork that is essentially the relative (or percentage) deviation from his regular market and

age determined price path. Normally, εijt could reflect innovations in artist creativity or

random demand forces in the art market. Since we assume in this paper that the random

demand forces in the market are the same across all artists, εijt chiefly reflects innovations

in artist creativity. Moreover, the variance of εijt, σ2i , measures the price diversity produced

by the artist. In general, the more diverse the price, the more diverse the artist’s creative

production. It reflects the artist’s willingness to try many different things, thus taking more

risks in the creative process.

In previous studies, σ2i is assumed to be constant across different artists; in our paper, σ2

could vary across artists. We will compute the unconditional σ2i as well as the conditional

σ2is, where s stands for the age of the artist. Our objective is to measure the price diversity

over the lifetime of the artist. This allows us to numerically compare which artist has

more price diversity to when the artist has the most price diversity over his lifetime, thus

showing his willingness to take the most risks. It further allows us to examine whether these

residual risks are related to success in the art market as well as in art history. Since we

have heteroscedasticity in Model 1, we will estimate the model using the Generalized Least

Square (GLS) method.

Our second model is the repeated sale model:

Ln(Pijt)− Ln(Pijt−1) = δ(t) + ηijt ηijt ∼ N(0, 2σ2i ) (2)

It is easy to see from Model 1 that the second model can be derived easily from a simple

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difference between prices in two subsequent sales and that it is simply made of the market

time dummies and the difference between two residuals. Thus, estimating residual variance

in Model 2 is equivalent to estimating Model 1 multiplied by a factor of 2. Model 2 can be

estimated using the repeated sales approach of Goetzmann (1993) using GLS. One can easily

use it to do mark-to-market in order to estimate Ln(PijT )(T = 2012)–which is the market

value of the painting estimated at the end of 2012.

One of the caveats for the above two models is that the sample may be subject to the

Heckman selection bias problem as the observed samples are the more marketable ones. A

recent paper by Korteweg and Sorensen (2012) extends Heckman’s model by explicitly exam-

ining the selection bias. Using transactions of residential properties in Alameda, California,

they have estimated the model (2) using an MCMC Bayesian approach. KS discover that

if the holding period is relatively long, the bias problem is quite small. Since the average

holding period of our sample is 10.6 years, the time far exceeds the 5.1 years in Korteweg

and Sorensen (2012). Thus, we believe the selection bias should be modest in our case.

Our third model is a simple GARCH extension of the hedonic model (1):

Ln(Pijst) = α1sij+α2s2ij+α3s

3ij+α4s

4ij+

∑δ(t)+θ(i)+XB+εijst, εijst ∼ N(0, his) (3)

where we assume a GARCH(1,1) model for the conditional variance his:

his = α0 + αhis−1 + βε2ijs; (4)

Here his denotes the conditional variance his for artist i where s is the age of the artist at the

time of creating the art (the date signed). This model specifies that the conditional variance

of artistic innovation follows a GARCH(1,1) process, where α measures the persistence of

artistic innovation while β measures the impact of a single innovation on future variance. It

is easy to see that the model (4) is an extension of the GARCH(p,q) model by Engle and

Bollerslev (1986), which can be estimated using maximum likelihood estimation.

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3 Data and Descriptive Analysis

We obtain the list of contemporary artists from the contemporary art sales catalogs of

Christie’s and Sotheby’s auction houses. We then collect auction data for these contemporary

artists from the online database of artinfo.com, artprice.com and websites of various auction

houses. Our data contains transactions of major auction houses around the world from 1980

to 2012. Our data for each artwork contains the following information: the name of the

artist, the title of the artwork, the year the painting was created, the year the painting was

auctioned, the sale price at auction, the lowest and highest estimates of the painting by the

auction house, the height, width, medium and shape of the painting, whether or not the

painting is signed, and the name of the auction house where the painting was auctioned. To

be included in the sample, we require an artist to have at least ten artworks. In the end, we

are left with 81,567 observations from 275 artists.

Table 1 in the Appendix reports the frequency distribution of the sample by the alphabetic

order of the first names of the artists. The distribution across artists is relatively even. Most

of the artists’ works comprise less than 0.5% of the data. There are a few exceptions. Andy

Warhol’s works account for 3% of the total observations, and Jean Dubuffet’s works make

up 2.43% of the sample. We have also constructed repeated-sales data based on auctions

from Sotheby’s and Christie’s as their sales catalog make it easier to track repeated sales

and information on exhibition and literature citation.

In order to measure the importance of artworks as well as artists, we collect data on

artwork citations in major art history books as well as major exhibitions. We then compile

the total citation and exhibition counts by artists based on our sample of of repeated sales

data. Sotheby’s and Christie’s auction catalogs list separately literature citations and the

exhibitions they could find for each piece they put up for sale in their catalogs. We count

each citation and each exhibition listed for the most recent auction to develop the values

available in our database. This information is only available from Sotheby’s and Christie’s

online catalogs. Their history goes back only as far as 1998. Earlier sales information on

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these two variables has to be found and hand collected in a library with a rich collection of

catalogs, such as the one at the Metropolitan Museum in New York. In addition, we also

measure the importance of artists directly by compiling artist rankings made by several art

history Ph.D. students from Florida State University.

Table 2 reports the frequency distributions of the sample by the characteristics of the

artworks. All panels include two columns for the frequency distribution. The first column

is the number of observations. The second column is the percentage frequency distribution.

Panel A is for the medium of the paintings, and oil paintings make up about 40% of the

sample. Panel B is for the shape of the paintings. More than 95% of the paintings are

rectangular. Panel C shows the signature status of the paintings and more than 65% of

the paintings are signed by the artists. Panel D displays the auction house status of the

paintings: the top three auction houses are Christie’s, Sotheby’s and Phillips. About 40%

of the paintings were auctioned in the top three auction houses. Panel E is the frequency

distribution by calendar years. The more recent years, the more observations. There are

because more artworks by contemporary artists are available for sale.

4 Residual Risk and Artistic Creativity

Table 3 reports the estimation results for Model 1. Similar to GW, we show that the price

of an artwork is related to the age of the artist when the work is created and that this is a

nonlinear relationship. Because of the nonlinearity, a one-year increase in age has different

impacts on the price of an art work, depending on the age of an artist. For example, holding

other variables constant, when the artist is 25 years old, a one-year increase in age will

increase the expected price of his work by about 5%. At age 34, a one-year increase in age

will increase the price by about 0.05% and at age 65, a one-year increase in age will decrease

the price by about 0.36%. Graph 1 plots the age-price profile for all artists in the sample,

and the average peaking age for contemporary artists is around 35, which is somewhat older

than what Galenson and Weinberg (2000) found for American artists born after 1920. In our

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paper, contemporary artists appear to peak about five years later than the modern American

artists studied in their paper.

Table 3 also reports other determinants of art prices. The brand name of the auction

houses has a significant impact on the prices. Being auctioned at Christie’s, Sotheby’s or

Phillips, compared to being auctioned at other auction houses, increases the price of an

artwork by 26%. A one-inch increase in height increases the price by 2%; a one-inch increase

in width increases the price by 1%. Oil paintings are generally more expensive, no matter

what medium. The next most expensive are sculpture and water color paintings. Whether

or not the artwork is signed by the artist is also important, which means a 9% difference in

price for an artwork with a signature versus one without. And we observe significant time

fixed effects and the artist fixed effects which we will elaborate in Graph 2 and Table 6. The

artist fixed effect essentially captures the distinct effect on market prices by each artist, thus,

it reflects the lifetime achievement or reputation of each artist.

Graph 2 plots both the contemporary art index based on repeated sales data and the year

fixed effects estimated from Model 1. This graph shows that the time series of the year fixed

effects tracks the repeated-sales index closely. Both indices show that the contemporary art

market rose rapidly from 1980 to the early 1990s, then had a crash from the early 1990s to

1995, because the overall art market was heavily influenced by the exit of Japanese collectors

at the time. The contemporary art market then rose rising steadily except for a short period

of decline in 2008 and 2009.

The focus of this paper is the residuals from Model 1 and their second moments. As an

example, Graph 3 plots the sorted residuals of the model by size and by five-year age intervals

for Andy Warhol. As the residual εijt measures the relative (or percentage) deviation from his

regular market and age-determined price path, our plots essentially show how his individual

works at different time were valued by the market. We can see that Warhol had many below

average works between 20 and 30 years old, while he produced some of his most expensive

works when he was in his 30s.

This is consistent with art history. Warhol was born in 1928. His first exhibition of the

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32 Campbell’s Soup Cans was in an art gallery in Los Angeles in 1962 when he was 34 years

old. From then on, most of Andy Warhol’s best work was done over a span of about eight

years, such as Death and Disaster series, as well as the photo silk screen-printing portraits

of Marilyn Monroe, Elizabeth Taylor, and so forth. However, Warhol also produced some of

his lowest priced works at the same age. What is really striking from Graph 3 is that his

best work from 36 to 40 years old sold for more than 148 times his average work while his

worst sold for 0.67% of the average. The difference is 22,090 times! Thus, we can observe

that while some innovations were highly valued by the market, others produced at the same

time were poorly received. His success seems to be always accompanied by failure, but his

failures early in life were seldom accompanied by successes. However, his innovation had a

large price dispersion throughout his life.

Comparing the residuals from ages 36 to 40 with those from ages 56 to 60, we can see that

Warhol had more successes as well as failures from ages 36 to 40, as there is more mass at

both sides of the residual distribution. We may try to describe this mass on both sides of the

residual distribution by computing the average squared residuals by age. It is essentially a

measure of the artist’s intensity of innovation at a certain age. The residual risk is essentially

reflects the intensity of the artist’s innovation at a certain age. The result is presented in

the top left panel of Graph 4. For Warhol, we can see this intensity peaks around 35, rises

again in his early 40s, then declines in his 50s. This intensity measure appears to correspond

closely to Warhol’s as it is perceived by art historians.

For comparison, Graph 4 also plots the age residual risk profile for other top ranked

artists. It presents several interesting results. First, there is clearly a large variation in the

pattern of residual risk across artists. While most artists tend to peak early in their careers,

Francis Bacon innovated intensely later in life. While William De Kooning’s residual risk

was concentrated in his 40s, Jasper Johns had multiple peaks in his 20s, 30s, and 40s. The

residual risk of artists fluctuated substantially over age and differently across artists2.

2Our residual risk results also seem to confirm the time-varying creativity profiles for the following artists.Roy Lichtenstein was born in 1923. He rose to fame with one of his best known work–Drowning Girl (1963),symbolizing the rise of Pop Art and the return of art to two dimensions (flat surface) from the emphasis

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To better understand the residual risk, Panel A of Table 4 shows the summary statistics

of the squared residuals from Model 1. We split the sample of artworks by each artist into

four different life periods: the whole lifetime, ages between 25 and 35, ages after 36, and

ages after 65. The mean for the residual risk measure is 0.937 for the whole sample, and the

standard deviation is about 0.60, with a minimum of 0.248 and a maximum of 4.850. The

mean of the residual risk decreases as artists age, and the standard deviation of residual risk

increases. Thus, in general, the residual risk of contemporary artists tends to peak around

their middle ages, then falls when artists grow older. However, the increasing standard

deviation in the older age group indicates that a large number of older artists remain highly

innovative. Panel B of Table 4 presents the correlation matrix of the four residual risk

measures. We can see there is a great deal of persistence in residual risk over the lifetime

of an artist. As a further study of this lifetime persistence, we employ the GARCH (1,1)

model of Engle and Bollerslev (1986) to investigate the persistence of residual risks. After

estimating the model for each artist, we discover that the average α for the sample artists

is 0.16 and the average β for the sample artists is 1.23. Again, the result confirms lifetime

persistence.

Since the artist fixed effect in Model 1 measures the average achievement by an artist over

his life as perceived by the market, in Table 5, we will examine the relationship between the

fixed effect and residual risk. Thus we regress the artists’ fixed effects on the four residual

risk measures derived from the squared residual from Model 1. It shows that the fixed effect

is positively related to the residual risk of the whole lifetime, the residual risk between 25

on the dimensionality of abstract expressionism. In the early 60s, Lichtenstein produced a series of comiccartoon style paintings, such as Whaam (1963), Head of Girl (1964), and Head with Red Shadow (1965).Jackson Pollock’s best works were made during the period between 1948 and 1953, when he was between 36and 41 years of age, in which he developed the dripping method. Examples of those works are No. 5 1948,No. 12 (1949), Number 28 (1951) and Number Blue Poles: Number 11, (1952). Jasper Johns was born in1930. His most famous painting Flag (1954-55) was created when he was 25. Another of his famous works,Three Flags (1958) was created when he was 28. Most of his famous works were created from 1955 to 1980,so his creativity was highest between the ages of 25 and 50. Willem De Kooning was born in 1904. Hisfamous works were created in the 1940s and 1950s when he was in his 40s and 50s, such as his most famouspaintings, Woman III (1953) and Woman V (1952-53). Francis Bacon was born in 1909. His creativityclimaxed in his 60s to 70s, echoing his lover affair with George Dyer. His most famous works were madefrom the late 1960s to early 1980s, such as Study for the Head of George Dyer (1966), Triptych, May-June1973 (1973), Study for Self-Portrait (1982) and Study for a Self Portrait-Triptych (1985-86).

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and 35, the residual risk between 36 to 65, and the residual risk after 65, respectively. The

coefficients are 0.734, 0.528, 0.785, and 0.705, while the t-statistics are 6.27, 3.76, 6.32, and

4.31, respectively. Thus, the residual risk measures are significantly and positively related

to the average lifetime achievement by an artist. It is worth noting that because we have

taken logs in art prices, our result here is not simply a mechanical one, due to high variance

as a result of high mean. Thus, like returns to large stocks (see Model 2), works by highly

successful artists could have lower squared residuals (variance), but they actually tend to

have higher squared residuals.

Table 6 reports the rankings of the artists based on the fixed effects and residual risk

estimated from Model 1. It shows that a vast majority of the artists overlap in two categories.

Examples are: Jeff Koons, Jasper Johns, Roy Lichtenstein, Andy Warhol, Yves Klein, Robert

Rauschenberg, Wayne Thiebaud, Jackson Pollock, Willem De Kooning, Gerhard Richter,

and Francis Bacon. This ranking table confirms our regression results that there is a high

correlation between the residual risk and the average market performance of each artist.

Since the residual risk measure is highly related to the fixed effect, one may wonder why

we need this measure. The answer is that it enriches our understanding of the creative

process. It shows that art creation by artists is a process full of uncertainties. Ex ante, the

most innovative artist must take a huge risk that his art may be hated or misunderstood by

the market. The most innovative artist, relatively speaking, is also likely to have the most

to lose as indicated by the long fat tails in Warhol’s residual distribution in his 30s. The

market may hate their work, but artists keep innovating despite this huge risk. Sometimes

they are rewarded for their efforts.

One interesting issue here is how the residual risk measure relates to the importance

of artworks as perceived by art critics and historians. There are two conventional ways of

measuring the importance of an artwork: first, by how many times it is cited in important

art history books; second, by how many times it was exhibited in major museums. As

noted in the data section, we measure the success of an artist by compiling the total number

of citations of his sample works that were sold by the two major auction houses and by

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compiling the total number of exhibitions of these works displayed in major museums. Table

7 and Table 8 show the regression results of the citation and the exhibition counts on the

artists’ fixed effects estimated from Model 1 and the four residual risks over the four age

periods. Both tables show that the citation and the exhibition counts are significantly and

positively related to the residual risk of the whole lifetime, the residual risk between 25

and 35, the residual risk between 36 and 65, and the residual risk after 65, respectively,

controlling for the artists’ individual fixed effects3. These results imply that the residual risk

measures has additional explanatory power in terms of how often the artist’s works are cited

and exhibited, even after we control for artist fixed (reputation) effects.

A third way of ranking the importance of artists is to ask art historians to rank artists

directly. We collect the ranking information from several Ph.D. students of art history at

Florida State University and we take the average of all the rankings. Table 9 shows the

regression results of the average relative rankings of the artists on the artists’ fixed effects

and the four residual risk measures. The table shows that the rankings of art historians are

significantly and positively related to the residual risk of the whole lifetime of the artist, the

residual risk between 25 and 35, the residual risk between 36 and 65, and the residual risk

after 65, respectively, controlling for the artists’ individual fixed effects. These results imply

that the residual risk measures has additional explanatory power in terms of the rankings of

artists, even after we control for artist fixed (reputation) effects.

5 Asset Pricing Analysis

In this section, we employ an asset pricing analysis to examine whether the residual risk

measure we developed in the above section was highly valued (or priced) by the art market.

To do this, we first mark-to-market, using a repeated sale index, all artworks sold during

the sample period to 2012, essentially computing the present value of all artworks sold. We

then compute the mean of the top five works’ market value (as of 2012)for each artist.

3We also performed the same regression using average citations and exhibitions per painting and theresults were similar.

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Table 11 shows the regression results of the mean of the top five works’ market value

(as of 2012) for an artist on the artists’ fixed effects and residual risk. It shows that the

top five works’ market values are significantly and positively related to the various measures

of residual risk after controlling for the artists’ individual fixed effects. These results imply

that the artist’s residual risk helps determine the top five works’ market value.

One interesting question arising from our study is whether residual risk estimated from

an earlier sample helps forecast artist fixed effects and residual risk in later samples. Table

12 shows that the artist fixed effects and residual risk estimated from 1996 to 2012 are

positively and significantly related to the artist fixed effect and residual risk estimated from

an earlier sample of 1980 to 1995. The result again confirms the results of Panel B of Table

4 that residual risk is not only quite persistent over the life of an artist but also persistent

over the sample period.

Another way of measuring whether residual risk is valued by the market is to ask if

artworks by artists with high residual risk outperform the art market. We will address the

question from both within and out of sample. To measure outperformance, we search our

data for repeated sales and we define outperformance as the annualized excess returns of the

artwork over the market return during the holding period. We use the time fixed effects in

Model 1 as well as the market index computed from the repeated sales model (2) to compute

the market return.

One important variable affecting art price could be liquidity. Collectors often need to

sell their artworks for various reasons, and artworks differ in transaction cost and the level

of difficulty in matching buyers with sellers. In the asset pricing literature, Duffie, Pedersen,

and Singleton (2003), Vayanos and Wang (2007) and Weill (2007) provide theoretical models

and empirical evidence to analyze the effects of liquidity on asset prices and trading volume,

based on a search process between buyers and sellers. Pastor and Stambaugh (2003) also

find the presence of a liquidity factor in equity pricing. It is quite intuitive that liquid assets

tend to have higher prices and thus may outperform the market. To study the effects of

liquidity, we compute the total number of auctioned paintings for each artist and use that

13

as a dependent variable.

Another variable related to art investment return is the “masterpiece effect”. Studies

such as Pesando (1993), Mei and Moses ((2002), (2005)) show that masterpieces (defined by

their high purchase prices) tend to underperform the market. Thus, we include the purchase

price to control for the “masterpiece effect”.

Table 13 shows the regression of outperformance for 3,361 paintings with repeated sales

against residual risk and several control variables over the whole sample period. The outper-

formance is defined as the (annualized) difference of the two adjacent log sale prices adjusted

respectively by the corresponding change in the Repeated Sales (RS) Contemporary Art In-

dex (Model 1) and the year fixed effects estimated from the above model (Model 2). We

find that , even controlling for reputation, liquidity, and the masterpiece effect, the adjusted

outperformance is significantly and positively related to the residual risk of the artist mea-

sured over his lifetime. We discover that reputation (the artist fixed effect) is significantly

and positively related to outperformance. This is not surprising as these artists on average

have high prices estimated over the sample period. We also discover that the liquidity (the

total number of auctioned paintings by the artist) is positively related to outperformance,

but the effect is statistically not significant. We further confirm the presence of a very strong

and negative “masterpiece effect”.

Table 14 provides an out-of-sample outperformance study by splitting the sample in

two ways: 1980-1995 and 1996-2010 vs. 1980-1999 and 2000-2010. The earlier sample is

used for computing residual risk and liquidity, while the latter sample is used for analyzing

outperformance. Due to the smaller sample, we drop reputation as a control variable. Again,

our results show that even controlling for liquidity and the masterpiece effect, residual risk of

the artist can significantly predict outperformance. The result is robust over different sample

splits and models used for controlling market returns. We also discover the presence of a

very strong and negative “masterpiece effect” though the liquidity effect is not significant.

It is worth noting, however, that residual risk becomes statistically insignificant once we

introduce reputation as a controlling variable.

14

In summary, we find that there is some evidence that the market seems to reward those

artworks created by artists with high residual risk as defined in this paper–but collectors

should avoid paying excessive amounts. We also note that “liquid” and well-known artists

seem to enjoy small excess returns. Given the fact that the residual risk is highly related to

artistic creativity as perceived by art historians and curators and that there is preliminary

evidence that it is also valued by collectors, we may use it as an alternative measure of

creativity in addition to those based on first moments used by Galenson and Weinberg

((2000) and (2001)).

6 Discussion and Conclusion

In May of 2012, Edvard Munch’s famous work of art, The Scream, sold for close to $120

million at Sotheby’s, setting the world record for any work of art sold at an auction. The

$120 million was almost the pure value of artistic creativity, as the material cost of the

painting is negligible. Creativity is at the center of almost all economic innovations. It is

the root source of total factor productivity as new technology, new business models, and

new markets are developed by creativity. Yet, the economics profession scarcely studies the

subject. With the exception of Galenson and Weinberg ((2000) and (2001)) and Galenson et.

al (Galenson and Kotin (2005), Galenson and Kotin (2008), etc.), few studies have employed

economic tools to systematically study the creative process. Art historians have even resisted

the introduction of economic analysis into art.

In this paper, we extend Galenson and Weinberg ((2000) and (2001)) by developing a

new measure of creativity for artists based on residual risk. Our empirical work has yielded

a few interesting results. First, we discover that our creativity measure is significantly and

positively related to the average lifetime achievement of an artist. Second, our creativity

measure has additional explanatory power in terms of how often the artist’s works are cited

and exhibited, even after we control for artist fixed (reputation) effects. Third, we find

that those artists with high creativity measures are highly valued by collectors. Moreover,

15

artworks by those artists with high creativity measures also tend to outperform the market

within and out of sample. Our creativity measure helps enrich our understanding of the

creative process. It shows that art creation is a process full of uncertainties. Ex ante, the

most creative artists need to take huge risks because their art could be misunderstood by

the market. The most creative, relatively speaking, are also likely to have the most to lose

as their log price distribution tends to have fat tails on the left side. But they must continue

to innovate despite this huge risk.

Our analysis is broadly consistent with a body of literature in corporate finance, which

studies firm innovation and idiosyncratic volatility of stock prices. The theory of firms

by Myers and Majluf (1984) suggests that firms with more growth opportunities are more

volatile and especially have more idiosyncratic volatility. Firms innovate through R&D, so

firms that invest more in R&D are expected to have more volatile cashflows. That is to

say innovation constantly creates winners and losers, so we should expect innovative firms to

have more idiosyncratic risk. Pastor and Veronesi (2009) study technological revolutions and

stock prices across countries, and they find that countries where technological revolutions

originate are associated with higher idiosyncratic volatility. Bartram, Brown, and Stulz

(2012) also find that the higher idiosyncratic volatility of US firms is driven by a higher

share of R&D in the sum of capital expenditures and more R&D than comparable firms in

foreign countries.

In order to correctly interpret our results, one needs to be aware of some important

assumptions underlying the analysis. First of all, we assume that the market value provides

an unbiased estimate of the creative value of art. As a result, we assume away all micro

factors that may affect the demand for and supply of art by collectors. This is certainly not

always the case, as Beggs and Graddy ((2008) and (2009)) find direct evidence of anchoring

and loss aversion in the art market. As a result, the conditional mean of the residuals

in Model 1 may not be zero. However, as long as the behavioral biases do not correlate

cross-sectionally with creativity, they should not affect the results of this paper. Second, the

market may not correctly price young artists who have yet to establish themselves in the

16

market. For example, Vincent van Gogh, created some of the most expensive paintings that

have been sold on the modern market, but he nonetheless was unable to sell his artworks

during his lifetime. In this study, we bypass this problem by focusing on artists whose works

are sold by the world’s top auction houses.

17

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20

Figure 1: The Age Quadratic Profile for All Artists.This graph shows quadratic age profile of the artists. The y-axis is the value of α1sij +α2s

2ij + α3s

3ij + α4s

4ij. The coefficients are estimated from Model 1.

55.

255.

55.

756

20 25 30 35 40 45 50 55 60 65 70 75 80 85Age

The Function Value of Age

21

Tab

le1:

The

Fre

quency

Dis

trib

uti

on

by

Art

ists

.T

he

tab

lere

por

tsth

efr

equ

ency

dis

trib

uti

onby

art

ists

.W

eob

tain

the

list

of

the

conte

mp

ora

ryart

ists

from

Ch

rist

ie’s

an

dS

oth

eby’s

au

ctio

n

hou

ses.

We

then

coll

ect

auct

ion

dat

ain

form

atio

nfo

rth

ese

conte

mp

ora

ryart

ists

from

the

data

base

of

art

info

.com

.T

he

data

base

conta

ins

the

tran

sact

ion

info

rmat

ion

ofal

lm

ajo

rau

ctio

nh

ouse

sfr

om

1980

to2012.

We

requ

ire

an

art

ist

toh

ave

at

least

10

ob

serv

ati

on

s.T

he

sam

ple

has

81,5

67

obse

rvat

ion

sfr

om27

5ar

tist

s.T

hes

eob

serv

atio

ns

are

the

on

esw

hic

hw

ere

au

ctio

ned

on

lyon

cein

his

tory

or

the

last

occ

urr

ence

of

are

pea

ted

sale

.

Th

eta

ble

rep

orts

two

colu

mn

sfo

rth

efr

equ

ency

dis

trib

uti

on

,th

efirs

tis

the

nu

mb

erof

ob

serv

ati

on

san

dth

ese

con

dis

the

pro

bab

ilit

yd

istr

ibu

tion

.

Artist

Nam

eFreq.

Percent

Artist

Nam

eFreq.

Percent

Artist

Nam

eFreq.

Percent

A.R

.PENCK

519

0.64

CHUCK

CONNELLY

15

0.02

GEORGE

MARTIN

10

0.01

ACHIL

LE

PERIL

LI

496

0.61

CLYFFORD

STIL

L28

0.03

GEORGESMATHIE

U1,136

1.39

AD

REIN

HARDT

111

0.14

CONRAD

MARCA-R

ELLI

169

0.21

GER

LATASTER

160

0.20

ADOLPH

GOTTLIE

B274

0.34

CY

TW

OMBLY

380

0.47

GERARD

SCHLOSSER

251

0.31

AGNESMARTIN

171

0.21

DAMIA

NLOEB

13

0.02

GERARD

SCHNEID

ER

978

1.20

AGOSTIN

OBONALUMI

398

0.49

DANA

SCHUTZ

20

0.02

GERHARD

RIC

HTER

1,145

1.40

AL

HELD

123

0.15

DAVID

HOCKNEY

626

0.77

GERRIT

BENNER

111

0.14

ALAN

REYNOLDS

257

0.32

DAVID

SALLE

244

0.30

GIL

LIA

NCARNEGIE

18

0.02

ALBERT

OEHLEN

159

0.19

DELIA

BROW

N12

0.01

GIO

RGIO

CAVALLON

58

0.07

ALBERTO

BURRI

244

0.30

DOMENIC

OGNOLI

124

0.15

GIU

LIO

PAOLIN

I172

0.21

ALBERTO

MAGNELLI

389

0.48

DONALD

BAECHLER

347

0.43

GIU

SEPPE

CAPOGROSSI

288

0.35

ALEX

KATZ

317

0.39

DONALD

SULTAN

209

0.26

GLENN

BROW

N40

0.05

ALEXANDRE

ISTRATI

450

0.55

EDUARDO

ARROYO

452

0.55

GRACE

HARTIG

AN

56

0.07

ALEXIS

ROCKMAN

26

0.03

ELAIN

EDE

KOONIN

G67

0.08

GRAHAM

SUTHERLAND

735

0.90

ALFONSO

OSSORIO

54

0.07

ELAIN

ESTURTEVANT

76

0.09

GUIL

LERMO

KUIT

CA

133

0.16

ALFRED

LESLIE

58

0.07

ELIZ

ABETH

PEYTON

130

0.16

GUNTHER

FORG

329

0.40

ALFRED

MANESSIE

R399

0.49

ELLSW

ORTH

KELLY

172

0.21

HAN

SNEL

65

0.08

ALIC

ENEEL

37

0.05

EMIL

IOVEDOVA

376

0.46

HANSHARTUNG

1,514

1.86

ALIG

HIE

RO

BOETTI

437

0.54

ENRIC

OBAJ

524

0.64

HANSRIC

HTER

366

0.45

ALLAN

D’A

RCANGELO

72

0.09

ENRIC

ODONATI

124

0.15

HELEN

FRANKENTHALER

263

0.32

ANDRE

LANSKOY

847

1.04

ENZO

CUCCHI

250

0.31

HOWARD

HODGKIN

79

0.10

ANDY

WARHOL

2,856

3.50

ERIC

FISCHL

208

0.26

ILYA

BOLOTOW

SKY

83

0.10

ANSELM

KIE

FER

246

0.30

ERNESTO

TRECCANI

128

0.16

INKA

ESSENHIG

H36

0.04

ANTON

ROOSKENS

497

0.61

ERNST

WIL

HELM

NAY

595

0.73

JACK

TW

ORKOV

88

0.11

ANTONICLAVE

647

0.79

ESTEBAN

VIC

ENTE

81

0.10

JACKSON

POLLOCK

84

0.10

ANTONITAPIE

S705

0.86

EUGENE

LEROY

122

0.15

JACQUESDE

LA

VIL

LEG

277

0.34

ANTONIO

SAURA

510

0.63

FIN

NPEDERSEN

117

0.14

JAMESBROOKS

88

0.11

ASGER

JORN

1,533

1.88

FIO

NA

RAE

44

0.05

JAMESROSENQUIST

227

0.28

BACCIO

MARIA

BACCI

35

0.04

FORREST

BESS

18

0.02

JAN

CREMER

68

0.08

BARNETT

NEW

MAN

30

0.04

FRANCESCO

CLEMENTE

295

0.36

JANNIS

KOUNELLIS

230

0.28

BARRY

MCGEE

41

0.05

FRANCESCO

VEZZOLI

15

0.02

JASPER

JOHNS

143

0.18

BENGT

LIN

DSTROM

356

0.44

FRANCIS

ALYS

98

0.12

JEAN

DUBUFFET

1,978

2.43

BRAM

VAN

VELDE

74

0.09

FRANCIS

BACON

113

0.14

JEAN

FAUTRIE

R610

0.75

BRIC

EMARDEN

136

0.17

FRANCOIS

DUFRENE

74

0.09

JEAN

HELIO

N1,039

1.27

BRID

GET

RIL

EY

227

0.28

FRANK

AUERBACH

252

0.31

JEAN-PAUL

RIO

PELLE

706

0.87

BURGOYNE

DIL

LER

26

0.03

FRANK

STELLA

505

0.62

JEFF

KOONS

312

0.38

CAMIL

LE

BRYEN

297

0.36

FRANZ

ACKERMANN

58

0.07

JENNIF

ER

BARTLETT

85

0.10

CAREL

WIL

LIN

K111

0.14

FRANZ

KLIN

E259

0.32

JENNY

SAVIL

LE

20

0.02

CARL-H

ENNIN

GPEDERSE

569

0.70

FRIE

DEL

DZUBAS

155

0.19

JIM

DIN

E448

0.55

CECILY

BROW

N84

0.10

FRIT

ZW

INTER

925

1.13

JOAN

BROW

N30

0.04

CHARLESLAPIC

QUE

983

1.21

GANDY

BRODIE

13

0.02

JOAN

MIT

CHELL

189

0.23

CHRIS

BEEKMAN

13

0.02

GARY

HUME

79

0.10

JOE

ZUCKER

25

0.03

CHRIS

OFIL

I127

0.16

GEN

PAUL

70

0.09

JOHN

CRAXTON

154

0.19

CHRISTIA

NSCHUMANN

40

0.05

GENE

DAVIS

97

0.12

JOHN

CURRIN

73

0.09

CHRISTOPH

RUCKHABERL

24

0.03

GEORG

BASELIT

Z526

0.64

JOHN

MCLAUGHLIN

63

0.08

CHRISTOPHER

WOOL

224

0.27

GEORGE

CONDO

430

0.53

JOHN

TUNNARD

256

0.31

22

Artist

Nam

eFreq.

Percent

Artist

Nam

eFreq.

Percent

Artist

Nam

eFreq.

Percent

JORG

IMMENDORFF

342

0.42

MIC

HAEL

RAEDECKER

41

0.05

RIC

HARD

PRIN

CE

275

0.34

JOSE

MARIA

SIC

ILIA

176

0.22

MILTON

RESNIC

K118

0.14

ROBERT

BECHTLE

28

0.03

JOSEF

ALBERS

594

0.73

MIM

MO

PALADIN

O653

0.80

ROBERT

COLESCOTT

30

0.04

JULESOLIT

SKI

255

0.31

MIM

MO

ROTELLA

748

0.92

ROBERT

COTTIN

GHAM

84

0.10

JULIA

JACQUETTE

13

0.02

MIQ

UEL

BARCELO

294

0.36

ROBERT

GOODNOUGH

186

0.23

JULIA

NOPIE

114

0.14

MORRIS

LOUIS

74

0.09

ROBERT

MOTHERW

ELL

458

0.56

JULIA

NSCHNABEL

279

0.34

NATALIA

DUMIT

RESCO

467

0.57

ROBERT

NATKIN

217

0.27

KAIALTHOFF

20

0.02

NATHAN

OLIV

EIR

A130

0.16

ROBERT

RAUSCHENBERG

624

0.77

KARA

WALKER

36

0.04

NEIL

JENNEY

63

0.08

ROBERT

RYMAN

99

0.12

KAREL

APPEL

2,460

3.02

NEO

RAUCH

106

0.13

ROBERTO

MATTA

927

1.14

KAREN

KIL

IMNIK

77

0.09

NIC

HOLASKRUSHENIC

K44

0.05

ROGER

RAVEEL

132

0.16

KEIT

HVAUGHAN

536

0.66

NIC

OLA

DE

MARIA

244

0.30

ROMAN

OPALKA

75

0.09

KELLEY

WALKER

32

0.04

NIC

OLE

EISENMAN

22

0.03

RONALD

DAVIS

56

0.07

KENNETH

NOLAND

391

0.48

NORBERT

BISKY

78

0.10

ROSSBLECKNER

288

0.35

KENNY

SCHARF

216

0.26

NORBERT

SCHW

ONTKOW

SK

21

0.03

ROY

LIC

HTENSTEIN

596

0.73

KIK

ILAMERS

11

0.01

NORMAN

BLUHM

206

0.25

RUDOLF

STIN

GEL

117

0.14

LARRY

POONS

155

0.19

OLIV

IER

DEBRE

686

0.84

SALVATORE

SCARPIT

TA

88

0.11

LARRY

RIV

ERS

419

0.51

OSCAR

JACQUESGAUTHI

176

0.22

SAM

FRANCIS

1,509

1.85

LEE

KRASNER

75

0.09

PAT

STEIR

42

0.05

SEAN

SCULLY

261

0.32

LEON

KOSSOFF

76

0.09

PAUL

JENKIN

S1,075

1.32

SERGE

CHARCHOUNE

670

0.82

LEON

POLK

SMIT

H43

0.05

PAUL

WONNER

70

0.09

SERGE

POLIA

KOFF

742

0.91

LEONARDO

CREMONIN

I101

0.12

PER

KIR

KEBY

309

0.38

SIG

MAR

POLKE

552

0.68

LISA

RUYTER

43

0.05

PETER

DOIG

143

0.18

STEPHEN

CONROY

33

0.04

LISA

YUSKAVAGE

56

0.07

PETER

HALLEY

161

0.20

SUZANNE

MCCLELLAND

17

0.02

LLOYD

FREDERIC

REES

408

0.50

PETER

SAUL

73

0.09

TANO

FESTA

764

0.94

LUC

TUYMANS

79

0.10

PHIL

IPGUSTON

212

0.26

TERRY

WIN

TERS

77

0.09

LUCEBERT

749

0.92

PHIL

IPPEARLSTEIN

134

0.16

THEO

WOLVECAMP

140

0.17

LUCIA

NFREUD

131

0.16

PHIL

IPTAAFFE

96

0.12

THEODOROSSTAMOS

482

0.59

LUCIA

NO

CASTELLI

267

0.33

PIE

RO

DORAZIO

1,049

1.29

THOMASSCHEIB

ITZ

66

0.08

LUCIO

FONTANA

1,479

1.81

PIE

RRE

ALECHIN

SKY

1,105

1.35

TOM

WESSELMANN

1,275

1.56

MANOLO

MIL

LARES

140

0.17

PIE

RRE

SOULAGES

321

0.39

TONY

BEVAN

52

0.06

MANOLO

VALDES

105

0.13

PYKE

KOCH

25

0.03

TURISIM

ETI

143

0.18

MARIO

MAFAI

99

0.12

RAFAEL

CANOGAR

161

0.20

VIC

TOR

VASARELY

1,587

1.95

MARIO

SCHIFANO

893

1.09

RAIN

ER

FETTIN

G370

0.45

WAYNE

THIE

BAUD

220

0.27

MARK

FRANCIS

105

0.13

RALPH

GOIN

GS

55

0.07

WIF

REDO

LAM

1,032

1.27

MARK

GROTJAHN

77

0.09

RAOUL

HYNCKES

16

0.02

WIL

HELM

SASNAL

68

0.08

MARK

ROTHKO

161

0.20

RAY

JOHNSON

46

0.06

WIL

LEM

DE

KOONIN

G436

0.53

MARK

TOBEY

783

0.96

RAY

PARKER

60

0.07

WIL

LEM

HUSSEM

164

0.20

MARLENE

DUMAS

217

0.27

RAYMOND

HAIN

S308

0.38

WIL

LIA

MBAIL

EY

50

0.06

MARTIA

LRAYSSE

149

0.18

RAYMOND

PETTIB

ON

282

0.35

WIL

LIA

MBAZIO

TES

99

0.12

MARTIN

KIP

PENBERGER

399

0.49

RIC

HARD

ANUSZKIE

WIC

Z140

0.17

WIL

LIA

MKENTRID

GE

84

0.10

MASSIM

OCAMPIG

LI

464

0.57

RIC

HARD

DIE

BENKORN

228

0.28

WIL

LIA

MNELSON

COPLE

182

0.22

MATTHIA

SW

EISCHER

77

0.09

RIC

HARD

ESTES

80

0.10

WIM

SCHUHMACHER

63

0.08

MEL

RAMOS

166

0.20

RIC

HARD

LIN

DNER

161

0.20

WOLF

KAHN

145

0.18

MIC

HAEL

CRAIG

-MARTIN

37

0.05

RIC

HARD

MORTENSEN

418

0.51

YVESKLEIN

331

0.41

MIC

HAEL

GOLDBERG

83

0.10

RIC

HARD

POUSETTE-D

AR

61

0.07

Tota

l81,567

100.00

23

Table 2: The Frequency Distributions.The table reports the frequency distribution of the sample by the characteristics of the paintings. We obtain

the list of the contemporary artists from Christie’s and Sotheby’s auction houses. We then collect auction

data information for these contemporary artists from the database of artinfo.com. The database contains

the transaction information of all major auction houses from 1980 to 2012. We require an artist to have at

least 10 observations. The sample has 81,567 observations from 275 artists. These observations are the ones

which were auctioned only once in history or the last occurrence of a repeated sale. All panels report two

columns for the frequency distribution. The first column is the number of observations. The second column

is the probability distribution. Panel A is for the medium of the paintings. Panel B is for the shape of the

paintings. Panel C is for the signature status of the paintings. Panel D is for the auction house status of

the paintings. The top three auction houses are Christie’s, Sotheby’s and Phillips. Panel E is the frequency

distribution by calendar years.

Panel A: The Frequency Distribution For the Medium of the Paintings.

Medium Freq. Percent

D (Drawing) 873 1.07O (Other) 42,880 52.57OB (Oil on Board, Panel, Wood) 3,335 4.09OC (Oil on Canvas) 23,917 29.32OO (Oil on Other) 3,020 3.7OP (Oil on Paper) 1,501 1.84P (Pastel) 1,102 1.35S (Sculpture) 2,066 2.53WC (Water Color) 2,873 3.52

Total 81,567 100

Panel B: The Frequency Distribution For the Shape of the Paintings.

Shape Freq. Percent

O (Other) 2,792 3.42R (Rectangular) 78,775 96.58

Total 81,567 100

Panel C: The Frequency Distribution For the Signature Status of the Paintings.

Signed or Not Freq. Percent

No 25,349 31.08Yes 56,218 68.92

Total 81,567 100.00

Panel D: The Frequency Distribution For the Auction House Status of the Paintings.

Top Three Auction House or Not Freq. Percent

No 48,528 59.49Yes 33,039 40.51

Total 81,567 100.00

24

Panel E: The Frequency Distribution By Calendar Years.

Sale Year Freq. Percent1980 683 0.841981 738 0.901982 615 0.751983 706 0.871984 908 1.111985 1,178 1.441986 1,261 1.551987 1,705 2.091988 1,785 2.191989 2,718 3.331990 2,489 3.051991 1,299 1.591992 1,530 1.881993 1,727 2.121994 2,063 2.531995 2,194 2.691996 2,481 3.041997 2,476 3.041998 2,575 3.161999 2,444 3.002000 2,628 3.222001 2,665 3.272002 2,690 3.302003 2,941 3.612004 3,526 4.322005 4,349 5.332006 5,189 6.362007 5,088 6.242008 4,224 5.182009 3,380 4.142010 3,853 4.722011 4,233 5.192012 3,226 3.96

Total 81,567 100.00

25

Table 3: The Estimation Results of the Hedonic Model.This table reports the estimation results for the hedonic regression of the logarithm of price on

various variables: Ln(Pijt) = α1sij+α2s2ij+α3s

3ij+α4s

4ij+

∑δ(t)+θ(i)+XB+εijt, εijt ∼ N(0, σ2i ).

where i denotes the artist i; j denotes the jth painting, s is the age of the artist at the time

of creating the art (the date signed) and t is the time of the transaction. This model assumes

that the logarithm of price is a polynomial function of the age of the artist when the painting is

created, hence the model captures the life cycle of residual risk. The time dummy δ(t) controls the

market return of the art index, and the artist dummy θ(i) denotes the fixed effect of the artist. X

represents the vector of the hedonic variables, such as the height, width, medium (oil or watercolor,

etc.), shape (rectangular, or other shapes), whether or not the painting is signed, or whether the

transaction was held in one of the three major auctions houses–Christie’s, Sotheby’s and Phillips

(CSP). We use nominal prices, as the time dummies would automatically pick up inflation. We

assume heteroscedasticity for the variance of the residual across artists and we estimate the model

using the Generalized Least Square (GLS) method; then we compute residual variance, based on

age. We use log prices to mitigate the artificial mean effects since the variance of the residual is the

linear function of price levels, which makes our results prone to the artificial inflation of variance if

we use the price level itself. When we use the logarithm of the price, however, the artificial mean

effect disappears. ∗ denotes significant at 10%; ∗∗ denotes significant at 5%; ∗∗∗ denotes significant

at 1%.

Variable Category Coefficient t-Stat p-ValueCAGE 0.473*** 71.66 0.00CAGE2 −0.014*** −58.28 0.00CAGE3 0.000*** 46.28 0.00CAGE4 −0.000*** −37.45 0.00CSP 0.259*** 37.81 0.00Height 0.021*** 89.68 0.00Width 0.012*** 61.39 0.00Medium D (Drawing)

O (Other) 0.433*** 12.58 0.00OB (Oil on Board, Panel, Wood) 1.097*** 29.38 0.00OC (Oil on Canvas) 1.338*** 38.36 0.00OO (Oil on Other) 1.068*** 27.85 0.00OP (Oil on Paper) 0.747*** 17.86 0.00P (Pastel) 0.320*** 7.52 0.00S (Sculpture) 0.465*** 8.70 0.00WC (Water Color) 0.331*** 8.76 0.00

Shape O (Other)R (Rectangular) −0.065* −1.83 0.07

Signed NoYes 0.092*** 11.19 0.00

Time Fixed Effects YesArtist Fixed Effects Yes

26

Table 4: The Summary Statistics of Residual Risk.This table reports the summary statistics of residual risk defined as the residual variance in the

hedonic regression of the logarithm of price on various variables: Ln(Pijt) = α1sij +α2s2ij +α3s

3ij +

α4s4ij +

∑δ(t) + θ(i) +XB + εijt, εijt ∼ N(0, σ2i ). where i denotes the artist i; j denotes the jth

painting, s is the age of the artist at the time of creating the art (the date signed) and t is the

time of the transaction. The time dummy δ(t) controls the market return of the art index, and

the artist dummy θ(i) denotes the fixed effect of the artist. X represents the vector of the hedonic

variables, such as the height, width, medium (oil or watercolor, etc.), shape (rectangular, or other

shapes), whether or not the painting is signed, or whether the transaction took place in one of

the three major auctions houses–Christie’s, Sotheby’s and Phillips. We use nominal prices, as the

time dummies would automatically pick up inflation. We assume heteroscedasticity for residual

variances across artists and we estimate the model using the GLS method. We separate the sample

into different age groups: all the ages, from 25 to 35, from 36 to 65 and after 65 years old.

Panel A: The Summary Statistics of Residual Risk.

Variable Mean Std Min MaxResidual Risk (All) 0.937 0.599 0.248 4.850Residual Risk (25 to 35) 0.884 0.567 0.256 4.038Residual Risk (36 to 65) 0.849 0.569 0.149 3.851Residual Risk (After 65) 0.697 0.699 0.108 5.151

Panel B: The Correlation Matrix of Residual Risk.

Residual Risk Residual Risk Residual Risk Residual Risk(All) (25 to 35) (36 to 65) (After 65)

Residual Risk (All) 1.000Residual Risk (25 to 35) 0.680 1.000Residual Risk (36 to 65) 0.921 0.444 1.000Residual Risk (After 65) 0.634 0.274 0.691 1.000

27

Table 5: Residual Risk and the Fixed Effects of Artists.This table reports the regression results of the artists’ fixed effects on residual risk defined as the

residual variance in the hedonic regression of the logarithm of price on various variables: Ln(Pijt) =

α1sij+α2s2ij+α3s

3ij+α4s

4ij+

∑δ(t)+θ(i)+XB+εijt, εijt ∼ N(0, σ2i ). where i denotes the artist i;

j denotes the jth painting, s is the age of the artist at the time of creating the art (the date signed)

and t is the time of the transaction. The time dummy δ(t) controls the market return of the art

index, and the artist dummy θ(i) denotes the fixed effect of the artist. X represents the vector of the

hedonic variables, such as the height, width, medium (oil or watercolor, etc.), shape (rectangular,

or other shapes), whether or not the painting is signed, or whether the transaction took place in

one of the three major auctions houses–Christie’s, Sotheby’s and Phillips. We use nominal prices,

as the time dummies would automatically pick up inflation. We assume heteroscedasticity for

residual variances across artists and we estimate the model using the GLS method. We separate

the sample into different age groups: all the ages, from 25 to 35, from 36 to 65 and after 65 years

old. The artists’ fixed effects are estimated from all the age groups. The numbers in the brackets

are t-statistics. ∗ denotes significant at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗ denotes significant

at 1%.

Artist Fixed Effect (All)1 2 3 4

Residual Risk (All) 0.734***(6.27)

Residual Risk (25 to 35) 0.528***(3.76)

Residual Risk (36 to 65) 0.785***(6.32)

Residual Risk (After 65) 0.705***(4.31)

Constant −0.096 0.133 0.01 0.277*(−0.74) (0.91) (0.08) (1.71)

Observations 274 203 244 101R-squared 13% 7% 14% 16%

28

Table 6: Artists’ Ranking Based on Fixed Effects and Residual Risk.This table reports the ranking results of the artists’ fixed effects and residual risk defined as the

residual variance in the hedonic regression of the logarithm of price on various variables:Ln(Pijt) =

α1sij + α2s2ij + α3s

3ij + α4s

4ij +

∑δ(t) + θ(i) +XB + εijt, εijt ∼ N(0, σ2i ). whereas i denotes the

artist i; j denotes the jth painting, s is the age of the artist at the time of creating the art (the date

signed) and t is the time of the transaction. The time dummy δ(t) controls the market return of the

art index, and the artist dummy θ(i) denotes the fixed effect of the artist. X represents the vector

of the hedonic variables. We use nominal prices, as the time dummies would automatically pick up

inflation. We assume heteroscedasticity for residual variances across artists and we estimate the

model using the GLS method. The numbers in the brackets are t-statistics. ∗ denotes significant

at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗ denotes significant at 1%.

ARTIST FULLNAME Fixed Effects ARTIST FULLNAME Residual Risk

1 FRANCIS BACON 4.750 JEFF KOONS 4.8502 JACKSON POLLOCK 4.003 ROBERT COLESCOTT 3.8883 MARK ROTHKO 3.975 JASPER JOHNS 3.7084 BARNETT NEWMAN 3.847 KARA WALKER 3.3355 JASPER JOHNS 3.546 ROY LICHTENSTEIN 2.4906 LUCIAN FREUD 3.453 GLENN BROWN 2.4307 CLYFFORD STILL 3.396 ROMAN OPALKA 2.4248 AGNES MARTIN 3.151 LUCIAN FREUD 2.3969 WILLEM DE KOONING 3.148 SALVATORE SCARPITTA 2.34810 YVES KLEIN 2.968 LUCIO FONTANA 2.17111 WAYNE THIEBAUD 2.961 ANDY WARHOL 2.16812 CY TWOMBLY 2.882 YVES KLEIN 2.16413 ROBERT RYMAN 2.872 ROBERT RAUSCHENBERG 2.01514 RICHARD DIEBENKORN 2.870 MARTIAL RAYSSE 2.00015 ALBERTO BURRI 2.823 WAYNE THIEBAUD 1.98216 ANDY WARHOL 2.710 ELAINE STURTEVANT 1.98217 ROY LICHTENSTEIN 2.653 RICHARD PRINCE 1.96518 JEAN DUBUFFET 2.608 GILLIAN CARNEGIE 1.95319 BRICE MARDEN 2.585 JACKSON POLLOCK 1.93420 LUCIO FONTANA 2.361 FRANK AUERBACH 1.91921 FRANZ KLINE 2.309 WILLEM DE KOONING 1.91522 JOSEF ALBERS 2.194 ENZO CUCCHI 1.90523 PYKE KOCH 2.166 GERHARD RICHTER 1.90424 PHILIP GUSTON 2.145 WILLIAM BAILEY 1.85825 JOHN CURRIN 2.105 CLYFFORD STILL 1.81826 AD REINHARDT 2.104 FRANCIS BACON 1.80427 JENNY SAVILLE 2.081 CHRISTOPHER WOOL 1.71228 MARK GROTJAHN 2.051 JEAN FAUTRIER 1.70029 GERHARD RICHTER 2.047 ALEXIS ROCKMAN 1.66830 MASSIMO CAMPIGLI 2.023 MICHAEL GOLDBERG 1.633

29

Tab

le7:

Resi

dual

Ris

kand

the

Lit

era

ture

Cit

ati

on.

Th

ista

ble

rep

ort

sth

ere

gres

sion

resu

lts

ofth

eci

tati

onin

form

atio

non

resi

du

alri

skd

efin

edas

the

resi

du

alva

rian

cein

the

hed

onic

regr

essi

onof

the

loga

rith

mof

pri

ceon

vari

ous

vari

able

s:Ln

(Pijt)

=α1s i

j+α2s2 ij

+α3s3 ij

+α4s4 ij

+∑ δ(

t)+θ(i)

+XB

+ε ijt,

ε ijt∼

N(0,σ

2 i).

wh

erei

den

ote

sth

ear

tist

i;j

den

otes

thejt

hp

ainti

ng,

sis

the

age

ofth

ear

tist

atth

eti

me

ofcr

eati

ng

the

art

(th

ed

ate

sign

ed)

andt

isth

eti

me

ofth

etr

ansa

ctio

n.

We

sep

arat

eth

esa

mp

lein

tod

iffer

ent

age

grou

ps:

all

the

ages

,

from

25to

35,

from

36to

65an

daf

ter

65ye

ars

old.

For

each

age

grou

ps,

we

obta

inre

sid

ual

risk

and

the

arti

sts’

fixed

effec

tses

tim

ated

from

the

ab

ove

met

hod

.W

eco

llec

tth

eci

tati

onan

dex

hib

itio

nin

form

atio

nfr

omC

hri

stie

’san

dS

otheb

y’s

on

lin

eca

talo

gsfo

rth

eh

isto

rygo

ing

bac

kto

1998

.E

arli

erin

form

atio

non

thes

etw

ova

riab

les

isco

llec

ted

from

the

li-

bra

ryat

the

Met

rop

oli

tan

Mu

seu

mof

Art

inN

ewY

ork.

We

take

the

aver

age

cita

tion

for

each

arti

st.

Th

enu

mb

ers

in

the

bra

cket

sar

et-

stati

stic

s.∗

den

ote

ssi

gnifi

cant

at10

%;∗∗

den

otes

sign

ifica

nt

at5%

;∗∗∗

den

otes

sign

ifica

nt

at1%

.1

23

45

67

89

10

11

12

ResidualRisk(A

ll)

15.886***

12.001***

(7.47)

(5.38)

ResidualRisk(25to

35)

18.030***

14.845***

(6.57)

(5.23)

ResidualRisk(36to

65)

18.160***

14.245***

(7.17)

(5.33)

ResidualRisk(A

fter

65)

11.122***

7.237**

(3.82)

(2.52)

FE

(All)

6.871***

4.668***

(6.76)

(4.46)

FE

(25to

35)

6.836***

4.462***

(5.06)

(3.32)

FE

(36to

65)

6.842***

4.391***

(5.97)

(3.74)

FE

(After

65)

7.302***

6.009***

(4.99)

(3.97)

Constant

−8.426***

−8.961***

−8.375***

0.505

2.193

2.133

3.072*

9.622***

−7.728***

−9.361***

−7.637***

4.185

(−3.57)

(−3.08)

(−3.26)

(0.17)

(1.54)

(1.12)

(1.94)

(4.82)

(−3.39)

(−3.30)

(−3.05)

(1.44)

Observations

252

189

224

94

252

189

224

94

252

189

224

94

R-squared

18%

19%

19%

14%

15%

12%

14%

21%

24%

23%

24%

26%

30

Tab

le8:

Resi

dual

Ris

kand

the

Muse

um

Exhib

itio

n.

Th

ista

ble

rep

orts

the

regr

essi

onre

sult

sof

the

exh

ibit

info

rmat

ion

onre

sid

ual

risk

defi

ned

asth

ere

sid

ual

vari

ance

inth

eh

edon

icre

gres

-

sion

ofth

elo

gar

ith

mof

pri

ceon

vari

ous

vari

able

s:Ln

(Pijt)

=α1s i

j+α2s2 ij

+α3s3 ij

+α4s4 ij

+∑ δ(

t)+θ(i)

+XB

+ε ijt,

ε ijt∼N

(0,σ

2 i).

wh

erei

den

otes

the

arti

sti;

jd

enote

sth

ejt

hp

ainti

ng,

sis

the

age

ofth

ear

tist

atth

eti

me

ofcr

eati

ng

the

art

(th

e

date

sign

ed)

an

dt

isth

eti

me

of

the

tran

sact

ion

.W

ese

par

ate

the

sam

ple

into

diff

eren

tag

egr

oup

s:al

lth

eag

es,

from

25to

35,

from

36to

65

an

daf

ter

65ye

ars

old

.F

orea

chag

egr

oup

s,w

eob

tain

resi

du

alri

skan

dth

ear

tist

s’fi

xed

ef-

fect

ses

tim

ate

dfr

omth

eab

ove

met

hod

.W

eco

llec

tth

eci

tati

onan

dex

hib

itio

nin

form

atio

nfr

omC

hri

stie

’san

dS

oth

eby’s

onli

ne

cata

logs

for

the

his

tory

goin

gb

ack

to19

98.

Ear

lier

info

rmat

ion

onth

ese

two

vari

able

sis

coll

ecte

dfr

omth

eli-

bra

ryat

the

Met

rop

oli

tan

Mu

seu

mof

Art

inN

ewY

ork.

We

take

the

aver

age

exh

ibit

for

each

arti

st.

Th

enu

mb

ers

in

the

bra

cket

sare

t-st

ati

stic

s.∗

den

ote

ssi

gnifi

cant

at10

%;∗∗

den

otes

sign

ifica

nt

at5%

;∗∗∗

den

otes

sign

ifica

nt

at1%

.1

23

45

67

89

10

11

12

ResidualRisk(A

ll)

15.397***

9.503***

(6.18)

(3.75)

ResidualRisk(25to

35)

19.455***

14.781***

(6.23)

(4.66)

ResidualRisk(36to

65)

17.771***

11.492***

(6.01)

(3.78)

ResidualRisk(A

fter

65)

8.857**

3.43

(2.50)

(1.01)

FE

(All)

8.827***

7.083***

(7.85)

(5.95)

FE

(25to

35)

8.911***

6.547***

(5.99)

(4.36)

FE

(36to

65)

9.020***

7.042***

(7.14)

(5.28)

FE

(After

65)

9.006***

8.393***

(5.35)

(4.69)

Constant

−5.390*

−7.568**

−5.186*

6.085*

3.525**

3.29

4.638***

13.802***

−4.331*

−8.154**

−4.001

11.225***

(−1.95)

(−2.29)

(−1.73)

(1.69)

(2.24)

(1.56)

(2.66)

(6.01)

(−1.67)

(−2.58)

(−1.41)

(3.27)

Observations

252

189

224

94

252

189

224

94

252

189

224

94

R-squared

13%

17%

14%

6%

20%

16%

19%

24%

24%

25%

24%

25%

31

Tab

le9:

Resi

dual

Ris

kand

the

Avera

ge

Rankin

gby

Art

Sch

ola

rs.

Th

ista

ble

rep

orts

the

regre

ssio

nre

sult

sof

the

aver

age

ran

kin

gof

the

arti

sts

onre

sid

ual

risk

defi

ned

asth

ere

sid

ual

vari

ance

inth

eh

e-

don

icre

gre

ssio

nofth

elo

gari

thm

ofpri

ceon

vari

ous

vari

able

s:Ln

(Pijt)

=α1s i

j+α2s2 ij

+α3s3 ij

+α4s4 ij

+∑ δ(

t)+θ(i)

+XB

+ε ijt,

ε ijt∼

N(0,σ

2 i).

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67

89

10

11

12

ResidualRisk(A

ll)

0.345***

0.227**

(3.85)

(2.42)

ResidualRisk(25to

35)

0.350***

0.203

(2.82)

(1.61)

ResidualRisk(36to

65)

0.291***

0.168

(2.92)

(1.62)

ResidualRisk(A

fter

65)

0.572**

0.520**

(2.50)

(2.22)

FE

(All)

0.211***

0.162***

(4.40)

(3.16)

FE

(25to

35)

0.242***

0.203***

(3.94)

(3.09)

FE

(36to

65)

0.206***

0.169***

(3.89)

(2.95)

FE

(After

65)

0.134

0.091

(1.39)

(1.02)

Constant

3.186***

3.219***

3.297***

3.089***

3.447***

3.375***

3.491***

3.678***

3.218***

3.202***

3.328***

3.170***

(26.47)

(21.77)

(25.31)

(11.91)

(50.45)

(37.37)

(46.40)

(22.84)

(27.75)

(22.95)

(26.61)

(11.70)

Observations

107

70

85

19

107

70

85

19

107

70

85

19

R-squared

12%

10%

9%

27%

16%

19%

15%

10%

20%

22%

18%

31%

32

Table 10: Residual Risk and the Mean and Median of the Present Market Value.This table reports the regression results of the mean and median of the present market value of

the artists’ works on residual risk defined as the residual variance in the hedonic regression of the

logarithm of price on various variables: Ln(Pijt) = α1sij + α2s2ij + α3s

3ij + α4s

4ij +

∑δ(t) + θ(i) +

XB+ εijt, εijt ∼ N(0, σ2i ). where i denotes the artist i; j denotes the jth painting, s is the age of

the artist at the time of creating the art (the date signed) and t is the time of the transaction. We

separate the sample into different age groups: all the ages, from 25 to 35, from 36 to 65 and after

65 years old. For each age groups, we obtain residual risk estimated from the above method. We

first obtain the prices of all the works ever sold on the auction market for an artist as of 2012 and

we take the mean and median of this series of prices. The numbers in the brackets are t-statistics.

∗ denotes significant at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗ denotes significant at 1%.

Mean of TMV at the Price of 2012 Median of TMV at the Price of 20121 2 3 4 1 2 3 4

Residual Risk (All) 0.733*** 0.767***(6.31) (6.37)

Residual Risk (25 to 35) 0.505*** 0.519***(3.86) (3.81)

Residual Risk (36 to 65) 0.792*** 0.807***(6.13) (6.02)

Residual Risk (After 65) 0.736*** 0.735***(4.40) (4.29)

Constant 9.765*** 10.001*** 9.808*** 9.876*** 9.741*** 9.994*** 9.794*** 9.859***(75.71) (72.81) (74.43) (60.01) (72.85) (69.92) (71.56) (58.40)

Observations 275 204 245 102 275 204 245 102R-squared 13% 7% 13% 16% 13% 7% 13% 16%

33

Table 11: Residual Risk and the Mean of the Top Five Works’ Market Value.This table reports the regression results of the mean and median of the top five market value of

the artists’ works on residual risk defined as the residual variance in the hedonic regression of the

logarithm of price on various variables: Ln(Pijt) = α1sij + α2s2ij + α3s

3ij + α4s

4ij +

∑δ(t) + θ(i) +

XB+ εijt, εijt ∼ N(0, σ2i ). where i denotes the artist i; j denotes the jth painting, s is the age of

the artist at the time of creating the art (the date signed) and t is the time of the transaction. We

separate the sample into different age groups: all the ages, from 25 to 35, from 36 to 65 and after

65 years old. For each age groups, we obtain residual risk estimated from the above method. We

first obtain the prices of the top works ever sold on the auction market for an artist as of 2012 and

we take the mean of this series of prices. The numbers in the brackets are t-statistics. ∗ denotes

significant at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗ denotes significant at 1%.

Mean of The Top 5 Works at the Price of 20121 2 3 4

Residual Risk (All) 3.667***(6.28)

Residual Risk (25 to 35) 2.523***(3.84)

Residual Risk (36 to 65) 3.967***(6.11)

Residual Risk (After 65) 3.681***(4.38)

Constant 48.845*** 50.023*** 49.048*** 49.382***(75.27) (72.53) (74.01) (59.76)

Observations 275 204 245 102R-squared 13% 7% 13% 16%

34

Table 12: Residual Risk Persistence Over Two Periods.This table reports the regression results of the artists’ fixed effects on residual risk defined as the

residual variance in the hedonic regression of the logarithm of price on various variables: Ln(Pijt) =

α1sij + α2s2ij + α3s

3ij + α4s

4ij +

∑δ(t) + θ(i) + XB + εijt, εijt ∼ N(0, σ2i ). where i denotes the

artist i; j denotes the jth painting, s is the age of the artist at the time of creating the art (the date

signed) and t is the time of the transaction. We separate the sample into two periods–from 1980

to 1995 and from 1996 to 2012. For each period, we obtain the residual risk and the artists’ fixed

effects estimated from the above method. The numbers in the brackets are t-statistics. ∗ denotes

significant at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗ denotes significant at 1%.

Fixed Effect (1996-2012) Variance (1996-2012)1 2 3 4 5 6

Fixed Effect 1.016*** 1.014*** 0.250*** 0.174***(1980-1995) (28.97) (27.14) (7.95) (5.90)

Variance 1.073*** 0.016 0.873*** 0.694***(1980-1995) (4.58) (0.14) (9.50) (7.66)

Constant 0.593*** 0.13 0.583*** 0.858*** 0.350*** 0.425***(14.63) (0.74) (7.06) (23.62) (5.10) (6.54)

Observations 201 201 201 201 202 201R-squared 81% 10% 81% 24% 31% 41%

35

Table 13: Residual Risk and the In-Sample Outperformance.This table reports the regression results of the outperformance of the artists on residual risk defined

as the residual variance in the hedonic regression of the logarithm of price on various variables:

Ln(Pijt) = α1sij +α2s2ij +α3s

3ij +α4s

4ij +

∑δ(t)+θ(i)+XB+εijt, εijt ∼ N(0, σ2i ). where i denotes

the artist i; j denotes the jth painting, s is the age of the artist at the time of creating the art (the

date signed) and t is the time of the transaction. We separate the sample into different age groups:

all the ages, from 25 to 35, from 36 to 65 and after 65 years old. For each age groups, we obtain

residual risk and the artists’ fixed effects estimated from the above method. The outperformance

is defined as the difference of the two adjacent log sale prices from t to t+ 1 adjusted respectively

by the corresponding change in the Repeated Sales (RS) Contemporary Art Index (Model 1) and

the year fixed effects estimated from the above model (Model 2). Log (Auction Number) is the

logarithm of the total number of auctions for an artist in each age group. Log (Sale Price) is the

logarithm of the auction price at time t. We cluster the standard errors by painting. The numbers

in the brackets are t-statistics. ∗ denotes significant at 10%; ∗∗ denotes significant at 5%; ∗ ∗ ∗denotes significant at 1%.

Model 1 Model 2Residual Risk (All) 0.016* 0.018*

(1.71) (1.80)FE (All) 0.037*** 0.041***

(3.53) (4.07)Log (Auction Number) 0.006 0.006

(0.93) (0.92)Log (Sale Price) −0.031*** −0.032***

(−4.03) (−4.69)Constant 0.187* 0.226**

(1.94) (2.52)Observations 3,361 3,361R-squared 2% 3%

36

Table 14: Residual Risk and the Ex-Ante Outperformance.This table reports the regression results of the outperformance of the artists on residual risk defined

as the residual variance in the hedonic regression of the logarithm of price on various variables:

Ln(Pijt) = α1sij + α2s2ij + α3s

3ij + α4s

4ij +

∑δ(t) + θ(i) + XB + εijt, εijt ∼ N(0, σ2i ). where i

denotes the artist i; j denotes the jth painting, s is the age of the artist at the time of creating the

art (the date signed) and t is the time of the transaction. We separate the sample into two periods

respectively–the first pair is from 1980 to 1995 and from 1996 to 2012, the second pair is from 1980 to

1999 and from 2000 to 2010. For the first period of each pair, we obtain residual risk estimated from

the above method. For the second period of each pair, we obtain the outperformance estimated as

the difference of the two adjacent log sale prices adjusted respectively by the corresponding change

in the Repeated Sales (RS) Contemporary Art Index and the year fixed effects estimated from the

above model. For Model 1 and 2, the outperformance of a painting is from year t to t+ 1 and the

residual variance is over the period from t−15 to t−1. For Model 3 and 4, the outperformance of a

painting is at year t and the residual variance is over the period from t− 20 to t− 1. Log (Auction

Number) is the logarithm of the total number of auctions for an artist in each period. Log (Sale

Price) is the logarithm of the auction price at time t. We cluster the standard errors by painting.

The numbers in the brackets are t-statistics. ∗ denotes significant at 10%; ∗∗ denotes significant at

5%; ∗ ∗ ∗ denotes significant at 1%.

1 2 3 41996-2010 2000-2010

Adj. by Adj. by Adj. by Adj. byRS Index Time FE RS Index Time FE

Residual Risk 0.144*** 0.129***(1980-1995) (3.85) (3.62)

Residual Risk 0.118** 0.115**(1980-1999) (2.15) (2.17)

Log (Auction Number) 0.018 0.02 0.028* 0.027(1.32) (1.43) (1.65) (1.62)

Log (Sale Price) −0.023** −0.029*** −0.038** −0.039***(−2.25) (−2.98) (−2.58) (−2.59)

Artist Fixed Effects YesConstant −0.039 0.128 0.123 0.187

(−0.34) (1.10) (0.75) (1.12)Observations 1,658 1,658 1,099 1,099

R-squared 1% 1% 1% 1%

37

Figure 2: The Contemporary Art Market Performance Plot.This graph shows both the contemporary art index of Mei & Moses and the fixed effects ofthe time estimated from Model 1.

-1-.

50

.51

1.5

22.

53

3.5

4

1980 1985 1990 1995 2000 2005 2010Year

Contemporary Art Index (Log) Time Fixed Effects (Log)

Contemporary Art Index (Log) and Time Fixed Effects (Log)

38

Figure 3: Andy Warhol Sorted Residual by Age Group.This graph shows the residual estimated from Model 1 sorted by age group for Andy Warhol.The X-axis is the nth painting created in that period.

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 10 20 30 40

ANDY WARHOL (Age 20-25)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 50 100 150

ANDY WARHOL (Age 26-30)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 50 100 150 200

ANDY WARHOL (Age 31-35)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 200 400 600

ANDY WARHOL (Age 36-40)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 50 100 150

ANDY WARHOL (Age 41-45)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 100 200 300 400

ANDY WARHOL (Age 46-50)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 200 400 600 800

ANDY WARHOL (Age 51-55)

-6-5

-4-3

-2-1

01

23

45

6R

esid

ual (

Sor

ted)

Plo

t of P

aint

ings

0 200 400 600

ANDY WARHOL (Age 56-60)

39

Figure 4: The Residual Risk Profiles for Top Ranked Artists.This graph shows the residual risk profiles for some top ranked artists. The residual riskmeasure is the average residual variances by the age of an artist. The residual is estimatedfrom Model 1.

02

46

810

Mea

n of

Cre

ativ

ity

15 20 25 30 35 40 45 50 55 60Age

ANDY WARHOL

02

46

810

Mea

n of

Cre

ativ

ity

20 30 40 50 60 70 80Age

FRANCIS BACON

02

46

810

Mea

n of

Cre

ativ

ity

20 25 30 35 40 45Age

JACKSON POLLOCK

02

46

810

Mea

n of

Cre

ativ

ity

20 30 40 50 60 70 80Age

JASPER JOHNS

02

46

810

Mea

n of

Cre

ativ

ity

25 30 35 40 45 50 55 60 65 70 75Age

ROY_LICHTENSTEIN

02

46

810

Mea

n of

Cre

ativ

ity

20 30 40 50 60 70 80 90Age

WILLEM DE KOONING

40