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Bordeaux Wine as a Financial Investment


Abstract and Figures

For repeat transactions data from monthly auction hammer prices, we analyze the level and quality of Bordeaux wine returns using the Fama-French Three-Factor Model and the Capital Asset Pricing Model. Returns average up to 0.75% per month above those predicted by these models. Further, investment grade wines benefi t from low exposure to market risk factors, thus offering a valuable dimension of portfolio diversifi cation. These fi ndings are consistent with simple theoretical con-siderations and support a documented growing interest in wine investments (JEL Classifi cation: G11, G12).
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© The American Association of Wine Economists, 2008
Journal of Wine Economics, Volume 3, Number 1, Spring 2008, Pages 51– 71
Bordeaux Wine as a Financial Investment*
Lee W. Sanninga, Sherrill Shafferb and Jo Marie Sharrattc
For repeat transactions data from monthly auction hammer prices, we analyze the level and quality
of Bordeaux wine returns using the Fama-French Three-Factor Model and the Capital Asset Pricing
Model. Returns average up to 0.75% per month above those predicted by these models. Further,
investment grade wines benefi t from low exposure to market risk factors, thus offering a valuable
dimension of portfolio diversifi cation. These fi ndings are consistent with simple theoretical con-
siderations and support a documented growing interest in wine investments (JEL Classifi cation:
G11, G12).
I. Introduction
While many people consider buying wine and aging it for future consumption, few have
historically viewed wine strictly as a fi nancial investment. Yet wine possesses characteris-
tics that allow it to be considered and analyzed as an investment vehicle. Most important
among these is an active trading market; monthly wine auctions at the top six auction houses
frequently exceed $15 million. In recent years, at least two mutual funds have formed to
invest specifi cally in wine: the Ascot Wine Management Fine Wine Fund, founded in 1999
by a Bahamian company, has exhibited annual gains ranging from 10.9% to 13%; and
the Orange Wine Fund, founded in 2001, is listed on the Euronext Stock Exchange in
Amsterdam. Information published by these funds indicates that they are managed with an
approach similar to that of hedge funds (International Herald Tribune, 2001).
Additional evidence that wines are increasingly viewed as investment vehicles comes
from recent proposed changes to British pension tax law. UK self-invested pension plans
(SIPPs) are similar to some US individual retirement accounts (IRAs) which allow indi-
* The authors are grateful for helpful comments from Jim Gunderson, Ivo Ph. Jansen, Fred Sterbenz, Kirk
Vandezande and an anonymous referee.
a Department of Economics and Finance, University of Wyoming, 1000 East University Ave., Laramie, WY,
82071, phone (307) 766-3848, e-mail: (corresponding author).
b Department of Economics and Finance, University of Wyoming, 1000 East University Ave., Laramie, WY,
82071, phone (307) 766-2173, e-mail:
c Department of Economics and Finance, University of Wyoming, 1000 East University Ave., Laramie, WY,
82071, phone (970) 225-1275, e-mail:
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52 Bordeaux Wine as a Financial Investment
vidual to invest virtually tax free. The proposed changes would have broadened the allow-
able assets to include “residential property and certain other assets such as fi ne wines” (HM
Treasury, 2005). Chancellor of the Exchequer, at the time Gordon Brown, subsequently
modifi ed his position and ultimately classifi ed these assets such that wine and other assets
can be put into SIPPs but are ineligible for tax relief.1
Given these recent trends in viewing wine as an investment vehicle, an open question
therefore is whether wine returns compare favorably with historical fi nancial returns on
other assets, both in mean value and in volatility or covariance. This paper addresses that
question, fi rst by developing a simple conceptual framework to formulate specifi c hypothe-
ses, and then by using both the Capital Asset Pricing Model (CAPM) and the Fama-French
Three-Factor Model to test the hypotheses empirically (Fama and French, 1993). Our
sample is formed from monthly repeat transactions data for individual wine-asset sales,
which yields a time series of monthly returns. Consistent with our predictions, we fi nd that
the wines in our sample exhibit large, positive excess returns along with low exposure to
commonly recognized market risk factors. These fi ndings provide quantitative evidence
that wine may be a viable investment asset, both alone and as part of a well diversifi ed
The remainder of this paper is organized as follows. The second section summarizes
related literature, section 3 presents key concepts and hypotheses, section 4 describes the
data, section 5 presents the empirical model, section 6 reports the results, and section 7
II. Related Literature
Wine exhibits unique characteristics relative to traditional fi nancial assets. Some of these
characteristics are similar to those exhibited by other tangible assets such as collectible art.
Wine does not pay a dividend, investors must pay for its storage, and the time required to
liquidate a collection of wine can extend to four or fi ve months. Further, a bottle of wine
may break or may be otherwise deemed worthless if stored improperly.
While the subject of wine as an investment has been sparsely studied relative to tra-
ditional fi nancial assets like stocks, mutual funds, bonds, etc., extant results and recom-
mendations are mixed. Krasker (1979) analyzed returns over the period of 1973–1977 and
found no risk premium for storing red Bordeaux and California Cabernet Sauvignon wines
produced since 1950. Jaeger (1981), by contrast, established risk premiums in excess of
12% using Krasker’s methodology but extending the sample period to eight years begin-
ning in 1969 and incorporating a signifi cantly lower measure of wine storage costs. Jaeger
argued that the four-year period studied by Krasker biased his procedure toward fi nding a
1 For a summary specifi c to wine-assets and SIPPs, see The Decanter.Com Guide to Wine and Sipps
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 53
2 In most of the Bordeaux region, it is the wine producer (chateau) and not the land that is classifi ed by growth.
The fi ve growth classifi cations were originally established to distinguish among the quality of the wines produced
in the area (where First Growth represents the highest quality).
3 Jaeger (1981) discusses risk as an important factor in wine investments, but does not incorporate an explicit
measure of risk in the empirical model.
subnormal rate of return. Additionally, the different assumptions about storage costs affect
the premiums measured. Jaeger’s estimate of storage cost was approximately $0.50 per
case annually while Krasker’s estimate was over $16 per case for annual storage.
Weil (1993) calculates the returns to an individual wine portfolio using detailed infor-
mation across a 13 year (1980–1992) acquisition and holding period. His results suggest
that the return to wine assets is approximately 9.5% and increases to 11% if the portfolio
is limited to Bordeaux wines. Weil concludes that these returns are much less than rates
of return to NYSE stocks over the same period. Interestingly, the Bordeaux wines in the
investor’s portfolio exhibit lower return variation than other wines. Weil notes that this is
not effi cient since Bordeaux wines have a higher return and less risk. We extend this fi nd-
ing by calculating the excess returns of wine, returns over (or under) a risk-adjusted market
More recently, Burton and Jacobsen (2001) used a repeat-sale regression—an approach
also used by Goetzmann (1993) to value the art market—to estimate the rate of return for
Bordeaux wines during 1986–96. Their fi ndings include an annual nominal rate of return
of almost 14% for a portfolio of 1982 wine but only 8.3% for a portfolio of 1961 wines.
An aggregate portfolio of wines earned a nominal rate of 7.9% annually while a portfolio
consisting of only those classifi ed as “First Growth” earned 6.7%.2 Of particular interest
is the comparison to the Dow Jones Industrial Average: only the 1982 vintage portfolio
outperforms the index over the period in question.
While these studies focused on establishing wine asset returns either in absolute terms
or relative to a simple market return, they did not explicitly account for the fi nancial risk
of wine assets.3 Our paper enhances this literature by both determining wine returns over
a more recent time period and by investigating the degree of exposure and covariance
between wine returns and common fi nancial risk factors.
Other studies have focused on the determinants of wine prices. Combris, Lecocq, and
Visser (1997 and 2000) explored whether market prices respond to various measures of
quality. Objective characteristics, including the name of the producer, ranking of the wine
(growth classifi cation), color of the wine, and vintage, were found to be signifi cantly asso-
ciated with the variation in prices. By contrast, a majority of the subjective characteristics,
including visual and olfactory fi ndings, gustatory fi ndings, and grade as determined by a
“jury” of four persons, were found not to be signifi cantly associated with market prices.
Jones and Storchmann (2001), on the other hand, found wine ratings (“Parker points”)
to be a signifi cant determinant of Bordeaux prices, along with sugar levels, acidity, and
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54 Bordeaux Wine as a Financial Investment
aging. Similarly, Cardebat and Figuet (2004) found sensory variables to be a signifi cant
determinant of Bordeaux prices for 1996–1999 vintages. While it is not surprising that
some of these non-fi nancial factors have been found to help explain or predict prices of spe-
cifi c wines, our empirical model does not include them because, from the perspective of a
diversifi ed investor, such factors correspond to fi rm-specifi c risk and the relevant fi nancial
question is to what extent such risk can be mitigated in a diversifi ed portfolio.
Another set of literature addresses the hypothesis that collectibles act as a hedge against
stock market risk by exhibiting a negative covariance with market returns or with infl ation.
Burton and Jacobsen (1999) summarize the research in this area. Specifi cally, they state
that while research has shown that returns on collectibles may be negatively correlated with
stock market returns (for items including coins, stamps, and art), there is no evidence that
collectibles are a hedge against stock market declines since returns on collectibles have
been historically fl at during the periods examined. Wines were not studied in this context.
Our paper contributes to the literature by specifi cally measuring the covariance of wine
returns with stock market returns.
III. Background Concepts and Hypotheses
One key issue that previous studies appear to have neglected is a formal comparison of
fundamental sources of value across equities, wine assets, and other collectibles such as art.
A standard and long-established approach to estimating the market value of an equity relies
on the discounted present value of its dividend payouts (Gordon, 1962). In this approach,
cash payouts to investors comprise the ultimate source of investment value. Under the
simplifying assumption that dividends grow at a constant rate g, and discounting at a con-
stant rate re, the discounted present value at time t of an infi nite stream of dividends can be
explicitly expressed in a simple form. The exact valuation and the corresponding rate of
return on the investment will depend on details of timing and whether or not dividends are
reinvested. For purposes of comparison, we calculate the valuation Pt as of the beginning of
period t, assuming that the dividend Dt is paid at the end of period t. Under these assump-
tions, the value of the equity is:
Pt = Dt / (reg), (1)
in the discrete-time case.4 A basic prediction of this model and an immediate consequence
of equation (1) is that the value of an equity grows at the rate g. The total nominal annual
net rate of return earned by an equity investor is the sum of this capital appreciation rate
g plus the dividend rate implied by equation (1), which equals:
4 Alternate assumptions about the timing of cash fl ows, or alternate notation, will yield a slightly different
expression for valuation.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 55
equity return = Dt/Pt + g = ( re g) + g = re. (2)
This simplifi ed and standard analysis incorporates uncertainty only implicitly; g is typi-
cally interpreted as an expected value while re is set at a level that incorporates an appropriate
risk premium. Thus, in general, g < re, a condition also needed as a technical requirement of
the model to ensure a positive and fi nite valuation. Further, as a practical matter, the principle
of opportunity costs suggests that the long-run growth rate of the aggregate economy consti-
tutes a lower bound on values of re that are relevant to investors, and any risk premium would
drive re above that bound. Combined with the accounting identity that no individual company
can grow faster than the entire economy forever, this logic also ensures g < re.
The source of fi nancial value of collectible artwork, by contrast, is ultimately derived
from the consumption value of viewing the item. While the value of viewing a particular
piece of art may vary over time and will typically vary from one individual to the next,
any single painting or sculpture is a unique object that (apart from accidental damage or
depreciation) will not change over time. Thus, at least in real terms, it is diffi cult to identify
any reason why the consumption value of viewing any individual piece of art should be
expected to increase over time, except possibly to the extent that close substitutes (such as
other paintings by the same artist) may grow scarcer due to damage or loss. Further, this
consumption value exists as a continuous stream, in that each owner has a constant oppor-
tunity to view the artwork while it remains in his possession.
This line of thought suggests that the pattern of returns to artwork resembles that of a
perpetuity or consol, namely a (roughly) constant expected value of consumption in each
period. If C denotes the value of viewing the artwork in each period, then the well-known
perpetuity formula gives the present value of the artwork as PV = C / ra for an appropriate
discount rate ra, which need not be the same value as re applied to equity in equation (1).
A notable property of this valuation is a complete absence of capital appreciation in equi-
librium, at least in real terms. The total rate of return to an investor in art is then the stream
of consumption value alone, which accrues in the form of viewing pleasure but not in any
measurable fi nancial form.5
One might similarly postulate that the fundamental source of fi nancial value for wine
likewise derives from its eventual consumption value. A crucial difference is that a bottle
of wine must be consumed at a single point in time, rather than conferring a continuous and
readily accessible stream of consumption benefi ts as is true of artwork.6
5 This prediction is broadly consistent with many previous empirical fi ndings: nominal returns to art investments
have been found to be lower than for either equity or bond investments by Baumol (1986), Buelens and Gins-
burgh (1993), Goetzmann (1993), Candela and Scorcu (1997), and Agnello and Pierce (1998) for paintings, and
by Pesando (1993) for prints. Some negative real returns were reported by Agnello and Pierce (1998) while real
annual returns of less than 1% were reported by Baumol (1986), Buelens and Ginsburgh (1993), and Candela
and Scorcu (1997).
6 We assume that non-gustatory consumption benefi ts of wine, such as any collectible value of wine labels or the
consumption value of viewing a well-stocked wine cellar, ultimately derive from drinking the wine and are thus
secondary in their fi nancial impact.
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56 Bordeaux Wine as a Financial Investment
Further, the owner chooses the time of consumption. As Jaeger (1981) notes, wines typi-
cally exhibit an optimal storage period after which the gustatory experience is maximized
and, while there is uncertainty regarding the optimal consumption date, rational behav-
ior would suggest that most bottles (especially those purchased for investment purposes)
would tend to be opened and their contents consumed near the expected optimal date. Thus,
the nature of the fi nancial returns to wine revolves around a single consumption event. If
the optimal consumption value is C, then the present value of a bottle of wine is given by
PV = C / (1 + rw)T where the optimal consumption date occurs T periods in the future, and
this present value grows at the rate rw in each period. The rate of return to a wine invest-
ment is thus rw.
Although this simple framework abstracts from explicit representation of uncertainty, it
not only captures important fi nancial features of equity, art, and wine assets, but also—as
a fi rst-order approximation—it implies two key hypotheses or predictions. First, although
the discount rate rw applied to wine valuation need not be the same as that applied to equity
valuation, both will typically be established as the risk-free rate of interest plus some risk
premium, and hence will be of similar orders of magnitude. Because the quality of wine is
subject to several sources of uncertainty not shared by equities, including the optimal time
of consumption, possibility of spoilage among some bottles, and uncertainty over how well
a particular vintage will age, it is plausible that rw > re in many cases. Since the analysis
above shows that the equilibrium return to investment equals re for equity and rw for wine,
one prediction is that, unlike the return to art, the rate of return to wine investments should
be similar to that of equities, and may even exceed them. This prediction is consistent with
the fi ndings of Jaeger (1981) and Weil (1993), though not with those of Krasker (1989),
and will be further tested below.
A second prediction follows from the observation that the fundamental source of fi nancial
value for wine derives from a consumption experience unique to each variety and vintage
of wine. The economic value of that consumption experience will typically be an increasing
function of an investor’s fi nancial wealth, which in turn is affected by the aggregate perfor-
mance of equity markets to the extent that equity holdings comprise a signifi cant share of
the investor’s total wealth. However, this is a second-order effect as the idiosyncratic risk
of wines will typically overwhelm this correlated component of risk. An implication is thus
that the fi nancial returns to wine should exhibit low correlations with returns on purely
nancial assets such as equities. This property, if true, would make wine an attractive invest-
ment as a means of diversifying purely fi nancial portfolios, especially if the fi rst prediction
is also true. We present the fi rst empirical test of this second hypothesis below.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 57
IV. Sample and Descriptive Statistics
The wine returns data in our sample are derived from an eight-year series of monthly auc-
tion “hammer price”7 data from The Chicago Wine Company on red Bordeaux8 vintages
ranging from 1893 to 1998.9 Founded by Philip H. Tenenbaum in May 1974, The Chicago
Wine Company (TCWC) conducted its fi rst fi ne wine auction in April 1977, making it the
second company to conduct wine auctions in the U.S. TCWC represents one of the larg-
est trading markets for wine assets. TCWC now conducts at least one live auction and one
silent auction per month, signifi cantly more annually than any other wine auction company
in Chicago or the U.S. It should be noted that these open auctions constitute secondary-
market transactions. Some wines sell as futures or are allocated on a limited basis and thus
represent primary-market transactions. These are similar to equity IPOs and not accessible
to most investors. While the returns to investing in wine futures are an interesting area, we
leave this to future research.
We use data on red Bordeaux wines, a choice based on several features of this wine.
First, red Bordeaux wines have been determined to benefi t from extended aging (Jaeger,
1981). Perhaps related to this property, red Bordeaux wines are purchased more often for
investment purposes than for consumption relative to white wines and wines from other
regions. Consequently, French wines, and in particular those from Bordeaux, comprise the
greater part of the wine auction market (Burton and Jacobsen, 2001). Annual classifi ed
growth Bordeaux sales are approximately $7 billion as of 2005.
Returns data are calculated for each month from repeat transactions. The returns are cal-
culated for every vintage10, or year of production, and for every individual wine producer.11
The calculation of monthly returns by vintage and producer is a three-step process. First,
an average price per bottle is calculated for each year and month of trade by the vintage
and producer.12 Next, the average return for each vintage and producer is calculated by the
7 A hammer price refers to the price at which the bidding stops and the item for sale is said to be “hammered
down” (Ashenfelter 1989).
8 Bordeaux refers to a wine-making area of France that straddles the Garonne and Dordogne Rivers. This area
is world-famous for its reputation of being home to the fi nest red wine producers. The area benefi ts from a long
and warm growing season as well as soil composition, made up of topsoil poor in nutrients and subsoil rich in
minerals (Clarke 1996).
9 These data are readily available on The Chicago Wine Company’s webpage of past auction hammer prices
10 Vintage refers to a particular year’s grape harvest and may also refer to the wine of a single year (Oz Clarke,
11 The sample is limited to wines with non-inferior bottle conditions. Wines with label conditions (stains, tears) as
well as less than full shoulder fi lls are eliminated. The resulting data set therefore has consistent bottle conditions.
This reduces return variance due to varying asset quality.
12 The prices used to calculate returns are the hammer prices as reported by TCWC. As is standard with calcula-
tion of traditional asset returns, brokerage costs, account fees, or other forms of transactions costs are not ac-
counted for since they vary across investors, investment horizon, and investment scale.
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58 Bordeaux Wine as a Financial Investment
difference in the log of the average price for the month of trade and the log of the average
price for the prior month of trade. If a trade did not occur in the prior month, the return is
designated as missing.13 In the third step, a modifi ed mean is calculated by winsorizing the
sample as in Nissim and Ziv (2001), Dittmar (2004), and others.14 For those portions of the
analyses that require average returns by vintage and growth classifi cation,15 the data are
categorized by classifi cation and then averages are calculated for every year and month of
trade by vintage and growth. Calculating averages as outlined above allows for a degree of
smoothing of the returns data, which has been shown to exhibit a wide range of values. In
addition, it mitigates the “declining price anomaly,” a phenomenon in which identical lots
of wine sold at a later time in a single auction are likely to sell for a lower price than those
purchased earlier in the day (Ashenfelter, 1989).
The wine returns data are based on approximately 90 producers and include vintage
years from 1893 through 1998. In total, the data include 13,662 wine asset returns rec-
ognized over the eight-year period 1996–2003. For the purpose of evaluating investment
potential relative to equities, this period is fairly short; on the other hand, it includes a
period of exceptionally high returns in the U.S. stock market, thereby providing a stringent
benchmark for identifying excess returns. Of the 90 producers present in the sample, 47
belong to a growth classifi cation.16 Within the data set, there are 276 unique vintage and
producer combinations and 83 unique vintage and growth groupings.
The three variables comprising the Fama-French Three-Factor Model, described below,
are published monthly by Kenneth French and may be downloaded directly from his web-
site.17 The three factors consist of Rm-Rf, a measure of the value-weighted return on all
NYSE, AMEX and NASDAQ stocks (from CRSP) net of the one-month Treasury bill rate,
SMB (Small Minus Big), a measure of the difference in the average return on small and
large market equity fi rms, and HML (High Minus Low), a measure of the difference in
average return between high and low book-to-market equity fi rms.18 Other data required
to estimate the model include monthly U.S. Treasury returns, obtained from the CRSP
government bond fi le.
13 To maximize sample size while maintaining econometric precision, we require a minimum of 15 observations
for any given vintage producer or vintage growth portfolio.
14 This method replaces extreme values in the sample with limits. In this study, the entire sample is divided into
groups; the groups representing the highest 2.5% and the lowest 2.5% return values are replaced. The replacement
values are those maximum and minimum values as calculated by respective vintage and producer. For example,
if a return for a 1982 Latour falls within the bottom 2.5% of the entire sample of returns, the value is replaced
with the minimum average return for the sample of 1982 Latour returns. This process resolves the problem of
inconsistent outliers resulting from data entry errors and lack of data cleaning. Our choice of a 2.5% threshold
lies between the 5% value used by Dittmar (2004) and the 0.1% value selected by Nissim and Ziv (2001).
15 These are for First through Fifth Growth and for unclassifi ed wines (No Growth).
16 It should be noted that only fi ve wines comprise the First Growth classifi cation (Haut Brion, Lafi te Rothschild,
Latour, Margaux and Mouton Rothschild).
18 For a detailed description of the estimation of the factor returns, see Fama and French (1993).
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 59
A. Descriptive Statistics
Table 1 presents descriptive statistics. Average monthly returns by vintage and producer
range from 4.38% (1961 Petrus)19 to 7.48% (1966 Haut Brion), with the average of all
monthly returns at a respectable 0.51%. When only those wines that are classifi ed in the
growth categories are analyzed, the average increases to 0.78%. Perhaps not that surpris-
ing to advocates of wine investment, more than 75 wines averaged 1% or more in monthly
returns (see Table 2). The average minimum monthly return for all returns by vintage and
producer is approximately 11%20, with an average maximum return of 14% (see Table 1).
The standard deviation of all monthly returns by vintage and producer is 6 %.
Table 1
Descriptive Statistics – Monthly Wine Returns
Avg #
Monthly %
Std Dev
Monthly %
Min Avg
Max Avg
Avg Min
Monthly %
Avg Max
Monthly %
Vintage &
Producer 37 0.51 6.05 4.38 7.48 11.26 13.98
Vintage &
Producer 139 0.78 7.20 2.71 7.48 12.45 16.07
Vintage &
Growth 49 0.88 7.08 2.86 4.18 13.19 17.54
Vintage &
Growth 147 1.03 7.29 2.39 4.03 13.10 17.99
Growth 49 0.70 7.81 2.39 2.65 13.85 18.58
Growth 48 1.45 7.12 0.22 3.85 12.88 17.66
Growth 53 1.08 7.16 0.26 2.29 12.15 18.91
Growth 33 0.91 6.84 0.83 4.03 12.57 16.73
Growth 44 1.07 6.50 1.25 3.85 13.36 16.45
No Growth 55 0.45 6.45 2.86 4.18 13.46 16.23
1includes only those wines classifi ed in one of the fi ve growth classifi cations.
Avg # Monthly Returns is the average number of observations for the vintage-producer assets or vintage – growth asset.
For example, the average vintage produce return series has 37 monthly observations with an average monthly return on .51%.
19 This number indicates that over the eight year trading period, 1961 Petrus has an average monthly return
of − 4.38%.
20 This number is calculated by observing the lowest return for each wine (vintage – producer), and averaging
these minimum returns across all wines (vintage – producer).
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60 Bordeaux Wine as a Financial Investment
Table 2
Top 75 Wine Assets by Average Monthly Return
Vintage Producer Avg Monthly
% Return Vintage Producer
Monthly %
1966 Haut Brion 7.48 1989 Trotte Vieille 1.76
1982 Lynch Bages 4.95 1983 Pichon Lalande 1.74
1990 Pichon Lalande 4.39 1988 Mouton Rotschild 1.72
1989 L’Enclos 4.32 1989 Lynch Bages 1.72
1989 Canon La Gaffelière 4.07 1994 Margaux 1.70
1961 Latour 3.68 1990 Pichon Baron 1.69
1982 Petit Village 3.25 1986 Pichon Lalande 1.66
1988 Lafi te Rothschild 3.18 1995 Meyney 1.53
1995 Pavillon Rouge du Margaux 2.77 1961 Lynch Bages 1.50
1970 Haut Brion 2.75 1989 L’Evangile 1.49
1989 Mouton Rothschild 2.73 1959 Haut Brion 1.48
1985 Margaux 2.72 1945 Mouton Rothschild 1.46
1986 Gruaud Larose 2.69 1986 Haut Brion 1.46
1990 Gruaud Larose 2.66 1995 Calon Ségur 1.45
1995 Lynch Bages 2.59 1982 La Mission Haut
1970 Ducru Beaucaillou 2.51 1995 La Mission Haut
Brion 1.38
1986 Cheval Blanc 2.49 1996 Bellefont-Belcier 1.36
1986 Talbot 2.49 1989 Pichon Lalande 1.33
1996 Léoville Poyferré 2.47 1989 Gruaud Larose 1.32
1996 Pape Clement 2.44 1982 Pavie 1.32
1990 Mouton Rothschild 2.29 1989 Belair 1.32
1989 Pichon Baron 2.24 1982 Gruaud Larose 1.28
1983 Léoville Las Cases 2.23 1986 Margaux 1.27
1985 Cos d’Estournel 2.20 1995 Ducru Beaucaillou 1.26
1990 Certan De May 2.12 1995 Léoville Las Cases 1.22
1988 La Mission Haut Brion 2.11 1982 Léoville Las Cases 1.22
1982 Cos d’Estournel 2.10 1996 Margaux 1.20
1990 Haut Brion 2.07 1989 Palmer 1.19
1982 Calon Ségur 2.00 1989 Fieuzal 1.17
1989 Figeac 2.00 1990 Troplong Mondot 1.17
1982 Lafi te Rothschild 2.00 1990 L’Evangile 1.11
1989 Ducru Beaucaillou 1.91 1970 La Mission Haut
1970 Lafi te Rothschild 1.90 1986 Lafi te Rothschild 1.08
1982 Pétrus 1.88 1970 Latour 1.06
1983 Margaux 1.87 1995 Grand Puy Lacoste 1.05
1998 Cheval Blanc 1.84 1998 Haut Brion 1.04
1989 Léoville Las Cases 1.81 1990 Calon Ségur 1.03
1990 Rauzan-Ségla 1.78
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 61
The average monthly returns as calculated by vintage and growth are similar though
somewhat greater due to the difference in the weighting structure. When calculating averages
by vintage and producer, each producer’s average monthly return is given a weight of one.
When calculating average returns by vintage and growth classifi cation, the returns represent
a mean of the average returns for all applicable producers in a given month. The monthly
averages by vintage and growth range from 2.9% (1961 “no growth”) to 4.2% (1979 “no
growth”). The average for all returns by vintage and growth is 0.88% per month, with an
average minimum of 13.2% and a maximum of 17.5%. When the sample is reduced to
those wines categorized in one of the fi ve growth classifi cations, the average monthly return
is over 1%. When analyzed further by growth classifi cation, Second Growth outperforms
the other classifi cations on average monthly return with 1.45% (see Table 1).
These monthly returns are similar to those found in prior studies. For the years 1986
to 1996, the annual nominal rate of return for an aggregate portfolio of red Bordeaux was
found to be 7.9% (Burton and Jacobsen, 2001). An average monthly return of 0.51 to
0.88% (6.2 to 11.1% annualized) occurs for all wines, while 0.78 to 1.03 (9.7 to 13.1%
annualized) occurs for classifi ed wines over the eight-year period from 1996 to 2003,
which includes several depressed years in the equity market. Thus, relative to other asset
classes, wine exhibits a strong positive return over the period.
There has been some debate over the extent to which additional non-pecuniary benefi ts
associated with wine investment add to the monetary rate of return. Specifi cally, to what
extent should investors consider the potential benefi t of consumption? Strict economic the-
ory maintains that the prices observed have already accounted for this option. Moreover, if
markets are well functioning and complete, the rate of return on wine assets (net of costs
and consumption benefi ts) should equal the rate of return on competing assets with identi-
cal risk (Burton and Jacobsen, 2001).
B. Trading Frequency
Unreported results reveal a number of interesting patterns in trading frequency of the
wines.21 Of particular importance is the percentage of total returns accounted for by each
growth classifi cation. While the First Growth wines only account for 5.6% of the number of
producers in the data, they account for approximately 20% of the trading volume. Another
20% of the trading activity comes from the trading of Second Growth wines. Third, Fourth
and Fifth Growth wines, in total, account for only 9.4% of total returns although they rep-
resent 30% of the producers in the sample. The No Growth wines represent about 50% of
both the number of producers and returns.
There also exist interesting patterns in monthly trading activity. For all growth classifi ca-
tions, wine trading volumes decline in February and March, increase in April and May, and
21 Available from the authors upon request
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62 Bordeaux Wine as a Financial Investment
decline again in June and July. Trading increases again in August, then declines in September
and October. November, December, and January represent three consecutive months of sub-
stantial percentage increases. On average, the number of trades increases approximately 20%
in November and another 20% in December. While beyond the scope of this study, future
research may uncover seasonal trading strategies with arbitrage opportunities.
V. Regression Model
The Fama-French Three-Factor Model (TFM) (Fama and French, 1993) was developed
in response to the research of the 1980s that revealed many empirical contradictions to
the central CAPM. The CAPM predicts that expected returns are a function of their mar-
ket β s and that the market β s suffi ce to describe the cross-section of expected returns on
U.S. equities. Specifi cally, Fama and French (1992) confi rm that, unlike the relationship
between average return and β, there exists a strong negative relationship between fi rm
size and average return and an even stronger positive relationship between book-to-market
equity and average return, and that these two relationships persist in competition with other
variables. Fama and French demonstrate that while the CAPM predictions hold for the pre-
1969 period, they disappear for the 1963–1990 period. Thus, they augment the CAPM by
including two additional variables, HML and SMB, as described above. The TFM has been
shown to absorb common time-series variation in equity returns by producing intercepts
closer to zero and increased R squared values. The empirical model is as follows:
it t i i t t i t i t it
−=+ − + + +
αβ β β ε
, (3)
where Rit is the return on a wine asset i (wine or wine portfolio) during month t, RFt rep-
resents the risk-free rate (one month bill rate), RMt represents the month t value-weighted
return on all NYSE, AMEX, and NASDAQ stocks, SMBt is the month t size factor HMLt is
the month t book-to-market related factor.
Under this model, evaluating the fi nancial performance of a wine asset is straightfor-
ward. The regression intercept represents the average abnormal return (positive or nega-
tive) and thus indicates whether an asset provides return in excess of the risk-adjusted
return. A positive intercept indicates that, on average, the asset earned a return higher than
suggested by the exposure to market risk factors.22
The regression analysis in this paper includes both the TFM and CAPM. We include the
CAPM, despite its relatively lower performance in explaining variations in asset returns,
in order to estimate single-factor β s and to facilitate more traditional risk benchmarking of
wine to other asset classes.23
22 The goal of this research is to use established models to analyze the quality of the returns that wines exhibit.
This goal is different from developing a model to best explain wine returns, which we defer to future research.
Our approach compares the fi nancial returns of wines to those of equities, thus addressing the benefi ts of adding
wine assets to an otherwise diversifi ed portfolio.
23 Single factor β s are universally used by practitioners while multi factor β s are used less commonly.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 63
The regression analyses include results based on average returns by vintage and pro-
ducer, and by vintage and growth classifi cation. The justifi cation for separate regressions
based on vintage comes from prior research on wine prices and returns. Vintage refers to
a particular year’s grape harvest and may also refer to the wine of a single year (Clarke,
1986). Research has shown that weather conditions during the growing season and the
period of harvest play a signifi cant role in forecasting long-run prices for wines (Corsi
and Ashenfelter, 2003). Burton and Jacobsen show striking differences in returns based on
vintage for red Bordeaux wines, with 1961 and 1982 vintage portfolios beating the market
return. These results correspond well with published data regarding the ranking of wine
quality by vintage. For example, 1982 is only one of two vintages rated as “exceptional”
while 1961 rated as “excellent” for vintages since 1960 (Conseil Interprofessionnel du Vin
de Bordeaux, 2004).
Growth classifi cation of a particular wine has also been shown in prior studies to con-
tribute signifi cantly to its price. We confi rm this by testing the signifi cance of growth clas-
sifi cations using our auction data and the TFM. F-tests (unreported) support fi xed differ-
ences in excess returns (alphas) and differences in beta coeffi cients (risk exposures) across
the six growth categories (fi ve classifi es growths and one “no growth”)24. Based on these
unreported results, individual regressions are estimated in the TFM and CAPM analyses
for each of the unique vintage and growth groupings. Further, given that the growth clas-
sifi cations are simply groupings of producers, individual regressions based on vintage and
producer are analyzed as well.
VI. Results
The results of our several hundred TFM regressions are summarized in Panel 1 of Table 3.
As shown, the average regression intercepts (Jensen’s alpha25) are positive. The average
intercept over the time period analyzed here for the 276 regressions by vintage and pro-
ducer is 0.186. Omitting the non-growth classifi cation, the average excess return (regression
intercept) for the 122 vintage- producer regressions increases to 0.419. Average Rm-Rf
provides the average of the beta coeffi cients on the market factor. Average SMB
provides the average of the beta coeffi cients on the size factor. Average HML pro-
vides the average of the beta coeffi cients on the book-to-market factor. The average
coeffi cients for these three risk exposures are small relative to traditional equities,
and many are in fact negative.
24 Detailed results available from the authors upon request.
25 This is a risk-adjusted performance measure that is the average return on a portfolio over and above that pre-
dicted, given the portfolio’s beta and the average market return. This is the portfolio’s alpha. In fact, the term is
sometimes discribed as “Jensen’s alpha.”
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64 Bordeaux Wine as a Financial Investment
Table 3
Average Regression Results
# Regressions 2Average
R Squared
Panel 1:
Fama French 3 Factor
Vintage & Producer 276 0.186 0.008 0.012 0.066 0.095
Vintage & Producer 1122 0.419 0.033 0.049 0.071 0.097
Vintage & Growth 83 0.606 0.006 0.034 0.042 0.092
Vintage & Growth 1 62 0.759 0.020 0.037 0.035 0.100
First Growth 22 0.478 0.104 0.106 0.030 0.094
Second Growth 18 1.330 0.040 0.149 0.036 0.128
Third Growth 10 0.716 0.049 0.037 0.073 0.049
Fourth Growth 8 0.459 0.126 0.264 0.013 0.127
Fifth Growth 4 0.450 0.305 0.053 0.008 0.088
No Growth 21 0.154 0.037 0.023 0.063 0.067
Panel 2:
CAPM Model
Vintage & Producer 276 0.168 0.005 0.031
Vintage & Producer 1122 0.389 0.019 0.030
Vintage & Growth 83 0.880 0.007 . 0.038
Vintage & Growth 1 62 1.046 0.002 0.041
First Growth 22 0.775 0.074 0.052
Second Growth 18 1.532 0.045 0.032
Third Growth 10 1.027 0.018 0.027
Fourth Growth 8 0.750 0.107 0.024
Fifth Growth 4 0.727 0.310 0.035
No Growth 21 0.439 0.053 0.026
1 includes only those wines classifi ed in one of the fi ve growth classifi cations.
2 because these are averages of numerous regression coeffi cients, t-statistics or p-values do not appear, but are discussed in the text and refl ected
in Figures 1a–2b.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 65
The results from the regression analysis establish two key fi ndings. First, average wine
returns are positive and in excess of risk adjusted returns. Second, as indicated by the
small risk factor loadings, wine returns covary minimally with market returns and other
commonly accepted risk factors.26 These results hold for regressions based on vintage and
producer as well as on vintage and growth, and are consistent with the predictions of the
theoretical framework presented above.
The results of the analysis by vintage and growth classifi cation are largely equivalent,
though somewhat greater due to the weighting structure discussed earlier. The average
intercept for the 83 regressions by vintage and growth is 0.606. Again, if only the wines
categorized within the fi ve growth classifi cations are analyzed, the average of the 62 vin-
tage-growth regression intercepts increases to 0.759. The signifi cance of these results can-
not be overstated. Investment grade wines provide, on average, substantially large, positive
returns in excess of those forecasted by the Fama-French market risk factors. Specifi cally,
these wines experienced monthly returns of 0.61 to 0.76% higher than those predicted by
the Fama-French risk factors. On an annual basis, these compound to an excess return of
7.6 to 9.5%.
Figures 1a and 1b present the distributions of excess returns (intercept values) for the
two sets of TFM regressions and their statistical signifi cance. Approximately one-fourth
of the intercepts for the regressions by vintage and growth are signifi cant, indicated by the
crosshatched portion of the bar graphs. Figure 1b indicates that the majority of the excess
returns are positive.
26 Because the covariance between wine returns and equity market returns is near zero, these standard fi nancial
models explain only a small portion of the total variation in wine returns (that is, the R-squared values are low).
On the other hand, these same models have been found to generate similar R-squared values of 0.03 (CAPM) to
0.05 (Fama-French) for individual monthly stock returns (Bartholdy and Peare, 2005).
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66 Bordeaux Wine as a Financial Investment
Figure 1a
Excess Return by Vintage and Producer
Total of 276 Fama-French Regressions by Vintage and Producer
–2.5 or more
1.00: 1.4 9
2.5 or more
Excess Return
Number of Coefficients
Figure 1b
Excess Return by Vintage and Growth
Total of 83 Fama-French Regressions by Vintage and Growth
–2.5 or more
2.5 or more
Excess Return
Number of Coefficients
Figure 1a displays the distribution of the intercept values (excess returns) from the 276 TFM regressions by
vintage and producer. Figure 1b displays the distribution of the intercept values from the 83 TFM regressions by
vintage and growth. Statistically signifi cant excess returns are displayed in the crosshatched areas. The majority
of the statistically signifi cant excess returns are positive.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 67
Other regression results presented in Panel 1 of Table 3 provide evidence that wine
assets covary minimally with commonly accepted market risk factors and thus provide
a valuable source of diversifi cation. The results show that there exists either a negative
relationship between the wine asset returns and the Fama-French factors or an absence of
a relationship (β s not signifi cantly different from zero). For the 83 regressions based on
returns by vintage and growth, the risk premium coeffi cient returned a signifi cantly posi-
tive value in only six of the regressions. The remaining coeffi cients are either signifi cantly
negative (nine) or not signifi cantly different from zero (68), with an overall average of
0.006. Results are similar for the coeffi cients for SMB, found to be signifi cantly positive
in fi ve cases, signifi cantly negative in fi ve cases and not signifi cantly different from zero in
the remaining 73 regressions (average of 0.034). Results are similar for the HML coef-
cients , signifi cantly positive in only two cases, signifi cantly negative in eight cases and
essentially zero in the remaining 73 (average of 0.042).
Further evidence that there exists a weak relationship between wine returns and the mar-
ket risk factors is evident in the average R squared value of 0.09. The Fama-French factors,
which have been shown to account for approximately 90% of the variation in stock returns,
explain only 9% of the variation in wine returns on average. These results continue to hold
in the regressions by vintage and producer. A mere 10% of the risk premium coeffi cients
are signifi cant with the majority signifi cantly negative. With respect to the factor SMB, just
over 8% of the coeffi cients are signifi cant, and again, the majority is negative; 10% of the
HML coeffi cients are signifi cant and 87% of those are negative.
The CAPM regression results presented in Panel 2 of Table 3 are comparable to those of
the TFM. The average intercepts for both sets of regressions are positive, indicating excess
returns exist. The average intercept is 0.168 for the regressions by vintage and producer and
0.880 for those regressions by vintage and growth classifi cation. The average coeffi cients
on the market factor are both effectively zero (0.007 by vintage and growth and 0.005
by vintage and producer) supporting the previous fi nding of low exposure to market return
risk. Approximately 30% of the vintage and growth regression intercepts are signifi cantly
positive with the balance unable to reject the null hypothesis of α = 0. In both sets of regres-
sions (by vintage and growth and by vintage and producer) only 10% of the coeffi cients
on the risk premiums are signifi cant, indicated by the crosshatched portion of Figures 2a
and 2b.
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68 Bordeaux Wine as a Financial Investment
Figure 2a
Betas by Vintage and Producer
Total of 276 CAPM Regressions by Vintage and Producer
Beta Values
Number of Coeffients
Figure 2b
Betas by Vintage and Growth
Total of 83 CAPM Regressions by Vintage and Growth
Beta Values
Number of Coefficients
Figure 2a displays the distribution of the CAPM Beta values (market risk) from the 276 CAPM regressions by
vintage and producer. Figure 2b displays the distribution of the CAPM Beta values from the 83 CAPM regres-
sions by vintage and growth. Statistically signifi cant betas are displayed in the crosshatched areas.
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Lee W. Sanning, Sherrill Shaffer and Jo Marie Sharratt 69
VII. Conclusion
The research presented here provides new insights into the returns and risks associated with
wine as an investment vehicle. This study reveals two notable characteristics of investment
grade wine, both predicted by simple theoretical analysis. First, investment grade wine
assets provide, on average, positive returns in excess of those forecasted by well accepted
models that have been shown to explain much of the variation in average stock returns. This
result is consistent with previous fi ndings by Jaeger (1981), but contrasts strongly with
earlier fi ndings by Krasker (1979). Although our sample period is fairly short, it includes a
period of exceptionally high equity returns which provide a stringent test of excess returns
for alternative investments. Second, investment grade wine assets benefi t from low expo-
sure to market risk factors and, as a result, provide a valuable source of diversifi cation for
investors seeking hedge investments. This property has not been previously studied in the
case of wine.
Using a well-documented investment analysis tool, we show that wines on average pro-
vide large, positive excess returns. Specifi cally, using the Fama-French three-factor model,
we document average excess returns of more than 0.60 to 0.75% per month and 7.5 to
9.5% per year over returns predicted by factors shown to account for risk. Furthermore,
our results suggest that wines have very little exposure to common market risk factors and
have effectively zero betas. The simple conceptual framework that we used to predict these
two properties appears not to have been developed elsewhere in the context of collectible
investments. The same framework also predicts roughly zero real fi nancial returns to art-
work, consistent with many previous empirical studies. The theoretical analysis indicates
that the contrasting empirical fi ndings for art and wine represent rational patterns rather
than market anomalies.
Future research could usefully expand on this systematic distinction in at least two
ways. First, the investor’s decision regarding the optimal time at which to consume a par-
ticular bottle of wine is a dynamic programming problem under uncertainty, similar to the
problem of optimal harvesting and related problems that have been extensively studied
elsewhere. Applying dynamic analysis to the optimization of wine investment and con-
sumption decisions may yield further testable empirical predictions.27 Second, because
consumption reduces the remaining stock of any particular vintage, the possibility of stra-
tegic consumption arises as a way of driving up the price of remaining units. Again, incor-
porating this feature could generate additional testable implications.
Another interesting extension would be to estimate the optimal share of wine in a diversi-
ed investment portfolio, taking account of the empirical pattern of wine returns (specifi -
cally, their mean, variance, and correlation with equity returns). We defer this task to future
research for several reasons. First, the large number of wine varieties, vintages, and labels
raises the issue of the appropriate method for computing such a share: should it be calculated
27 The authors are grateful to Kirk Vandezande for raising this point.
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70 Bordeaux Wine as a Financial Investment
for each individual wine, or for some diversifi ed basket of wines? If the latter, what is the
optimal share of each wine within the wine basket? Either way, such calculations would be
a highly computationally intensive task. Second, the empirical results reported here raise
the possibility that such an optimal share might turn out to be so large as to be infeasible in
aggregate due to constraints in the supply of wine. That is, while a few small investors might
be able to implement the optimal strategy, any systematic attempt by large numbers of insti-
tutional investors to follow the same strategy might bid up the market price of wine far above
the levels observed under the current mix of investment and consumption objectives among
wine buyers, ultimately arbitraging away any risk-adjusted excess returns.
Previous research on wine investments has pointed out many negative aspects of such
investments. Investing in wine can be risky and the range of returns is quite wide. These
characteristics have not changed. However, this paper provides alternative research that sup-
ports the argument for investment in wine assets under current market conditions. Since a
hedging strategy is one that offsets or protects against risk, and since wine assets do not fl uc-
tuate according to market risk factors, investors committed to researching those wine assets
expected to deliver strong returns can construct a credible case for assembling a wine cellar.
And, if by some chance one of your wine assets declines substantially in economic value, you
can always exercise the implicit option to consume, and drink your Bordeaux.
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... In the aftermath of the global financial crisis, investors have become more interested in nontraditional assets that could possibly act as safe-havens (e.g., see Bredin et al., 2015;Low et al., 2016, andBredin et al., 2017, among others), or more precisely be uncorrelated with equities during stress periods (Bouri, 2015). In this regard, fine wine as an alternative investment instrument has recently been the subject of substantial financial media coverage, and more scholars have pointed toward its valuable role in equity portfolios (see, among others, Sanning et al., 2008;Masset and Henderson, 2010;Kourtis et al., 2012;Bouri, 2015;Bouri and Roubaud, 2016). In particular, the growth in the wine industry of 1474 percent since 1988 has outperformed equity, gold and real estate markets (Pruszynski, 2015). ...
... Numerous studies consider the relationships between fine wines and conventional assets, like equities and bonds, to highlight the diversification benefits of fine wine (Sanning et al., 2008;Masset and Henderson, 2010;Kourtis et al., 2012). Interestingly, very recent studies focus on the safe haven property of fine wines by examining the wine-equity correlation during stress periods using DCC-GARCH models (Bouri, 2015;Bouri and Roubaud, 2016), and provide some weak evidence of the safe-haven property of fine wine. ...
... These findings confirm the view that fine wines do not fluctuate according to market risk factors because they are affected, in addition to supply-demand imbalances, by unique factors such age effects, external quality ratings, climate conditions, and grape quality (Hadj et al., 2008). Using different data, methodology, and sample period, our findings corroborate with that reported by Sanning et al. (2008) who, using the CAPM and the Fama-French model, show that the fine wine market and market risk factors are uncorrelated. ...
Full-text available
Unlike the extant literature on safe-havens, where one aims to relate the movements in an asset considered with extreme episodes in equity markets, we test this property for fine wine, by relating it to global uncertainty. Using a nonparametric k-th order causality-in-quantiles test, we show that, while uncertainty does affect returns and/or variance of the alternative wine indices considered, this effect is restricted to only certain parts of the conditional distribution. In particular, wine seems to be unaffected by global uncertainty, and hence, acts as a safe-haven at extreme ends of the market, i.e., during bear or bullish times; but not during normal times (around the median of the conditional distribution of returns and/or volatility).
... It is crucial that wine be held in a secure facility with automated atmospheric conditions. Fine wine has outperformed almost every other major financial index over the past two decades according to many studies (Haeger & Storchmann, 2006;Sanning et al., 2008;Bouri et al., 2018). Fine wine investments can also provide diversification benefits, as they display a low correlation with traditional financial assets, and their returns become less volatile over time (Coffman & Nance, 2009). ...
... The results provided by the literature are mixed, however, when risk-adjusted returns are considered. Sanning et al. (2008) analyze the returns of Bordeaux wine and find an average monthly abnormal return equal to 0.75% due to low exposure to market risk factors. In contrast, Masset and Weisskopf (2015) indicate that wine fund managers fail to deliver positive risk-adjusted returns for investors, as their abilities to select securities and time the market seems to be restricted over the period 2000-2013. ...
... The majority of the studies indicate that wine returns lag behind the returns of equities Burton & Jacobsen, 2001;Haeger & Storchmann, 2006;Dimson et al., 2015). However, Sanning et al. (2008) find the returns of fine wine to outperform equities' returns. In parallel, the empirical findings show that the performance of wine prevails over bonds (Burton & Jacobsen, 2001;Haeger & Storchmann, 2006;Jaeger, 1981). ...
Motivated by the growing necessity of portfolio diversification, this paper investigates the dynamic connectedness among fine wine, equities, bonds, crude oil, commodities, gold, copper, shipping and real estate by applying the Diebold and Yilmaz (2012) approach, based on the time-varying parameter vector autoregressive (TVP-VAR) model of Antonakakis et al. (2020), for the period 1/1/2010-5/31/2021. Our results indicate moderate volatility spillovers among the markets over time, whereas total connectedness is prone to exogenous shocks, reaching its peak during stress periods. Equities, crude oil, gold and fine wine are the net contributors of spillovers, whereas real estate, commodities, copper, bonds and shipping constitute the net receivers of the diffused shocks. Furthermore, we estimate and compare the hedging ability of fine wine, before and after the emergence of the coronavirus pandemic, to instruct investors in rebalancing their portfolio strategies during COVID-19. The empirical findings suggest that fine wine can form an effective hedging tool to reduce the risk deriving from adverse movements of the markets and its hedging ability was enhanced during COVID-19, with few exceptions. Regardless time period, the highest hedging effectiveness can be achieved by taking a long position in the volatility of crude oil and a short position in the volatility of fine wine.
... With a wine-only portfolio, one hopes that wines have a higher rate of return than other investments (Burton and Jacobsen, 2001;Fogarty, 2006;Jaeger, 1981;Krasker, 1979;Jones and Storchmann, 2001), although not all studies have found long-term positive returns. More recently, Sanning, Shaffer, and Sharratt (2008) show that wine has exhibited large excess returns. However, any study conducted immediately prior to the 2011 wine market correction is likely to be overly optimistic. ...
... So, any return modestly greater than zero could be beneficial if uncorrelated or, better yet, anti-correlated to other instruments. Initial studies did find low or negative correlation to typical investment vehicles (Fogarty, 2010;Masset and Henderson, 2010;Masset and Weisskopf, 2018;Sanning, Shaffer, and Sharratt, 2008), but intrinsic return remains a question. ...
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Wine investment returns can come from overall market trends or price increases with age. Because of the short wine price histories available, market and maturation effects are difficult to separate. Consequently, researchers often obtain dramatically different estimates of investment returns. We find that data sample bias may be the hidden cause of the disparate estimates. In wine auction data, the sample bias refers to a shift in the distribution of which wines are traded as a function of their age. Such sample bias in panel data sampled across many different wine labels can distort the estimation of price increases versus age and consequently impact the estimation of market trends. This analysis shows that segmenting the analysis such that the data panels contain wine labels with similar trading characteristics can lead to a more stable estimation. The analysis here looks at data from Bordeaux, Italy, Australia, and California. An Age-Period-Cohort (APC) analysis is applied to data panels from each region. Then the data in each region is segmented by a measure of popularity in order to reduce sampling bias. Data thus segmented is then re-analyzed to demonstrate the difference in estimating price appreciation lifecycles and market trends.
... Moreover, investors do not hesitate to include fine wines in their portfolio as diversification assets by considering both their advantageous risk-return profile and their low exposure to financial risk (Sanning et al., 2008;Masset & Henderson, 2010;Chu, 2014;Le Fur et al., 2016a, b;Faye et al., 2015;Masset et al., 2016). Other empirical studies show that fine wine can be considered a riskless asset and may perform better than other collectible assets (Dimson et al., 2015;Ben Ameur & Le Fur, 2020). ...
This study analyzes the efficiency hypothesis for three major wine indices using different parametric and non‐parametric tests for the short and long‐term horizons. Based on monthly data over the period 2003–2019, our analysis identifies two main findings. First, in the short term, there is market inefficiency allowing for investment and diversification opportunities and a need for greater market transparency. Second, the spread between wine markets and financial markets can disappear in the long term, that is, there is a long‐run relationship that is relatively compatible with efficiency, except for the Bordeaux Legends 50 index. This implies greater efficiency and therefore better information and fewer investment and diversification opportunities.
... Wine can be viewed as an investment asset encouraging a strong motivation to purchase for special occasions, gifts, investments or to consume immediately (Dimson et al., 2015). To stay financially sustainable, wineries often diversify their portfolios to reduce risk (Sanning et al., 2008). This was made more evident when circumstances beyond wine stakeholders control occurred, namely a global pandemic encouraged innovation and wine sales diversification to mitigate risk (Samitas et al., 2022). ...
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This exploratory study identifies the economic benefits for wine regions, but specifically where wine stakeholders invest in their own event portfolio creating fun and social occasions. There is little empirical research about the utility of an event portfolio in regional areas and even less in relation to their application to Victorian wine events making the study innovative in nature. It is the first attempt to represent the viewpoint of wine stakeholders. A qualitative geographical bounded case study approach was applied to explore the rational and benefits of developing a wine event portfolio in two Victorian wine regions from a wine stakeholders' perspective. The findings suggest that while destination marketing organizations were familiar with the term 'event portfolio' the concept was quite new for event organizers at individual wineries. Nonetheless, they were planning for multiple events across the year and aimed to capture a range of benefits by holding more than one annual event. These benefits for wine stakeholders' representing individual wineries are an increase in wine sales, diverse demographics of winery visitors and repeat visitation throughout the calendar year. This is despite the increase in financial risk and time commitments by wine stakeholders' hosting wine events at their winery. The paper concludes by suggesting the future research should focus on other viewpoints and replicating this study in other wine regions highlighting the importance and greater benefits of investing in regional development focusing on wine event tourism.
... Consequently, most studies use ask price data [Combris et al. (2000) for Burgundy; Masset et al. (2015) for Bordeaux en primeur wines; Cardebat and Figuet (2009) for Alsace, Beaujolais and Provence; and Benfratello et al. (2009) for Piedmont]. The few studies that employ market price data focus on Bordeaux wines and rely on auction data [Sanning et al. (2008), Masset et al. (2016)]. Interestingly, however, even the seminal study of Dimson et al. (2015) relies on a mix of auction hammer prices and ask prices from Berry Bros. and Rudd, an important London wine merchant. ...
Purpose This study aims to identify the price determinants of fine wines in a small and competitive market. These characteristics are found in many lesser-known wine-producing countries and are often difficult to analyse because of lack of data. Design/methodology/approach This study hand-collects and transcribes wine-related data for 149 Swiss wineries and 2,454 individual wines over the period 2014–2018 directly from wine lists provided by wineries. This study uses multivariate ordinary least squares regressions to analyse the relation between wine attributes and prices and to assess the effect of a currency shock caused by the sudden appreciation of the Swiss franc in 2015 as well as a reduction in information asymmetries induced by the novel coverage of Swiss wines by The Wine Advocate. Findings Prices mainly depend on collective reputation, production techniques and product positioning. Surprisingly, following a sharp appreciation of the Swiss franc, producers did not reduce prices. The arrival of a highly influential wine expert on the market also had a positive price effect on rated wines and producers. Both hint at wineries attempting to position themselves relative to competitors. Originality/value Few studies examine the price drivers in lesser-known wine markets, where competition is fierce. This study’s results show that wine pricing differs from other more famous and larger wine regions. In addition, to the best of the authors’ knowledge, this study is also the first to analyse the impact of a currency shock and a reduction in information asymmetries on wine prices.
... For instance, the seminal work by Krasker (1979) and Jaeger (1981) explored how stored wines perform compared to riskless assets such as treasury bonds. Further studies have highlighted the diversification benefits of fine wines in investors' portfolios, both in terms of return expectations and risk reduction (e.g., Sanning et al., 2008;Kourtis et al., 2012;Chu, 2014;Le Fur et al., 2016;Faye et al., 2015;Aytac and Mandou, 2016). Indeed, most empirical studies provide evidence that fine wine can be considered as a riskless asset, and may perform better than other collectibles assets (Ben Ameur & Le Fur, 2020;Dimson et al., 2015). ...
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This study investigates the impact of both economic policy uncertainty (EPU) and business cycles on the fine wine market. We use a nonlinear autoregressive distributed lag model to measure the influence of these two variables on three major Liv-ex indices over the period 2005M01–2020M12. Our results are multiple. First, fine wine prices are relatively unaffected asymmetrically by EPU, while the economic cycle has a more pronounced asymmetric effect, especially in the short run. Second, uncertainty in Europe and the USA affect fine wine prices more than in China. Third, in the short term, fine wine prices react more strongly to changes in business cycles than to uncertainty. Finally, prices of the five first growths of Bordeaux are asymmetrically influenced by EPU, unlike of the rest of the most prestigious Bordeaux wines. The study also has implications for investment. We argue that a strong and professional strategic intelligence watch would help stakeholders in the secondary wine market to improve their returns, especially when European and US wines are involved. While short-runners should focus on information relative to changes in the business cycle, long-term investors would find it more interesting to closely monitor policy decisions liable to have long-term effects on wine prices (such as taxation, monetary measures…).
This paper investigates digital object in video games as an alternative investment during the period 2015 to 2020. We use the Steam Community Market of the Counter-Strike Global Offensive game publisher. The database contains 19,717 observations divided into knives, machine guns, machine pistols, pistols, rifles, sniper rifles, and stickers. From the hedonic approach based on implicit prices, indices for each category of Skin are constructed as well as an overall index. The results show a sharp increase of the overall index from the beginning of 2019 up to the end of 2020 essentially explained by the transition of the game from pay-to-play to Free-to-play in December 2018. The stickers index is very high due to the scarcity of supply of some stickers and the use of specific periods. The machine guns index highlights a high increase coupled with the highest risk. The knives index presents the lowest growth. Low correlations between the overall index and gold and art markets indicate that there are potential gains of portfolio diversification. Implications of these findings for individual and professional investors, gamblers, and game publishers are discussed.
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This paper examines the risk and returns of classic car price indices over the 1994–2021 period. We calculate the central tendency, dispersion, shape of risk and returns, the unit root tests, and correlations. The results indicate a moderated volatility, a low range of returns, and a weak expectation of financial gain given the ancillary costs related to the auction, transport, insurance, guarding, maintenance, and restoration. There are low correlations among the classic car markets. These results provide a better understanding of the risk and returns of the classic car market for many actors such as individual and professional investors, collectors, and wealth managers. Investing in a classic car is more of a passion and emotional investment than a simple desire for financial gain.
This paper proposes a price index for modern and contemporary paintings based on estimates and auction prices. We use this index for the evaluation of the Italian art market and for comparisons with returns on other assets. During the period 1983-1994 art prices increased in line with inflation but returns on paintings were lower than returns on financial assets. In the long run art prices are unrelated to financial assets prices, but a positive correlation with real estate prices emerges.
ABSTRACT Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market {3, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in {3 that is unrelated to size, the relation between market {3 and average return is flat, even when {3 is the only explanatory variable. THE ASSET-PRICING MODEL OF Sharpe (1964), Lintner (1965), and Black (1972)
RESUME : Le but de ce papier est d'examiner comment le climat affecte la qualité des vins produits dans les zones du Barolo et du Barbaresco en Italie. Comme des données des prix ne sont pas largement disponibles pour ces vins, nous mesurons la qualité des millésimes à partir des jugements subjectifs de qualité de plusieurs experts. Ces mesures de qualité sont seulement ordinales; nous estimons donc leurs déterminants en utilisant une fonction probit ordinale, qui inclut de multiples indicateurs de la qualité et une structure commune sous-jacente de comment le climat affecte la qualité. La méthode fournit des mesures des déterminants de la qualité des jugements sur la qualité des millésimes et des suggestion sur la fiabilité de chaque expert. ABSTRACT : In this paper we examine how the weather determines the quality of wines produced in the Barolo and Barbaresco regions of Italy. Since pricing data are not widely available for these wines, we measure wine vintage quality by a variety of subjective measures of quality taken from the published opinions of several experts. Since these measures of quality are only ordinal, we estimate their determinants using an ordered probit function that incorporates multiple indicators of quality and a common underlying structure of how the weather determines quality. The method provides measures of the determinants of vintage quality ratings and suggestions on the reliability of each expert.
We investigate the relation between dividend changes and future profitability, measured in terms of either future earnings or future abnormal earnings. Supporting “the information content of dividends hypothesis,” we find that dividend changes provide information about the level of profitability in subsequent years, incremental to market and accounting data. We also document that dividend changes are positively related to earnings changes in each of the two years after the dividend change.
This paper identifies five common risk factors in the returns on stocks and bonds. There are three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity. There are two bond-market factors, related to maturity and default risks. Stock returns have shared variation due to the stock-market factors, and they are linked to bond returns through shared variation in the bond-market factors. Except for low-grade corporates, the bond-market factors capture the common variation in bond returns. Most important, the five factors seem to explain average returns on stocks and bonds.
We show that Baumol's conclusion that returns on bonds are higher than returns on paintings is too pessimistic. There are segments in the market for which returns are significantly higher than returns on bonds and stocks, during long periods of time (20 to 40 years); since tastes do change slowly (though not in a predictable way), this may imply that beating the market is not impossible. We also construct price indexes for paintings over the last 200 years.