Commodity Market Capital Flow and Asset Return Yogo.pdf · Commodity Market Capital Flow and Asset Return ... To formalize our observation, we regress the monthly excess returns on a portfolio of commodity futures onto lagged 12-month open-interest growth. ... commodity-speciﬁcpredictorssuchasaggregatebasis(i.e.,theratiooffuturestospotprice averaged
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Commodity Market Capital Flow and Asset Return
Harrison Hong Motohiro Yogo
February 28, 2010
We establish several new findings on the relation between capital flow in commod-ity markets and asset returns. Capital flowing into commodity markets, as measuredby high open-interest growth, predicts high commodity returns and low bond returns.Open-interest growth is a more powerful and robust predictor of commodity returnsthan other known predictors such as the short rate, the yield spread, the basis, andhedging pressure. It is positively correlated with commodity returns but has informa-tion for future returns beyond that contained in past commodity prices. Open-interestgrowth also predicts changes in inflation and inflation expectations. These findingssuggest that open-interest growth contains information about future inflation that getspriced into commodity and bond markets with delay. Our findings are consistent withrecent theories of gradual information diffusion and have implications for macroeco-nomic forecasting models.
This paper subsumes our earlier work titled Digging into Commodities. For comments and discussions,we thank Erkko Etula, Hong Liu, David Robinson, Nikolai Roussanov, Allan Timmermann, and seminarparticipants at Boston College, Centre de Recherche en Economie et Statistique, Dartmouth College, Ford-ham University, PanAgora Asset Management, Stockholm School of Economics, University of California SanDiego, University of Pennsylvania, University of Southern California, Washington University in St. Louis,the 2008 Economic Research Initiatives at Duke Conference on Identification Issues in Economics, and the2010 Annual Meeting of the American Finance Association. We thank Jennifer Kwok, Hui Fang, YupengLiu, James Luo, Thien Nguyen, and Elizabeth So for research assistance. Hong acknowledges a grant fromthe National Science Foundation. Yogo acknowledges a grant from the Rodney L. White Center for FinancialResearch at the University of Pennsylvania.
Princeton University and NBER (e-mail: firstname.lastname@example.org)University of Pennsylvania and NBER (e-mail: email@example.com)
We analyze how capital flow in commodity markets is related to commodity and bond re-
turns. Our analysis is motivated by the recent volatility in commodity prices and the renewed
interest in the behavior of these markets, which have not been seen since the energy crisis of
the 1970s. Once largely ignored by the investment community, commodities have emerged as
an important asset class. By some estimates, index investment in this asset class increased
from $13 billion at the end of 2003 to $317 billion in July 2008, just prior to the financial
crisis (Masters and White, 2008). During the same period, the influx of new investors led to
elevated levels of capital flow as measured by open interest in commodity futures, which grew
from $103 billion to $509 billion. This capital flow has led to inquiries about how trading
affects asset price formation in these markets, underscored by the recent Congressional hear-
ings on the impact of excessive speculation. Hence, understanding the link between capital
flow and commodity price fluctuations is important not only for investors but also for public
Our analysis covers 30 commodities across four sectors (agriculture, energy, livestock,
and metals) over the period of 1965 through 2008. Using hand-collected data on open inter-
est from the Commitments of Traders since 1965, we establish several new findings on the
relation between capital flow and returns in commodity markets. Figure 1 summarizes our
main finding. The first series is the percentage change in open interest over the previous
12 months, averaged across all commodities. The second series is the return on fully collat-
eralized commodity futures over the previous 12 months, averaged across all commodities.
During the recent commodity boom from 2003 to 2008, capital flowed into the sector at a
persistently high rate, more so than in any other period over the previous thirty years. Only
the energy crisis of the 1970s witnessed higher activity. During these two historic periods
and also more generally, open-interest growth and commodity returns are highly correlated.
But the most interesting finding in this plot is that open-interest growth seems to lead com-
modity returns. In other words, capital flowing into commodity markets appears to predict
subsequent appreciation of commodity prices.
To formalize our observation, we regress the monthly excess returns on a portfolio of
commodity futures onto lagged 12-month open-interest growth. We find that a standard
deviation increase in open-interest growth increases expected commodity returns by 0.64%
per month. Similarly, we find that a standard deviation increase in open-interest growth
increases expected spot-price growth by 0.41% per month. Both of these estimates are
economically large and statistically significant. Open-interest growth is a more powerful and
robust predictor than a number of other variables that are known to predict commodity
returns. These include common predictors such as the short rate and the yield spread and
commodity-specific predictors such as aggregate basis (i.e., the ratio of futures to spot price
averaged across commodities) and aggregate hedging pressure (i.e., the net short position
of hedgers averaged across commodities).1 Open-interest growth is a more robust predictor
than these other variables in two important ways. First, aggregate open-interest growth
predicts returns on sector portfolios, in contrast to other variables that predict returns for
only particular sectors. Second, open-interest growth is the only variable that continues
to demonstrate forecasting power in the most recent period since 1987, when there are the
greatest number of commodities in the database.
Open-interest growth is most closely related to 12-month commodity returns. We find
that past aggregate commodity returns forecast the subsequent months return. In other
words, there is momentum in the time series of aggregate commodity returns. However, in a
horse race between these variables, open-interest growth entirely drives out the forecasting
power of past commodity returns. This means that open-interest growth contains informa-
tion about future returns that is not fully captured by past commodity prices. A potential
1Bessembinder and Chan (1992) are the first to establish that the same variables that predict bond andstock returns (such as the short rate, the default spread, and the dividend yield) also predict commodityreturns. There is mixed evidence that basis predicts returns on commodity futures. Fama and French (1987)are the first to establish that basis predicts returns for some commodities. They emphasize that there ismore consistent evidence for the theory of storage. A number of other studies have documented mixedevidence for the theory of backwardation, controlling for systematic risk and using an empirical proxy forhedging pressure (Carter, Rausser, and Schmitz, 1983; Chang, 1985; Bessembinder, 1992; de Roon, Nijman,and Veld, 2000).
interpretation of these findings is that capital flows into commodity markets in response to
news, which get impounded into commodity prices with delay. In the 1970s, for example,
there were news about supply shocks to oil. In the most recent period, there were news
about strong demand for commodities from the emerging economies.
To test this hypothesis, we examine whether open-interest growth predicts inflation and
excess bond returns. Consistent with the hypothesis, we find that high open-interest growth
predicts rising inflation and also a rising nominal short rate. In addition, high open-interest
growth predicts low bond returns with a t-statistic over 3. A standard deviation increase in
open-interest growth decreases expected bond returns by 0.32% per month. Open-interest
growth is the only predictor that survives in the most recent period since 1987, when the
short rate and the yield spread fail to predict bond returns. Hence, open-interest growth
not only contains powerful information about future commodity returns, but also important
information about future bond returns.
To summarize, our novel finding is that capital flow into the commodity sector contains
information about inflation news and bond returns that is not fully captured by commodity
prices. As we discuss in the body, our findings are most consistent with recent theories
of gradual information diffusion in asset markets (see Hong and Stein, 2007, for a review).
These theories suggest that when market prices under-react to news, trading activity emerges
as a useful additional predictor of future returns. Moreover, the fact that capital flow can be
useful for predicting economic activity like inflation expectations has important implications
for macroeconomic forecasting models.
Our work is part of a second generation of commodity papers that have recently emerged
in response to renewed interest in commodity markets. In a pioneering study that lays
out the agenda, Gorton and Rouwenhorst (2006) emphasize that commodities have a high
Sharpe ratio and a low correlation with other asset classes. They argue that this evidence
is consistent with the theory of backwardation in particular and market segmentation more
generally. Acharya, Lochstoer, and Ramadorai (2009) find that producers hedging demand,
as captured by their default risk, predicts commodity returns. Etula (2009) finds that the
supply of speculator capital, as captured by changes in broker-dealer balance sheets, predicts
commodity returns, especially in energy. Relative to these studies, we share the view that
market segmentation is a key driver of predictability in commodity markets. However, our
focus on the implications of gradual information diffusion is quite different from these other
studies that focus on limited risk-bearing capacity in commodity markets. More closely
related is a group of studies that document momentum in the cross section of commodity
returns (Erb and Harvey, 2006; Gorton, Hayashi, and Rouwenhorst, 2007; Miffre and Rallis,
2007; Asness, Moskowitz, and Pedersen, 2009). We find momentum in the time series of
aggregate commodity returns, which interacts with capital flow into commodity markets.
The rest of the paper proceeds as follows. Section 2 describes the commodity market data
and the construction of the key variables used in our empirical analysis. Section 3 reports
summary statistics for commodity returns, spot-price growth, and the predictor variables.
Section 4 presents our main finding that open-interest growth predicts commodity returns.
We also present evidence that open-interest growth predicts inflation news and bond returns
to illuminate the economic mechanism behind our findings. Section 5 concludes.
2. Commodity Market Data and Definitions
2.1. Commodity Market Data
Our data on commodity prices are from the Commodity Research Bureau, which has daily
prices for individual futures contracts as well as spot prices for many commodities beginning
in December 1964. Gorton and Rouwenhorst (2006) also use this database, and additional
details can be found in the appendix to their paper. As they point out, the database mostly
contains data for contracts that have survived until the present or that were in existence for
an extended period between 1965 and the present. Many different types of contracts fail to
survive because of lack of interest from market participants, and they are consequently not
recorded in the database. Consequently, the computed returns on commodity futures may
be subject to survivorship bias.
Following Gorton and Rouwenhorst (2006), we work with a broad set of commodities
contained in the database. Table 1 is a list all our commodities, together with the date
of the first recorded futures price for each commodity. We categorize commodities into
four broad sectors. Agriculture consists of 15 commodities and tends to contain the oldest
contracts. Energy consists of five commodities. Heating oil is the oldest contract in energy,
which starts in November 1978. Data for crude oil are available only since March 1983.
Livestock consists of five commodities, and metals consists of six commodities. A potential
concern with using a broad set of commodities is that not all contracts are liquid. In results
that are not reported here, we have confirmed our main findings on a subset of 17 relatively
liquid commodities that are in the AIG Commodities Index.
Following Gorton and Rouwenhorst (2006), we exclude futures contracts with one month
or less to maturity. These contracts are typically illiquid because futures traders do not
want to take delivery of the underlying physical commodity. We therefore rule out investment
strategies that require holding futures contracts to maturity. While Gorton and Rouwenhorst
(2006) isolate the contract that is closest to maturity for each commodity, we include all
contracts with more than one month to maturity.
We also use data on open interest (i.e., the number of futures contracts outstanding)
as well as the long and short positions of noncommercial traders (or hedgers) for each
commodity. Since January 1986, the data are available electronically from the Commod-
ity Futures Trading Commission. Prior to that date, we hand-collected data from various
volumes of the Commitments of Traders in Commodity Futures. Data for December 1964
through June 1972 are from the Commodity Exchange Authority (19641972). Data for
July 1972 through December 1985 are from the Commodity Futures Trading Commission
(19721985). There is a 11 month gap from January through November of 1982, during
which the Commodity Futures Trading Commission did not collect data due to budgetary
Figure 2 shows the share of total dollar open interest that each sector represents. The
figure shows that agriculture dominates the early part of the sample, while energy becomes
the biggest sector later in the sample. The relative size of the four sectors is much more
balanced in the second half of the sample starting in 1987. These stylized facts have two im-
portant implications for our empirical analysis. First, we construct the aggregate commodity
portfolio as an equal-weighted portfolio of the four sectors, which ensures that the portfolio
composition is consistent throughout the sample. Second, we examine the predictability of
commodity returns by sector and in subsamples to check the robustness of our main results.
The subsample since 1987 is perhaps more representative of what we can expect from com-
modity markets going forward because it has a more balanced representation across the four
2.2. Aggregate Commodity Returns
To construct aggregate commodity returns, we first compute the return on a fully collater-
alized position in commodity futures as follows. Let Rf,t be the monthly gross return...