Who Influences the Fundamental Value ofCommodity Futures in Japan?

Kentaro Iwatsubo
Clinton Watkins
December 2018
Discussion Paper No.1830

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANWho Influences the Fundamental Value ofCommodity Futures in Japan??
Kentaro Iwatsubo∗, Clinton Watkins
Graduate School of Economics, Kobe University
December 2018

Abstract
We present evidence on asymmetric information content in the trades of six investor groups
transacting in the gold, platinum, gasoline and rubber futures markets on the Tokyo Commodity
Exchange. Microstructure theory suggests that traders with greater information on the efficient
price should be more profitable in the long run. Foreign investors have the greatest influence
over the efficient price in the gold market, investment funds in the platinum market and retailinvestors in the gasoline market. Both trade and non-trade related innovations have an equal
influence on the efficient price of rubber, with trades by investment funds having the largest
information content in this market. We relate differences in the relative influence of investor
groups to differences in market interconnectedness, the nature of the commodity and associatedfundamental information.
Keywords: Commodities, Futures, Market microstructure, Asymmetric information, Investor
behaviour
JEL: C22, G14, G15, Q02

1. Introduction
Financial market analysts and investors routinely attempt to identify the market positioningof “smart money” traders, those who are believed to possess valuable private information on
the future course of securities prices and trade profitably. The market positioning of such smart
money traders is believed to reveal their return expectations. Mimicking the market positions of
the smart money is also expected to be a profitable trading strategy if implemented efficiently?

The authors would like to thank Mr. Hiroshi Yamaoka of TOCOM for providing us with the transaction data
and the participants at the IAS Focused Program on Time Series Analysis of Higher Moments and Distributions ofFinancial Data, Hong Kong University of Science and Technology for helpful comments and discussions.
∗
Corresponding author
Email address: iwatsubo@econ.kobe-u.ac.jp (Kentaro Iwatsubo)

enough with respect to trading costs and time. On the other hand sophisticated investors often
want to invest against “dumb money” traders with the expectation that such traders will beforced to close out of their positions, resulting in a gain for those betting against the dumbmoney positions.
Who are the smart money investors that trade on private fundamental information on the
future path of a security’s prices? Central issues in the market microstructure literature include
the means by which information enters prices and which traders are informed. Those investors
who are informed possess private information and can influence the “efficient price” of financial
securities. Their trades have a lasting impact on a security’s price as they incorporate new
private information into the price, and accordingly their investing activities are expected to be
profitable in the long run. Uninformed investors, such as liquidity traders and noise traders, do
not have a lasting impact on market prices as their trades convey no information. The influenceof uninformed investor trades on market prices may be reversed over time, and accordingly
such investors are not expected to be profitable over the long run. As a caveat to this, liquidity
providers’ trades may be profitable over the long run if the liquidity premium they earn issufficient. Noise traders are not expected to be long-run profitable.
Hasbrouck (1991a,b) models the interaction between trades and price revisions as a bivariate structural vector autoregression (VAR) to analyse the influence of order flow on a security’s
efficient price. The approach provides a method to isolate the variance of innovations to the nonstationary permanent component of price, which is considered as the efficient price, and the stationary temporary price component. The variance of the efficient price can be decomposed into
the proportions attributable to trade-related and non-trade-related information. Trade-related
information may be interpreted as private information contained in the unexpected trades ofinvestors, and non-trade-related information as public information that hits the market. Thus
private information is presumed to enter the price of a security through the trading activity of
informed investors.
In this study, we adapt Hasbrouck’s approach to identify information asymmetry between
six investor groups transacting in the gold, platinum, gasoline and rubber futures markets on
the Tokyo Commodity Exchange (TOCOM). Using a multivariate structural VAR, we isolate
the influence of each investor group’s trades on the efficient price in each commodity market
which reveals the extent to which the trades of that group are informed. Investor groups having
a substantial influence on the efficient price in market are expected to have superior investment2performance over the long run. Previous analyses use VAR models that cannot identify the
profitability of trader groups as they do not distinguish permanent versus temporary influencesof trades on price.
Different types of investor have different motivations for trading. For instance, hedgers
seek to offset risk while speculators attempt to capitalise on taking appropriately compensatedrisk. Investor heterogeneity in this sense allows financial markets to perform their essential risk
management functions. Investors with similar attributes, such as their motivation for taking or
reducing risk (speculator or hedger), level of sophistication (retail or institutional), their role infacilitating markets (broker or market maker), or domicile (domestic or foreign), may exhibita degree of homogeneity in trading. We take advantage of investor classifications recently
introduced by TOCOM to present new evidence on the information content of the trades ofdifferent investor groups trading in commodity futures markets.
We shed light on why differences in the relative influence of investor groups in may be
due to differences in market interconnectedness, the nature of the commodity and associatedfundamental information. Exchange-traded commodity futures are typically thought of as standardised internationally traded goods with prices that conform to the law of one price. However,there are differences between futures markets for a given commodity in terms of regulation, contract specification, liquidity and the type of market participants active on different exchanges.
This implies differences in the centralisation of price discovery, the relationship between supply
and demand fundamentals, and the importance of global or region specific economic and financial variables for prices. Commodities have different geographical centres of consumption andproduction. A popular assumption is that domestic equity investors possess more information
on locally listed firms, but it is less clear in commodities which investor groups should be better
informed.
We select the gold, platinum, gasoline and rubber markets for analysis as they provide aninteresting comparison. TOCOM futures contracts for these commodities have differentiatedinternational connectedness vis a vis contracts for similar underlying commodities on futures
exchanges in other countries. The underlying commodities have differing degrees of internationalisation in demand and supply, storage and standardisation. This would suggest different cross-market information and empirical linkages. TOCOM is a relatively small venue forgold trading, while gold futures trading internationally is homogeneous. This contrasts withplatinum, for which TOCOM is a globally important trading venue, again for a homogeneous3

commodity. The Tokyo market for gasoline trades a grade of fuel conforming to a domestic
standard, and while it’s price is related to international oil prices, domestic demand and supplyof the fuel is an important factor. TOCOM is an internationally important venue for the rubber
trade, however the underlying commodity is one of several grades of rubber traded in Asia.
Comparison of commodities with distinct market characteristics allows us to examine whether
different types of investors influence the prices for futures where the underlying commodities
have different cross-market linkages. Further, despite TOCOM’s importance both internationally and domestically in commodities, relatively few studies have been published examining the
microstructure of markets on this exchange.
We find that the efficient price in the gold, platinum and gasoline markets is mainly influenced by trade-related innovations. Foreign investors have the greatest influence over theefficient price in the gold market, investment funds in the platinum market and retail investors
in the gasoline market. Both trade and non-trade related innovations have an equal influenceon the efficient price of rubber, with trades by investment funds having the largest information
content in this market. We explore why these results are obtained.
The article proceeds as follows. We discuss relevant literature in Section 2, our data in
Section 3 and explain our model in Section 4. Section 5 presents our empirical results and theseare discussed in Section 6. Section 7 concludes.
2. Literature Review
Market participants, regulators and researchers are interested in whether certain types ofinvestors influence prices. An important constraint on empirical work is the lack of suitabletransaction level data required to investigate the influence of trades (Dewally et al., 2013). The
variety of approaches to, and forms of the data available for, answering questions on the influence of investors in various financial markets means these questions have not been definitively
answered.
We examine salient evidence from equity and commodity markets, briefly surveying theliterature showing i) foreign investors at an informational disadvantage to domestic investors, ii)
foreign investors with a relatively small market impact, iii) foreign investors at an informational
advantage, and iv) that in commodity futures some speculative investors are profitable, while
hedgers tend to trade at a loss. To our knowledge, there is no published research comparing the
profitability of different types of investors in commodity futures markets from the perspective of4

whether their trades influence the efficient price. Our paper adds to the literature by developing
this area.
The information disadvantage of foreign to resident investors is frequently cited as a potential justification for home bias (Karolyi and Stulz, 2003). Kang and Stulz (1997) show that
foreign investors hold more stocks of large firms than small firms in Japan, and suggest thismay indicate foreign investors have a greater information disadvantage in trading small stocks.
Foreign investors may be at a disadvantage due to their distance from a firms’ headquarters(Coval and Moskowitz, 1999, 2001) or language difference (Grinblatt and Keloharju, 2001). In
an intraday study of Korean stock market trade and price data, Choe et al. (2005) find foreign
investment managers pay more than their domestic peers when they buy stocks, and receive
less when they sell for medium and large trades. The market moves more against foreign investors before they trade than it does against other investor groups. This may be evidence that
foreign investors follow intraday return-chasing strategies because they are at an information
disadvantage to domestic investors. Korean retail investors have an edge over foreign investors.
Domestic investors earn higher profits than foreign investors on the Jakarta Stock Exchange
according to Dvorak’s (2005) analysis of transaction data. Clients of global brokerages have an
information advantage trades longer than one month, while those of domestic brokerages havean advantage over shorter horizons. However, domestic clients of global brokerages have higher
profits than foreign clients. Lakonishok et al. (1992) argue that institutional investors trading isneither as stabilising nor destabilising. In general, the US tax-exempt institutional investors intheir sample pursued a wide variety of investment strategies, the effects of which tend to cancel
out. Institutional investors do not herd in large cap stocks, while they found some evidence ofherding in small cap stocks.
Choe et al. (1999) find that before the Korean crisis of 1997, foreign investors engage inpositive feedback trading and herd. However during the crisis, evidence of positive feedback
trading is much weaker and they find no convincing evidence to suggest foreign investors destabilised the Korean equity market. Rather the market impact of domestic retail investors outweighed that of foreigners. Inspired by Choe et al. (1999), Yang (2017) examines whether
foreign investors destabilise the Korean equity market during the 2008 global financial crisis.
Foreign ownership levels and transaction volumes were far higher in 2008 than 1997 due in partto the relaxation of restrictions on foreign investors in 1998. Sales by foreign investors during
the crisis were not followed by significantly negative returns over the next 25 minutes of trade.
5

Korean individual investors had a greater price impact than foreign investors. However, theprice impact per trade of foreigners increased sharply during the crisis. Using a VAR model offoreign equity inflows and returns for six Asian emerging equity markets, Richards (2005) findsthat foreign flow surprises influence returns for a few days beyond the day of the inflow. Foreign equity returns are also included in the model and are shown to be as important as domestic
returns in accounting for the variance in flows suggesting that “push” factors are important.
Several studies provide evidence that foreign investors are more informed or profitable
than domestic investors. Seasholes (2000) shows that foreign investors time Taiwanese firm’s
earnings announcements well, and Grinblatt and Keloharju (2000) provides evidence from the
Finnish equity market that foreigners are better stock pickers than domestic investors. Usinga trivariate VAR model consisting of NIKKEI equity index returns, USD/JPY currency returns
and net equity purchases for a weekly sample from 1995 to 2001, Karolyi (2002) find that
foreign investors follow a positive feedback strategy. A positive shock to equity returns is associated with a positive response in net foreign purchases, which decays within about one month.
Domestic companies and banks followed a negative feedback strategy. Foreign investors generated positive investment performance and appeared to be good market timers, while the performance of domestic investors was negative, with financial companies being the worst. Kamesaka
et al. (2003) examined the trading patterns of seven investor groups on the Tokyo Stock Exchange from 1980 to 1997 using a VAR model of net investment flow and TOPIX returns, andGranger causality to identify market timing trading patterns. Foreign investors and Japaneseretail investors followed positive feedback strategies. Foreigners were profitable while retailtraders were not, consistent with foreigners trading on information and retail investors trading
according to a behavioural model. Securities firms and banks engaged in negative feedback
strategies that were profitable.
Recent studies of commodities futures in the United States use non-aggregated transactionlevel data to infer whether the trading activities of certain types of investors are informed or
profitable. Fishe and Smith (2012) use daily positions of traders in twelve commodities to
identify who is informed on an intraday and overnight basis. Brokers are over-represented in
the overnight informed group, and funds in the intraday informed group. Commercial hedgers
are under-represented both overnight and intraday. Informed investors are likely to have greater
trading experience, activity, position size, as well as trade on both sides of the market. In
the crude oil, gasoline and heating oil markets, Dewally et al. (2013) show that likely hedgers
6trade at a loss while likely speculators make profits. At the aggregate level, speculator profits
are generated from absorbing risk from, or providing liquidity to, hedgers. However at theindividual level, some investors have an informational or trading skill advantage. de Roonet al. (2000) show that commercial hedgers bring price pressure to futures markets when they
adjusted their positions.
3. TOCOM Commodity Futures Data
In the following sections we explain the characteristics of the commodities selected foranalysis, define the investor groups and discuss our data and variables. The four commodities we select differ in terms of the level of centralisation of trading in the commodity and its
derivatives, and relationships with similar commodities that are traded on exchanges outside
Japan. Further, different types of economic and financial variables from different geographic
locations are relevant to fundamental information on commodity demand and supply expected
to be incorporated in the efficient prices. This provides an interesting comparison regarding
the investor groups that influence the efficient price, and the extent to which trade-related andnon-trade related information influence the efficient price.
3.1. Futures Markets
Gold futures and other derivatives are traded on several exchanges around the world. Hauptfleisch et al. (2016) note that the trade in gold is largely decentralised. Examining whether the
price of gold futures is set by London or New York, they find that although both markets contribute to price discovery, the New York futures play a larger role on average despite the New
York exchange turnover being less than 10 percent of that of the London exchange. TOCOM
plays a relatively minor role in the market for gold. For instance, gold futures trade in Tokyo
represented approximately 6 percent and 5 percent of the volume of trade on the Commodity
Exchange of New York (COMEX) by weight of metal in 2015 and 2016, respectively. Luceyet al. (2014) find that the London and New York markets for gold dominate returns and volatility
and affect trade in Tokyo, but there is no significant and consistent effect from Tokyo to othermarkets. Investment and jewellery make up the bulk of Gold demand, although the metal is also
used in industrial processes (O’Connor et al., 2015). As primarily a financial asset wih massive
stocks in comparison to annual physical production and consumption, it is difficult to identify
non-financial sources of fundamental information on the gold price. There do not appear to be7

compelling reasons to suspect that Japanese domestic investors have a substantial informational
advantage over foreign investors in the gold market.
In contrast, trade in platinum futures is relatively concentrated and centred on TOCOM
and the New York Mercantile Exchange (NYMEX). Tokyo’s importance in the global platinum
market stems from the use of platinum in the the automotive industry. Over 40 percent of
global platinum production is used in automotive catalytic converters (McDonald and Hunt,
1982; Eller and Sagerer, 2011). Japanese firms trading in physical platinum for the automotive
industry have traditionally used TOCOM to transact in platinum futures for hedging purposes
via liquid far dated contracts1 . TOCOM was the largest futures market for platinum by weightof metal until around the time of the global financial crisis. In 2008, 3.5 million kilograms
was traded on TOCOM, or 4.4 times that of NYMEX. Following the crisis, the trade volume
on TOCOM declined while that on NYMEX increased. From 2000 to 2007, the average ofannual futures volumes on TOCOM equated to around 6.3 million kilograms of platinum. Over2008 to 2017 the average annual volume declined to just over 2 million kilograms. In the firstsix months of 2018, futures contracts equivalent to around 4.4 million kilograms were tradedon NYMEX, about five times the amount traded on TOCOM over the same period. However
despite the decline in TOCOM volume, a substantial share of the global platinum futures tradestill occurs on the TOCOM and important end users of physical platinum continue to use theexchange for hedging. While both gold and platinum are considered precious metals, gold tends
to be demanded by investors as a safe haven financial asset while platinum is stockpiled during
good economic times for industrial use (Chng and Foster, 2012).
TOCOM trades futures contracts based on an underlying domestic grade of regular gasoline
conforming with the Japanese Industrial Standard (JIS) K2202 Grade 2, which is refined within
Japan or imported, and delivered at refineries or storage tanks in Tokyo, Kanagawa or Chiba2 .
Important factors affecting the gasoline futures price include global crude oil prices (specifically the Dubai crude price), as well as the domestic production and consumption of gasoline,
refinery capacity and outages, imports and inventory levels. The gasoline “crack spread”, orthe differential between the futures prices for gasoline and crude oil, represents the non-crude
oil-related components of the gasoline price. The crack spread is time-varying and driven predominantly by domestic factors including private automobile use and transport demand, gaso1

The nature of the platinum futures trade differs between TOCOM and NYMEX. On TOCOM, the far dated
contracts are liquid while the near dated contracts are not actively traded (this is also the case for the other commodities examined in this research). The reverse is true for NYMEX.
2
TOCOM also trades cash settled contracts based on barge and lorry gasoline.

8line refinery output, inventory levels, imports and exports of gasoline, seasonal patterns andeconomic growth. Information on demand, supply and inventory levels is readily available, as
is reporting of refinery outages. Within Japan, the trade of gasoline futures is concentrated on
TOCOM.
Asia is the centre of the global rubber industry, both in production and consumption. Thailand, Indonesia and Vietnam are the largest producers of natural rubber and China is the largest
consumer, by country (IHS Markit, 2017). The Singapore Exchange (SGX) and TOCOM havetraditionally been the two most important futures markets in contract volume terms, and for thepricing of natural rubber (Meng and Liang, 2013; Chang et al., 2011). The Shanghai Futures
Exchange (SHFE) has been rising in prominence in recent years, and there are some indications that the importance of TOCOM has declined with a portion of trade shifting to Singapore.
Craymer (2016) notes that the liquidity of the rubber futures market on TOCOM had declined,volatility had increased and market participants believed prices had become less connected with
fundamentals in the physical market. There are also a number of less globally-important exchanges in the region including in India and Thailand.
Several grades of rubber are traded on futures exchanges in Asia. TOCOM has traded contracts based on the Ribbed Smoked Sheets Number 3 grade (RSS-3) since 1952 and recently
launched a new contract for Technically Specified Rubber (TSR-20) on 9 October 20183 . We
model the TOCOM RSS-3 contract since there is no data available for the TSR-20 contract during our sample period. SGX similarly trades RSS-3 and TSR-20. The SHFE trades a contract
based on domestic Sinochem brand natural (SCR) rubber and imported RSS-3 as the underlying
commodity, and recently the China Securities Regulatory Commission approved the launch ofa TSR-20 contract. The National Multi-Commodity Exchange of India trades a contract basedon RSS-4 rubber. A common thread linking platinum, gasoline and rubber is automobile transportation. Chng (2009) notes that more than 50 percent of global demand for these commodities
originates from the automobile industry and finds cross-market interactions amongst the futures
on TOCOM. Around 60 percent of natural rubber production is used to produce tyres and tubes(International Rubber Study Group, 2018b).
3

RSS rubber is rolled into sheets and dried in a smokehouse, while TSR (also known as block rubber) is washed,
shredded, granulated under controlled conditions, and dried as a block (International Rubber Study Group, 2018a).
TSR is precisely graded based on the rubber’s chemical and quality properties, while RSS rubber is graded byvisual inspection.

93.2. Investor Group Definitions
With the introduction of TOCOM’s J-GATE trading system in September 2016, all trades on
the exchange are classified as originating from six investor groups: (i) Commercials (denotedin TOCOM documentation as 40), (ii) Prop/Market Makers (5B), (iii) Funds/Investment Trust(5C), (iv) General Investors (5D), (v) Agency (5E), (vi) Foreign Investors (5F). The classification of an investor placing a trade is determined from information reported by the broker thatplaces the trade on the exchange. The investor classification provided by TOCOM is considerably more detailed than those provided by other commodity futures exchanges in their publicly
available data. The classifications used with J-GATE are more intuitive and useful for examining information asymmetry among investor groups than TOCOM’s investor classifications usedprior to the introduction of the new trading system4 . The new classifications are more easily
attributable to different types of investors, while the old groups were aligned with how trades
were placed on the exchange.
Commercials are entities hedging physical positions. Although the commercials classification includes foreign firms, most are domestic Japanese firms. We abbreviate the group nameslightly to ”Commercial”. Prop/Market Makers are proprietary traders, futures commission
merchants (FCMs) and market making brokers or dealers that have direct access to trade on TOCOM. We call this group “Dealer”. Funds/Investment Trusts covers all types of funds managedby financial institutions, which may be active or passive, and includes Toshin, exchange tradedfunds (ETFs), mutual funds, hedge funds and Commodity Trade Adviser (CTA) funds. We refer to this group as “Fund”. General Investors are mainly individual domestic retail traders who
access the markets via on-line and traditional individual broker services, and we refer to thisgroup as “General”. Agency, referred to simply as “Agency”, includes financial intermediaries
who cannot place their trades directly on TOCOM and access the markets via domestic intermediaries who are members of the exchange. This classification may include trades originatingfrom investors that could be classified under the other groups. Foreign Investors include both
short and long-term investors, and perform similar investment activities to the domestic market
participants classified as Prop/Market Makers and Funds/Investment Trusts. Foreign Investors
access TOCOM via foreign FCMs. We refer to the Foreign Investors category as “Foreign”.
4

Prior to the introduction of J-GATE, investors were classified according to seven groups: (i) commercials
with direct access to TOCOM (1A), (ii) commercials without direct access to TOCOM (21), (iii) commercials
with access to TOCOM but trading through intermediaries (31), (iv) other commercials (3A), (v) intermediaries(1B), (vi) intermediaries that trade through other intermediaries (32), (vii) individual and institutional investors,
and financial institutions (22).

10Using our terminology, we have the following six investor groups: Commercial, Dealer, Fund,
General, Agency, and Foreign.
3.3. Data and Variables
The sample period runs from 20 September 2016 to 28 February 2018. The beginning of thesample corresponds to the introduction of TOCOM’s J-GATE trading system, under which all
trades are classified as originating from the six investor groups described in Section 3.25 . With
the introduction of J-GATE, the exchange trading time was extended to begin earlier in morningand finish later at night. Trading hours span 08:45 Japan Standard Time (JST) to 05:30 the nextday. TOCOM runs a day session from 8:45 to 15:15 JST. The night session runs from 16:30 to
05:30 JST for gold, platinum and gasoline, and 16:30 to 19:00 JST for rubber. The trading day
is defined as beginning at the start of the TOCOM night session and running through to the endof the day session on the following day.
We obtained daily futures close price and transaction data for gold, platinum, gasoline andrubber from TOCOM6 . Prices are denominated in Japanese yen per gram for the gold and platinum contracts, yen per kilolitre for gasoline and yen per kilogram for rubber futures.
Contract months for gold and platinum futures are all even months of the year, and six nonexpired contracts will exist at any time. For gasoline and rubber, futures contracts are generated
for six consecutive months, and as for gold and platinum, six non-expired contracts will trade at
any time. Contract months begin from the current month (up to the last trading day) for rubber
and the month following the current month (again up until the last trading day) for gasoline.
An important characteristic of the trade in these futures on TOCOM is that the farthest contract
is the most liquid and actively traded while the near contract is illiquid. This contrasts withmost other futures exchanges where trading activity is concentrated around the near contract.
Accordingly, we use the prices for the farthest contract in each commodity to calculate thereturn over the trading day, rt , as:rt =Pt − Pt−1
× 100
Pt−1

(1)

where Pt represents the close price at the end of the day session at time t.
5
The investor group classifications used in J-GATE differ from those used under the previous trading system,
and accordingly, we are limited to using data beginning in September 2016 for the analysis of the new investor
groups.
6
The data is publicly available from http://www.tocom.or.jp.

11The transaction data comprises of the number of contracts for each commodity bought
and sold on each trading day by each investor group. TOCOM aggregates investor grouptransactions over all six non-expired contract maturities. While we would prefer to measuretransactions in the farthest contract only, the aggregated data is reasonable for our purpose astransactions in the farthest contract constitute a high proportion of total volume over all nonexpired maturities. We calculate the following daily trade ratio, xg,t , for each investor group,g = 1, . . . , 6, at time t defined as:
xg,t =?
B?g,t − S g,t?
B?g,t + S g,t(2)?
where B?g,t and S g,t
represent the number of futures contracts bought and sold by investor groupg at time t, respectively. The resulting trade ratio series are stationary and range between -1 and
1. A positive (negative) trade ratio shows the investor group bought (sold) more contracts thanit sold (bought) during the trading day, the same time interval over which returns are calculated.
Plots of the daily prices, returns and trade ratios are provided in Figures 1 to 4. Panel (a)shows price, panel (c) has the return, panel (b) gives the trade ratio for Commercial, Dealer
and Fund, while panel (d) contains the trade ratio for General, Agency and Foreign. Summary
statistics for the transactions series and returns are given in Tables 1, 3, 5 and 7. Correlations
between the different investor group trade ratios are provided in Tables 2, 4, 6 and 8.

12

Figure 1: Gold Futures Market(a) Price(b) Trade Ratio (Commercial, Dealer, Fund)(c) Return(d) Trade Ratio (General, Agency, Foreign)

Table 1: Summary Statistics for Gold Trades and Return

Mean
MedianMaximum
Minimum
Std. Dev.
Skewness
Kurtosis
Obs.

Commercial
Buy
Sell

Dealer
Buy
Sell

Fund
Buy
Sell

General
Buy
Sell

AgencyBuy
Sell

Foreign
Buy
Sell

Return

6248
6292
11991
326
2986
0.03
2.35
354

13382
12602
28053
5569
5807
0.94
3.00
354

601
459
1201
336
251
0.84
2.01
354

50212
47802
82821
24505
13562
0.38
2.16
354

6745
6209
11509
3422
1997
0.73
2.53
354

12017
13008
27273
1337
5969
-0.07
2.11
354

0.017
0.022
1.480
-2.4650.474-0.490
2.776
353

23876
21724
49951
4193
10375
0.36
2.34
354

14774
13938
25434
4107
5646
0.13
1.88
354

35
25
99
12
20
1.58
4.81
354

40898
41257
63654
20954
9778
0.08
2.38
354

6023
6007
9009
3311
1322
0.15
2.47
354

3682
2539
15317
188
3248
1.44
4.78
354

Table 2: Correlations Amongst Gold Trade Ratios

Commercial
DealerFund
GeneralAgencyForeign

Commercial

DealerFund

GeneralAgencyForeign

1.00
0.74
-0.36
-0.88
-0.88
0.54

1.00
-0.57
-0.80
-0.79
0.42

1.00
0.31
0.42
-0.16

1.00
0.96
-0.79

1.00
-0.79

1.00

13

Figure 2: Platinum Futures Market(a) Price(b) Trade Ratio (Commercial, Dealer, Fund)(c) Return(d) Trade Ratio (General, Agency, Foreign)

Table 3: Summary Statistics for Platinum Trades and Return

Mean
MedianMaximum
Minimum
Std. Dev.
Skewness
Kurtosis
Obs.

Commercial
Buy
Sell

Dealer
Buy
Sell

Buy

4055
3710
8140
1083
1744
0.67
2.61
354

3627
3640
7781
700
1447
0.02
3.03
354

282
266
476
224
51
1.66
4.59
354

26599
25953
35920
14910
6206
-0.28
1.76
354

4136
3266
10167
1003
2666
0.71
2.30
354

Fund
Sell
2
0
42
0
8
4.02
18.56
354

General
Buy
Sell

AgencyBuy
Sell

Foreign
Buy
Sell

Return

37895
38520
53752
20792
7965
-0.09
2.40
354

5056
5266
6383
2625
972
-0.91
2.77
354

5027
3768
15705
1093
3486
0.87
2.72
354

0.006
0.000
4.014
-3.204
0.862
0.185
1.322
353

16615
16938
23572
6435
3810
-0.45
2.51
354

1678
1697
2544
816
335
-0.37
2.68
354

6991
7157
19301
162
4071
0.79
3.87
354

Table 4: Correlations Amongst Platinum Trade Ratios

Commercial
DealerFund
GeneralAgencyForeign

Commercial

DealerFund

GeneralAgencyForeign

1.00
0.72
0.22
-0.71
-0.73
0.40

1.00
0.17
-0.71
-0.75
0.21

1.00
-0.08
-0.09
0.27

1.00
0.94
-0.61

1.00
-0.48

1.00

14

Figure 3: Gasoline Futures Market(a) Price(b) Trade Ratio (Commercial, Dealer, Fund)(c) Return(d) Trade Ratio (General, Agency, Foreign)

Table 5: Summary Statistics for Gasoline Trades and Return

Mean
MedianMaximum
Minimum
Std. Dev.
Skewness
Kurtosis
Obs.

Commercial
Buy
Sell

Dealer
Buy
Sell

Fund
Buy
Sell

General
Buy
Sell

AgencyBuy
Sell

Foreign
Buy
Sell

Return

4344
4301
6334
1791
1192
-0.15
1.85
354

1577
1479
2859
676
492
0.66
2.80
354

7
0
65
0
13
1.69
4.69
354

2649
2619
3749
1436
557
0.14
2.02
354

301
283
560
94
118
0.43
2.35
354

497
203
2435
20
588
1.47
4.20
354

0.111
0.170
8.774
-4.157
1.245
1.070
6.886
353

3574
3593
5777
1567
1082
0.10
2.20
354

1756
1577
3114
410
720
0.13
1.75
354

42
28
171
23
30
2.52
9.32
354

2436
2393
3454
1269
365
0.46
3.07
354

257
247
503
131
62
1.11
4.72
354

1296
1502
2356
48
671
-0.49
1.84
354

Table 6: Correlations Amongst Gasoline Trade Ratios

Commercial
DealerFund
GeneralAgencyForeign

Commercial

DealerFund

GeneralAgencyForeign

1.00
-0.16
0.34
-0.25
-0.57
-0.73

1.00
0.58
-0.43
-0.09
-0.03

1.00
-0.52
-0.43
-0.19

1.00
0.84
-0.21

1.00
-0.02

1.00

15

Figure 4: Rubber Futures Market(a) Price(b) Trade Ratio (Commercial, Dealer, Fund)(c) Return(d) Trade Ratio (General, Agency, Foreign)

Table 7: Summary Statistics for Rubber Trades and Return

Mean
MedianMaximum
Minimum
Std. Dev.
Skewness
Kurtosis
Obs.

Commercial
Buy
Sell

Dealer
Buy
Sell

Fund
Buy
Sell

General
Buy
Sell

AgencyBuy
Sell

Foreign
Buy
Sell

Return

2254
2332
4587
336
1174
0.17
1.91
354

1676
1526
4435
620
660
1.26
5.04
354

24
14
122
9
22
2.56
9.56
354

8538
8434
13780
2529
3055
0.01
1.92
354

792
793
1130
300
175
-0.40
2.77
354

8575
8002
19430
1835
3598
0.79
3.64
354

0.072
0.061
6.969
-6.9432.297-0.012
0.747
353

4195
4029
9615
1337
1585
0.88
4.04
354

1675
1515
4472
326
690
1.10
4.40
354

5
0
35
0
6
1.65
6.13
354

5798
4782
12327
2672
2626
0.94
2.44
354

1229
1063
2684
517
456
1.13
3.61
354

8993
8076
21464
2016
3873
0.98
3.87
354

Table 8: Correlations Amongst Rubber Trade Ratios

Commercial
DealerFund
GeneralAgencyForeign

Commercial

DealerFund

GeneralAgencyForeign

1.00
-0.27
-0.04
-0.04
0.25
-0.18

1.00
0.35
-0.21
-0.05
0.25

1.00
-0.18
-0.18
0.23

1.00
0.76
-0.89

1.00
-0.77

1.00

The time series of prices for the four commodity futures display reasonably different behaviour over the sample period. Gold and gasoline prices trend up over the sample while plat16

inum prices go sideways and the price of rubber declines from a peak in early 2017. A common
feature of the price series is a rise in prices in late 2016 to early 2017. This coincides with aperiod of substantial depreciation in the Japanese yen over the last quarter of 2016, from around
100 to the US dollar to 117. The contracts are yen denominated, suitable for hedging domestic
exposures.
Average daily returns are close to zero for all of the commodities over the sample. The
gasoline and rubber markets appear to be the most volatile. The maximum and minimum daily
returns are substantial in gasoline and rubber, and to a lesser extent also in platinum. In standard deviation terms, the gold futures are the least risky securities, followed by platinum, andgasoline while rubber futures returns have the greatest dispersion. Gold returns are negatively
skewed, while platinum and gasoline returns are positively skewed. Gasoline returns are particularly leptokurtic.
The transactions summary statistics, trade ratio plots and correlations reveal a number ofinteresting characteristics of the trading activities of the six investor groups in each market.
The gold market has the greatest median buy and sell transactions across all groups. Generalinvestors are the largest traders in the market judging by the median of the daily number ofbuy and sell transactions, while investment Funds are the smallest participants in the market.
The Commercial, Dealer, Foreign and Agency groups all have reasonably large transactions
volumes. Dealer, General and Agency investor median buy and sell volumes are broadly balanced, while Foreign and Fund investors have more buys than sells. Commercial investors havemore sells than buys. The trade ratios for General and Agency investors are almost perfectly
correlated in the gold market, suggesting that the trades recorded under Agency may largely
originate from retail investors are placed with the exchange via brokers without direct trading
access to TOCOM. The Foreign trade ratio has a high negative correlation with the trade ratiosof General and Agency investors, and a positive correlation with Commercial and Dealer investors. Commercial and Dealer trade ratios are highly correlated, however the trade ratio forCommercial investors is usually more negative than Dealers, suggesting relatively short market
positioning. Fund investor transactions have a low correlation with those of other groups, andthe trade ratio varies between 0.5 and one, suggesting the Funds group may be dominated bylong-only or passive index tracking investors.
The relationships between the six investor groups’ trades in the platinum market are similar
to those in the gold market. General investors are the largest participants and Funds are the17smallest. Commercial investors are also substantial traders in platinum futures. As in the gold
market, the General and Agency trade ratios are almost perfectly correlated with each other,and negatively correlated with the Commercial, Dealer and Foreign trade ratios. Commercial
and Dealer trading is positively correlated as in the gold market. The Commercial trade ratio isnegative over the sample, while the Dealer trade ratio fluctuates above and below zero. Looking
at the trade ratio plots, it would appear that in both gold and platinum the General and Agency
investors are trading against the Commercials and Dealers. Foreign investors also generally
place more sells than buys, while General and Agency investors place more buys than sells.
Funds predominantly make buy transactions, with their trade ratio close to one, but with somevariation over the sample.
The characteristics of gasoline trading differ from those of the metals markets. There isnoticeably less trading activity in gasoline than in the gold or platinum. Some of the correlations
between investor group trade ratios also differ. Commercials are the largest gasoline market
participants, followed by General investors. Fund participation is very low during the sample.
Again, General and Agency trade ratios are positively correlated, but to a lesser extent thanin the gold and platinum markets. In contrast to the metals markets, Commercial and Dealer
trading has a low negative correlation, and Dealer and Fund trading is moderately positively
correlated. The Fund trade ratio is positively correlated with Dealers and negatively with that forAgency investors. The Commercial and Foreign trade ratios are the most negatively correlatedof all trade ratios in gasoline. In general, the trade ratio correlations seen in the metals markets
are greater in absolute value terms than those seen in the gasoline and rubber markets.
Trading in the rubber market also differs somewhat from that in the other three commodities.
Foreign investors place the most trades during the sample, followed by General investors who
are amongst the largest traders in all four markets. Commercials the third largest traders inrubber, versus the largest in gasoline and second largest in the metals. Again Fund participationis very low. However in rubber, the Fund trade ratio is relatively volatile. Of the groups withlarger trade volumes, Foreign investors buy and sell about the same number of contracts inmedian terms, while General buy more than they sell, and Commercials sell more than theybuy. Dealers have balanced buys and sells and the dealer trade ratio is close to zero for the
sample. As is the case for gold, platinum and gasoline, the General and Agency trade ratios
are positively correlated, albeit more moderately for rubber. General and Agency trades arenegatively correlated with those of Foreign investors. The other rubber trade ratio correlations
18

are relatively low.
4. Modelling the Information Content of Trades
The following subsections describe the model we use to determine the influence of investor
groups’ trading on the efficient price and differentiate between trade-related and nontraderelated information.
4.1. VAR Model of Orderflow and Return
We adapt the structural bivariate VAR model proposed by Hasbrouck (1991a,b) of orderflow
and price revisions to examine the information content of the trades of the six trader groups ineach commodity futures market. Our multivariate model can be expressed in vector form as:
BYt = Φ0 + Φ1 Yt−1 + Φ2 Yt−2 + · · · + t  100000 x 

1,t

 
1
0
0
0
0
 0
 
 x2,t 
 0
 
 
0
1
0
0
0

X 
x 3,t  t where: Yt =   , Xt =   and B =  0
0
0
1
0
0
r 
 x4,t t 

0
0
0
1
0
 
 0
 x5,t  
 0
0
0
0
0
1
x 

6,t

−c1 −c2 −c3 −c4 −c5 −c6(3)−b1 −b2 −b3 
−b4 .
−b5 
−b6 
1 In our model, Yt contains the trade information Xt for each of the six investor groups and thefutures mid-price return rt . Xt contains the trade ratios for the six investor groups, xg,t , whereg = 1, . . . , 6, corresponding to the investor groups Commercial, Dealer, Fund, General, Agency
and Foreign, respectively. The Φi are 7 × 7 coefficient matrices, where i represents the numberof lags included in the VAR. In contrast with typical VAR applications, contemporaneous traderatios and returns enter the system as explanatory variables. There are contemporaneous relationships between trade ratios and returns, and returns and trade ratios, but not between the traderatios which constitute restrictions to identify the model. The bg coefficients are those on thecontemporaneous returns variable in the six trade ratio relationships, while the cg coefficientsare those on the six contemporaneous trade ratio terms that appear in the returns relationship.
The first six rows of the system represent, for each investor group, the relationship between the
trade ratio as the dependent variable, and its lags and the contemporaneous return as explanatory
19

variables. The innovations include unexpected trading activity. The final row is the relationshipbetween return as the dependent variable and its lags plus the contemporaneous trade ratio foreach investor group.
The number of lags to be included in the VAR model for each commodity was determinedusing the Akaike Information Criterion (AIC). Lags included in the models are as follows: forgold two lags, platinum three, gasoline two and rubber one. Each VAR model was estimatedusing maximum likelihood. The empirical results are discussed in the Section 5.
4.2. The Efficient Price and Mispricing
Hasbrouck (1991b) provides a method to isolate the variance of the permanent component of
a security’s price, and the proportions attributable to trade- and non-trade-related information.
The permanent component of a securities price is interpreted as the efficient price reflecting thefundamental valuation of the security. Only where trades influence the permanent component ofprice do investors influence the efficient price. The trade-related part of the permanent component may be interpreted as the private information incorporated in the security’s price throughthe unexpected trades of informed investors. The non-trade-related part of the permanent component reflects public information. We adapt this approach to estimate the influence tradeson the efficient price and asymmetry in the information content of the trades of the different
investor groups.
The price of a security, pt , may be decomposed into the efficient price, mt , and mispricing,st :pt = mt + st(4)

where mt follows a random walk process, st is a mean-zero covariance stationary process, andlimh→∞ E (st+h ) = 0.
The permanent component of price, or efficient price, can be modelled as:
mt = mt−1 + ωt

(5)



where ωt ∼ N 0, σ2ω , and E (ωt , ω s ) = 0 for t , s.
The efficient price, mt , is driven by the previous period’s efficient price and an innovationthat reflects new fundamental information, ωt , which is incorporated in the efficient price attime t. The efficient price shock, ωt , has a permanent influence on the security price, while themispricing shock, st , is has only a temporary effect. The st component represents transitory20

effects on the security’s price, or mispricing, arising from non-information based microstructure effects, liquidity provision and noise trading. The variance of the information innovation,
σ2ω , measures the variation in the permanent component of the price related to fundamental
information.
The structural VAR model shown in equation (3) can be inverted to the Vector Moving
Average (VMA) representation:
Yt = I + θ1 L + θ2 L2 + θ3 L3 + . . . t = θ (L) t(6)

where L is the lag operator, the θi are 7 × 7 matrices of coefficients, and t is a white noise error
process with E (t ) = 0 and Var (t ) = Ω.
The variance of the shock to the permanent component of the security’s price is estimatedfrom the VMA representation in (6) as:
σ2ω = [θ (1)]7 Ω [θ (1)]07(7)

where [θ (1)]7 denotes the seventh row of [θ (1)] that corresponds to the returns relationship, and[θ (1)] = (I + θ1 + θ2 + . . . ).
The variance of the trade-related component for an investor group, g, is: 0σ2ω,xg = θ? (1) 7 Ω θ? (1) 7(8)where θ? represents θ from the VMA with the coefficients related to all other investor groups
and and the coefficients related to returns set to zero.
Similarly the variance of the non-trade related component is: 0σ2ω,r = θ?? (1) 7 Ω θ?? (1) 7(9)where θ?? represents θ from the VMA with the coefficients related to all investor groups set tozero.
As the trading behaviour of the different investor groups is correlated, we expect Ω to be anon-diagonal covariance matrix. Accordingly, we use Cholesky factorisation to extract σ2ω . SetΩ = F 0 F, where where F is the upper triangular Cholesky factor7 , and let d = [θ (1)]7 F 0 . Then
7

We order the factorisation with the trade ratios first, then returns.

21the variance of the permanent shock is the sum of the squares of the elements of d:
σ2w =

X

di2

(10)

We can obtain the variance due to trade-related, σ2ω,xg , and non-trade-related, σ2ω,r , components using this procedure, and express these relative to the total variance of the permanent
component σ2ω . The relative trade-related variance for an investor group provides a measure of
the relative influence of that group’s trades on the efficient price. The relative non-trade-related
variance gives a measure of the influence of public information on the efficient price.
4.3. The Price Impact of Trade
The price impact of trade by an investor group is calculated as the sum of the coefficientsin θ? for that group multiplied by a shock to that group’s trade. This can be seen clearly if the
VMA model can be re-written as:
 
  
 x  a (L) b (L)(L)
g1  1,t 1
 1,t   1     
g2 (L) 2,t  x2,t  a2 (L) b2 (L)       x3,t   3,t        4,t  x4,t  =          5,t  x5,t         x6,t    6,t       a7 (L) b7 (L) c7 (L) d7 (L) e7 (L) f7 (L) g7 (L) 7,t 
rt

(11)

Then the ultimate effect of unexpected trade by the Commercial investor group (g = 1) on
price thirty days after the shock is:
Ψ30 =

30
X

a7,k 1,0



(12)

k=0

which is equivalent to the cumulative impulse response of return to a shock to the Commercial
trade ratio, 1,0 . This measure includes both the permanent and temporary influences on a security’s price. Over a sufficiently long period subsequent to the shock, the temporary componentsof the price impact would be expected to reverse out, leaving the permanent influence on price.
While the trades of uninformed investors influence prices in the short-run, their trades do nothave a lasting long-run impact on price because the short-run influence is reversed over time.
22

In this sense, the price impact on trade is one way to isolate which investor groups influence
price permanently, and thus which groups influence the efficient price. However, the permanent
component that we can isolate using the impulse response function does not indicate whether
the information that appears to be related to the trades is private or public. A positive price impact on trade suggests that the investor group trades as an informed investor. That is, a positive
shock in the trade ratio is associated with a positive response in returns. Whereas, a negative
price impact on trade suggests that the investor group trades as a liquidity provider8 , that is apositive shock to the trade ratio is associated with a negative response in returns. A high (low)absolute number suggests unexpected trades have a large (small) total long-run influence on the
security’s price. The relative magnitudes of the price impact of trade across markets can beinterpreted as indicating relative liquidity levels. In a more liquid market, a given shock to trade
will have a lower impact on price.
4.4. Interpreting the Information Content of Trades
Using the information content of trade modelling, we differentiate three types of investor.
Informed investors successfully use private information to trade at a profit. Their trades containfundamental information that is impounded in the market price and has a persistent influenceon the market price, affecting the efficient price. We assume that the long-run price impact of
informed investors is positive, that is, the CIRF is positive and significant within the 30-day
horizon. Informed investors have a positive long-run price impact and a relatively large impact
on the efficient price, represented by a large share in the variance decomposition of the efficient
price. Informed investors may or may not represent a large share of the forecast error variancedecomposition.
We also allow for investors who influence the efficient price, but who have an indeterminate
or negative long-run price impact of trade. Such investors may be hedgers who’ trades containfundamental information on expected future demand or supply conditions for the commodity.
Although the trades contain fundamental information, hedgers may on average trade at a loss,
and this compensates risk bearers in the market. Hedgers are likely to have a negative long-run
price impact of trade, but may have constitute a substantial share of the variance decompositionof the efficient price if their trades contain fundamental information. Liquidity providers operating over a longer trade horizon than 30 days may also have a negative price impact of trade8

A negatively signed cumulative impulse response function does not necessarily imply that a group is unprofitable, as a liquidity provider may earn a premium for providing liquidity over a period longer than 30 days.

23and their trades may constitute a substantial share of the long-run price impact of trade. We assume that 30 trade days reflects the long-run, but acknowledge that the trade horizons for someinvestors may be much longer. To that extent, they should appear to influence the efficient price,
but their long-run price impact of trade may be negative even though over a longer horizon they
may be profitable.
Investors who trade on noise are not expected to be profitable over the long run. Their
trades do not contain fundamental information, and do not have a persistent affect on the price,and thus do not influence the efficient price. Such investors’ trades may be a large share of
the forecast error variance decomposition because their trades may influence the price overthe short run. If so, the short-run influence will be reversed out and thus have no persistentinfluence. This is because the forecast error variance decomposition calculation attributes agreater weight to short-run influences on price, and thus may show a substantial influence on
price by noise traders. Noise traders share of the variance decomposition of the efficient pricewill be close to zero.
5. Empirical Results
We present the following empirical results from the VAR model: the long-run price impact of trade, the forecast error variance decomposition and the variance decomposition of theefficient price.
5.1. Long-Run Price Impact of Trade
The cumulative impulse response function (CIRF) is used to indicate the long-run priceimpact of trade, following Hasbrouck (1991a). We evaluate the CIRF at 30 days, which we
assume is sufficient time for the short-term influence of trade on return to reverse out. The longrun interpretation of the CIRF requires this assumption that temporary influences on price willreverse out within the 30-day period over which the CIRF is calculated. However as discussed
in the previous section, the long-run price impact of trade does not isolate the efficient price, nordoes it provide a useful means to distinguish between trade- and nontrade-related information.
Table 9 summarises the price impact of trade evaluated at 30 days. This represents the
cumulative response of return to a positive shock in the trade ratio of each investor group, ineach market. A positive sign on the long-run price impact of trade allows for the possibility
that the investor group may be informed, as a positive trade ratio shock corresponding withan increase in buys relative to sells is followed by positive returns. A negative sign opens the24Table 9: Long-Run Price Impact of Trade Evaluated at 30 Days

Commercial
DealerFund
GeneralAgencyForeign
ReturnGold

Platinum

Gasoline

Rubber

-0.04
-0.02
0.06
0.37
0.04
0.17
-0.04

-0.29
-0.15
0.20
-0.31
-0.13
-0.26
0.29

-0.43
-0.94
0.14
0.75
0.26
0.25
0.16

-0.79
1.09
1.13
-1.01
-0.84
0.55
2.57

possibility that the investor group is a liquidity provider, since a positive shock to the trade ratio
is followed by negative returns. We also include in the table the response of return to a shockin return, to evaluate persistence in returns shocks. Figures 5 to 8 contain plots the CIRF overthe same 30-day horizon. The dotted lines represent two standard deviations either side of the
CIRF estimate. We omit the return charts given that we are primarily interested in the impact ofshocks in the trade ratios on return.
Figure 5: Cumulative Impulse Response Functions of Return for Gold(a) Commercial(b) Dealer(c) Fund(d) General(e) Agency(f) Foreign

In the gold market (see Figure 5), General investors have a relatively large positive andsignificant long-run price impact and thus appear to be informed. The impact of Foreign investors is less than half that of General investors, but it is relatively large when compared withthe other groups and is borderline significant. While the CIRFs for commercial and dealer investors are negative, suggesting the groups are are liquidity providers, the values are small andnot significant. Shocks to returns do not have a persistent influence on returns.
The CIRFs for platinum are shown in Figure 6. Funds appear to be informed, with a positive25

Figure 6: Cumulative Impulse Response Functions of Return for Platinum(a) Commercial(b) Dealer(c) Fund(d) General(e) Agency(f) Foreign

and borderline significant long-run price impact. The remaining groups have negative long-run
price impacts, of which only Commercial and General appear significant. The CIRF of Generalinvestors declines markedly over the 30 day horizon suggesting that much of the initial priceimpact is transitory. In contrast, the cumulative impact of Commercials increases over time.
The cumulative impact of Foreign investors is of similar magnitude to Commercials, but is not
significant. Commercial and General investors appear to be the liquidity providers in platinum.
Shocks to platinum returns have a significant and positive persistent impact on returns.

Figure 7: Cumulative Impulse Response Functions of Return for Gasoline(a) Commercial(b) Dealer(c) Fund(d) General(e) Agency(f) Foreign

The long-run price impact of trades by the General group in the gasoline market (see Figure7) is positive and significant, suggesting General investors may be informed. Dealers, and to26

a lesser extent Commercials, have a negative and significant CIRF suggesting they may beliquidity providers. The Fund, Agency and Foreign long-run price impacts are relatively low
and statistically insignificant. Shocks to returns are insignificant, and thus not persistent in thismarket.
Figure 8: Cumulative Impulse Response Functions of Return for Rubber(a) Commercial(b) Dealer(c) Fund(d) General(e) Agency(f) Foreign

The CIRFs for rubber futures shown in Figure 8 suggest that Funds and Dealers are informed, while General investors are liquidity providers. The impacts of trades by Commercial,
Agency and Foreign investors are relatively low and are not significant. A shock to returns
produces a large and significant CIRF for returns.
As discussed in Section 4, the long-run price impact of return can be interpreted as anindicator of market liquidity. Our results indicate that the gold futures market is most liquid,followed by platinum, gasoline and finally the rubber market is the least liquid. Accordingly,
shocks to trade in gold have a relatively low impact on returns, while shocks in rubber trade
have a relatively high impact. This ranking of market liquidities is intuitively consistent withthe characteristics of the futures markets discussed in Section 3.3. The median number of daily
futures contract transactions over all investor groups in gold, platinum, gasoline and rubber are
86,228, 56,508, 9,002, and 21,724, respectively. This CIRF ranking of relative liquidities isbroadly in line with the ranking of gold, platinum and gasoline by median daily transactions
volumes. However, despite having more than double the daily median volume of gasolinetransactions by number of contracts, the rubber market is less liquid in terms of the long-run
price impact of trade.

27

5.2. Forecast Error Variance Decomposition
A VAR forecast error variance decomposition for each commodity calculated over 30 days
is provided in Table 10. This corresponds to a decomposition in terms the price of a security,
pt , as shown in equation (4). While the standard decomposition is informative in terms of theimportance of unexpected trading by each of the investor groups on returns, it is not ideal forour purpose of determining each group’s influence on the efficient price, mt , in equation (5). Ourprior is that the transitory influence of an investor group’s unexpected trades on price (mispricing) will reverse out over time. However, the standard variance decomposition places a higher
weight on the shorter lag influences of an innovation relative to the longer lag influences. Initialmispricing influences will be more important in the decomposition than subsequent reversals
even if mispricing is reversed within the period over which the decomposition is calculated.
Table 10: Forecast Error Variance Decomposition (%, 30 days)

Commercial
DealerFund
GeneralAgencyForeign
ReturnGold

Platinum

Gasoline

Rubber

0.88
0.71
0.70
89.23
2.21
1.42
4.85

1.91
0.32
0.65
86.67
2.58
1.95
5.92

8.08
20.14
0.43
11.42
3.41
15.73
40.79

1.59
2.80
32.02
5.16
8.07
0.83
49.53

Noting the above caveat, our interpretation of the forecast error variance decomposition is
as follows. Trade-related information has the most influence over price in the gold and platinum
markets, while both trade- and nontrade-related information appear broadly equal in importancefor gasoline and rubber prices. The unexpected trades of General investors have the greatest
influence over prices in the gold and platinum markets. Dealer, Foreign and General investors
are important in Gasoline. Funds have the greatest trade-related influence over the rubber price.
5.3. Variance Decomposition of the Efficient Price
Table 11 provides the variance decomposition of the efficient price, mt in equation (5), foreach commodity market. The efficient price in the gold, platinum and gasoline markets is mainly
influenced by trade-related innovations, which can be interpreted as the private information contained in investors’ trades. Foreign investors have the greatest influence over the efficient pricein the gold market, followed by Commercials. The trades of Dealer, Fund, General and Agency
28

investors are relatively unimportant for the efficient price of gold, at least at the level of aggregation we are working with. Funds, followed by commercials, have the greatest influence overplatinum’s efficient price. While trade-related information accounts for most of the variation in
the efficient price of platinum, almost 20 percent is accounted for by nontrade-related information that we interpret as public information. In the gasoline market, General investors have themost influence over the efficient price, followed distantly by Dealers. Both trade and non-traderelated innovations have an equal influence on efficient price of rubber, with trades by Funds
having the largest information content in this market, followed by General investors.
Table 11: Variance Decomposition of the Efficient Price (% Share)Gold

Platinum

Gasoline

RubberCommercial
DealerFund
GeneralAgencyForeign
Return

39.48
8.06
5.80
0.41
0.36
45.64
0.24

27.55
0.43
29.16
6.36
3.13
14.90
18.47

4.86
17.79
4.08
57.94
3.97
7.98
3.38

0.29
1.25
27.70
17.50
5.65
0.28
47.34

Share by trades

99.76

81.53

96.62

52.66

6. Discussion
In this section we discuss our results for each commodity and relate the results to the structure and characteristics of markets.
6.1. GoldWe find that Commercials and Foreign trades have a persistent influence on the gold futures
price. Both groups have a relatively large influence on the efficient price. With a positive longrun price impact, foreign investors are deemed informed. The CIRF estimate for Commercials
is not significant, so we cannot say whether Commercials are informed or liquidity providers.
However, looking at the trade ratio for Commercials it is clear that as a group they are mostly
net sellers, likely for hedging purposes. Foreign investors are usually net buyers during thesample period and the trade ratios of these two groups are positively correlated at around 0.5.
We interpret these results as intuitively consistent with the structure of the gold market, inwhich there is a single global price for a globally traded homogeneous commodity, where tradeis decentralised over numerous markets around the globe, and where Tokyo does not play a29significant role in price discovery. Foreign investors may have access to greater information on
global macroeconomic and financial conditions, and be able to better forecast demand for gold
as a safe haven or hedge against global inflation.
General investor trades account for almost all of the forecast error variance decomposition
but almost none of the variance decomposition of the efficient price. This suggests that Generals
have a substantial short-term influence on gold prices, but no persistent impact. TOCOM gold
is a favoured market for Japanese retail investors, and the lack of a persistent impact of theGeneral group is consistent with the notion that retail investors are not well informed on thefundamental drivers of the gold futures price.
Gold is the most liquid market in our study and is likely relatively informationally efficient.
Accordingly, there is little role for nontrade-related (or public) information either as a shortterm or persistent influence on price as there is a high degree of competition among informed
traders.
6.2. Platinum
Fund, Commercial and Foreign investor trades influence the efficient price of platinum futures. Funds have a positive long-run impact on price and thus are informed, while commercials
have a negative long-run impact and so are liquidity providers. The insignificant CIRF estimate
for Foreign investors means we are unable to distinguish whether they are informed investors
or liquidity providers. Compared with the gold market, the three groups each have a relatively
small influence over the efficient price. Private information is more dispersed over investor
groups, and public information also has an influence on the efficient price.
The influence of Commercials on the efficient price is consistent with the role of TOCOM
is a globally important hedging venue for companies engaged in the international trade andconsumption of physical platinum. The platinum futures trade and hedging activities are concentrated in a small number of markets, of which TOCOM is one. Further, given the large
automotive industry in Japan, we expect that commercials and other domestic investors haveprivate information on the demand and supply of platinum. Hedging activities by commercials
give rise to profit opportunities for other investor groups willing to take on risk. As a globally homogeneous commodity, foreign investors may possess private information gleaned fromother locations and influence the efficient price. Platinum futures trade is concentrated within asmall number of exchanges. See Iwatsubo et al. (2018) for explanation of the linkages between
the Tokyo and New York markets. While funds are predominantly small buyers of platinum
30futures, Commercials are always net sellers and Foreign have sustained periods of both net buying and net selling. Trades of the three groups have a relatively evenly shared influence on theefficient price. Given the groups’ relative positioning, the result is reasonable despite the low
variation in the Fund trade ratio. Similar to our results for the gold market, General investors
have a large short-term influence on price but little persistent impact. In platinum, Generalinvestors are liquidity providers.
Platinum is the second most liquid market in our study after gold. In this market, nontraderelated information has a small influence on the efficient price, suggesting that public information shocks have some influence on the efficient price.
6.3. Gasoline
The variance decomposition of the efficient price indicates that General and Dealer investor
trades have a persistent influence on the price of gasoline. General investors have a relatively
substantial influence on the efficient price, and with a significant and positive 30-day CIRF,
they are informed investors. Of the four markets analysed, General investors in gasoline havethe largest share of the variance decomposition of the efficient price in any market. Dealers are
liquidity providers and have a smaller influence on the efficient price than General investors.
The influence of General investors on the efficient price is consistent with the importanceof domestic Japanese information in the gasoline market. As discussed in Section 3.1, thegasoline futures price is related to both international crude oil prices and domestic gasoline
supply and demand information. Gasoline is, to a large extent, a domestic consumer good.
Domestic investors may have an informational advantage by being located close the sources of
information about domestic gasoline demand and supply, inventory levels, refinery conditions
and outages, and imports and exports of gasoline. Information on domestic gasoline demand,
supply and inventories is readily available.
In addition to Dealer and General Investors, the forecast error variance decomposition suggests Foreign investor trades have an influence on price. However, the variance decompositionof the efficient price shows the influence is reversed out within 30-days and Foreign investors
do not influence the efficient price of gasoline futures. Similarly, Commercials have also havemore influence in the short-term thanon the efficient price.
The gasoline futures market is less liquid than the markets for gold and platinum. Nontraderelated (public) information influences price in the short-term as indicated by the share of theforecast error variance decomposition attributed to return. However, the efficient price is not
31

influenced by public information. In the short-term it appears there may be less competition
amongst traders, but in the longer term the market appears efficient.
6.4. Rubber
Funds have the largest trade-related influence on the efficient price of rubber futures, followed by General investors. However, the information share of each group is relatively small.
Funds have a significant and positive CIRF and thus are informed investors, while General are
liquidity providers. Funds are relatively minor participants in the rubber market when compared with the other investor groups by the number of contracts traded, but Funds’ trade ratio isrelatively volatile suggesting that, as a group, their investment strategies are conducted over ashort horizon. General investors as a group appear to contrarian to a degree, betting against thepeak in the rubber price in early 2017. It would appear that Funds may have benefited from thetrades of general investors.
The large influence of nontrade-related information on price distinguishes the rubber market
from the others we analyse. Public information accounts for around 50 percent of both theforecast error variance decomposition and the variance decomposition of the efficient price.
Public information has a greater influence on the efficient price than the two most influential
investor groups combined. We rationalise this as follows. The long-run price impact of tradesuggests that the rubber market is the least liquid of the four markets analysed. The 30-day
CIRFs for rubber are by far the greatest of the four commodities. During our sample, thestandard deviation of rubber returns is relatively high. Reports in the financial press suggest thatrubber market liquidity has declined substantially and that prices are at times detached fromthe underlying physical rubber market fundamentals (for example, see Tan (2015); Craymer(2016)), suggesting the market may be inefficient. The large influence of public informationon the efficient price is consistent with a low degree of competition among informed investors.
Surprising news has a substantial impact on the efficient price because the market is inefficient.
Further, characteristics of the rubber market and its interconnectedness may mean that it
is more difficult for investors to acquire private information that is useful for predicting future
prices. The market is segregated over two main grades of rubber, RSS-3 and TSR-20, andthree main exchanges in Tokyo, Singapore and Shanghai. Additional grades of rubber are used
for trade in markets in other locations. Information on rubber demand, supply and inventories
is relatively opaque and costly to obtain. The financial press suggests large consumers andproducers of rubber are using TOCOM less for hedging (see Craymer (2016)).
32

7. Conclusion
We present evidence on asymmetric information content of the trades of six investor groups
transacting in the gold, platinum, gasoline and rubber futures markets on TOCOM. Our results differ from the previous literature primarily by estimating the influence of investor groups’
trades on the efficient price. We find that the efficient price in the gold, platinum and gasoline
markets is mainly influenced by trade-related innovations, which we interpret as private information. In the platinum market, nontrade-related, or public, information has a non-negligible
influence on the efficient price. Foreign investors have the greatest influence over the efficient
price in the gold market, followed by Commercials. In the platinum market, the trades of both
Funds and Commercials influence the efficient price. General investors, and to a lesser extent
Dealers, influence the efficient price in the gasoline market. Both trade and non-trade related
innovations have an equal influence on the efficient price of rubber, with trades by Funds having
the largest information content in this market, followed by General investors. Given the implications of microstructure theory, our results suggest these investor groups in their respective
markets are profitable traders over the long run. We relate differences in the relative influenceof investor groups to differences in market interconnectedness, the nature of the commodity andassociated fundamental information.

33

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36

Appendix A. Contemporaneous Effects
Tables A.12 to A.15 contain the contemporaneous coefficients, the bg and cg for g = 1, . . . , 6,from the VAR model of equation (4) for each commodity. The return relationship, labelled
Eq.7, refers to the last row of the model where return is a function of the trade ratios of each
investor group, and the estimates correspond to the cg coefficients. Our prior is that the investor
group trades generally have both a contemporaneous and lagged influence on return. All traderatio contemporaneous coefficients are significant in the model for copper, the coefficients for
all groups except fund are significant in platinum and gasoline, while the coefficients on fund,general and agency are significant in the rubber market. In each market, there are both positive
and negative relationships between contemporaneous return and the trade ratios. A priori, return
may or may not have a contemporaneous relationship with each trade ratio in the relationships
denoted EQ.1 to Eq.6 for each market. We find significant estimates for the contemporaneous
return coefficient, bg , for each investor group in the gold market, for all except General inplatinum, for Dealer, General and Agency in gasoline, and for none of the investor groups in
the rubber market.
Table A.12: Contemporaneous Coefficients for Gold
Dependent variable

Explanatory variable

Coeff.

Eq.1
Eq.2
Eq.3
Eq.4
Eq.5
Eq.6

Commercial
DealerFund
GeneralAgencyForeign

ReturnReturnReturnReturnReturnReturn0.035
0.036-0.008-0.290-0.066
0.199

Eq.7

ReturnCommercial
DealerFund
GeneralAgencyForeign

14.682
11.6053.326
64.024-35.243
4.390

Standard Error

P-value

***
***
*
**
***
***

0.006
0.004
0.004
0.115
0.004
0.016

0.000
0.000
0.090
0.012
0.000
0.000

***
***
***
***
***
***

1.330
1.595
1.432
2.567
2.324
0.469

0.000
0.000
0.020
0.000
0.000
0.000

Standard Error

P-value

0.001
0.003
0.001
0.051
0.003
0.012

0.000
0.000
0.076
0.334
0.000
0.000

3.763
1.321
3.639
3.997
2.958
0.675

0.004
0.001
0.647
0.000
0.000
0.000

Table A.13: Contemporaneous Coefficients for Platinum
Dependent variable

Explanatory variable

Coeff.

Eq.1
Eq.2
Eq.3
Eq.4
Eq.5
Eq.6

Commercial
DealerFund
GeneralAgencyForeign

ReturnReturnReturnReturnReturnReturn

0.007
0.024
0.002
0.049
-0.018
0.077

***
***
*

Eq.7

ReturnCommercial
DealerFund
GeneralAgencyForeign-10.750-4.216-1.668-67.258
23.450-5.247

***
***

37

***
***

***
***
***

Table A.14: Contemporaneous Coefficients for Gasoline
Dependent variable

Explanatory variable

Coeff.

Eq.1
Eq.2
Eq.3
Eq.4
Eq.5
Eq.6

Commercial
DealerFund
GeneralAgencyForeign

ReturnReturnReturnReturnReturnReturn-0.002-0.0070.001-0.019-0.013-0.001

Eq.7

ReturnCommercial
DealerFund
GeneralAgencyForeign23.98916.7510.334
18.396-7.79110.713Standard Error

P-value

0.001
0.004
0.008
0.002
0.002
0.005

0.244
0.082
0.880
0.000
0.000
0.859

4.088
2.156
0.543
2.410
1.907
1.435

0.000
0.000
0.539
0.000
0.000
0.000

*
***
***

***
***
***
***
***

Table A.15: Contemporaneous Coefficients for Rubber
Dependent variable

Explanatory variable

Coeff.

Standard Error

P-value

Eq.1
Eq.2
Eq.3
Eq.4
Eq.5
Eq.6

Commercial
DealerFund
GeneralAgencyForeign

ReturnReturnReturnReturnReturnReturn

0.000
0.000
0.004
-0.002-0.003
0.003

0.002
0.001
0.040
0.002
0.004
0.002

0.835
0.909
0.912
0.292
0.422
0.107

Eq.7

ReturnCommercial
DealerFund
GeneralAgencyForeign-3.732-1.383
3.260-11.326-8.696-3.075

2.305
3.225
0.731
3.100
2.167
1.940

0.106
0.668
0.000
0.000
0.000
0.113

38

***
***
***