Received: 4 March 2017 DOI: 10.1002/fut.21853
|
Accepted: 4 March 2017
RESEARCH ARTICLE
The effects of investor attention on commodity futures markets Liyan Han1
| Ziying Li1 | Libo Yin2
1 School of Economics and Management, Beihang University, Beijing, China 2 School
of Finance, Central University of Finance and Economics, Beijing, China Correspondence Libo Yin, Associate Professor, School of Finance, Central University of Finance and Economics, No. 39 South College Road, Haidian District, Beijing 100081, China. Email:
[email protected]
This study utilizes the search volume for key terms on Google as a direct and timely proxy for investor attention in order to examine how attention impacts commodity futures prices, We provide significant evidence for attention’s influence on 13 commodity futures and the interaction between attention and returns, even after controlling for important macroeconomic variables. We also examine the impact of investor attention on market efficiency. Results show that rising attention, on one hand, increases information efficiency and attenuates arbitrage opportunities, whereas, on the other hand, decreases market efficiency by facilitating herd behavior.
Funding information National Natural Science Foundation of China, Grant numbers: 71671193, 71371022; Program for Innovation Research in the Central University of Finance and Economics
1 | INTRODUCTION Commodity related institutional investments grew sharply from less than $20 billion in 2003 to more than $250 billion in 2010. By the first quarter of 2016, managed futures funds had almost $333.9 billion of assets under management (AUM) according to Barclays Hedge.1 Such a rapid influx of money into commodity markets has the potential to dramatically affect commodity futures trading volumes, prices, and volatilities as well as the performance of managed futures funds. The growing importance of commodity market investments has led researchers to examine the factors that might impact the behavior of commodity futures prices and conditions that influence the efficiency of market pricing. Information efficiency plays a central role in modern financial theory. Fama (1970) argues that in an efficient market prices fully and correctly reflect available information and change only in response to the arrival of new information.2 This makes most changes in prices unpredictable because the arrival of new information is unpredictable. Information efficiency depends on (at least some) investors paying sufficient attention to the asset so they can respond quickly to news that impacts the market.3 While extreme returns, trading volume and sentiment for news and headlines are used as indirect proxies for investor attention,4 an increase in these proxies does not guarantee growing attention, unless investors actually are aware of the news of extreme returns or turnovers. With the use of the internet penetrating throughout daily life, investors not only passively absorb information from the news but also actively gather it from the Internet. The search volume on Google has been utilized as a direct, active, and
1
Available online at: http://www.barclayhedge.com/
2
Marsh and Rosenfeld (1986) show that prices may bounce between bid and ask prices even in the absence of the arrival of new information.
3
Grossman and Stiglitz (1980) have shown that perfectly efficient capital markets are impossible when information production is costly.
4
Barber and Odean (2008) provide a comprehensive review of these proxies. In the commodity futures market, evidence of the relationship between news sentiment and futures return has been provided (Gao & Süss, 2015; Smales, 2014). J Futures Markets. 2017;37:1031–1049.
wileyonlinelibrary.com/journal/fut
© 2017 Wiley Periodicals, Inc.
|
1031
1032
|
HAN
ET AL.
timely proxy for investor attention, more specifically retail investor attention (Ben-Rephael, Da, & Israelsen, 2016), and has proved influential in price efficiency in the equity market.5 As the expansion of the market inevitably exposes commodity futures to significantly more attention, empiricists have begun to investigate how investor attention impacts commodity prices. Peri, Vandone, and Baldi (2014) test the relationship between Internet searches and noise trading in corn futures prices. Li, Ma, Wang, and Zhang (2015) uses the Google search volume index (GSVI) to measure investor attention and investigate the relationships among the GSVI, different trader positions, and crude oil prices. Still, evidence for the role of investor attention on commodity futures prices is limited in scope. Following the emerging literature, we examine how investor attention impacts the behavior of commodity futures prices and ascertain the extent of market efficiency for each futures with respect to investor attention. Our primary contributions can be summarized as follows. First, we confirm attention’s influence on the returns of a range of commodity futures categories including: grains, softs, metals, and energy, unlike existing research that focuses on a specific commodity category. We consider the search queries for certain keywords as investor attention toward the futures contract. Using cocoa as an example, our study focuses on the search volume of “cocoa,” “cocoa futures,” “cocoa cost,” “cocoa price,” “cocoa production,” and “cocoa supply.” The returns of 13 futures contracts that we examine have a statistically significant relation with investor attention. The significant interaction between attention and futures returns indicates that they are mutually influential, which is consistent with the theory that attention represents the information flow to investors. The effectiveness remains when controlling for four macroeconomic variables: VIX, Economic Policy Uncertainty (EPU), the Aruoba-DieboldScotti (ADS) business conditions index, and the Treasury bill-Eurodollar futures (TED) spread, which implies that the information contained in investor attention is not included in macroeconomic sources. Second, our study sheds some new light on the findings in the literature concerning the exact role of investor attention from a perspective of market efficiency. Consistent with the evidence that searches in Google Trends directly represent retail investor attention (Ben-Rephael et al., 2016), our study highlights that the investor attention serves as an indicator for informational efficiency, but the exact role of investor attention in market efficiency remains elusive. According to Grossman and Stiglitz (1980), more information leads to more informative prices, that is, a more informationally efficient market. If increased attention leads to more information absorbed by the market, then increased attention should improve market efficiency (Vozlyublennaia, 2014). However, some researchers argue that more investor attention can create extra noise and, consequently, reduce market efficiency (Da, Engelberg, & Gao, 2015). The results show that rising attention increases information efficiency and attenuates arbitrage opportunities but conversely decreases market efficiency by facilitating herd behavior. The specific effect depends on the futures contracts and the relative magnitude of the conflicting influences. Third, although there is an extensive body of theoretical and empirical literature that examines the cointegration of spot and futures prices to ascertain the efficiency of commodity futures markets (i.e., Kellard et al., 1999; Saberi, Stoorvogel, & Sannuti, 2011; Switzer & El-Khoury, 2007), research that examines a broad swath of commodities to compare market efficiency among different futures markets is limited. Our study shows that metals futures are the most efficient of the four commodity categories examined while grains futures are the least efficient. Briefly stated, the ranking of commodity futures contracts from strongest to weakest in terms of market efficiency are metals > energy > softs > grains. Moreover, the out-of-sample results suggest timevarying market efficiency for some commodity futures contracts. Specifically, our proxy for investor attention predicted cocoa and copper futures with more accuracy than historical models for some periods, but the predictive power vanished in recent years, coincident with the financialization of the futures markets (Cheng & Xiong, 2014). Some researchers argue that financialization has increased trading in commodity futures markets and enhanced market efficiency. However, natural gas and cotton futures show a decline in measured market efficiency during the period from 2010 to 2012. In addition, our study finds both discrepant results among futures contracts that belong to the same category and similarity among contracts from different categories. We posit investors’ heterogeneous preferences in trading commodity futures as a possible explanation, and this heterogeneity is not limited by the contract’s category. For example, while copper and silver belong to metals futures, the evidence suggests that investors trading copper futures tend to take into account information over 3 consecutive days, while silver futures market participants tend to trade on information shortly after it arrives. Meanwhile, cocoa, natural gas, and gold futures display similar interactions with attention. Of course, an alternate explanation is that market participants do not behave differently but the evidence is limited. One way to further investigate this hypothesis, for example, is comparing results of CBOE wheat futures and CME Kansas wheat futures. Yet this paper does not provide additional evidence for this possibility.
5 The existing literature includes the studies of Aouadi, Arouri, and Teulon (2013), Da et al. (2011, 2015), Hou and Ding (2015), Siganos (2013), Vozlyublennaia (2014), and so forth.
HAN
ET AL.
|
1033
In the next section, we present a literature review and discuss how our findings differ from existing studies. Section 3 describes the data set that are employed in our study. Section 4 reports the significance of attention and its interaction with past returns. Section 5 presents the results of the out-of-sample analysis and illustrates time-varying market efficiency. Finally, Section 6 presents the conclusion.
2 | LITERATURE REVIEW The volatile nature of commodity futures prices has been widely studied. Traditionally, spot price, convenience yield, interest rate, and long-term price are believed to be key determinants in the term structure of futures prices (Lautier, 2005) and macroeconomic fluctuations can reflect changes in the supply and demand of commodities (i.e., Alquist & Coibion, 2014; Kilian & Murphy, 2014). With noncommercial traders, such as hedge funds or other managed money vehicles, participating in commodity futures markets, in addition to commercial hedgers (including farmers and producers), some researchers have attempted to relate the changed composition of participants to the price fluctuations (Hamilton & Wu, 2014; Tang & Xiong, 2012). However, several studies reveal that the increased trading volumes of commodity futures contracts from speculators cannot be directly credited for the recent fluctuations in commodity futures prices (Brooks, Prokopczuk, & Wu, 2015; Fattouh, Kilian, & Mahadeva, 2012; Manera, Nicolini, & Vignati, 2013; Tse & Williams, 2013). Instead, the increased investor attention to commodity market is proposed as a possible factor. Early investigations of investor attention issues in stock markets involve analyses of news announcements and the mass media effect (Barber & Odean, 2008; Dellavigna & Pollet, 2009), but several potential shortcomings are documented. For example, there is no guarantee that all the information in media announcements is obtained by investors, or that the mass media with no new information may create a sentiment that appears relevant to securities prices (Da, Engelberg, & Gao, 2011). Departing from market-based measures6 and other exogenous proxies,7 Da et al. (2011) propose the Google Search Volume Index (GSVI) as a more direct, active, and timely proxy of investor attention and find that the GSVI is correlated with, but different from, existing proxies of investor attention in a sample of Russell 300 stocks. Since their proposal, using the search for stocks’ ticker symbols to estimate Internet search activity for a given security as investor attention is commonly used (Ding & Hou, 2015; Drake, Darrenand, & Thornock, 2012; Vlastakis & Markellos, 2012). Additionally, the search volumes for economyrelated keywords are examined (Dzielinski, 2012; Vozlyublennaia, 2014). Further, Ben-Rephael et al. (2016) compare abnormal institutional investor attention (AIA) using news searching and news reading activity for specific stocks on Bloomberg terminals with retail investor attention measured by Google search activity. Empiricists have studied the efficiency of commodity futures markets, by examining convergence of spot and future prices. Kellard et al., 1999 propose that the degree of inefficiency in a market may be measured in terms of the ability of the futures price to forecast the subsequent spot price relative to the forecast produced by the best fitting quasi-ECM and suggest the soybean market is efficient, the gasoil market is 1% inefficient, the hogs market is 7% inefficient, the Brent crude market is 12% inefficient, and the cattle market is 47% inefficient. Peroni and Mcnown (1998) presents a critique of tests of market efficiency and support the weak and semistrong efficiency in three energy futures markets by testing the equivalence of the data generation processes of the spot and futures prices. Switzer and El-Khoury (2007) investigates the efficiency of the New York Mercantile Exchange (NYMEX) Division light sweet crude oil futures contract market and crude oil futures contract prices are found to be cointegrated with spot prices and unbiased predictors of future spot prices. Saberi et al., 2011 study the market efficiency and unbiasedness among WTI futures with different maturities tested. Additionally, Westerlund, Norkute, and Narayan (2015) advance a factor analytical approach to the efficient futures market hypothesis. Still, there is a limited amount of research that investigates the market efficiency in commodity futures markets. Besides, evidence for the role of investor attention on commodity futures prices is limited in scope and its impact on the market efficiency of commodity futures has not been studied extensively. Several studies pertinent to the impact of investor attention or investor sentiment on futures returns have been conducted. For example, Smales (2014) utilizes commodity specific news sentiment data to examine the relationship between news sentiment and returns in the gold futures market over the period 2003–2012. Peri et al., 2014 focus on corn futures prices and show that search activity on Google intensifies the volatility produced by negative shocks. Li et al. (2015) verify the feedback loop between GSVI activity and crude oil prices, and contend that GSVI activity improves the forecast accuracy of crude oil prices in recursive 1-week-ahead forecasts. In summary, the influence of investor attention on commodity futures
6
Market-based measures include trading volume (Baker & Stein, 2004), dividend premium (Fama & French, 2002), mutual fund flows (Frazzini & Lamont, 2008), and option implied volatilities (Whaley, 2000). 7 There are various exogenous proxies for investor attention and include: investor mood (Dowling and Lucey, 2005; Edmans et al., 2007), salient events such as record-breaking events (Yuan, 2015), aviation disasters (Kaplanski & Levy, 2010).
1034
|
HAN
ET AL.
TABLE 1 Statistics of futures returns Commodities
Obs.
Exchange
Mean
Std.
Skew
Kurt
Jarque–Bera
Grains Corn
3,281
CME
0.0003
0.0198
−0.3403
11.2262
9,314.5
Soybeans
3,283
CME
0.0002
0.0183
−0.5663
18.8322
34,463.4
Chicago wheat
3,284
CME
0.0002
0.0213
0.2147
4.8109
473.9
Cocoa
3,202
ICE
0.0003
0.0185
−0.2031
6.3824
1,548.4
Coffee
3,192
ICE
0.0001
0.0273
−15.2276
563.8573
4.20E + 07
Cotton
3,031
ICE
0.0002
0.0204
−0.5849
17.9781
28,505.5
Sugar
3,062
ICE
0.0007
0.0230
0.2066
9.5646
5,519.9
Copper
3,265
NYMEX
0.0005
0.0202
0.1809
11.0695
8,876.3
Gold
3,270
NYMEX
0.0004
0.0146
0.3111
28.6124
89,432.3
Silver
3,286
NYMEX
0.0006
0.0219
−0.6853
8.2577
4,042.0
Softs
Metals
Energy WTI crude oil
3,286
NYMEX
0.0005
0.0249
0.3244
8.3025
3,907.3
Heating oil
3,292
NYMEX
−0.0004
0.0224
−0.2486
6.3315
1,556.3
Natural gas
3,268
NYMEX
0.0003
0.0328
1.0199
9.4376
6,209.6
This table displays the summary statistics for the returns of commodity futures that are examined in this study. The second column shows the number of observations for each series and the third column indicates in which exchange the commodity futures is traded. We examine daily data from January 2004 to January 2017 for each futures but observations for each of the futures vary because the futures are not traded on exactly the same dates. In the following columns, average, standard deviation (std. dev.), skewness, and kurtosis are reported for each series. The last column is the statistic of Jarque–Bera test of whether sample data have the skewness and kurtosis matching a normal distribution. Samples from a normal distribution have an expected value of zero for JB statistic. Any deviation from this increases the JB statistic.
returns has not been broadly confirmed; the existing evidence for investor attention reflecting information and pricing efficiency in commodity futures markets is limited in scope. Simply stated the literature regarding the exact impact of investor attention on market prices yields mixed results.
3 | DATA AND METHODOLOGY 3.1 | Indexed and off-index commodity futures We focus on 13 individual futures contracts that are actively traded in the U.S. across four categories: softs, grains, metals, and energy. Specifically, they are as folloes: cocoa, coffee, cotton, sugar (i.e., softs), corn, soybeans, Chicago wheat (i.e., grains), WTI crude oil, heating oil, natural gas (i.e., energy), copper, gold, and silver (i.e., metals). We examine daily data from January 2004 to January 2017 and calculate daily log returns. Because the futures are not traded on exactly the same dates, observations for each of the futures vary. Each time series contains approximately 3,200 observations. The details concerning selected commodities are illustrated in Table 1.
3.2 | Investor attention We collect the daily Search Volume Index (SVI) of several keywords from Google Trend8 to measure investor attention. In addition to the SVI of the futures’ name, keywords that combine the name with identifiers of “price,”9 “futures,” “production,” and “supply” are downloaded. Each keyword has approximately 3,200 observations, except for searches for: “heating/WTI oil 8
Available online at: http://www.google.com/trends
Note that in addition to searching for “cocoa price,” we also searched for “cocoa prices” and “price of cocoa.” Given that these two search terms are essentially the same as “cocoa price” we only present the results of the search with the strongest significance. If none of them is statistically significant, we present the result of the search for “cocoa price.”
9
HAN
|
ET AL.
1035
supply,” “heating/WTI oil production,” “soybean supply.” To mitigate the outliers, we winsorize each series at the 5% level (2.5% in each tail) and compute the daily percentage change of each search term. Since some keywords have no search volume for certain periods, for example, if SVIi;t equals zero in a certain series for which percentage computation would not make sense, we revise the data by replacing those zeros with ones so that ΔSVIi;t remains unchanged when both SVIi;t and SVIi;t1 are zero, but when SVIi;t becomes positive, the changes would be presented by an augmented positive ΔSVIi;t .
3.3 | Macroeconomic variables We include the CBOE Volatility Index (VIX), Economic Policy Uncertainty (EPU), the Aruoba-Diebold-Scotti business conditions index (ADS), and the Treasury bill EuroDollar spread (TED) as control variables for in-sample analysis. The VIX is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices. The daily news-based Economic Policy Uncertainty Index is based on newspaper archives from the Access World News NewsBank service, whose database contains the archives of thousands of newspapers and other news sources from across the globe. The ADS is designed to track real business conditions at a high frequency. Its underlying (seasonally adjusted) economic indicators (weekly initial jobless claims, monthly payroll employment, industrial production, personal income less transfer payments, manufacturing and trade sales, and quarterly real GDP) blend high- and low-frequency information and stock and flow data. The TED is calculated as the spread between 3-month LIBOR based on U.S. dollars and the yield on recently issued 3-month U.S. Treasury bills. Daily data for each are available online.10 We download and adjust the time series for the same time period.
4 | IN-SAMPLE ANALYSIS OF INVESTOR ATTENTION AND FUTURES RETURNS 4.1 | Multiple univariate regressions Given the significant impacts of investor attention in the form of SVI on stock markets (Da et al., 2011, 2015; Vozlyublennaia, 2014; etc.), we expect similar effects between investor attention and futures. To our knowledge, investor attention effects are seen immediately and over the short run (Stambaugh, Yu, & Yuan, 2012), so we commence our analysis by using the following benchmark model: m
Rt ¼ αRt1 þ ∑βk Sk;ti þ c
ð1Þ
i
where Rt1 denotes the daily one-lag log returns of futures and Sk;ti denotes the corresponding search volume for one specific keyword for term k with i lags. m refers to the longest lag, in this case 5 for 1 week. The unreported results show that there is autocorrelation within Sk , which may affect the significance of the results. Therefore, we split the multivariate regression into multiple “univariate” regressions as follows: Rt ¼ αRt1 þ βk;i Sk;ti þ c i ¼ 0;1; . . .5
ð2Þ
For the sake of brevity, only portions of the results are displayed in Table 2. For soft futures, the search volume is influential instantaneously on futures returns, and the effect attenuates in 5 days. In contrast, for grain futures, St4 and St5 are statistically significant, while St and St1 display little significant relationship. For energy and metals futures, the influence of search volume tends to remain throughout a week. This inconsistency among commodity futures suggests different levels of information efficiency in these markets. According to Fama (1970), market efficiency, refers to the condition where information is “fully reflected” into prices. We use the speed at which new information is incorporated into prices as a measure of the degree of market efficiency—the faster the new information is incorporated into prices the higher degree of market efficiency. Preliminarily, the significance of the timely relationship between the search volume and soft futures returns reveals a relatively higher level of market efficiency, whereas the significance of the lag relationship for grains futures returns suggests its market efficiency is at the lower level. The market efficiency of the energy and metals futures markets is more complicated and is discussed in more detail in section 5.2. 10
We download VIX from available online at: http://www.cboe.com/micro/vix/historical.aspx, EPU from available online at: http://www.policyuncertainty. com/us_daily.html, ADS from available online at: https://www.philadelphiafed.org/research-and-data/real-time-center/business-conditions-index, and TED from available online at: https://fred.stlouisfed.org/series/TEDRATE
1036
|
HAN
ET AL.
TABLE 2 Multiple univariate regressions on search terms Cocoa
S (t)
S (t−1)
S (t−2)
S (t−3)
S (t−4)
S (t−5)
−0.00079
0.00226
0.00082
−0.00163
0.00249
0.00063
−0.00001
−0.00006
−0.00004
0.00009**
0.00004
−0.00002
−0.00006
−0.00002
0.00004
0.00005
−0.00013***
0.00010**
0.00091*
−0.00055
0.00074
−0.00095*
−0.00025
0.00040
0.00012
0.00012
0.00004
−0.00005
−0.00010**
0.00006
0.00006
−0.00261
0.01681
−0.00302
0.00572
−0.00176
0.00020
0.00042
0.00056
−0.00039
0.00080
0.00012
0.00076
0.00054
−0.00026
0.00055
0.00001
-0.00052
0.00122
0.00001
−0.00014
0.00204**
−0.00017
−0.00100
0.00147
0.00055
0.00044
−0.00008
0.00045
−0.00083
−0.00052
−0.00713
−0.00180
−0.00592
−0.00641
0.00647
0.00000
−0.00018
0.00048
0.00087
−0.00152**
−0.00004
−0.00006
−0.00022*
−0.00004
−0.00015
0.00129***
0.00171*
0.00091*
0.00000
−0.00070
−0.00105
0.00017
0.00061
−0.00085
0.00004
−0.00053
0.00016
0.00027
−0.00013
0.00063
−0.01154
0.00179
0.01014
0.00645
−0.00331
−0.00079
0.00110
0.00054
−0.00096
0.00095
0.00098
0.00012
−0.00115
−0.00010
0.00096
−0.00063
0.00001
−0.00230***
0.00196**
0.00011
−0.00023
0.00029
−0.00024
0.00099
−0.00067
(0.00369) Cost
0.00009** (0.00004)
Futures
0.00004 (0.00004)
Price
0.00014*** (0.00005)
Production
0.00066 (0.00049)
Supply
0.00008* (0.00004)
Coffee
−0.00008 (0.01070)
Cost
0.00010 (0.00119)
Futures
−0.00025 (0.00083)
Price
−0.00020 (0.00140)
Production
−0.00253*** (0.00098)
Supply
−0.00064 (0.00097)
Cotton
−0.00216 (0.00502)
Cost
0.00021 (0.00060)
Futures
0.00004 (0.00004)
Price
−0.00099** (0.00044)
Production
0.00066 (0.00049)
Supply
0.00040 (0.00056)
Sugar
0.00265 (0.00804)
Cost
0.00059 (0.00116)
Futures
−0.00036 (0.00090)
Price
0.00176*** (0.00084)
Production
−0.00029
(Continues)
HAN
|
ET AL.
TABLE 2
1037
(Continued)
S (t)
S (t−1)
S (t−2)
S (t−3)
S (t−4)
S (t−5)
0.00095
−0.00120*
0.00110
0.00021
−0.00070
−0.00282
0.00652
0.00149
−0.00400
0.00128
0.00044
−0.00113*
−0.00051
0.00020
−0.00019
−0.00036
0.00046
−0.00060
0.00017
0.00097
0.00090
−0.00129*
0.00062
−0.00043
0.00166**
−0.00055
0.00040
0.00037
−0.00151**
0.00132**
0.00064
0.00010
−0.00038
0.00020
−0.00055
−0.00023
0.00016
0.00013
0.00089
−0.00222
0.00005
0.00001
0.00002
0.00006
0.00001
−0.00003
−0.00051
−0.00039
0.00022
−0.00083
−0.00059
0.00004
−0.00004
0.00038
−0.00136**
0.00934*
−0.00989*
−0.00219
0.00234
−0.00628
−0.00392***
0.00012
0.00063
−0.00069
0.00147
0.00020
0.00100
−0.00162
−0.00046
0.00190*
−0.00132
−0.00108
−0.00065
0.00248*
−0.00095
0.00055
−0.00143
0.00160
−0.00090
0.00036
−0.00045
−0.00263**
0.00091
0.00077
0.00077
0.00015
−0.00194
−0.00064
0.00041
0.00117
0.00101
−0.00011
0.00016
0.00108*
−0.00043
0.00059
−0.00020
−0.00049
−0.00069
0.00061
0.00087
−0.00122*
−0.00038
−0.00174*
0.00113
(0.00085) Supply
−0.00074 (0.00072)
Corn
0.00187 (0.00435)
Cost
−0.00001 (0.00068)
Futures
0.00022 (0.00061)
Price
−0.00121 (0.00084)
Production
−0.00031 (0.00062)
Supply
−0.00027 (0.00054)
Soybean
−0.00223 (0.00157)
Cost
Futures
−0.00003 (0.00007)
Price
−0.00125** (0.00063)
Production
−0.00012 (0.00060)
Supply Natural gas
0.00245 (0.00532)
Cost
−0.00022 (0.00130)
Futures
−0.00186* (0.00112)
Price
0.00084 (0.00151)
Production
−0.00031 (0.00108)
Supply
−0.00238** (0.00105)
Heating oil
−0.00078 (0.00153)
Cost
0.00063 (0.00061)
Futures
0.00005 (0.00080)
Price
0.00193**
(Continues)
1038
|
TABLE 2
HAN
ET AL.
(Continued)
S (t)
S (t−1)
S (t−2)
S (t−3)
S (t−4)
S (t−5)
−0.00110
−0.00526
−0.00237
−0.00244
0.01385**
0.00021
−0.00024
0.00020
0.00035
0.00160**
0.00028
−0.00072
0.00124**
−0.00100
−0.00013
0.00077
0.00192*
0.00407***
0.00270**
−0.00021
−0.00144**
−0.00017
0.00018
0.00092
−0.00086
−0.00144**
0.00013
0.00039
−0.00042
0.00104
0.00193
−0.00065
−0.00729
0.00775
0.00912
−0.00084
−0.00001
−0.00058
0.00113
−0.00034
−0.00014
0.00051
0.00043
−0.00064
0.00146**
0.00192*
−0.00078
−0.00352**
−0.00052
0.00396**
0.00016
−0.00018
−0.00004
0.00022
0.00145***
0.00021
-0.00062
0.00006
0.00028
0.00084
0.00658
−0.00054
−0.01579*
0.00366
0.01997**
0.00201*
−0.00053
−0.00044
0.00240**
0.00030
0.00038
0.00018
−0.00010
0.00051
−0.00073
−0.00230*
0.00506***
−0.00361**
−0.00012
0.00141
−0.00119
0.00078
0.00009
−0.00061
0.00000
−0.00047*
0.00048
0.00057
0.00030
−0.00014
(0.00101) Production
Supply Copper
0.00833 (0.00563)
Cost
0.00046 (0.00080)
Futures
−0.00064 (0.00062)
Price
−0.00076 (0.00121)
Production
0.00133** (0.00066)
Supply
0.00091 (0.00070)
Gold
0.00003 (0.00676)
Cost
0.00114 (0.00083)
Futures
0.00037 (0.00055)
Price
0.00313*** (0.00108)
Production
−0.00051 (0.00051)
Supply
0.00036 (0.00059)
Silver
0.00671 (0.00927)
Cost
−0.00154 (0.00104)
Futures
−0.00053 (0.00056)
Price
0.00225* (0.00134)
Production
−0.00009 (0.00070)
Supply
0.00036 (0.00079)
Table 2 reports the test results of multiple univariate regressions on each search term. The dependent variables are contemporaneous futures returns. The independent variable is the percentage change of the search query within that day and in the past 5 days. Of course, the variable of past returns with one lag is also included for control. The data of futures returns and Google search volumes is obtained at daily frequency for January 2004 to January 2017. For brevity, only portions of the results are displayed. The first column shows the keywords of search terms and the search data are obtained from Google Trend. Searches for “heating/WTI oil supply,” “heating/WTI oil production,” “soybean supply” are insufficient so the corresponding parts remain blank. The first row shows the coefficients and the second row shows the standard deviations. ***Significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
HAN
|
ET AL.
1039
When i ¼ 0, the significant coefficient on St indicates a positive contemporaneous relationship between the search volume and futures returns. For example, a 10% increase in the search for “gold price” corresponds to three-basis point increase in the returns of gold futures, which is a significant amount of money given the par value per contract. The futures of silver, WTI oil, heating oil, cocoa, and sugar present similar relationships with investor attention. Interestingly, for cotton and soybean futures, increasing the search for price leads to a decline in returns. These seemingly ambivalent results also occur when we examine the relationship between futures returns and the search for “production” or “supply.” Intuitively, when investors have concern about the price of particular futures, the corresponding search volume would increase. However, the futures returns do not necessarily change because the increase in search volume for “price” does not directly indicate whether the traders feel optimistic or pessimistic toward this market. Since the search volume can be of either positive or negative role, we infer that investor attention represented by searches in Google Trends is “neutral,” unlike investor mood that has clear indication for investors’ motivation nor investor sentiment that usually refers to pessimism. Consistent with the evidence that searches in Google Trends directly represent retail investor attention (Ben-Rephael et al., 2016), we highlight that the investor attention serves as an indicator for informational efficiency. In other words, rising investor attention reflects an increase in the speed at which new information is incorporated but does not indicate which direction the price is going toward. Moreover, we examine the reversals in the adjacent coefficients on Sti of the same search term for one contract because reversals for a larger span could be meaningless regarding the autocorrelation in the search volume. For instance, the significant coefficient on St indicates a positive contemporaneous relationship between the search volume and futures returns for “copper production,” while the coefficient on St1 is negative. “Silver price” and “cocoa price” have identical results, “natural gas” displays similar results between St1 and St2 , and “coffee production” presents results with exactly the opposite coefficients. In the equity markets, a price increasing in the short term and decreasing in the long run is attributed to net-buying behavior (Barber & Odean, 2008). When individual investors are buying, they must choose from a large set of available alternatives, but when they are selling, they can only sell what they own. Therefore, the shocks to retail attention should lead to net buying from these uninformed traders, for which a positive St1 predicts higher prices and a positive St corresponds to a decline. The framework of Barber and Odean (2008) nevertheless could not explain the results of “coffee production,” whose price is decreasing in the short term (negative St ) and increasing in the long run (positive St1 ). On the other hand, the results of “copper price” and “gold price” present a trend rather than reversals. For “copper price,” coefficients of consecutive St2 , St3 , and St4 are all positive and both St and St1 of “gold price” play a positive role on gold futures returns. We relate this finding to the study of Han, Hu, and Yang (2016), in which a simple moving average timing strategy generates superior performance to the buy-and-hold strategy by exploiting trends in commodity futures prices. The results suggest the possibility of exploitable trends in gold and copper futures prices from the perspective of attention. Meanwhile, we note that although gold, copper, silver all belong to the metals category, inconsistent results are shown. We interpret this as a signal to the heterogeneity in commodity futures markets. For example, the evidence suggests that investors trading copper futures tend to take into account information over 3 consecutive days, while silver futures market participants tend to trade on information shortly after it arrives. There are other examples in section 4.3 concerning the discrepancy among futures contracts of the same category. In summary, among the 13 futures, all of their returns are significantly related to the search volume of one or more key search terms, in a contemporaneous or lagged manner, suggesting that search volume is a factor affecting the prices in the commodity futures markets.
4.2 | Allocate the Increment of the Search Volume This paper omits further discussion of the specific impact of each key search term and considers that investor attention for one futures should include the search for all the relative information instead of merely the information for price or production. Attention is measured as the sum of the incremental contributions of each statistically significant key search term. The incremental contributions of insignificant key search terms are not included. The method for allocation is simple, as shown in the following formula: ( nk
Ati ¼ ∑ð1Þ Sk;ti k
nk ¼ 0; if βk > 0 nk ¼ 1; if βk < 0
ð3Þ
If only one search term is statistically significant, the allocated attention directly equals the search for this term. Note that, for example, we allocate the increment based on the significance of Sk;t for At and that of Sk;t1 for At1 . Thus, there would be
1040
|
HAN
ET AL.
i time series for attention when we attempt to examine the influence of i lags. Herein, we concentrate on the most timely effects of At and At1 so that the results can also serve as robustness checks for each other. We regress futures returns on At and At1 separately as: Rt ¼ αRt1 þ γk;i Ak;ti þ c i ¼ 0; 1
ð4Þ
Recall that Table 2 shows that corn, wheat, sugar, and heating oil futures do not necessarily show that the search terms are significant when i = 0 and i = 1, their corresponding regressions are blank, as presented in Table 3. After allocation, the significance is at a stronger level (i.e., it increases from a statistically significant 10% to 5% or from 5% to 1%), and this strengthened significance remains consistent between At and At1 . To discern the effects of investor attention from those of the volatility of the stock markets, macroeconomic uncertainty, business conditions, and liquidity risk, we include four macroeconomic variables: VIX, EPU, ADS, and TED. The results are presented in Appendix 1, as the inclusion of control variables neither diminishes the role of attention nor significantly changes its magnitude, which shows that the significance of investor attention is not replaced by macroeconomic variables.
4.3 | Interactive model Next, we proceed to investigate the relationship between attention and futures returns. Based on the feedback loop between returns and attention that has been discovered in stock markets (Vozlyublennaia, 2014) and crude oil futures markets (Li et al., 2015), we use an interactive model as follows: Rt ¼ α1 Rt1 þ γ1 At1 þ δ1 At1 Rt1 þ c
ð5Þ
where coefficient α1 measures the change in the impact of the past returns on the current return when there is no change in attention and γ1 functions in the opposite manner. The coefficients on the interaction term δ1 measure the change in the effect of attention on the futures returns conditional on a unit increase in the past return, and ðγ1 þ δ1 Þ measures the total impact of attention exerted upon returns based on each unit increase of past returns. The returns of cocoa, cotton, WTI oil, natural gas, and gold futures are shown (see Table 4) to rapidly interact with attention, but they are slightly different in their manner of interaction. The results controlling for macroeconomic variables are presented in Appendix 2. For cotton, natural gas, and gold futures with positive γ1 and negative δ1 , At1 is more influential when the past returns are negative, suggesting herd behavior in the market; when traders are pessimistic concerning the negative returns, it is more likely they will follow others’ decisions, and thus, there is a higher chance for the price of futures to move in one direction. Conversely,
TABLE 3 Regressions on allocated attention A (t−1)
A (t)
A (t−1)
A (t)
WTI oil
Natural gas
Heating oil
Copper
Gold
Silver
−0.00264***
0.00434***
−0.00144***
0.00194*
0.00159***
(0.00094)
(0.00127)
(0.00048)
(0.00107)
(0.00053)
0.00010*
−0.00208***
0.00133**
0.00307***
0.00225* (0.00134)
0.00193**
(0.00006)
(0.00076)
(0.00101)
(0.00066)
(0.00107)
Cocoa
Coffee
Cotton
Sugar
Soybean
−0.00014***
0.00204**
0.00129***
(0.00004)
(0.00098)
(0.00044)
0.00008***
−0.00253***
0.00008***
0.00176***
−0.00125**
(0.00002)
(0.00098)
(0.00002)
(0.00084)
(0.00063)
Table 3 reports the test results of regressions on the allocated attention series. The allocated attention is measured as the sum of the incremental contributions of each statistically significant key search term in Table 2. Note that we allocate the increment based on the significance of Sk,t for At and that of Sk,t−1 for At−1. Herein, we concentrate on the most timely effects of At and At−1 and they are regressed, respectively. The variable of past returns with one lag is also included for control but for brevity only portions of the results are displayed. Since corn, wheat, sugar, and heating oil futures do not necessarily have search terms significant when i = 0 and i = 1 (see Table 2), their corresponding regressions are blank. ***Significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
HAN
|
ET AL.
1041
TABLE 4 Results of interaction models Cocoa
Coffee
Cotton
WTI oil
Natural gas
Copper
Gold
Silver
R (t−1)
A (t−1)
R (t−1)*A (t−1)
C
0.02839
−0.00012***
−0.00523**
0.00053
(0.01837)
(0.00004)
(0.00204)
(0.00034)
−0.03640
0.00204**
−0.00692
−0.00010
(0.02362)
(0.00098)
(0.04718)
(0.00049)
0.10859***
0.00125***
−0.06330***
−0.00017
(0.01917)
(0.00044)
(0.02224)
(0.00038)
−0.07676***
−0.00257***
−0.07961**
0.00072
(0.01784)
(0.00094)
(0.03469)
(0.00044)
−0.06221***
0.00430***
−0.09685**
0.00065
(0.01752)
(0.00127)
(0.03930)
(0.00058)
−0.12545***
−0.00147***
0.02076
0.00085**
(0.01788)
(0.00048)
(0.02451)
(0.00037)
−0.15507***
0.00235**
−0.42406***
0.00059**
(0.01732)
(0.00107)
(0.07037)
(0.00025)
−0.02316
0.00161***
−0.01934
0.00069*
(0.01766)
(0.00053)
(0.02436)
(0.00039)
This table reports estimation results for the effect of allocated attention with one lag A (t−1) on futures returns R (t) conditional on past returns R (t−l). The regressions include interaction terms between lagged attention and lagged returns. The coefficients on the interaction term measure the change in the effect of attention on the futures returns conditional on a unit increase in the past return, and measures the total impact of attention exerted upon returns based on each unit increase of past returns. ***Significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
when the past returns are positive, one-lag attention has a lower magnitude. The reasoning behind this phenomenon is as follows: As positive returns are viewed as normal by investors, an increase in attention benefits the information efficiency with useful information quickly absorbed by the market. Thus, the arbitrage opportunities diminish, and the prices of futures become less predictable by attention. In contrast, for WTI oil futures with both γ1 and δ1 negative, At1 has a stronger impact on current returns when the past returns are positive. We relate to the coefficient α1 , or the autocorrelation of futures returns, to interpret this result. WTI oil is shown to relate negatively with its one-lag returns. With both γ1 and δ1 negative, it means when one-lag returns are positive, increasing attention intensifies this negative autocorrelation of WTI oil futures returns, indicating that the market is restoring a balance, which ideally refers to zero excess returns. Conversely, when one-lag returns are negative, the increase in attention attenuates the autocorrelation of WTI oil futures returns, consistent with the theory that higher information efficiency leads to less predictability of prices. Both scenarios reveal a high level of market efficiency in the WTI oil futures market. Interestingly, regarding these different manners of interaction, we note that the role of investor attention on market efficiency is mixed. On one hand, rising attention for cotton, natural gas, and gold futures decreases market efficiency by facilitating herd behavior when the past returns are negative. On the other hand, it increases information efficiency and attenuates arbitrage opportunities when past returns are positive. Nevertheless, the results of WTI oil futures show that whatever past returns are attention plays a positive role on the market efficiency. In short, these mixed results suggest that the market efficiency in commodity futures tends to depend on the futures contracts and historical market returns. Moreover, we note from these results that there are apparent discrepancies among futures contracts in the same category and similarities among contracts in different categories. While cotton and cocoa both belong to softs, they actually have different interactions with investor attention. Cocoa, natural gas, and gold futures, however, display similar interactions with attention. This finding serves to support our analysis in section 4.1 concerning the heterogeneous preferences of investors in trading commodity futures. All in all, we construe the significance of interaction as evidence that attention reflects the information flows to investors and support that investor attention and returns are mutually influential.
1042
|
HAN
ET AL.
FIGURE 1
(a) CSPE for bench model of cocoa. (b) CSPE for interactive model of cocoa. (c) CSPE for bench model of coffee. (d) CSPE for interactive model of coffee. (e) CSPE for bench model of cotton. (f) CSPE for interactive model of cotton. (g) CSPE for bench model of copper. (h) CSPE for interactive model of copper. (i) CSPE for bench model of gold. (j) CSPE for interactive model of gold. (k) CSPE for bench model of silver. (l) CSPE for interactive model of silver. (m) CSPE for bench model of natural gas. (n) CSPE for interactive model of natural gas. (o) CSPE for bench model of WTI oil. (p) CSPE for interactive model of WTI oil. Figure 1 (a–p) present the differences between the cumulative square prediction error (CSPE) for the historical average benchmark forecast and the CSPE for the forecasts based on the individual predictive regression models. If the curve is above (below) the horizontal zero line, the restricted model has a lower (higher) cumulative squared prediction error than the historical average. Meanwhile, the CSPE figure reveals the timing when the predictive power of attention switches to surpass (fall behind) that of the historical average. A predictive regression model that always outperforms the historical average for any out-of-sample period will thus, have a curve with a slope that is always positive; the closer a predictive regression model is to this ideal, the greater its ability to consistently outperform the historical average in terms of CSPE (Rapach et al., 2009) [Color figure can be viewed at wileyonlinelibrary.com]
5 | OUT-OF-SAMPLE ANALYSIS AND MARKET EFFICIENCY Up to this point, we have analyzed the significance of search volume and the interaction between attention and futures returns. We confirm that the information efficiency reflected by attention in the commodity futures market is consistent with the traditional theory concerning market efficiency and predictability of prices. With the goal of obtaining a further understanding of
HAN
|
ET AL.
FIGURE 1
1043
Continued.
the market efficiency in commodity futures markets, we attempt to use investor attention to predict futures returns in an out-ofsample analysis. A statistically advantageous prediction refers to relatively lower market efficiency and vice versa.
5.1 | Definitions of CSPE We conduct an out-of-sample analysis on the three models. Two of them are the unrestricted models that were presented in the previous section as Eqs. (2) and (5). The third model is a benchmark model where Rj;tþ1 is forecast as the historical average from 0 to period t. b c and Using data from 0 to period t, the forecast stock returns at period t + 1 are estimated by Rd j; tþ1 ¼ β1 Rj;t þ γb1 Aj;t þ b b b c . The predicted stock return of the benchmark model for month t + 1 is Rj;t . Herein, the Rd j; tþ1 ¼ β1 Rj;t þ γb1 Aj;t þ δ1 Rj;t Aj;t þ b initial parameter is estimated based on weekly data from January 2004 to December 2007. We introduce the cumulative square prediction error (CSPE) into our analysis. The differences between the cumulative square prediction error (CSPE) for the historical average benchmark forecast and the cumulative square prediction error for the forecasts based on the individual predictive regression models are plotted in Figure 1. If the curve is above (below) the horizontal zero line, the
1044
|
FIGURE 1
HAN
ET AL.
Continued.
restricted model has a lower (higher) cumulative squared prediction error than the historical average. Meanwhile, the CSPE figure reveals the timing when the predictive power of attention switches to surpass (fall behind) that of the historical average. A predictive regression model that always outperforms the historical average for any out-of-sample period will thus have a curve with a slope that is always positive; the closer a predictive regression model is to this ideal, the greater its ability to consistently outperform the historical average in terms of CSPE (Rapach, Strauss, & Zhou, 2009).
5.2 | OOS results Figure 1 reveals that cotton and coffee futures maintain a positive CSPE throughout the sample period, meaning their futures returns are better predicted by attention the majority of the time. Conversely, with regard to gold and silver futures, attention has little advantage in predicting the returns. These reveal that the market efficiency of coffee and cotton futures is relatively weaker, while that of gold and silver futures is strong. Other futures reside between these two levels. There are more intriguing findings from the CSPE results. First, some futures present an improvement in their market efficiency. The cocoa futures market efficiency improves after 2010, and that of copper futures improves after 2012. This possibly results from the process of financialization, that is, commodity futures became a popular asset class for investors similar to stocks and bonds (Cheng & Xiong, 2014). Consequently, trading volume in cocoa and copper futures markets is increased and market efficiency improves, diminishing the predictability of futures returns. Second, some futures display a decline in market efficiency. For instance, the level of investor attention was not a good predictor of natural gas futures returns in 2010, but soon afterward the returns predicted by attention have a higher accuracy. This could be because at that time a significant amount of hot money from emerging markets flowed into the natural gas futures market. However, the reason for the dramatic changes in the accuracy of forecasting cotton futures returns in 2012 remains elusive. Third, the interaction model of WTI oil has apparent merit over the benchmark model. Referring to the results in section 4.3, a strong interaction indicates higher information efficiency for WTI oil futures. Meanwhile, the fact that it is less predictable by attention suggests it has a higher level of market efficiency than natural gas. Based on the previous discussion concerning market efficiency of these 13 commodity futures markets, we can briefly rank them (see Figure 2). The sugar and grains futures have the weakest efficiency as they tend to be impacted by further lagged
HAN
ET AL.
|
1045
FIGURE 2
Ranking futures contracts by market efficiency. Figure 2 displays the rank of market efficiency for the 13 commodity futures examined in this study. We rank them based on the previous discussion. Cotton and coffee futures rank the second weakest as they are better predicted by one-lag attention the majority of the time, while cocoa futures are slightly better because their efficiency improves after 2010. Next are natural gas futures and WTI oil futures because of their better interaction with attention. Heating oil futures are considered to be at a higher level of efficiency than WTI oil futures given the insignificant coefficient of one-lag investor attention in section 4.2. Finally, metals futures have the strongest market efficiency [Color figure can be viewed at wileyonlinelibrary.com]
attention. Cotton and coffee futures rank the second weakest as they are better predicted by one-lag attention the majority of the time, while cocoa futures are slightly better because their efficiency improves after 2010. Next are natural gas futures and WTI oil futures because of their better interaction with attention. Heating oil futures are considered to be at a higher level of efficiency than WTI oil futures given the insignificant coefficient of one-lag investor attention in section 4.2. Finally, metals futures have the strongest market efficiency.
6 | CONCLUSION We investigate the impact of investor attention on four categories of commodity futures: energy, grains, softs, and metals. We select the most actively traded contracts for each category. Investor attention, represented by search volume on Google, is shown to impact the price of commodity futures in a timely fashion and affect futures returns. Based on the results, we analyze the market efficiency for each futures contract and rank them. We confirm that the search volume in Google Trends is statistically significant for commodity futures, even when controlling for macroeconomic variables. The significance is stronger after allocating the increment of search volume to each individual futures contract. The timely interaction between investor attention and commodity futures returns indicates that attention and returns are mutually influential, which is in accordance with the theory that attention reflects the information flow to the investors. More interestingly, when examining the exact role of investor attention in market efficiency, our study follows neither the approach of Peri et al. (2014) nor that of Li et al. (2015). Our results show that rising attention increases information efficiency and attenuates arbitrage opportunities, but it can also decrease market efficiency by facilitating herd behavior. The specific effect depends on the futures contracts. During an out-of-sample analysis, we further analyze the predictability of futures returns and report time-varying market efficiency in commodity futures markets. We rank the contracts from strongest to weakest market efficiency as follows: metals > energy > softs > grains. Some results remain intriguing. The market efficiency of cocoa futures improves after 2010, and that of copper futures improves after 2012, whereas the efficiency in the natural gas futures market weakens in approximately 2010, and that in cotton futures market declines in approximately 2012. Additionally, our study presents discrepant results among futures contracts that belong to the same commodity category. However, there are similarities among contracts from different commodity categories. This finding is a reminder that the characteristics for each commodity futures contract may explain several seemingly ambivalent results in the empirical analysis. Further investigation into this heterogeneity is needed.
1046
|
HAN
ET AL.
ACKNOWLEDGMENTS This research is financially supported by the National Natural Science Foundation of China under projects No. 71671193 and No. 71371022, the Program for Innovation Research in the Central University of Finance and Economics. ORCID Libo Yin
http://orcid.org/0000-0003-0193-6735
REFERENCES Alquist, R., & Coibion, O. Commodity-price comovement and global economic activity (March 2014). NBER Working paper No. w20003. Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35(3), 674–681. Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271–299. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785–818. Ben-Rephael, A., Da, Z., & Israelsen, R. D (2016). It depends on where you search: Institutional investor attention and under-reaction to news. Kelley School of Business Research Paper No. 15–82. Available at SSRN: https://ssrn.com/abstract=2692181 or https://doi.org/10.2139/ssrn.2692181. Brooks, C., Prokopczuk, M., & Wu, Y. (2015). Booms and busts in commodity markets: Bubbles or fundamentals? Journal of Futures Markets, 35(10), 916–938. Cheng, I. H., & Xiong, W. (2014). Financialization of commodity markets. Annual Review of Financial Economics, 6, 419–441. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499. Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all fears investor sentiment and asset prices. Review of Financial Studies, 28(1), 1–32. Dellavigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. Journal of Finance, 64(2), 709–749. Ding, R., & Hou, W. (2015). Retail investor attention and stock liquidity. Journal of International Financial Markets, Institutions and Money, 37, 12–26. Dowling, M., & Lucey, B. M. (2005). Weather, biorhythms, beliefs and stock returns—Some preliminary Irish evidence. International Review of Financial Analysis, 14(3), 337–355. Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2012). Investor information demand: Evidence from google searches around earnings announcements. Journal of Accounting Research, 50(4), 1001–1040. Dzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167–175. Edmans, A, Garcia, D, & Norli, Ø. (2007). Sports sentiment and stock returns. The Journal of Finance, 62(4), 1967–1998. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. Fama, E. F., & French, K. R. (2002). The equity premium. The Journal of Finance, 57(2), 637–659. Fattouh, B., Kilian, L., & Mahadeva, L. The role of speculation in oil markets: What have we learned so far? (March 2012) CEPR Discussion Paper No. DP8916. Frazzini, A., & Lamont, O. A. (2008). Dumb money: Mutual fund flows and the cross-section of stock returns. Journal of Financial Economics, 88(2), 299–322. Gao, L., & Süss, S. (2015). Market sentiment in commodity futures returns. Journal of Empirical Finance, 33, 84–103. Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70(3), 393–408. Hamilton, J. D., & Wu, J. C. (2014). Risk premia in crude oil futures prices. Journal of International Money & Finance, 42(2), 9–37. Han, Y., Hu, T., & Yang, J. (2016). Are there exploitable trends in commodity futures prices? Journal of Banking & Finance, 70, 214–234. Hou, W, & Ding, R. (2015). Retail Investor attention and stock liquidity. Journal of International Financial Markets Institutions & Money, 37, 12–26. Kaplanski, G., & Levy, H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics, 95(2), 174–201. Kellard, N., Newbold, P., Rayner, T., & Ennew, C. (1999). The relative efficiency of commodity futures markets. Journal of Futures Markets, 19(4), 413–432. Kilian, L., & Murphy, D. P. (2014). The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics, 29(7753), 454–478. Lautier, D. (2005). Term structure models of commodity prices. Journal of Alternative Investments, 8(1), 42–64. Li, X., Ma, J., Wang, S., & Zhang, X. (2015). How does Google search affect trader positions and crude oil prices? Economic Modelling, 49, 162–171. Manera, M., Nicolini, M., & Vignati, I. (2013). Financial speculation in energy and agriculture futures markets: A multivariate GARCH approach. Energy Journal, 34(3), 55–81. Peri, M., Vandone, D., & Baldi, L. (2014). Internet, noise trading and commodity futures prices. International Review of Economics & Finance, 33(3), 82–89. Peroni, E., & Mcnown, R. (1998). Noninformative and informative tests of efficiency in three energy futures markets. Journal of Futures Markets, 18(8), 939–964. Rapach, D. E., Strauss, J. K., & Zhou, G. (2009). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23(2), 821–862.
HAN
ET AL.
|
1047
Saberi, A., Stoorvogel, A. A., & Sannuti, P. (2011). Market efficiency among futures with different maturities: Evidence from the crude oil futures market. Journal of Futures Markets, 31(5), 487–501. Siganos, A. (2013). Google attention and target price run ups. International Review of Financial Analysis, 29(5), 219–226. Smales, L. A. (2014). News sentiment in the gold futures market. Journal of Banking & Finance, 49, 275–286. Stambaugh, R. F., Yu, J, & Yuan, Y. (2012). The short of it: Investor sentiment and anomalies. Journal of Financial Economics, 104(2), 288–302. Switzer, L. N., & El-Khoury, M. (2007). Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets. Journal of Futures Markets, 27(1), 61–84. Tang, K., & Xiong, W. (2012). Index investment and financialization of commodities. Financial Analysts Journal, 68(6), 54–74. Tse, Y., & Williams, M. R. (2013). Does index speculation impact commodity prices? An intraday analysis. Financial Review, 48(48), 365–383. Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808–1821. Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17–35. Westerlund, J., Norkute, M., & Narayan, P. K. (2015). A factor analytical approach to the efficient futures market hypothesis. Journal of Futures Markets, 35(4), 357–370. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12–17. Yuan, Y. (2015). Market-wide attention, trading, and stock returns. Journal of Financial Economics, 116(3), 548–564.
How to cite this article: Han L, Li Z, Yin L. The effects of investor attention on commodity futures markets. J Futures Markets. 2017;37:1031–1049. https://doi.org/10.1002/fut.21853
1048
|
HAN
ET AL.
APPENDIX 1 Regressions on Attention Controlling for Macroeconomic Variables R (t−1) Cocoa
Coffee
Copper
Cotton
Gold
Silver
WTI oil
Natural gas
Cocoa
Coffee
Cotton
Copper
Gold
Silver
Heating oil
WTI oil
Natural gas
Soybean
Sugar
A (t−1)
VIX
EPU
ADS
TED
C
0.01903
−0.00014***
0.00200
0.00021
−0.00020
−0.00361
0.00062*
(0.01792)
(0.00004)
(0.00450)
(0.00036)
(0.00029)
(0.00457)
(0.00035)
−0.04035*
0.00194*
−0.01940***
−0.00008
−0.00070*
−0.01006
0.00002
(0.02357)
(0.00100)
(0.00667)
(0.00053)
(0.00038)
(0.00685)
(0.00051)
−0.12481***
−0.00149***
−0.00256
0.00035
0.00019
0.00089
0.00072*
(0.01763)
(0.00049)
(0.00492)
(0.00039)
(0.00032)
(0.00503)
(0.00038)
0.09014***
0.00112**
−0.03261***
−0.00052
0.00045
−0.00667
0.00008
(0.01824)
(0.00044)
(0.00501)
(0.00040)
(0.00034)
(0.00514)
(0.00040)
−0.14782***
0.00188*
0.01243***
−0.00015
0.00023
−0.00191
0.00055**
(0.01756)
(0.00108)
(0.00353)
(0.00028)
(0.00023)
(0.00360)
(0.00026)
−0.02147
0.00160***
0.00258
0.00004
0.00039
0.00320
0.00073*
(0.01774)
(0.00054)
(0.00536)
(0.00044)
(0.00035)
(0.00546)
(0.00040)
−0.08682***
−0.00287***
−0.00130
−0.00040
0.00001
−0.00614
0.00079
(0.01764)
(0.00095)
(0.00605)
(0.00048)
(0.00040)
(0.00617)
(0.00046)
−0.06212***
0.00540***
−0.00196
−0.00128
−0.00012
−0.01020
0.00098
(0.02094)
(0.00152)
(0.00854)
(0.00086)
(0.00066)
(0.00952)
(0.00075)
R (t−1)
A (t)
VIX
EPU
ADS
TED
C
0.01607
0.00008***
0.00161
0.00016
−0.00019
−0.00438
−0.00002
(0.01792)
(0.00002)
(0.00450)
(0.00036)
(0.00029)
(0.00458)
(0.00036)
−0.03978*
−0.00264***
−0.01973***
−0.00007
−0.00071*
−0.00980
0.00048
(0.02352)
(0.00100)
(0.00666)
(0.00053)
(0.00038)
(0.00685)
(0.00051)
0.08881***
−0.00097**
−0.03296***
−0.00053
0.00044
−0.00712
0.00058
(0.01823)
(0.00044)
(0.00501)
(0.00040)
(0.00034)
(0.00514)
(0.00040)
−0.12691***
0.00148**
−0.00223
0.00037
0.00017
0.00086
0.00023
(0.01761)
(0.00067)
(0.00492)
(0.00039)
(0.00032)
(0.00503)
(0.00038)
−0.14537***
0.00320***
0.01251***
−0.00014
0.00025
−0.00174
0.00056**
(0.01752)
(0.00109)
(0.00352)
(0.00028)
(0.00023)
(0.00360)
(0.00026)
−0.02256
0.00221
0.00273
0.00002
0.00040
0.00320
0.00051
(0.01774)
(0.00136)
(0.00536)
(0.00044)
(0.00035)
(0.00547)
(0.00040)
−0.05771***
0.00197*
−0.01615***
0.00038
0.00023
0.01364**
−0.00060
(0.01763)
(0.00101)
(0.00543)
(0.00043)
(0.00036)
(0.00556)
(0.00041)
−0.08808***
0.00010*
−0.00187
−0.00037
−0.00002
−0.00603
0.00040
(0.01765)
(0.00006)
(0.00606)
(0.00048)
(0.00040)
(0.00618)
(0.00046)
−0.06085***
−0.00314***
−0.00232
−0.00123
−0.00025
−0.00876
0.00119
(0.02091)
(0.00097)
(0.00854)
(0.00086)
(0.00066)
(0.00952)
(0.00077)
−0.01053
−0.00130**
0.00208
0.00042
0.00056
−0.00425
0.00028
(0.01771)
(0.00064)
(0.00447)
(0.00036)
(0.00029)
(0.00457)
(0.00034)
−0.03460*
0.00173**
0.01027*
−0.00024
−0.00033
−0.00533
0.00058
(0.01833)
(0.00085)
(0.00589)
(0.00049)
(0.00034)
(0.00594)
(0.00044)
Appendix 1 provides results of regressions on allocated attention controlling for four macroeconomic variables: VIX, EPU, ADS, and TED. VIX is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices. EPU is the daily news-based Economic Policy Uncertainty Index, based on newspaper archives from the Access World News NewsBank service. ADS is designed to track real business conditions at a high frequency and TED is calculated as the spread between 3-month LIBOR based on U.S. dollars and the yield on recently issued 3-month U.S. Treasury bills. The data are obtained at daily frequency for January 2004 to January 2017. We include these four variables to discern the effects of investor attention from those of the volatility of the stock markets, macroeconomic uncertainty, business conditions, and liquidity risk. ***Significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
HAN
|
ET AL.
1049
APPENDIX 2 Interactive Models Controlling for Macroeconomic Variables
Cocoa
Coffee
Cotton
WTI oil
Natural gas
Copper
Gold
Silver
R (t−1)
A (t−1)
R (t−1)*A (t−1)
VIX
EPU
ADS
TED
C
0.0327*
−0.0001***
−0.0053***
0.0018
0.0002
−0.0002
−0.0036
0.0006*
(0.0187)
(0.0000)
(0.0020)
(0.0045)
(0.0004)
(0.0003)
(0.0046)
(0.0003)
−0.0394
0.0019*
−0.0096
−0.0194***
−0.0001
−0.0007*
−0.0100
0.0000
(0.0240)
(0.0010)
(0.0479)
(0.0067)
(0.0005)
(0.0004)
(0.0069)
(0.0005)
0.1068***
0.0011**
−0.0574**
−0.0322***
−0.0005
0.0004
−0.0067
0.0000
(0.0193)
(0.0004)
(0.0224)
(0.0050)
(0.0004)
(0.0003)
(0.0051)
(0.0004)
−0.0777***
−0.0028***
−0.0766**
−0.0016
−0.0004
0.0000
−0.0062
0.0008
(0.0181)
(0.0009)
(0.0349)
(0.0061)
(0.0005)
(0.0004)
(0.0062)
(0.0005)
−0.0665***
0.0054***
−0.1070**
−0.0027
−0.0013
−0.0001
−0.0100
0.0010
(0.0210)
(0.0015)
(0.0448)
(0.0085)
(0.0009)
(0.0007)
(0.0095)
(0.0008)
−0.1286***
−0.0015***
0.0216
−0.0026
0.0003
0.0002
0.0009
0.0007*
(0.0181)
(0.0005)
(0.0248)
(0.0049)
(0.0004)
(0.0003)
(0.0050)
(0.0004)
−0.1577***
0.0023**
−0.4310***
0.0122***
−0.0001
0.0002
−0.0013
0.0006**
(0.0175)
(0.0011)
(0.0709)
(0.0035)
(0.0003)
(0.0002)
(0.0036)
(0.0003)
−0.0238
0.0016***
−0.0204
0.0025
0.0000
0.0004
0.0030
0.0007*
(0.0180)
(0.0005)
(0.0246)
(0.0054)
(0.0004)
(0.0004)
(0.0055)
(0.0004)
Appendix 2 provides results of interaction models between allocated attention and past futures returns, controlling for four macroeconomic variables: VIX, EPU, ADS, and TED. ***Significant at the 1% level, **significant at the 5% level, *significant at the 10% level.
