Cross-Market Investor Sentiment in Commodity Exchange-Traded Funds

Chen, Hsiu-Lang Article Cross-Market Investor Sentiment in Commodity ExchangeTraded Funds Credit and Capital Markets – Kredit und Kapital Provided in Cooperation with: Duncker & Humblot, Berlin Suggested Citation: Chen, Hsiu-Lang (2015) : Cross-Market Investor Sentiment in Commodity Exchange-Traded Funds, Credit and Capital Markets – Kredit und Kapital, ISSN 2199-1235, Duncker & Humblot, Berlin, Vol. 48, Iss. 2, pp. 171-206, https://doi.org/10.3790/ccm.48.2.171 This Version is available at: https://hdl.handle.net/10419/293753 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Credit and Capital Markets 2 / 2015 Cross-Market Investor Sentiment in Commodity Exchange-Traded Funds Hsiu-Lang Chen1* Abstract This study shows how the investor sentiment in the stock market affects prices of commodity exchange-traded funds (ETFs). The study provides quantitative evidence that the tracking errors of commodity ETFs differ in the bullish versus the bearish stock market, and the aggregate tracking error of commodity ETFs is sensitive to the well-known sentiment measures. The study exploits a profitable trading strategy based on investor sentiment in the stock market and commodity market. The sentiment-driven demand for commodity ETFs could exist even after consideration of trading costs, and it is a short-term phenomenon. This unique evidence indicates investor sentiment affects asset valuation across markets. Anleger-Sentiment bei Rohstoff-Exchange-Traded-Funds auf verschiedenen Märkten Zusammenfassung Diese Studie zeigt, wie sich Sentiment-Faktoren am Aktienmarkt auf die Preise von Rohstoff-Exchange-Traded-Funds (ETFs) auswirken. Die Studie liefert quantitative Evidenz dafür, dass die Tracking Errors von Rohstoff-ETFs sich in aufwärts und abwärts tendierenden Aktienmärkten unterscheiden und dass der aggregierte Tracking Error von Rohstoff-ETFs deutlich mit den verbreiteten Senti- * I am grateful to the referee for his or her comments. For their helpful comments, I thank the participants of the Annual Meetings of Midwest Finance Association held in Chicago, USA, March 14–16, 2013, of the Bicentenary Conference of the Italian Academy of Management (AIDEA) held in Lecce, Italy, September, 19–21, 2013, of the workshop “Determinants and Impact of Commodity Price Dynamics” in Münster, Germany, June 26, 2014, and of seminars at University of Illinois at Chicago, USA. I am thankful for data / information inquiry assistance provided by Eva Nelson at the Center for Research in Security Prices (CRSP). I also thank Chun Zhang, Jiayin Hou, and Saembyeol Park for research assistance. Address correspondence to Professor Dr. Hsiu-lang Chen, Department of Finance, University of Illinois at Chicago, 601 South Morgan Street, Chicago, IL 60607, USA, or e-mail: [email protected]. Credit and Capital Markets, 48. Jahrgang, Heft 2, Seiten 171–206 Abhandlungen OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 172 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 ment-Indikatoren zusammenhängt. Sie verwendet eine profitable Handelsstrategie basierend auf dem Anleger-Sentiment in Aktienund Rohstoffmärkten. Die vom Sentiment getriebene Nachfrage nach Rohstoff-ETFs existiert auch unter Berücksichtigung von Transaktionskosten und stellt ein kurzzeitiges Phänomen dar. Wie die Ergebnisse der Studie zeigen, wirkt sich das Sentiment auch auf Preisbildung auf verschiedenen Märkten aus. Keywords: Investor Sentiment, Tracking Errors, Commodity ETFs JEL Classification: G10, G23 I. Introduction It is well known that liquid financial markets are not always as orderly as the efficient market advocates might suggest (see Grossman / Stiglitz (1980)). In a model of two types of investors, rational arbitrageurs who are sentiment-free and irrational traders prone to exogenous sentiment, DeLong / Shleifer / Summers / Waldmann (1990) argue that rational arbitrageurs mainly face limits from short time horizons or from costs and risks of trading and short selling. Shleifer / Vishny (1997) show how agency problems between an arbitrageur and his or her source of capital can also hinder arbitrage. As a result, sentiment-based demands might drive prices away from their fundamental values. Empirically, examination of this issue is still contentious. The absence of precise valuation models for stocks makes measuring deviations from theoretical prices difficult. Similar problems arise from the difficulty in measuring investor sentiment. The study of passively managed commodity exchange-traded funds (ETFs) might be able to mitigate these issues.1 Most public statements by institutional investors emphasize the primary advantage of commodity investments as diversification, providing a return that has little correlation with core equity and bond holdings. Since their introduction in 2004, commodity ETFs have grown from just over $1 billion to $109 billion by the end of 2011, with total net assets 1 An ETF is an investment company, typically an open-end investment company (open-end fund), whose shares are traded intraday on stock exchanges at market-determined prices. Investors may buy or sell ETF shares through a broker just as they would the shares of any publicly traded company. The first ETF – a broadbased domestic equity fund tracking the S&P 500 index – was introduced in 1993. Until 2008, the U.S. Securities and Exchange Commission’s (SEC) exemptive relief was granted only to ETFs that tracked designated indexes. According to the 2012 Investment Company Fact Book, by the end of 2011, the total number of index-based and actively managed ETFs had grown to 1,134, and total net assets were $1.05 trillion. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 173 Credit and Capital Markets 2 / 2015 almost tripling in the last two years. By construction, a passively managed commodity ETF tracks its underlying index.2 Unlike the discounts on closed-end mutual funds, the market price of an ETF is close to the value of its underlying index assets net of the expense ratio, because its portfolio composition is transparent, and authorized participants (institutional investors) are allowed to assemble a basket of underlying index assets in exchange for shares of the ETF. The tracking error (TE) of an ETF can be defined as follows: (1) TE RR FF jjBI jB I ≡− = () − () ΛΛ ββ . Where Rj is the gross return on an ETF j while RBI is the total return on the ETF’s benchmark index BI. F(.) is a valuation model. Λ is the risk premium vector of 1xK associated with K factors while β is the factor loading vector of Kx1. Because of investment mandates, a passively-managed ETF has to closely track the benchmark index and commonly hold the index underlying assets. Thus, a valuation model, whatever it is, as well as the same risk factors should apply to both the ETF and its benchmark index. As a result, the tracking errors of the ETF are free of any complex pattern of compensation for systematic risk. In examining tracking errors of ETFs, this study can put aside the concern of whether control variables are effective to separate investor sentiment from economic fundamentals in predicting stock returns in a regression framework commonly used in the literature.3 Even though investor sentiment is taken into consideration, the sentiment will have a similar impact on the ETF and its index underlying assets as long as both are traded in the same market. For example, the tracking errors of S&P 500 ETFs are unlikely influenced by investor sentiment in the stock market. Commodity ETFs, however, are traded in a regular stock exchange. The fluctuations in the noise trader sentiment in the stock market likely af2 For example, United States Oil Fund, LP (USO), a commodity ETF, declared on its prospectus dated on April 10, 2006, that the price of USO’s units on the American Stock Exchange would closely track the spot price of a barrel of WTI light, sweet crude oil, less USO’s expenses. USO sought to achieve its investment objective by investing in a mix of oil futures contracts and other oil interests such as options on oil futures contracts, forward contracts for oil, and over-the-counter transactions based on the price of oil and other petroleum-based fuels. 3 See Brown / Cliff (2005), Lemmon / Portniaguina (2006), Baker / Wurgler (2006), and others. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 174 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 fect many assets traded in the market, including commodity ETFs.4 It is labeled as the cross-market sentiment effect. Although arbitrage can be undertaken by market participants who can buy the inexpensive asset and short sell the more expensive one, commodity ETFs and their underlying securities are not traded in the same market. If arbitrageurs lack the capacity to engage in arbitrage across multiple markets, as Shleifer / Vishny (1997) point out that arbitrage markets are specialized, the tracking errors of commodity ETFs would be affected by investor sentiment in the stock market. Are investors investing in commodity ETFs likely to enjoy the exciting celebration as do stock investors for a bullish stock market? A psychology experiment conducted by Moreland / Beach (1992) supports this conjecture. With a controlled condition of no interaction, they show that students’ mere exposure to the same classroom has strong effects on attraction and similarity to others. People also tend to conform to the judgments and behaviors of others. In a sequential decision model, Banerjee (1992) shows that people will do what others are doing rather than use their own information. This study empirically examines if the investor sentiment in the stock market affects prices of commodity ETFs. Several studies have documented the interaction between the sentiment and the broad stock market returns. Brown / Cliff (2004) show that sentiment levels and changes are strongly correlated with contemporaneous market returns. Using a direct survey measure of investor sentiment, Brown / Cliff (2005) provide evidence that optimism is associated with overvaluation and low returns over the subsequent one to three years as the valuation level returns to its intrinsic value. Ben-Rephael / Kandel / Wohl (2012) find that investor sentiment, proxied by net exchanges between equity funds and bond funds, creates noise in aggregate market prices. Others have used investor sentiment to explain anomalies in the asset pricing. Lee / Shleifer / Thaler (1991) argue that arbitrage against noise traders is risky because arbitrageurs do not have infinite horizons, and conclude that fluctuations in discounts of closed-end funds are mainly driven by changes in individual investor sentiment. Baker / Wurgler (2006, 2007) argue that investor sentiment drives the relative demand for speculative investments, which are typically hard to value and arbitrage, and therefore, possibly causes cross-sectional effects on stock 4 This paper simply views investor sentiment as optimism or pessimism about stocks in general and thus assumes investor sentiment is positive (negative) in a bullish (bearish) stock market. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 175 Credit and Capital Markets 2 / 2015 returns.5 They document that speculative and hard-to-arbitrage stocks have lower (higher) future returns on average than bond-like stocks when sentiment is measured to be high (low). Baker / Wurgler / Yuan (2012) provide further international evidence for the forecasting power of investor sentiment.6 In this study, I investigate whether investor sentiment in the stock market affects daily tracking errors of commodity ETFs. Ultimately, I explore whether I can quantify the cross-market sentiment effects by exploiting profits from a long-short investment strategy. The findings of this study complement the existing literature that asserts that investor sentiment affects the broad market returns and the cross-section of stock returns. This study also contributes to the ETF literature. Elton / Gruber / Comer / Li (2002) identify that both the management fee and the loss of return from dividend reinvestment cause the underperformance of Standard and Poor’s Depository Receipts (commonly referred as Spider) relative to the S&P 500 Index, which Spider tracks. After correcting measurement errors in net asset value (NAV), Engle / Sarkar (2006) show the average premium of equity ETFs was less than 5 basis points (bps) and the standard deviation was less than 20 bps. Delcoure / Zhong (2007) and Levy / Lieberman (2013) analyze the “stale pricing” problem of securities traded in foreign country markets in generating a premium of country ETFs. Levy / Lieberman (2013) further find that whereas country ETF prices are mostly driven by their NAV returns during synchronized trading hours, the S&P 500 index has a dominant effect during non-synchronized trading hours. Because this study aims to determine whether investor sentiment in one market affects asset prices in another market, incorporating all possible channels through which investor sentiment might have influence is important. If the replication of an index a commodity ETF tracks is imperfect, investor sentiment could amplify the tracking errors. Therefore, I mainly confine this study to the ETF’s price relative to its underlying index, not relative to its NAV. In addition, I use the underlying index to gauge the prospect of the commodity market relative to that of the 5 D’Avolio (2002) documents that stocks that are young, small, unprofitable, or experiencing extreme growth tend to be more costly to buy and to sell short. Wurg ler / Zhuravskaya (2002) also find such stocks have a high degree of idiosyncratic variation in their returns, which makes betting on them riskier. 6 In addition to investor sentiments, several studies have addressed the interdependencies between consumer sentiments (consumer confidence), stock returns, and macroeconomic activities. See Lemmon / Portniaguina (2006) and Beckmann / Belke / Kühl (2011). OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 176 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 stock market and to construct the index-adjusted performance measure for an investment strategy exploiting the sentiment effect. The result of this study suggests that in addition to the stale pricing problems, behavioral factors may account for some mispricing in ETFs. In short, the first hypothesis I test is whether investor sentiment in the stock market affects the tracking errors of commodity ETFs after controlling the investor sentiment in the commodity market. If the cross-market sentiment effect exists, the tracking errors of commodity ETFs reflect investor sentiment in the stock market. Thus, the second hypothesis I investigate is whether the aggregate tracking errors of commodity ETFs load significantly on sentiment measures commonly used in the literature. The third hypothesis to be empirically tested is whether investor sentiment in the stock market can predict future returns on a long-short strategy involving commodity ETFs and the Spider. The rest of the paper proceeds as follows. Section II. describes the data. Section III. presents statistics on tracking errors and tracking-error volatility for commodity ETFs. Section IV. tests whether investor sentiment in the stock market affects prices of commodity ETFs. Section V. exploits a profitable trading strategy based on investor sentiment in the stock market and commodity market. Section VI. investigates the impact of transaction costs on the trading strategy. Section VII. further performs a Fama-French risk-factor model for a robustness check, and section VIII. concludes. II. Data The CRSP daily return files, CRSP Survivor-Bias-Free Mutual Fund database, and SEC’s EDGAR database constitute the main data sources. I retrieve share returns, trading volumes, number of shares outstanding, closing prices, and closing bid / ask prices of all commodity ETFs as well as Spider (the ticker symbol: SPY) from CRSP return files.7 I hand-collect all historical expense ratios for all commodity ETFs, and Spider from SEC’s EDGAR database. I construct the daily gross returns by adding the expense ratios to the share returns for these ETFs. Daily net asset 7 An ETF is a security with a common share code of 73 in CRSP. There are six commodity ETFs in the sample, which are mistakenly classified as closed-end funds with a CRSP share code of 74. These include funds with ticker symbols of CORN, DNO, UNG, UNL, USCI, and USO. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 177 Credit and Capital Markets 2 / 2015 value (NAV) returns are directly retrieved from the CRSP Mutual Fund database. I only consider ETFs that have at least one-year daily returns, and have specified in the prospectus the underlying indexes that ETFs will track.8 I retrieve the data of indexes tracked by ETFs from the web sites of the companies that publish the indexes, as well as the data of service providers. The sample of this study includes 33 commodity ETFs from November 18, 2004, to December 31, 2011 (see Appendix 1 for listing). An ETF’s tracking error is the ETF’s gross return minus its benchmark index return. I decompose daily ETF tracking errors into two components, mispricing component and imperfect index replication component. The mispricing component is the ETF’s share returns minus its NAV returns while the imperfect index replication component is its NAV returns plus expense ratios minus its benchmark index returns. The monthly data of Baker and Wurgler’s measures of sentiment and discounts on closed-end equity fund are obtained from Jeffrey Wurgler’s website at www.stern.nyu.edu / ~jwurgler. The monthly data of the University of Michigan sentiment measure are obtained from the University of Michigan Surveys of Consumers at www.sca.isr.umich.edu. Data of daily VIX (Chicago Board Options Exchange Market Volatility Index) values are obtained from finance.yahoo.com. III. Tracking Errors and Tracking-Error Volatility I first present the quartile distribution of tracking errors of commodity ETFs over the entire sample period. The sample contains nine commodity ETFs designed to provide double returns, inverse returns, or double inverse returns of the indexes they track. For these leveraged / inverse ETFs, I define their tracking errors accordingly.9 Panel A of Table 1 shows that 8 The criterion of requiring ETFs having at least one-year daily returns is to ensure that the test of cross-market sentiments based on individual ETFs has a reasonable number of sample observations. Only two newly-established commodity ETFs, AGOL (Inception: 2011 / 01 / 14) and NAGS (Inception: 2011 / 02 / 01), do not satisfy this criterion. In addition, I exclude four commodity ETFs (ticker symbols: BNO, UGA, WITE, and GLTR). I cannot have the complete data for the indexes the first two ETFs track. The last two ETFs track an index comprising a customized deposit of bullion metals. 9 For example, the tracking error of ProShares Ultra Gold ETF (UGL) is its gross returns minus double returns on the daily performance of gold bullion as measured by the U.S.Dollar p.m. fixing price for delivery in London, the benchmark index UGL tracks. Similarly, the tracking error of ProShares UltraShort Gold ETF (GLL) is its gross returns minus inverse double returns on the benchOPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 178 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 commodity ETFs entail non-trivial tracking errors, whereas Spider tracks the S&P 500 Index nearly perfectly. The median commodity ETF trails its index by 0.8 basis points (bps) and on average a commodity ETF trails its index by 2.2 bps per day over the sample period. Given the underperformance of 2.2 bps, about one third of it is attributable to the commodity ETF’s mispricing and two third of it is attributable to ETF’s imperfect index replication. To present the stability of an ETF in tracking its index, I calculate the volatility of the ETF’s daily tracking errors over the sample period. Panel B of Table 1 shows the median tracking-error volatility (TEV) among these 33 commodity ETFs is about 1.905 %. The median volatility of the mispricing components and imperfect index replication components is 0.875 % and 1.202 %, respectively. For a reference comparison, I also construct the quartile distribution of TEV for Spider in such a way that the volatility of Spider’s tracking errors is calculated each time for a period in which TEV of a commodity ETF is calculated. Spider only entails a median tracking-error volatility of 0.164 % and its mispricing component has the equivalent volatility. The standard deviation of the cross-sectional TEVs of commodity ETFs is about 1.731 %. The big deviation might indicate the large variety of ways in which commodity ETFs implement tracking strategies—some investing in underlying assets directly whereas some using financial instruments to gain exposure to the underlying assets. This is also reflected in an even higher standard deviation of the cross-sectional volatilities of their components of imperfect index replication, which is 1.884 %. One must take the large variation in tracking implementation into consideration when I empirically test whether the tracking errors of commodity ETFs can possibly indicate investor sentiment in the stock market. Table 2 shows the quartile distribution of tracking errors over two contrast periods, bullishand bearish-period. I define a bullish stock market versus a bearish stock market according to daily returns of RMRF, one of the Fama-French three factors. The bullish (bearish) stock markets include days that RMRF is positive (negative). Spider trails the S&P 500 Index in a bullish stock market, whereas overshoots the S&P 500 Index in a bearish market in an almost identical magnitude of about 2bps. However, commodity ETFs exhibit an opposite effect. The median commark index. According the description on page 21 of the initial prospectus on November 21, 2008, these two funds will not invest in bullion, but rather will use financial instruments to gain exposure to these precious metals. Not investing directly in bullion may introduce additional tracking errors. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 185 Credit and Capital Markets 2 / 2015 vided by the dollar volume.11 It captures the daily price response associated with one dollar of trading volume. The turnover is the ratio of trading volume to the number of shares outstanding. Amihud / Mendelson (1986) document that turnover is negatively related to illiquidity costs. Investor sentiment measure (SENT) is constructed by Baker / Wurgler (2007) and directly retrieved from Wurgler’s website.12 Given that the sentiment measure is only available annually and monthly up to December 2010, daily tracking errors and liquidity measures are converted to monthly data first. Instead of retrieving monthly returns and liquidity variables directly, I average the daily data for each month for each ETF in the hopes that the averaging can reduce noises in the tracking-error calculation so the aggregate tracking errors of commodity ETFs can represent investor sentiment in the stock market. I calculate the cross-sectional average of monthly data in an equal weight for the portfolio of commodity ETFs. ETFs that use financial instruments to gain leverage exposure to their underlying assets may introduce additional tracking errors that are not necessarily related to investor sentiment. As a result, I exclude leveraged and inverse ETFs from the analysis.13 I have 24 non-leveraged / non-inverse commodity ETFs in total and 74 months between November 2004 and December 2010. I regress monthly tracking errors of the commodity ETF portfolio on the liquidity measures and sentiment measures with a control variable of the market excess returns, RMRF. Table 4 shows the aggregate tracking errors of commodity ETFs load positively and significantly on the sentiment regardless of whether or not I consider liquidity. In Model 4, a one standard deviation increase in the level of sentiment (0.301) is associated with a 1.7 bps increase in the average of daily tracking errors of commodity ETFs over a month, which represents 26 % of the standard deviation of the dependent variable.14 Because the tracking error has adjusted for any fundamental influence associated with the economy by deducting returns on an index tracked by an ETF, I expect the aggregate 11 The daily dollar volume is the trade volume times the daily closing price. 12 Baker / Wurgler (2006, 2007) construct their sentiment index based on six proxies: the trading volume; the dividend premium; the closed-end fund discount; the number and first-day returns on IPOs; and the equity share in new issues. 13 The results, not reported but available upon the request, are basically the same even though leveraged and inverse ETFs are included. To avoid any confounding between the leverage effect and the sentiment effect, I decide to exclude them hereafter. 14 (0.058 · 0.301) / 0.066 = 0.2645. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 186 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 tracking errors no longer strongly co-move with the stock market excess returns. An insignificant coefficient of RMRF in the regression confirms this expectation. When negative liquidity shocks hit, sentiment-driven demand, perhaps due to limits to arbitrage, will amplify the aggregate tracking errors as shown in the coefficients of two liquidity measures. As a reference, I apply the same test to the tracking errors of Spider. As expected, the tracking errors of Spider are not sensitive to investor sentiment at all because sentiment, if any is present, affects both Spider and S&P 500 company stocks simultaneously and similarly. Shleifer / Vishny (1997) argue that markets in which fundamental uncertainty is high and slowly resolved are likely to deter arbitrage activity. Given that Spider and S&P 500 stocks are very liquid and are traded in the same market, and that Spider’s NAV values are disseminated at a 15-second frequency throughout the trading days, arbitrage forces used to correct the mispricing on Spider are more effective. Therefore, sentiment-driven mispricing will wane much sooner on Spider than on commodity ETFs. This observation leads to consider both Spider and a commodity ETF for further testing the cross-market sentiment effect in Section V. For a robustness check, I consider other three measures commonly used in the sentiment literature. The first one is discounts on closed-end equity funds (CEFD). CEFD widely used as an indicator of investor sentiment in the literature deserves a further consideration even though it is one of the six variables used for Baker / Wurgler’s (2006,2007) sentiment index. Lee / Shleifer / Thaler (1991), Swaminathan (1996), and Neal / Wheatley (1998) all conclude that the discounts on closed-end funds reflect investor sentiment and can predict the size premium. The second one is the Michigan Consumer Sentiment Index (CSI) which is based on surveys. Lemmon / Portniaguina (2006) show that the sentiment component of CSI forecasts time-series variation in the size premium after allowing for time-series variation in market beta. Doms / Morin (2004) find, after controlling for economic fundamentals, that the measures of consumer confidence still respond to the sentiment. The third one is Chicago Board Options Exchange Market Volatility Index (VIX) which is the implied volatility of S&P 500 index options and is commonly termed as the investor fear gauge. Ben-Rephael / Kandel / Wohl (2012) document that investor sentiment proxied by net exchanges between equity funds and bond funds is strongly negatively related to VIX while it is weakly positively related to the CSI. If the aggregate tracking errors of commodity ETFs proxy for investor sentiment in the stock market, it is expected OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 187 Credit and Capital Markets 2 / 2015 that the tracking errors load positively on CSI while negatively on both CEFD and VIX. Panel C of Table 4 clearly shows that the loadings on the sentiments are significant and have the expected signs. The result still holds when daily tracking errors are compounded over a month first and these monthly compounded tracking errors are used as the aggregate tracking errors of commodity ETFs. If the tracking errors of commodity ETFs indeed reflect investor sentiment in the stock market, the daily tracking errors of commodity ETFs can serve as an indicator of daily sentiment for the stock market. Thus, the daily tracking errors of commodity ETFs complement indicators of annual and monthly sentiment measures provided by Baker / Wurgler (2006, 2007). Table 4 The Link between Aggregate Tracking Errors and Investor Sentiments The calculation of daily tracking errors for an ETF is described in Table 1. This table calculates two daily liquidity measures, Amihud illiquidity and turnover, for each ETF. Amihud (2002) illiquidity is the absolute return (|r|) divided by the dollar volume ($Vol). The turnover is the ratio of trading volume to the number of shares outstanding. The investor sentiment measure (SENT) is constructed by Baker / Wurgler (2007) and directly retrieved from Wurgler’s website. The sentiment measure is only available annually and monthly up to December 2010. Thus daily tracking errors and liquidity measures are converted to monthly data. The table averages the daily data for each month for each ETF and then calculates the cross-sectional average of monthly data for the entire commodity ETF. To avoiding the confounding, the table excludes leveraged and inversed ETFs from the analysis for this table. There are 24 non-leveraged / non-inversed commodity ETFs in total and 74 months between November 2004 and December 2010. Monthly tracking errors of the commodity ETF portfolio are regressed against the liquidity measures and sentiment measures. To control the conditions of the general economy and the stock market, the table adds monthly RMRF, one of the Fama-French three factors, to the independent variables. Both tracking errors and RMRF are in a percentage format. The t-value associated with a coefficient estimate is in parentheses. As a reference, the tracking errors (TEs) of Spider are regressed against the same variables. TEs and liquidity measures for Spider are constructed in the same way to obtain monthly data. For brevity, the table only reports results of two models for SPY in the last two columns. Note that Amihud illiquidity is defined as 106x |r| / $Vol for commodity ETFs and as 108x |r| / $Vol for Spider. Panel A presents regression results while Panel B presents statistics for the regression variables. In Panel C, the analysis is extended to other investor sentiment measures, which include monthly closed-end equity fund discounts (CEFD), monthly (Continued on the next page) OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 188 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 Michigan Consumer Sentiment Index (CSI), and daily Chicago Board Options Exchange Market Volatility Index (VIX). Daily VIX values are converted to monthly data by simply averaging daily values over a month. In Panel C, the dependent variable of compounded tracking errors is added. For each commodity ETF in each month, the compounded tracking error is the compounded daily returns on the ETF minus the compounded daily returns on its benchmark over the month. The dependent variable is the cross-sectional average of monthly data for the entire commodity ETF. The data of CEFD end at February 2011 while the data of CSI and VIX end at December 2011. Panel A. Regression Results Independent Variable Dependent Variable Tracking Errors of Commodity ETFs TEs of SPY Model 1 Model 2 Model 3 Model 4 Ref 1 Ref 2 Constant –0.015 0.007 –0.028 –0.005 0.0002 0.002 (–2.02) (0.52) (–2.81) (–0.39) (0.10) (0.32) RMRF 0.002 0.001 0.003 0.002 –0.0003 –0.0004 (1.06) (0.68) (1.71) (1.43) (–1.03) (–1.10) Turnover –0.308 –0.341 –0.008 –2.06) (–2.33) (–0.65) Amihud Illiquidity 0.746 0.836 5.994 (1.91) (2.20) (0.07) SENT 0.064 0.058 0.065 0.058 –0.0009 –0.0008 (2.53) (2.33) (2.59) (2.37) (–0.17) (–0.14) Adjusted R2 6.058 10.153 9.440 14.809 0.00 0.00 # Observations (Months) 74 74 74 74 74 74 Panel B. Statistics for the Regression Variables in Panel A over 74 Months Commodity ETFs Spider (SPY) Variable SENT RMRF TE Turnover Amihud TE Turnover Amihud AVG –0.027 0.320 –0.016 0.070 0.016 0.00007 0.289 0.00005 STD 0.301 4.851 0.066 0.050 0.020 0.012 0.150 0.00002 (Table 4 – Continued) OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 189 Credit and Capital Markets 2 / 2015 V. Using Sentiment to Predict Returns So far I have shown investor sentiment in the stock market will likely swing the ETFs’ tracking errors given that commodity ETFs are traded in the stock market. In other words, the positive sentiment in the stock market drives the prices of commodity ETFs above the intrinsic values of their underlying commodities. I follow the suggestion by Baker / Wurgler (2007) that the strongest tests of the effects of sentiment involve return predictability. To possibly quantify the impact of sentiment in the stock market on commodity ETFs, I perform a long-short investment strategy involving a commodity ETF and Spider, depending on whether investor sentiment is Panel C. Regression Results Based on Other Investor Sentiment Measures Independent Variable Dependent Variable: Commodity ETFs Average Tracking Errors Compounded Tracking Errors Constant –0.005 0.004 –0.094 0.022 –0.242 –0.068 –1.795 0.225 (–0.39) (0.33) (–1.82) (1.27) (–0.94) (–0.27) (–1.84) (0.70) RMRF 0.002 0.002 0.002 0.001 0.063 0.060 0.046 0.039 (1.43) (1.33) (1.12) (0.81) (2.05) (2.00) (1.75) (1.47) Turnover –0.341 –0.321 –0.327 –0.341 –4.036 –3.652 –3.976 –4.277 (–2.33) (–2.23) (–2.37) (–2.52) (–1.46) (–1.35) (–1.53) (–1.67) Amihud Illiquidity 0.836 1.262 1.113 1.512 16.681 24.358 21.235 27.211 (2.20) (3.11) (2.84) (3.14) (2.32) (3.18) (2.88) (2.98) SENT 0.058 1.000 (2.37) (2.17) SENT_CEFD –0.004 –0.068 (–2.60) (–2.48) SENT_CSI 0.001 0.019 (1.78) (1.68) SENT_VIX –0.002 –0.029 (–2.10) (–1.79) Adjusted R214.809 15.953 11.706 12.986 11.655 13.270 9.213 9.627 # Observations (Months) 74 76 86 86 74 76 86 86 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 190 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 simply positive or negative. A zero-cost investment in an efficient market should generate zero return after adjusting for risks. I investigate whether current investor-sentiment levels predict future returns on the longshort strategy as sentiment wanes differently on commodity ETFs and Spider, and as arbitrage forces accumulate to correct mispricing of these two securities at different paces. Additionally, using a commodity ETF and Spider, instead of a commodity ETF and its underlying assets, in the long-short investment strategy offers a practical advantage. Unlike in equity ETFs, an authorized participant in the creation / redemption of a commodity ETF may have to deliver / receive a combination of cash and physical assets underlying the commodity ETF for a metal commodity ETF (e. g. SPDR Gold ETF [GLD]), or a combination of cash and treasuries for a non-metal commodity ETF (e. g. United States Oil ETF [USO]). Types of assets for delivery in exchange for commodity ETF shares vary accordingly and may be illiquid or not available for short. A further impediment to arbitrage if a commodity ETF and its underlying assets are used in a long-short strategy is that both may not be traded in synchronized hours. For example, United States Commodity Index Fund (USCI) tracks the total return of the SummerHaven Dynamic Commodity Index, which is comprised of 14 futures contracts that will be selected on a monthly basis from a list of 27 possible futures contracts in the sectors of energy, livestock, grains, industry metals, precious metals, and softs. It might be challenging for retail investors engaging in arbitrage between USCI shares and its underlying securities—most are actively traded futures contracts with scheduled expirations. Not only are the open-outcry trading hours for these futures contracts varying and different from the trading hours of USCI shares at the NYSE Arca stock exchange, but futures contracts are also expiring constantly.15 Shleifer / Vishny (1997) argue that arbitrage markets are specialized, and arbitrageurs typically lack the experience and reputation to engage in arbitrage across multiple markets. As a result, to avoid the criticism that the existence of mispricing in commodity ETFs might not be due to the investor sentiment but the difference in market structure between commodities and equities, I use a commodity ETF and Spider, instead of a commodity ETF and its underlying assets, in the long-short investment strategy to 15 A constant, scheduled expiration in futures contracts introduces an additional complexity for arbitrage because of uncertain “contango” and “backwardation” phenomena in describing the price relationship between the near month futures contracts and the next month futures contracts. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 191 Credit and Capital Markets 2 / 2015 quantify the impact of sentiment in the stock market on commodity ETFs. I am mindful that the proposed long-short strategy is likely exposed to economic risk factors related to the fundamental difference between the commodity market and the stock market, and I have to take this into consideration. In the single-market strategy, I explore an investment opportunity based on investor sentiment in the stock market. At the beginning of each day, I long an ETF and short SPY if the stock market was bullish the prior day, whereas I short an ETF and long SPY if the stock market was bearish the prior day. Note that sentiment-driven mispricing wanes sooner on Spider than on commodity ETFs shown in the previous section. In the cross-market strategy, I explore an investment opportunity based on investor sentiment in both stock and commodity markets. At the beginning of each day, I long an ETF and short SPY if the stock market was bullish and the ETF market was bearish the prior day (BullSBearC). I short an ETF and long SPY if the stock market was bearish and the ETF market was bullish the prior day (BearSBullC). Using ETFs’ share returns, I calculate daily performance for each strategy for each ETF. Table 5 shows that the proposed investment strategy is profitable. For example, the single-market strategy on the basis of individual ETFs generates 14.1 bps per day on average, whereas the cross-market strategy results in about 19.9 bps. Both are significant at the level of 1 %. As a reference, I perform a plain strategy of long ETF and short SPY constantly. Without relying on sentiment signals, the plain strategy results in zero performance. This result indicates that both commodity ETFs and Spider are exposed to systematic risk factors similarly during the sample period, and thus the profit from the proposed long-short strategy is not just compensation for bearing the systematic risk. Given it is costly to execute short selling, it is definitely subject to the argument of limits to arbitrage if most of profits of the proposed long-short strategy are from the short position. I attribute the strategy performance to each of the long and short position held by the strategy. The result shows that the long position generates significant returns and contributes more than 73 % of the overall profits. When I pool all performance of strategies across commodity ETFs, the average performance of sentiment strategies is significant and positive. The strong evidence also appears in a singular strategy under either single-market or cross-market sentiment consideration. For example, a senOPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 192 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 Table 5 Investment Strategies Based on Cross-Market Investor Sentiments In the stock market, a bullish (bearish) day is the day when the daily RMRF, one of the Fama-French three factors, is positive (negative). In a commodity market, a bullish (bearish) day for an ETF is the day when the daily return on an index tracked by the ETF minus daily one-month T-bill rate is positive (negative). The table further classifies the extreme bullish (bearish) market for which the daily excess returns on the market are ranked at the top (bottom) third over the entire bullish (bearish) market period. For each commodity ETF, two long-short investment strategies are performed depending on signals in a single market or cross-markets. In the single-market strategy, at the beginning of each day, the table longs an ETF and shorts SPY if the stock market was bullish the prior day, whereas the table shorts an ETF and longs SPY if the stock market was bearish the prior day. In the cross-market strategy, at the beginning of each day, the longs an ETF and shorts SPY if the stock market was bullish and the ETF market was bearish the prior day (BullSBearC). The table shorts an ETF and longs SPY if the stock market was bearish and the ETF market was bullish the prior day (BearSBullC). Using ETFs’ share returns, the table calculates daily performance for each strategy for each ETF. Panel A reports the distribution of average performance of each strategy on the basis of individual commodity ETFs. Panel B pools daily performance of each strategy across all commodity ETFs and reports the performance distribution based on all fund-days. Numbers in performance are in a percentage format. The table reports the t-value associated with the test if the average performance is zero. As a reference, the table also reports the performance of a plain strategy, simply longing an ETF and shorting SPY daily, without relying on any investment signal. The average performance of each strategy is further split into performance attributed to each of the long and short position by the strategy. The significance level of returns equaling to 0 in either long or short position is indicated by *** (1%), ** (5%), and * (10%). To avoid the confounding, leveraged and inverse ETFs are excluded from the analysis for this table. The sample period from November 18, 2004, to December 31, 2011, contains 24 non-leveraged / non-inverse commodity ETFs. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 193 Credit and Capital Markets 2 / 2015 Strategy #Obs Quartile Distribution AVG STD t AVG 25% 50% 75% Long Short Panel A. On the basis of individual ETFs Without Signals 24 –0.031 0.013 0.061 0.007 0.074 0.48 0.032** –0.025*** With Signals Single-Market 24 0.109 0.150 0.182 0.141 0.097 7.10 0.103*** 0.038*** Cross-Market 24 0.147 0.166 0.262 0.199 0.112 8.68 0.190*** 0.010 Panel B. On the basis of pooling observations across all ETFs Without Signals 25690 –1.023 0.020 1.095 0.010 2.102 0.80 0.031** –0.020** With Signals Single-market 25525 –0.981 0.069 1.142 0.156 2.097 11.91 0.107*** 0.049*** Bullish 13861 –0.888 0.076 1.089 0.152 1.950 9.18 0.115*** 0.037*** Extreme Bullish 5337 –0.909 0.123 1.256 0.233 2.212 7.69 0.169*** 0.064*** Bearish 11664 –1.101 0.061 1.206 0.161 2.260 7.71 0.098*** 0.064*** Extreme Bearish 4669 –1.027 0.269 1.533 0.391 2.541 10.50 0.220*** 0.171*** Cross-Market 11068 –1.000 0.125 1.216 0.200 2.125 9.92 0.181*** 0.019 BullSBearC 5839 –0.883 0.163 1.263 0.245 2.041 9.18 0.228*** 0.017 Extreme BullSBearC 684 –1.159 0.414 2.040 0.548 2.749 5.22 0.584*** –0.036 BearSBullC 5229 –1.120 0.075 1.176 0.151 2.215 4.92 0.129*** 0.021 Extreme BearSBullC 691 –1.238 0.127 1.658 0.375 2.840 3.47 0.209** 0.166* OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 194 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 timent strategy based on positive sentiment in the stock market generates 15.2 bps per day on average, whereas a strategy based on positive sentiment in the stock market and negative sentiment in the commodity market results in 24.5 bps. Both are significant at the 1 % level. Again, the majority of strategy performance is generated from the long position in a long-short investment. To test the sentiment effects further, I split the time series into extreme bullish (bearish) days depending on whether the daily market excess returns are ranked at the top (bottom) third over the entire bullish (bearish) market period.16 If the sentiment effects indeed exist, I anticipate that investors are more excited in the extreme bullish market while more pessimistic in the extreme bearish market. As a result, a sentiment strategy based on these extreme signals is expected to generate higher returns, and Panel B of Table 5 confirms this expectation. For example, a sentiment strategy based on extreme positive sentiment in the stock market generates 23.3 bps per day on average, whereas a strategy based on extreme positive sentiment in the stock market and extreme negative sentiment in the commodity market results in 54.8 bps. Again, both are significant at the 1 % level. Although the proposed long-short strategy involves two liquid securities, a commodity ETF and Spider, performance of the strategy might still be exposed to economic risk factors related to the fundamental difference between the commodity market and the stock market. To take this possibility into consideration, I regress performance of long-short sentiment strategies on the return difference between the S&P 500 Index and the commodity index which the ETF tracks. According to the position of an ETF’s trading signal in the timeline, I further classify each strategy into three mutually exclusive groups depending on whether a sentiment signal is fresh new, in the middle of a consecutive signal sequence, or at the tail of a consecutive signal sequence. I pool daily per16 It is questionable if the strategy following the extreme sentiment signal is executable in practice, because ex ante I cannot identify if the prior market excess return is ranked as extreme or not. Alternatively, I can define extreme bullish (bearish) days depending on whether the daily market excess returns are greater (less) than a certain cutoff, for example, 1.5 % (–1.5 %). A sentiment strategy based on extreme positive sentiment with this certain cutoff of 1.5 % in the stock market generates significant 36.8 bps per day on average and its long position contributes 24.2 bps. I can have such a strategy for 2,842 fund-days over the sample period. In short, the main result will not change if the alternative definition of extreme sentiment signals is used. The result is available upon the request. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 201 Credit and Capital Markets 2 / 2015 Table 8 The 4-Factor Alphas of Investment Strategies Table 5 defines investment strategies. In each strategy, at the beginning of each day, this table forms three portfolios by including ETFs that have consecutive trading signals over the prior 1-, 2-, and 3-day intervals. Using ETFs’ share returns, the table calculates daily performance for each ETF for up to five days following the trading signal. To increase the power of the tests, the table constructs overlapping portfolios by following the methodology used in Jegadeesh / Titman (1993). Each portfolio is equally weighted and held for up to five days following the portfolio formation. For each holding period, the portfolio’s daily performance is regressed against the 4-factor (Fama-French 3 factors plus a momentum) portfolios retrieved directly from the French website and the intercept in a percentage is reported. This table only analyzes non-leveraged / non-inverse commodity ETFs. The significance level of alphas equaling to zero is indicated by *** (1 %), ** (5 %), and * (10 %). The table also reports the average number of ETFs in each portfolio and the percentage of days for which a strategy is executed over the entire sample period from November 18, 2004, to December 31, 2011. Strategy # prior consecutive signals # ETFs % days covered Holding Days +1 +2 +3 +4 +5 Singlemarket 1 14 99 0.172*** 0.116*** 0.072*** 0.048** 0.044** 2 15 47 0.260*** 0.257*** 0.220*** 0.189*** 0.172*** 3 15 22 0.316*** 0.309*** 0.309*** 0.237*** 0.212*** Bull 1 14 54 0.141*** 0.140*** 0.140*** 0.141*** 0.140*** 2 14 28 0.158*** 0.156*** 0.156*** 0.156*** 0.155*** 3 15 14 0.083 0.081 0.080 0.081 0.081 Bear 1 14 45 0.110** 0.110** 0.110** 0.110** 0.110** 2 15 19 0.218*** 0.218*** 0.218*** 0.218*** 0.218*** 3 15 8 0.305** 0.305** 0.305** 0.305** 0.305** CrossMarket 1 7 87 0.187*** 0.133*** 0.086*** 0.048** 0.052** 2 4 29 0.344*** 0.320*** 0.239*** 0.213*** 0.200*** 3 3 8 0.346*0.359*0.363*0.337*0.257 BullSBearC 1 7 46 0.166*** 0.151*** 0.142*** 0.126*** 0.132*** 2 4 17 0.239** 0.201** 0.188** 0.185** 0.184** 3 3 5 –0.138 –0.158 –0.160 –0.160 –0.160 BearSBullC 1 7 40 0.108*0.114** 0.127** 0.126** 0.120** 2 4 13 0.261** 0.250** 0.266** 0.295** 0.293** 3 3 3 0.444 0.478 0.487 0.487 0.363 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 202 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 gle-market sentiment strategy based on the prior-day signal generates the 4-facor alpha of 17.2 bps, and its performance decays quickly to 4.4 bps in five days. A similar result is shown in the cross-market sentiment strategy. VIII. Conclusion This study explores how investor sentiment in the stock market affects prices of commodity ETFs. I provide quantitative evidence that the tracking errors of commodity ETFs differ in the bullish versus the bearish stock market, and thus, the aggregate tracking error of commodity ETFs is sensitive to sentiment measures commonly used in the literature. I further exploit a profitable trading strategy based on investor sentiment in the stock market and commodity markets. I use commodity ETFs and Spider in a long-short strategy according to the prior sentiment signals. The sentiment-driven demand for commodity ETFs exists and is a short-term phenomenon. Only strategies following fresh positive or extreme positive sentiment in the stock market can generate significantly positive index-adjusted alphas of 25.2 bps and 32.1 bps per day on average, respectively. These strategies indeed offer attractive alphas which far exceed the cap of trading costs and are profitable in more than half of fund-days. Following the methodology used in Jegadeesh / Titman (1993), I document that the portfolio in the single-market sentiment strategy based on the prior-day signal generates the 4-factor alpha of 17.2 bps, and its performance decays speedily to 4.4 bps in five days. A recent study by Ben-David / Franzoni / Moussawi (2014) documents that stocks owned by equity ETFs exhibit significantly higher intraday and daily volatility due to arbitrage activity between equity ETFs and the underlying stocks. Will such arbitrage trades still propagate the liquidity shocks from commodity ETF prices to the underlying securities, given that both are traded in different markets? I leave this interesting question for future research. OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 203 Credit and Capital Markets 2 / 2015 References Amihud, Y. (2002): Illiquidity and Stock Returns: Cross-Section and Time-Series Effects, Journal of Financial Markets, Vol. 5, pp. 31–56. Amihud, Y. / Mendelson, H. (1986): Asset Pricing and the Bid–Ask Spread, Journal of Financial Economics, Vol. 17, pp. 223–249. Anderson, R. L. / Bancroft, T. A. (1952): Statistical Theory in Research, McGraw-Hill Book Company, New York. Baker, M. / Wurgler, J. (2006): Investor Sentiment and the Cross-Section of Stock Returns, Journal of Finance, Vol. 61, pp. 1645–1680. – (2007): Investor Sentiment in the Stock Market, Journal of Economic Perspectives, Vol. 21, pp. 129–151. Baker, M. / Wurgler, J. / Yuan, Y. 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OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 Cross-Market Investor Sentiment 205 Credit and Capital Markets 2 / 2015 Appendix 1 List of Commodity ETFs The sample period in this study starts on November 18, 2004, the first inception date in commodity ETFs, and ends by December 31, 2011. Commodity ETFs investing in underlying physical assets directly are indicated by “P” while some using financial instruments to gain exposure to the underlying assets are indicated by “F” in the last column. Ticker Name Inception Date Type of Assets AGQ PROSHARES ULTRA SILVER 20081201 F CMD PROSHARE U / S DJ-UBS(AIG) COMMODITY 20081124 F CORN TEUCRIUM CORN FUND 20100608 F DBA POWERSHARES DB AGRICULTURE FUND 20070105 F DBB POWERSHARES DB BASE METALS FUND 20070105 F DBC POWERSHARES DB COMMODITY INDEX TRACKING FUND 20060203 F DBE POWERSHARES DB ENERGY FUND 20070105 F DBO POWERSHARES DB OIL FUND 20070105 F DBP POWERSHARES DB PREC METALS FUND 20070105 F DBS POWERSHARES DB SILVER FUND 20070105 F DGL POWERSHARES DB GOLD FUND 20070105 F DNO UNITED STATES SHORT OIL FUND 20090924 F GCC GREENHAVEN CONTINUOUS CMDTY 20080124 F GLD SPDR GOLD TRUST 20041118 P GLL PROSHARES ULTRASHORT GOLD 20081201 F GSG ISHARES S&P GSCI COMMODITY-INDEXED TRUST ETF 20060710 F IAU ISHARES GOLD TRUST 20050121 P PALL ETFS PALLADIUM TRUST 20100108 P PPLT ETFS PLATINUM TRUST 20100108 P SCO PROSHARE U / S DJ-UBS(AIG) CRUDE OIL 20081124 F SGOL ETFS GOLD TRUST 20090909 P (Continued on the next page) OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13 206 Hsiu-Lang Chen Credit and Capital Markets 2 / 2015 Ticker Name Inception Date Type of Assets SIVR ETFS SILVER TRUST 20090724 P SLV ISHARES SILVER TRUST 20060421 P UCD PROSHARE ULT DJ-UBS(AIG) COMMODITY 20081124 F UCO PROSHARE ULT DJ-UBS(AIG) CRUDE OIL 20081124 F UGL PROSHARES ULTRA GOLD 20081201 F UHN UNITED STATES HEATING OIL LP 20080409 F UNG UNITED STATES NATURAL GAS FUND, LP 20070418 F UNL UNITED STATES 12 MONTH NATURAL GAS FUND 20091118 F USCI UNITED STATES COMMODITY INDEX FUND 20100810 F USL UNITED STATES 12 MONTH OIL 20071206 F USO UNITED STATES OIL FUND LP 20060410 F ZSL PROSHARES ULTRASHORT SILVER 20081201 F (Appendix 1 – Continued) OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/ccm.48.2.171 | Generated on 2023-01-16 13:25:13