New Evidence on the Financialization* of Commodity Markets

Transcription

1 1 New Evidence on the Financialization* of Commodity Markets Brian Henderson Neil Pearson Li Wang February 2013 * Financialization refers to the idea that non-information-based commodity investments by retail and institutional investors who buy commodities for portfolio reasons (e.g., diversification) have important impacts on commodity prices.

2 2 Background From 2003 to 2011, financial commodity investment increased from $15 to $400 billion The periods of most rapid increases in financial commodity investment approximately coincided with the periods of significant increases in commodity prices, especially the 2008 commodities boom Do these financial commodity investments have a causal impact on commodities prices? Some researchers say yes, e.g. Tang and Xiong (2011), Mou (2010), Singleton (2012) Others find no evidence, e.g. Stoll & Whaley (2010), Buyuksahin and Robe (2009, 2011)

3 Related Literature Evidence of financialization: Purchases by non-commercial traders have causal impacts on commodity futures prices or expected returns (Masters 2008, Gilbert 2010, Singleton 2012, Hamilton and Wu 2012). Evidence from correlations (Silvennoinen and Thorp 2010; Tang and Xiong 2011) Index rolling affects futures prices (Mou 2010). No evidence for financialization: Position changes computed from CFTC data do not Granger cause futures prices (Buyuksahin and Harris 2009) Rolling of positions does not impact commodities prices (Stoll and Whaley 2009) Hedge fund positions rather than index investment explain the recent increase in correlations between stock and commodity returns (Buyuksahin and Robe 2011) 3

4 Total Financial Commodity Investment 4

5 Summary We study the impact of issues of commodity-linked notes (CLN s) on commodity prices. CLN s are issued by financial institutions and sold to retail investors The sample of CLN s is useful because: Issuers hedge trades reflect the retail demand We know when the issuers hedge the issues, and thus know when to look for the price impact of the retail demand The CLN issues do not convey information to the market, because neither the CLN purchases nor the issuers hedging trades are based on information about commodity prices The hedge trades executed by the financial institutions that issue the CLN s have significant non-transitory impacts on commodity futures prices, consistent with the financialization of commodity prices 5

6 What are CLN s? Medium-term notes issued by financial intermediaries Payoffs based on the price (return) of a commodity, a commodity futures contract, a commodities index, or basket of commodities. Example: Accelerated Return Notes (ARN) Pricing Date: 16 Dec Linked to price of frontmonth crude oil futures Total proceeds: $11,316,090 No periodic interest payment Not listed on any exchange 6

7 CLN Issuer Hedges Using Futures Retail Investors purchasing CLNs Financial Intermediary Hedging Futures Market Hedging OTC Swap Re-hedging CLNs are priced based on the closing price of underlying commodity futures on the pricing date. On the pricing date, issuer hedges by buying futures or swaps, which are then re-hedged using futures

8 Identification 8 Trades that hedge CLN s sold to retail investors are plausibly exogenous Issuers will execute hedge trades regardless of changes in futures prices on the pricing date this eliminates the reverse causality from price changes on the pricing date to the hedge trades Sophisticated investors with valuable information are unlikely to buy high-cost and low-leverage CLN s rather than low-cost futures contracts this rules out common causality in which both CLN issuances and price movements are caused by retail investors private information about commodity prices.

9 9 Data SEC/EDGAR database: CLN pricing supplements (form 424B2 or 424B3) All public issues from Jan to Aug Issued by 20 banks and financial intermediaries 1491 issues, 106 of which are ETNs For each CLN: pricing date, maturity date, underlying asset, commissions, total proceeds, listing information, CUSIP Total proceeds are around $59 billion, 11% of the total financial commodity investments. Bloomberg: Daily closing price and open interest for futures contracts, S&P 500, Morgan Stanley Emerging Market Index, JP Morgan Bond Index, US Dollar Index Federal Reserve: Inflation compensation (Gürkaynak, Sack, and Wright 2010)

10 10 Numbers of Issues and Proceeds ($millions), by Year Agriculture Energy Industrial Metals Platinum and Palladium Gold and Silver Divers. Index and Comm. Baskets Full Sample Year No. $ No. $ No. $ No. $ No. $ No. $ No. $ , , , , , , , , , , , , , , , , , , , , , , ,093 Total 132 6, , , , , ,937 Average Proceeds Numbers of issues and proceeds for 2011 are based on data only through August 2011

11 Hypotheses Expect price impact (positive abnormal returns) around the pricing dates of CLN issues due to hedge trades Due to the possibility of delayed hedge trades, we focus on two-day (pricing date and next day) abnormal returns. Price impact should be increasing in the size of the hedge trade Use issue size as a proxy for hedge trade size. Expect to see a larger price impact for larger issues. Do not expect significant price impact for issues linked to diversified commodity indices because the hedge trades are spread across the many index commodities 11

12 Abnormal Futures Returns Following Tang and Xiong (2011), we use the return R t = ln(f t ) ln(f t 1 ) on the front-month futures contract We focus on the abnormal return Rt R t, where the benchmark return is based on a factor model R t = ˆ α + ˆ β + ˆ β BOND S& P500 R R BOND, t S& P500, t + ˆ γr t 1 + ˆ β + ˆ γ EMA EMA R R EMA, t EMA, t+ 1 + ˆ β USD + ˆ β R USD, t inflation R inflation, t 12 The factor model is estimated separately for each combination of commodity and pricing date using data from the 60 days prior to the pricing date

13 Price Impact: Average Abnormal Returns of Underlying Commodity Futures, Excluding S&P GSCI Roll Periods 13 Panel A: Individual commodities and commodity baskets Proceeds $2 million Proceeds $5 million Proceeds $10 million Days Days Days Day 0 Day 1 [0,1] Day 0 Day 1 [0,1] Day 0 Day 1 [0,1] Average abnormal return 0.22% 0.14% 0.37% 0.21% 0.19% 0.40% 0.31% 0.20% 0.51% t-statistic Number of returns > Number of returns Probability under H Panel B: Individual commodities (excluding commodity baskets) Proceeds $2 million Proceeds $5 million Proceeds $10 million Days Days Days Day 0 Day 1 [0,1] Day 0 Day 1 [0,1] Day 0 Day 1 [0,1] Average abnormal return 0.23% 0.13% 0.37% 0.22% 0.18% 0.40% 0.33% 0.19% 0.52% t-statistic Number of returns > Number of returns Probability under H

14 14 Price Impact by Commodity Sector (Energy/Other) Panel A: Energy Commodities Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.31% 0.65% 0.96% 0.41% 0.72% 1.14% 0.75% 0.68% 1.43% t-statistic Number of returns > Number of returns Probability under H Panel B: Other Commodities Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.20% -0.01% 0.18% 0.16% 0.05% 0.22% 0.20% 0.08% 0.27% t-statistic Number of returns > Number of returns Probability under H

15 Do CLN Issues Convey Information? We reject the possible alternative explanation that CLN issues convey information to the market because futures contracts/etf s are a better trading vehicle for an investor with information: Futures have high liquidity, low transactions costs, embedded leverage, and no credit risk ETF s have high liquidity, low transactions costs, and less credit risk than CLN s CLN s have low liquidity (and are only available when an issuer is selling a new issue), are costly to exit prior to maturity, have high commissions and embedded fees, and carry the credit risk of the issuer Implausible that CLN investors are sophisticated enough to have valuable information about commodity prices but so unsophisticated that they are unaware of the advantages of using futures contracts 15

16 Do CLN Issues Convey Information? 16 But, there are some fixed-income managers whose mandates limit their investments to notes and bonds They are allowed to invest in CLN s because CLN s are notes, but are not permitted to buy futures or ETF s Is it possible that some such fixed-income managers have valuable information about future commodity prices and trade on that information using CLN s? If this is the case then the CLN issues might convey information to the market.

17 Average Abnormal Returns for High-Commission ( 1%) Issues, Excluding S&P GSCI Roll Periods 17 Panel A: Individual commodities and commodity baskets Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.29% 0.19% 0.48% 0.34% 0.20% 0.55% 0.45% 0.32% 0.76% t-statistic Number of returns > Number of returns Probability under H Panel B: Individual commodities (excluding commodity baskets) Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.29% 0.15% 0.44% 0.37% 0.15% 0.52% 0.48% 0.25% 0.73% t-statistic Number of returns > Number of returns Probability under H

18 Average Abnormal Returns for Low-Commission (<1%) Issues, Excluding S&P GSCI Roll Periods 18 Panel A: Individual commodities and commodity baskets Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.16% 0.09% 0.26% 0.11% 0.16% 0.27% 0.21% 0.10% 0.31% t-statistic Number of returns > Number of returns Probability under H Panel B: Individual commodities (excluding commodity baskets) Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.18% 0.11% 0.29% 0.11% 0.18% 0.29% 0.21% 0.14% 0.35% t-statistic Number of returns > Number of returns Probability under H

19 Do CLN Issues Convey Information? In order to believe that the price impact is due to information conveyed by the fact of the CLN issue, you have to believe that there are investors: ho are sophisticated enough to have information about future commodity prices But are so unsophisticated that they chose to trade on the information using illiquid, high-commission CLN s rather than low-commission CLN s, liquid, lowcommission ETN s, ETF s, and commodity futures 19

20 20 Selection Bias? Issuer can cancel issue up until the close of trading on the pricing date. Could the positive abnormal returns be due to the fact that issues tend to be completed on days when commodity prices go up? If this selection bias is important, it should also affect issues based on diversified indexes. In particular, the price impact for issues based on diversified indexes is an upper bound on the magnitude of the selection bias. This upper bound is close to zero. If the price impact is due to selection bias, it should be independent of the issue size. But, we find that price impact is increasing in the issue size.

21 21 Price Impact on Diversified Commodity Indexes Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return -0.04% 0.01% -0.03% -0.07% 0.04% -0.02% -0.03% 0.00% -0.03% t-statistic Number of returns > Number of returns Probability under H No evidence of any price impact around the pricing dates of issues based on diversified commodity indexes

22 Small Issues (Proceeds < $2 million and proceeds < $5 million) 22 Proceeds < $2 million Proceeds < $5 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return -0.14% -0.21% -0.35% 0.07% -0.09% -0.02% t-statistic Number of returns > Number of returns Probability under H No evidence of any price impact around the pricing dates of small issues

23 Are the Price Impacts Temporary or Permanent? Post-Issue Returns 23 Panel A: Individual commodities and baskets Day relative to pricing date Total Proceeds $2 million Average abnormal return 0.37% 0.17% 0.13% 0.12% 0.05% t-statistic Number of returns > Number of returns Probability under H Total Proceeds $5 million Average abnormal return 0.40% 0.17% 0.10% 0.11% 0.06% t-statistic Number of returns > Number of returns Probability under H Total Proceeds $10 million Average abnormal return 0.51% 0.10% -0.01% 0.05% 0.07% t-statistic Number of returns > Number of returns Probability under H

24 24 Post-Issue Returns Panel B: Individual commodities (excluding baskets) Day relative to pricing date Total Proceeds $2 million Average abnormal return 0.37% 0.20% 0.13% 0.14% 0.06% t-statistic Number of returns > Number of returns Probability under H Total Proceeds $5 million Average abnormal return 0.40% 0.21% 0.10% 0.13% 0.06% t-statistic Number of returns > Number of returns Probability under H Total Proceeds $10 million Average abnormal return 0.52% 0.14% -0.02% 0.07% 0.06% t-statistic Number of returns > Number of returns Probability under H

25 25 Why Are There Price Impacts? Shleifer (1986) argues that there are downward sloping demand curves due to differing opinions about fundamental values. Singleton (2011) provides evidence about disagreement and its importance in the oil market Petajisto (2009) provides a model in which the pressure is borne by intermediaries, and applies his model to explain the empirical evidence on index inclusions and deletions. Price impacts are consistent with idea that demand due to hedge trades shifts the demand curve to the right

26 26 Include issues during Goldman Roll Main results exclude issues in the Goldman roll. We find similar results when we include issues that take place during the Goldman roll. Panel A: Individual Commodities and Baskets Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.08% 0.21% 0.29% 0.06% 0.21% 0.26% 0.16% 0.22% 0.39% t-statistic Number of returns > Number of returns Probability under H Panel B: Individual Commodities (Excluding Baskets) Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.07% 0.20% 0.27% 0.05% 0.20% 0.25% 0.17% 0.22% 0.39% t-statistic Number of returns > Number of returns Probability under H

27 27 Mean-adjusted returns Obtain similar results when we use the average return over 60- day window leading up to the pricing date as the benchmark. Panel A: Individual commodities and commodity baskets Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.19% 0.13% 0.33% 0.21% 0.18% 0.38% 0.26% 0.24% 0.50% t-statistic Number of returns > Number of returns Probability under H Panel B: Individual commodities (excluding commodity baskets) Proceeds $2 million Proceeds $5 million Proceeds $10 million Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Day 0 Day 1 Days [0,1] Average abnormal return 0.21% 0.11% 0.33% 0.22% 0.16% 0.37% 0.28% 0.23% 0.51% t-statistic Number of returns > Number of returns Probability under H

28 Relative Issue Size and Price Impact 28 Right-hand Side Variable Coefficient Estimates and t-statistics (in parenthesis) (1) (2) (3) Constant (0.89) (0.86) (-0.08) RelativeIssueSize (3.03) AbnormalVolume (1.78) 2-DayVolume (1.81) 2 AverageVolume (-1.06) Number of Observations Dependent variable is the abnormal futures return over the twoday period Days[0,1]

29 29 Right-hand side variables: Relative Issue Size = total proceeds divided by dollar open interest of the two-nearest expiring futures contracts Abnormal Volume = average volume in the reference commodity s front month futures contract during Days [0,1] minus the trailing 60-day average daily volume, normalized by the open interest in the two-nearest expiring futures contracts 2-DayVolume = volume during Days[0,1], normalized by the open interest in the two-nearest expiring futures contracts. AverageVolume = trailing 60-day average daily volume, normalized by the open interest in the two-nearest expiring futures contracts

30 30 Conclusion The hedge trades associated with CLNs have statistically and economically significant price impacts on the underlying commodity futures markets. The average two-day abnormal return around the pricing dates of CLNs is 37 to 51 basis points. No evidence of reversals. These results support the view that non-information based financial trading has an important impact on commodity futures prices, i.e. it is evidence consistent with the financialization of commodity futures markets