Passive Investors and Managed Money in Commodity Futures Part 2: Liquidity Prepared for: The CME Group Prepared by: October, 2008

2 Table of Contents SectionSlide Number Objectives and Approach3 Findings Corn4-12 Soybeans13-21 Chicago Wheat22-30 KC Wheat31-39 MN Wheat40-48 Cotton49-57 Natural Gas58-66 Crude Oil67-75 Summary76-79

3 Objectives and Approach The objective of this section is to examine the association, if it exists, between the market presence of passive and trend-following traders and liquidity in the studied markets. The liquidity measure is plotted in time-series fashion to identify recent trends. Growth in volume and open interest is also presented. A series of scatter diagrams are used to illustrate the relationship between market presence of each trader group and average liquidity. For the purposes of this work, liquidity is measured as trading volume as a percentage of open interest during the last 200 trading days.

4 Findings - Corn Overall, there has been at least a moderate increase in average liquidity for Corn contracts over the study period. In 2005 and 2006, liquidity generally averaged between 13% and 19%. From the beginning of 2007 onward, average liquidity per contract consistently ranges between 17% and 23%. Liquidity is seasonal, being lowest for the September contract and highest for the May contract.

5 Findings - Corn

6 Volume and Open Interest Growth - Corn

7 Findings - Corn The correlation between the presence of Non- Commercials and liquidity in the studied corn contracts is the strongest and it is positive. Correlations between the presence of Commercials and Indexers and liquidity were much weaker and negative. Interestingly, liquidity appears to decline whenever small traders make up a larger percentage of the market.

8 Findings - Corn Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

9 Findings - Corn

10 Findings - Corn

11 Findings - Corn

12 Findings - Corn

13 Findings - Soybeans There doesn’t appear to be a consistent, overall trend in average liquidity for Soybeans contracts over the study period. Average liquidity per contract seems to have declined from the early to mid-2005 through mid and stabilized through 2007 before showing signs of an increase in Overall, though, average liquidity has bounced between 22% and 36% per contract over the study period.

14 Findings - Soybeans

15 Volume and Open Interest Growth - Soybeans

16 Findings - Soybeans Correlation between the presence of any of the large trader groups and average liquidity was very weak for soybean futures. The data do not indicate a strong correlation between the presence of either Indexers or Money Managers and liquidity. The presence of small traders is negatively associated with liquidity.

17 Findings - Soybeans Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

18 Findings - Soybeans

19 Findings - Soybeans

20 Findings - Soybeans

21 Findings - Soybeans

22 Findings – Chicago Wheat Average liquidity has increased over time in Chicago Wheat futures. Average liquidity per contract moved between 14% and 22% during 2005 and From early 2007 on, average liquidity has consistently been above 20% and peaked as high as 28%.

23 Findings – Chicago Wheat

24 Volume and Open Interest Growth – Chicago Wheat

25 Findings – Chicago Wheat The presence of each of the major trading groups exhibits an extremely weak correlation to average liquidity for Chicago Wheat futures. There is not enough evidence in the charts on the following pages to suggest that the market presence of any trading group has even a modest impact on liquidity.

26 Findings – Chicago Wheat Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

27 Findings – Chicago Wheat

28 Findings – Chicago Wheat

29 Findings – Chicago Wheat

30 Findings – Chicago Wheat

31 Findings – Kansas City Wheat For Kansas City Wheat, average liquidity per contract has shown a rather strong down-trend over the study period. In 2005 and 2006, average liquidity was consistently 15% or higher, peaking around 20%. That steadily declined over time, and liquidity for recent Kansas City Wheat contracts has consistently been below 10%. Recently, volume has declined faster than open interest.

32 Findings – Kansas City Wheat

33 Volume and Open Interest Growth – Kansas City Wheat

34 Findings – Kansas City Wheat Although the correlation is not very strong (R- squared = 0.27), the data suggests that the presence of Indexers may have a positive impact on liquidity in the Kansas City Wheat futures market. Interestingly, the presence of Money Managers may have a very modest but negative impact on liquidity. There are no strong or definitive patterns between market presence and liquidity for the Commercial, Non-Commercial and Small Trader groups.

35 Findings – Kansas City Wheat Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

36 Findings – Kansas City Wheat

37 Findings – Kansas City Wheat

38 Findings – Kansas City Wheat

39 Findings – Kansas City Wheat

40 Findings – Minneapolis Wheat Average liquidity has varied widely over the study period for the Minneapolis Wheat futures market, ranging anywhere from 11% to 20%. Although liquidity has varied more in recent months, the general trend has been toward modestly lower liquidity. Significant seasonality is present, with liquidity routinely peaking in the May contract. Recently volume has fallen faster than open interest, causing the liquidity metric to decline.

41 Findings – Minneapolis Wheat

42 Volume and Open Interest Growth – Minneapolis Wheat

43 Findings – Minneapolis Wheat The following charts indicate that any correlation between the market presence of each trading group and liquidity in Minneapolis Wheat futures is extremely weak. Indexers are conspicuously absent in this futures market.

44 Findings – Minneapolis Wheat Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

45 Findings – Minneapolis Wheat

46 Findings – Minneapolis Wheat

47 Findings – Minneapolis Wheat

48 Findings – Minneapolis Wheat

49 Findings - Cotton Liquidity was consistently above 10% and as much as 15% or higher during For cotton, average liquidity per contract appears to be declining over time but this is heavily influenced by a substantial decline in liquidity during early The sharp drop in the liquidity measure during early 2008 may have been driven by reduced future volumes as price limits became binding more frequently and volume appeared to shift toward the options market.

50 Findings - Cotton

51 Volume and Open Interest Growth - Cotton

52 Findings - Cotton The presence of Commercials exhibits a very modest positive correlation to liquidity. A very modest negative correlation may exist between the presence of Non-Commercials and liquidity in this market. The presence of Indexers and Money Managers displays a much weaker and positive correlation to liquidity.

53 Findings - Cotton Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

54 Findings - Cotton

55 Findings - Cotton

56 Findings - Cotton

57 Findings - Cotton

58 Findings – Natural Gas For Natural Gas contracts, liquidity was steady – possibly even declining slightly – from early 2005 through early From there, average liquidity increased sharply and peaked in mid-2007 only to retreat to previous levels before rebounding again into and through While liquidity once averaged between 15% and 25%, it has regularly been above 25%, and even up to 40%, since the middle of last year.

59 Findings – Natural Gas

60 Volume and Open Interest Growth – Natural Gas

61 Findings – Natural Gas There appears to be little or no correlation between the market presence of the trader groups and average liquidity in this market.

62 Findings – Natural Gas Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

63 Findings – Natural Gas

64 Findings – Natural Gas

65 Findings – Natural Gas

66 Findings – Natural Gas

67 Findings – Crude Oil Average liquidity was consistently around 40% during 2005 and After a sudden upward shift, liquidity averaged mostly between 50% and 60% from early 2007 into Liquidity for the July and August 2008 contracts soared to more than 70%, however. It’s safe to say that Crude Oil futures have seen a marked increase in liquidity over the study period.

68 Findings – Crude Oil

69 Volume and Open Interest Growth – Crude Oil

70 Findings – Crude Oil As the following charts suggest, the presence of Indexers, as a trading group, does have some positive correlation with liquidity in Crude Oil futures. The presence of the other major trading groups exhibits much weaker correlation with liquidity.

71 Findings – Crude Oil Regression Equation: Liquidity = a + b x Market Presence R 2 = the correlation coefficient squared.

72 Findings – Crude Oil

73 Findings – Crude Oil

74 Findings – Crude Oil

75 Findings – Crude Oil

76 Summary Overall, it appears that liquidity increased over the study period (2005 through mid-2008) for Corn, Chicago Wheat, Natural Gas, and Crude Oil. The most pronounced increase in liquidity was in Crude Oil futures. Liquidity has declined over time in Kansas City Wheat, Minneapolis Wheat and Cotton. Liquidity has been relatively stable in Soybean futures with little change over time.

77 Summary The strongest observed positive associations were the Non-Commercials trading in the corn futures market and the Indexers trading Kansas City wheat and crude oil futures. In all three instances, however, we observed R- square statistics that were no higher than 0.35, suggesting a relatively weak correlation by most guidelines for statistical analysis.

78 Summary Liquidity tends to be seasonal in many contracts, with certain months “favored” over others. In corn, Dec liquidity is almost always higher than in Sep. This seasonality may mask trends in liquidity to some degree. If there were enough data we would have preferred to isolate by contract month (e.g., only compare Dec contracts with other Dec contracts). Unfortunately, there were only three years of data available.

79 Summary Overall, there is little evidence to suggest that any one particular trader group has a strong impact on liquidity—either positive or negative. It is more likely that, in markets where liquidity gains have occurred, all of the trader groups contributed in some manner. The factors that fostered increased trading for one group, likely did so for all of them. We must remember that correlation does not imply causation. However, the lack of correlation makes a strong case for the absence of causation.