{"id":4952,"date":"2019-09-27T14:56:19","date_gmt":"2019-09-27T12:56:19","guid":{"rendered":"https:\/\/quantpedia.com\/?p=4952"},"modified":"2025-06-04T14:19:11","modified_gmt":"2025-06-04T12:19:11","slug":"a-deeper-look-on-commitments-of-traders-report","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/a-deeper-look-on-commitments-of-traders-report\/","title":{"rendered":"A Deeper Look on Commitments of Traders Report"},"content":{"rendered":"

Sun Tzu once wrote (paraphrasing) \u201cknow yourself, know your enemy, and you shall win a hundred battles without loss”. This proverb is true also in financial markets as it is always easier to prepare trading\/investment strategy when you know who are other market participants and what their intentions probably are. A new academic research paper written by Robe & Roberts gives a more in-depth insight into the CFTC\u2019s weekly \u201cCommitments of Traders\u201d report. The COT\u2019s report offers a small number of trader groupings; therefore, its usefulness is very limited. However, Robe & Roberts use trader-level data that originate from the CFTC\u2019s Large Trader Reporting System (LTRS), which allows them to create a very detailed look at the composition of agricultural futures market.<\/strong><\/p>\n\n\n\n

Authors:<\/strong> Robe, Roberts<\/p>\n\n\n\n

Title: <\/strong>Who Holds Positions in Agricultural Futures Markets<\/p>\n\n\n\n

Link:<\/strong> https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3438627<\/a><\/p>\n\n\n\n

Abstract:<\/strong><\/p>\n\n\n\n

\nWe use non-public data regarding all trader-level futures positions, \nreported to the U.S. grain and oilseed derivatives market regulator (the\n CFTC), in order to describe the nature of market participants, the \nmaturity structure of their holdings, and the aggregate position \npatterns for nine different categories of traders that we separate based\n on their main lines of business. We provide novel evidence about the \noverall extent of calendar spreading and about the contribution of \ncommercial traders to total spreading activity. <\/p>\n\n\n\n

Our sample\u2019s 3,854 traders account for 86 to 93 percent of the total futures open interest at the end of an average day in 2015\u20132018. Well over 90 percent of their positions have maturities of less than a year. Among our nine trader categories, just three (hedge funds and commercial dealers\/merchants, plus commodity index traders on the long side) account for about four fifths of all reported trader positions. In fact, fewer than 200 \u201cpermanent\u201d large traders (overwhelmingly from these three categories) make up the bulk of the daily open interest in the four largest agricultural futures markets. <\/p>\n\n\n\n

In the aggregate, the\n positions of commercial dealers and hedge funds (including commodity \npool operators, commodity trading advisors, managed money traders, and \nassociated persons) are highly negatively correlated. This correlation \nis strikingly strong for short positions: as a result, the sum total of \ncommercial dealers\u2019 and hedge funds\u2019 respective shares of the short open\n interest fluctuates relatively little over time. <\/p>\n\n\n\n

We show, for \nthe first time, that calendar spreads account for more than a third of \nall large trader positions; that much of the intra-year variation in the\n total futures open interest can be tied to changes in the extent of \ncalendar spreading; that about half of all spread positions involve \ncontracts expiring in 4 to 12 months (either spreading with \nshorter-dated contracts, or involving only maturities of 4 to 12 \nmonths); and that commercial traders who are not swap dealers (dealers \nand merchants, mostly) make up from a quarter to two fifths of all \ncalendar spread positions. Again, commercial dealers\u2019 and hedge funds\u2019 \nshares of the spread open interest are negatively correlated. None of \nthese patterns can be inferred from public data, as the CFTC\u2019s \nCommitments of Traders Reports (COT) do not break out spreads for \n\u201ctraditional\u201d commercial traders in general and commercial dealers and \nmerchants in particular.\n\n<\/p>\n\n\n\n

Notable quotations from the academic research paper:<\/strong><\/p>\n\n\n\n

” This table reports the shares (in percent) of the reported open interest (OI) for nine trader categories in the four largest U.S. agricultural futures markets. The Commodity Index Traders (CIT) category comprises the commercial swap dealers and managed money traders with substantial indexing business. Commercial trader categories include commercial dealers (AD), commodity swap dealers (AS, excluding commodity index traders), and other traditional commercial traders (AT). Non-commercial traders include managed money traders (MMT, excluding commodity index traders), floor brokers and traders (FBT), financial dealer intermediaries (FDI), traders with multiple registrations other than MMT (FMX), and other large market participants that are not registered with the CFTC (NRP). Each individual reported trader\u2019s OI is calculated based on averages of daily long and short positions. Individual totals are then summed up across all large traders in a given category. The figures exclude small traders whose positions are not reported to the CFTC, which account from 10 to 15 percent of the total open interest. Because CITs\u2019 positions are overwhelmingly long, the \u201copen interest\u201d for that group of traders (calculated as the average of their long and short positions) understates by approximately half their contribution to the long-only open interest.<\/p>\n\n\n\n

\"Trader<\/figure>\n\n\n\n\n\n\n
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The public COTs\u2019 categories mix rather dissimilar kinds of traders. First, three of the four disaggregated COT groupings include traders that are principally involved in commodity indexing as well as traders that are not. We isolate the former to create a distinct group of \u201ccommodity index traders\u201d (CIT). Second, under the label \u201cOther non-commercials,\u201d the COTs include floor traders, brokers, and several other disparate types of financial intermediaries. We disaggregate this \u201ccatch-all\u201d group into several smaller, but more homogeneous, sub-categories. Third, in the same vein, a single commercial COT category lumps together all large \u201cProducers,\u201d \u201cProcessors\u201d (mills, biofuel refineries, etc.), elevators and other kinds of physical-market \u201cDealers and Merchants\u201d, plus all the other large commercial \u201cEndusers\u201d that are not commodity swap dealers. Intuitively, one would expect those diverse kinds of market participants to trade differently. Using the non-public information on each trader\u2019s daily positions and main line(s) of business, we document that, indeed, commercial dealers are\u2014in the aggregate\u2014often net short (resp. long) when all other traditional commercial traders are\u2014 collectively\u2014net long (resp. short).<\/p>\n\n\n\n

\"Commitment<\/figure>\n\n\n\n

Next, our data allow us to disaggregate the COT information not only by finer trader subgroups, but also by maturity. A big drawback of the public COT reports is that they sum up trader positions across all contract expirations. This aggregation obscures dissimilarities in the respective extents to which different trader groups\u2019 are active at the near and far ends of the futures curve and in their calendar spreading styles.<\/p>\n\n\n\n

In 2015-2018, we find no (wheat) or almost no (corn, soybeans) activity at maturities beyond three years. For virtually all of our nine trader sub-categories, on average 93 (corn) to 97 percent (wheat) of the open interest is concentrated within the front year expiry range. Noncommercial traders tend to focus on the near end of the futures term structure: two thirds of the aggregate non-commercial positions involve contracts expiring in 130 days or less, vs. only about half of all commercial positions. This focus on the two nearest maturities is strongest for managed money traders: on average, up to 70 percent of their aggregate positions in the four largest agricultural futures markets involve contracts expiring within four months.<\/p>\n\n\n\n

Last, but certainly not least, the ability to disaggregate trader positions by maturity allows us to provide novel evidence on calendar spreading. While the public COTs show the aggregate net long, net short, and calendar spread positions held in the aggregate by three broad categories of large traders (commercial swap dealers, managed money traders, and other non-commercial traders), the COTs do not break out calendar spreads for the other types of commercial traders. Using the CFTC\u2019s non-public trader-level data, we tabulate spread positions for all of our nine (more granular) trader sub-categories. We show, to our knowledge for the first time, that\u2014in the aggregate\u2014calendar spreads account for over one third of large trader positions and that non- CIT commercial traders (mostly dealers and merchants) contribute, on average in 2015\u20142018, from 28 percent (SRW wheat) to 40 or more percent (beans, corn, HRW wheat) of all calendar spread positions in each market. This novel evidence provides empirical support for a long tradition, in the agricultural economics literature, of prominent authors\u2019 arguing that calendar spreading is a key component of many commercial traders\u2019 activities in grain and oilseed futures markets\u2014a fact that is all but absent from the corresponding finance literature.”<\/p>\n\n\n\n

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Sun Tzu onced wrote (paraphrasing) \u201cknow yourself, know your enemy, and you shall win a hundred battles without loss”. This proverb is true also in financial markets as it is always easier to prepare trading\/investment strategy when you know who are other market participants and what their intentions probably are. A new academic research paper written by Robe & Roberts gives a more in-depth insight into the CFTC\u2019s weekly \u201cCommitments of Traders\u201d report. The COT\u2019s report offers a small number of trader groupings; therefore, its usefulness is very limited. However, Robe & Roberts use trader-level data that originate from the CFTC\u2019s Large Trader Reporting System (LTRS), which allows them to create a very detailed look at the composition of agricultural futures markets.<\/strong><\/p>\n

Authors:<\/strong> Robe, Roberts<\/p>\n

Title: <\/strong>Who Holds Positions in Agricultural Futures Markets<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[155],"class_list":["post-4952","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-alternative-data"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4952","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=4952"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4952\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=4952"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=4952"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=4952"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}