تاجران، بودجه، و دلالان کوچک در بازار آتی انرژی : تجزیه و تحلیل از تعهدات سی اف تی سی از گزارشات معامله گران
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|14991||2004||21 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 9100 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
- تولید محتوا با مقالات ISI برای سایت یا وبلاگ شما
- تولید محتوا با مقالات ISI برای کتاب شما
- تولید محتوا با مقالات ISI برای نشریه یا رسانه شما
پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Economics, Volume 26, Issue 3, May 2004, Pages 425–445
The Commodity Futures Trading Commission (CFTC)'s Commitments of Traders (COT) data are examined for crude oil, unleaded gasoline, heating oil, and natural gas futures contracts. The collection procedures for the COT data are first examined, followed by Granger causality tests to determine if relationships between trader positions and market prices exist. A positive correlation between returns and positions held by noncommercial traders, and a negative correlation between commercial positions and market returns, are found. Furthermore, positive returns result in an increase in noncommercial net positions in the following week, whereas the net long positions held by commercial hedgers decline following price increases. However, traders' net positions do not lead market returns in general.
The Commodity Futures Trading Commission (CFTC) collects data on the composition of open interest for all futures contracts. A subset of this data is released to the public through the CFTC's Commitments of Traders (COT) report. The open interest is divided into reporting and nonreporting traders, where reporting traders hold positions in excess of CFTC reporting levels. Reporting traders are further categorized as commercials or noncommercials. Commercials are associated with an underlying cash-related business and they are commonly considered to be hedgers. Noncommercials are not involved in an underlying cash business; thus, they are referred to as speculators. Furthermore, reporting level noncommercial activity is generally considered to be that of managed futures or commodity funds. Overall, the COT data are broadly discussed in terms of hedgers (reporting commercials), funds (reporting noncommercials), and small speculators (nonreporting traders). As illustrated in the opening quotes, the CFTC's COT report is widely anticipated and closely analyzed by commodity futures traders. In particular, futures traders tend to focus attention on positions held by reporting noncommercials (typically funds). Some analysts suggest that the anticipatory buying of futures contracts in front of the activity of reporting noncommercials can be a profitable strategy. At the same time, other analysts suggest that large fund positions signal market reversals; thus, fund activity can be viewed as a contrary indicator. Still, others argue that following the commercial trade is a profitable strategy (Welling, 1998). Regardless of the supposition, these views are rarely supported by statistical evidence. The COT data are also used by academics to examine a number of issues including the flow of funds among trader groups (Hieronymus, 1971), the forecasting ability of traders Hartzmark, 1991, Leuthold et al., 1994 and Buchanan et al., 2001, and the existence of risk premia (Chatrath et al., 1997), and is used as a measure of investor sentiment Wang, 2001 and Wang, 2002. Yet, the source, reliability, and definitions underlying the data set are rarely scrutinized. Given the widespread use of these data by both academics and industry professionals, it is essential that users have a thorough understanding of how the COT report is compiled and what information it contains. Given this, the overall objective of this study is to examine the information contained in COT reports, with focus on the COT data specific to the energy futures markets, namely, crude oil, gasoline, heating oil, and natural gas futures. First, the COT data for these futures markets are assessed in terms of collection procedures, trader definitions, and trader categorizations. In doing this, we fully describe the data collection procedures utilized by the CFTC and highlight the strengths and weaknesses of the COT data. It is important that researchers understand these issues, or they risk misinterpreting the results of their studies. Second, the COT data for these markets are evaluated in terms of how they relate to market prices. In particular, we ask the question: “Are trader positions useful for predicting market returns?” This is not meant to be a test of market efficiency or trading profitability; rather, we are investigating the informational content of these data in a broad sense. Similar in spirit to that of Buchanan et al. (2001), we evaluate whether traders' positions relate to futures prices and subsequent price movements. In doing this, we use a Granger causality framework to examine if returns lead trader positions, and if trader positions lead returns. We also determine the impact of extreme trader positions using a market timing framework similar to that of Cumby and Modest (1987). The CFTC's COT data are widely used by traders and academics alike, but are not always well documented or understood. This research represents the first comprehensive look at the COT data for the energy futures complex (crude oil, unleaded gasoline, heating oil, and natural gas). While Buchanan et al. (2001) provide insight into the information content of the COT data for the natural gas futures market, namely, the ability of trader positions to predict the direction of futures returns, we provide a more comprehensive look at this issue using different techniques over a larger cross-section of energy futures markets. Furthermore, this study pays special attention to the information contained in the COT reports beyond its ability to predict direction of returns. Namely, this research provides vital information with regards to the analysis of the data collection procedures, and interpretation of the trader categories used by the CFTC. Thus, we provide a more complete picture of the COT data's potential applicability, as well as how to interpret empirical results from studies where these data are used. Given the importance and size of the energy futures complex, as well as the role energy futures markets play in price discovery Fleming and Ostdiek, 1999 and Foster, 1996, it is important that researchers as well as traders in the energy complex have an understanding of this unique, publicly available data. The remainder of the paper is organized as follows. In Section 2, we present various academic studies, which use the COT data, and mention how a misunderstanding of the COT data could potentially lead to interpretative problems. In Section 3, we examine the CFTC large-trader reporting system, and specifically examine the information in the Commitment of Traders report in Section 4. Section 5 outlines the data, methods, and results used in linking the COT data to energy futures market returns, while Section 6 provides a summary of the research and conclusions.
نتیجه گیری انگلیسی
The CFTC collects detailed daily information on the positions held by reporting traders. A subset of that information is released to the public in the biweekly COT reports. A futures market’s open interest is disaggregated into positions held by reporting and nonreporting traders, and reporting traders are further identified as commercials or noncommercials. These groups are commonly referred to as funds (reporting noncommercials), hedgers (reporting commercials), and small speculators (nonreporting traders). The collection methodology underlying the COT data leads to the following conclusions. First, the data provide no information about nonreporting traders other than that they do not hold positions in excess of reporting levels. Second, the trading motives in the reporting commercial classification are likely to extend beyond just hedging. That is, pure hedging positions are a subset of those represented by CFTC reporting commercials. Finally, reporting noncommercials are the trader category least prone to reporting error. Since there are no incentives to self-classify as a speculator, the reporting noncommercial positions likely reflect a pure subset of true speculative positions. The empirical analysis focused on traders’ positions in crude oil, gasoline, heating oil, and natural gas futures from 1992 through 1999 (378 weekly observations). The empirical analysis shows that the largest positions are held by reporting commercials and the smallest by reporting noncommercials. Noncommercials are a relatively small percent of the total market (between 10% and 12% of the open interest for the tested markets), but they are active traders who may change from extreme net long positions to extreme net short positions over the course of a week. The contemporaneous relationship between the PNL for each CFTC trader class and market returns (Rt) is analyzed. The results indicate that reporting noncommercials increase their long positions in rising markets, and commercials decrease their long positions in rising markets. The fact that the noncommercials and commercials show inverse changes in their positions is not surprising, since longs and shorts must balance. Importantly, this contemporaneous relationship can support a number of competing theoretical models such as hedging pressure or positive feedback trading. The lead–lag relationship between net positions and market returns is analyzed in a Granger causality framework. The results clearly indicate that positive futures returns Granger cause increases in the net long positions held by reporting noncommercial traders, whereas commercials are net sellers following price increases. There is no consistent evidence that traders’ PNL positions contain any general predictive information about market returns. That is, PNL positions do not generally lead market returns. Furthermore, there is little evidence that extreme PNL positions in energy futures provide information about market returns. The above findings are important for accurately interpreting prior empirical results and theoretical models. First, any research that assumes positions at the end of a time period that are the same as those held during the time period must be carefully evaluated (see Chang, 1985; Bessembinder, 1992; Catrath et al., 1997; De Roon et al., 2000). The contemporaneous correlation between returns and positions will generate results showing that commercial traders create hedging pressure, which results in a risk premium flowing to noncommercials. Or, it will appear that noncommercials are profitable traders and commercials are not. The lead–lag relationships presented in this research, however, show that neither group’s positions are systematically useful in predicting returns. In fact, for both groups, returns lead positions. That is, commercials are net sellers the week following an increase in prices, and noncommercials are net buyers. It is not clear that the COT data provide any information concerning the profitability of trader groups in energy futures markets. The finding that traders’ positions are not useful in predicting returns cast a doubt on their usefulness as an independent indicator of market direction. In this analysis, it is assumed that the COT data are available immediately (on Tuesday). Historically, there is lag between when the data are collected and when they released to the public (Friday of the same week). In the interim, traders’ positions can change dramatically, especially those held by noncommercials. Thus, it is even more unlikely that the public release of the data is useful in predicting returns. However, our tests certainly do not rule out the possibility that these data can be used in conjunction with other information to forecast energy prices. Regardless, the CFTC’s COT is a unique data set that provides numerous opportunities for additional research in energy futures markets.