Returns to Individual Traders of Futures: Aggregate Results

RDX : 絕對好文 一定要讀 但是因為是自己OCR加上手動校的 所以可能有錯 或者有的注釋行沒有刪掉

Michael L. Hartzmark

University of Michigan and University of Chicago

By means of a data set previously unavailable for academic research, actual trading histories of individual futures traders are examined. With this more detailed data I am able to (1) test the risk/return hypothesis directly, (2)include a much larger segment of the market than before, and (3) use actual instead of hypothetical trading strategies. It is shown that the commercial (hedging) traders are most profitable, while noncommercial (speculative) traders earn negative or zero profits. Because speculators are not receiving rewards for the risks they willingly absorb, the theory of normal backwardation and its extensions can be rejected.

I. Introduction

This paper brings economists much closer to resolving the decades-old debate over who wins, who loses, and for what reasons in futures markets. On one side, proponents of the theory of normal backwardation argue that, to be induced to trade, speculators, acting as insurers, must be paid a risk premium for absorbing the risks hedgers wish to offset (Keynes 1930; Blau 1944; Cootner 1960a, 1960b; Hicks 1978). Since futures trading is a zero-sum game, this theory implies that speculators receive positive returns on average as compensation for risk, while hedgers "pay" to reduce their business risks. The alternative theories postulate that the supply of speculative services (with respect to expected dollar profits) is horizontal at a zero return. Thus the risk premium will be bid to zero and the returns for bearing risk will disappear (Telser 1958, 1960, 1967; Rockwell 1964, 1977; Hartzmark 1984).1 In these alternative theories, factors such as luck, superior forecasting ability, or market power determine the relative rewards individuals gain from participating in the futures market. Instead of acting to redistribute risk, the speculator may then serve the role of providing liquidity and enhancing the efficiency of the price discovery function that futures markets fulfill.

To resolve this controversy directly, a 4.5-year sample from nine futures markets is taken from the Commodity Futures Trading Commission's (CFTC) confidential files on the daily positions of large reporting traders.2 From this uniquely comprehensive daily record, the actual trading histories of 4,567 individual large traders are constructed. The dollar profits of individual traders are then precisely calculated. The main results show that noncommercial traders (speculators) fail to earn positive profits on average. In fact, it is the commercial traders (hedgers) who, on average, obtain positive returns.3 This new evidence, based on actual transactions, allows us to reject the theory of normal backwardation and its variants.

The previous evidence relies on either hypothetical traders, indirect price data, or small and unrepresentative samples of the participants in the market. In addition, the empirical results offer mixed support. Previous studies using actual trading histories have two limitations (Stewart 1949; Hieronymus 1977; Teweles, Harlow, and Stone 1977). First, and most serious, the profits of only small speculators (representing only a small segment of the market) are calculated, so the results cannot be generalized. Second, all the observations come from a single brokerage firm. This may bias the results since there may be some degree of dependence across individuals,4 and the other accounts traders may have are excluded. The present study is far more comprehensive because it is focused on the group of large traders who hold more than 50 percent of the positions on one or both sides of most of the markets in the sample (Hartzmark 1984, table 4). In addition, these traders are required to report all positions in all their accounts, so the problems of dependence and exclusion are eliminated.

Another group of studies on trader profitability utilizes the CFTC month-end large-trader reports including both commercial and noncommercial positions (Houthakker 1957; Rockwell 1964, 1977; Chang 1985). Even though they include a much broader sample of participants over a much longer time period, they are suspect because the estimates of profits are based on hypothetical trading rules rather than on actual tradingbehavior.5 This is necessary since the month-end reports include the positions of traders on only one day of the month and are aggregated across traders and maturity IIlonths. In this paper, daily data are disaggregated to the level of the individual trader and his individual contract holdings. This enables realistic estimates of performance to be made and allows for the examination of the moments of the distribution of individual returns.

II. Description of the Data Base

The data used in this paper come from two sources. The daily settlement prices and market activity variables are from the CFTC permanent files. The information on the individual large traders is generated from the CFTC reports on the end-of-day commitments of large traders. In all markets, traders who, either at the beginning or the end of a trading day, hold commitments exceeding certain levels are required by the CFTC to report their trading activity with their speculative and hedge, long and short, positions in all contract maturity months.6

This data set does not include the positions of day traders or scalpers since these participants seldom carry their positions overnight. In addition, since actual transactions prices are unavailable, the intraday profits and losses earned by large traders cannot be calculated from the data.7 There are a total of 1.28 million daily observations on the positions that individual traders hold in each maturity month (see table 1, col. 8). There are 585,000 observations in the data base where, for each trader, the positions are aggregated across each maturity month on a daily basis (see col. 9).

Daily profits for each trader for each contract held are calculated by multiplying the end-of-day positions by the change in the settlement price between the current day and the following day. This is the same procedure used by the central clearinghouse to mark each trader's account to the market price at the end of each trading session. These dollar profits are then used to measure performance. A percentage rate of return is not used because in futures markets this measure has little meaning. Since the net supply of contracts in futures markets is zero, there is no meaningful way to determine the magnitude of total investment in the market (the denominator for any percentage rate of return).8 In addition, the opportunity cost of investing is quite small (Telser 1981; Hartzmark 1986).9

Nine markets are covered over the period July 1, 1977, to December31, 1981.10 These include (1) oats traded on the Chicago Board of Trade (CBT), (2) wheat traded on the CBT, (3) wheat traded on the Minneapolis Grain Exchange (MGE), (4) wheat traded on the Kansas

City Board of Trade (KBT), (5) pork bellies traded on the Chicago Mercantile Exchange (CME), (6) live cattle traded on the CME, (7) feeder cattle traded on the CME, (8) U.s. T-bonds traded on the CBT, and (9) 90-day T-bills traded on the International Monetary Market (IMM).

A naive rule is employed to classify a participant as a commercial or a noncommercial trader. All traders who report only hedging positions are considered to be commercial traders (or pure hedgers). All traders who report only speculative positions are considered noncommercial traders (or pure speculators). For those traders who report both hedge and speculative positions, the confidential files kept by the CFTC are consulted to determine the line of business of the traders.

The distinction between comn1ercial and noncommercial traders is not always clear-cut. Certain commercial traders do, as shown later, speculate on their individual price expectations. However, to be designated as a commercial trader, the activity of the participant who holds both speculative and hedging positions must be donlinated by hedging commitments and the trader's main line of business (as revealed in the CFTC files) must be directly related to the underlying commodity.
III. The Overall Performance of Large Traders

The main results of this study are that large commercial traders do not sustain significant losses and that large noncommercial traders do not earn significant positive profits. In fact, it is the large commercial traders who earn significant positive profits when returns are aggregated across all nine markets. For the most part, these results are consistent across all nine markets. Overall, the evidence lends no support for the theory of normal backwardation.

Table 1 shows that large traders earn significant12 positive dollar profits over the July 1977 to December 1981 period (and therefore small traders lose).13 The $728 million that commercial traders earn is about 85 percent of the $853 million in total net profits earned by all large traders. This disproportionate success of large commercial traders holds for all contract markets except the cattle markets.14 The two new financial markets are most profitable for commercial traders, although most of the profits are realized after October 1979.15

In the nine markets combined, the noncommercial traders earn $125 million. Although positive, this profit figure does not differ significantly from zero. Noncommercial traders earn negative dollar returns in three of the nine market sand are most successful in the live cattle market (the market in which commercial traders perform the worst). However, for noncommercial traders, mean individual and monthly returns are positive and significantly different from zero in only three markets.

As a group, large traders perform better when they are net short than when they are net long (see cols. 2 and 3). Large traders earn negative returns in six markets when they are net long. When all nine markets are combined, long losses total $992 million. The two financial futures markets dominate these results. Without them, net long losses become gains of$169 million. When large traders are net short, they earn positive dollar returns in seven markets with a combined total of $1,846 million across all nine markets. The financial markets again dominate the net short results.

According to the CFTC reports on the Commitments of Traders in Commodity Futures, hedgers are net short, on average, in all but the feeder cattle market over the July 1977 to December 1981 period (Hartzmark 1984, table 4). This fact, combined with the results just reported, shows that commercial traders (except those in the live cattle market) make money on their hedged positions. Hedgers are thus "getting paid" while they reduce their risks, quite the contrary to the theory of normal backwardation.16
In column 4 the losses of all unsuccessful traders are summed to determine Total Losses, and in column 5 the profits of all successful traders are summed to get Total Gains. Overall, for commercial traders aggregate gains are twice as large as aggregate losses. For the noncommercial traders, aggregate losses and gains are almost equivalent. The fact that the non commercial gains and losses are so balanced suggests that random elements may be a more important factor in determining this group's performance relative to that of the commercial group.

Overall, only 46 percent of the large traders in the sample earn positive dollar profits17 even before commissions are subtracted (see col. 7).18 This proportion is roughly equal for commercial and noncommercial traders. The theory of normal backwardation would predict that the proportion of successful traders should be higher for the noncommercial group since they are earning the risk premium.

There is a large degree of skewness of the individual return distributions. When the individual returns of all traders in all markets are pooled, the mean dollar profit per trader is $186,760, while the median is -$4,530 (Hartzmark 1984). The five most successful traders (all commercial participants), or 0.1 percent of the sample, earn 58 percent of the total net profits (or $492 million). This skewness is observed in all nine markets as indicated by an average skewness coefficientof8.49 (Hartzmark 1984). The skewness coefficient or the pooled sample is 30.19.19 The fact that so few participants earn such a large proportion of the returns is another contradiction of the theory of normal backwardation. In that theory, speculators (insurers) can naively position themselves opposite the hedgers and walk away with positive profits, on average.

A. Returns to Traders Holding Both Speculative and Hedge Positions

Table 2 classifies the returns to traders according to whether the individual trader reveals only speculative, only hedge, or both types of positions.20Traders who hold both types of positions earn 77 percent of the total net profits in all markets. The returns to pure speculators (defined as those who hold only speculative positions), when aggregated across all nine markets, are negative or at least insignificant. Thus pure hedgers and those traders who do some hedging earn a substantial proportion of all the profits. Moreover, this pattern is fairly consistent across all markets, with the cattle markets being the major exception.

Among traders holding both speculative and hedge positions, the commercial traders (who are mostly hedging, by definition) are most successful. If this subset of commercial traders earns most of their profits from speculative activity, then the risk premium hypothesis could not be rejected. However, as shown earlier, the commercial traders earn the bulk of their profits on their net short hedging positions.21 Therefore, it cannot be the case that the performance of these traders is the result of earning substantial positive profits on their speculative positions (for absorbing the risks of other commercial traders) that more than offset their losses on their own hedging commitments.

B. Comparison with Previous Results

In table 3 the methods using the month-end reports of traders employed by Houthakker (1957) and Rockwell (1964, 1977) are used to calculate profits over the 1977-81 period.22 Two important results emerge. First, the Houthakker and Rockwell methods consistently overstate profits earned by noncommercial traders and understate profits earned by commercial traders. Second, the profits derived by applying the Houthakker and Rockwell methods to my data are consistent with the pattern of returns they find in their time period and in the comparable markets.23 Therefore, the different results I have found do not appear to be period or market specific.

Profit calculations using the month-end data (denoted Houthakker and Rockwell) offer results much different from the profit calculations using daily information. Noncommercial traders appear to earn significant positive returns in five of nine markets and for all markets combined. By contrast, the commercial traders apparently earn significant positive returns only in pork bellies and significant negative returns in CBT wheat. In all markets combined the estimated commercial returns are not significantly different from zero.

While the differences between the mean monthly return using the daily data (denoted Daily)24 and the mean returns using the Houthakker and Rockwell methods are statistically significant in only a few markets, these methods significantly overstate the profits earned by noncommercial traders when all nine markets are combined. Additionally, the probability is small that the observed systematic biases (i.e., consistent over-and underestimates) would randomly appear in each of the nine markets.25

These comparisons imply that the results using the publicly available month-end reports are misleading. They appear to miss substantial within-month profits earned byl arge commercial traders.26

Overall, then, it appears that speculators in futures markets do not earn significant positive profits and that hedgers do not suffer significant losses. From this we can infer that it is unlikely that there is a risk premium implicit in the futures price for a commodity or that the premium is buried by differences in price expectations. What we do not know is why certain traders earn profits and others sustain losses. Is it because of individual differences in the ability to forecast prices? Inherent luck? Or the ability to manipulate prices? The results presented in this paper show that any answer to this question must rest on something other than risk.