The relationship between farmers' behavioral attitudes and use of futures contracts is examined, taking into account non-directly observable variables and the heterogeneity of farmers. The relationships are tested on a stratified data sample of 440 farmers. Cluster analysis and covariance structure equation models are used to validate the relationships. Farmers are found not to be homogenous regarding the factors influencing their use of futures. Heterogeneity at the segment level masked important effects at the aggregate level, notably risk attitude. Furthermore, several psychological constructs for farmers related to market orientation, risk exposure, market performance and entrepreneurial behavior play important roles in their use of futures contracts.

nterest has long existed among followers of commodity futures markets about the I relationship between trading volume and price variability. Critics of futures markets argue that increased trading vohme, especially by speculators, leads to increased price volatility. This can lead to greater uncertainty and less market efficiency, suggesting that regulatory limits curbing speculative positions and daily trading activities be established.Others argue that increased price variability due to changing economic conditions attracts more trading, creating more market liquidity and leading eventually to less price volatility than if volume did not increase. This suggests that there be less regulation of traders and their activities.In between these scenarios is the one where daily trading activity and price volatility increase and decrease simultaneously. That is, one activity does not necessarily lead or lag the other, but that they move up and down in concert as economic conditions change. A high degree of simultaneity between volume and price changes can indicate liquid and efficient markets where the response to new information is nearly instantaneous.

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A NOTABLE and provocative development in the recent literature has been the application of the theory of random walks to the analysis of price behavior in the stock and commodity futures markets. The basic hypothesis of the random walk theory is that a particular price series behaves as a simple stochastic process. Successive price changes are independent random variables, implying that the past history of a series generates no information that would be useful in predicting future price changes.Several authors have tested this independence hypothesis, but with mixed results. Many of the studies on stock market prices are contained in a book edited by Cootner [5], and some show evidence that stock prices behave as a random walk,' while others reject the theory.2 Similar recent testing on commodity futures prices has added to the controversy. Tied to this assortment of results has been a variety of analytical techniques, each with its own justification, ranging from sophisticated statistical tools, such as spectral analysis, to mechanical trading rules which attempt to generate speculative profits.Among the studies on commodity futures prices, Larson [13], using autocorrelograms, found evidence to support Working's [17] theory of anticipatory prices which implies that prices move randomly. Stevenson and Bear [16], who used an assortment of statistical tools and mechanical trading rules, concluded that corn and soybean futures prices move in a systematic rather than a random fashion. This tended to agree with earlier work done by Houthakker [11] who applied a stop-loss scheme to corn trading. On the other hand, Cargill and Rausser [3], utilizing spectral analysis on various futures contracts for 1967, including corn, concluded that "a simple stochastic process appears consistent with commodity markets price behavior."A major shortcoming of the entire analysis and a possible reason for the lack of wide acceptance for any single result is the failure to subject identical data to both statistical and mechanical filter tests. The consequence has been varying results, depending upon data and tests used. Only Stevenson and Bear have attempted to apply alternative statistical tools and mechanical trading rules to similar data. However, as will be noted later, one may quarrel with their choice of statistical tools-namely, serial correlation and runs analysis. It is the purpose of this paper to rectify this void in the literature by applying both a sophisticated statistical tool and mechanical filter tests to similar data. Spectral analysis along with several mechanical filters will be employed to test whether the live cattle futures prices move in a random or a systematic fashion.

Cash cattle prices are found to be more accurate indicators of subsequent cash cattle price conditions than are the futures prices for distant contracts. This apparent relative inability of futures prices for live beef cattle to reflect later spot prices is becoming more pronounced over time.