We investigate analyst forecasts in a unique setting, the natural gas storage market, and study the contribution of analysts in facilitating price discovery in futures markets. Using a high-frequency database of analyst storage forecasts, we show that the market appears to condition expectations regarding a weekly storage 452 Gay, Simkins, and TuracJournal of Futures Markets release on the analyst forecasts and beyond that of various statistical-based models. Further, we find that the market looks through the reported consensus analyst forecast and places differential emphasis on the individual forecasts of analysts according to their prior accuracy. Also, the market appears to place greater emphasis on analysts' long-term accuracy than on their recent accuracy. forecast accuracy. We consider a few alternative weighting schemes and find supporting evidence that the market looks through the consensus forecast and places differential emphasis on the forecasts of analysts according to their prior accuracy. Further, it appears that the market focuses more on analysts' longterm forecast accuracy than their recent accuracy.Our investigation also entails two sidebars that create interesting information environments for analyzing market behavior. First, gas in storage is highly cyclical and involves two calendar periods during which supply and demand fundamentals alternate in importance and thus present differing challenges to analysts. During the "injection season" (April to October), storage typically accumulates because demand is lower during the warmer months and new inventories are being added in advance of the next winter heating season. During the "withdrawal season" (November to March), inventories are drawn down due to high consumption demand in the winter heating season. Demand shocks are mainly weather driven and are larger in magnitude and less predictable than supply shocks, which are typically technology driven. Hence, weekly storage changes are typically greater and more difficult to predict during the withdrawal season. 3 We find that analyst forecasts are indeed less accurate and more dispersed during the demand-driven withdrawal season than during the supply-driven injection season.Second, we consider a major change in responsibility for conducting the weekly survey. The survey was originated in January 1994 by the American Gas Association (AGA)-the industry trade group. In May 2002, the U.S. Department of Energy's "Energy Information Administration" (EIA) assumed responsibility from the AGA. The EIA implemented several changes to the survey procedures with the intention of reducing noise and producing more accurate estimates. We find that analyst accuracy improved and forecast dispersion decreased following the EIA takeover.Though we do not claim that our weighting schemes are optimal, our study adds to the literature on combining forecasts to produce a superior forecast. A seminal paper by Bates and Granger (1969) first suggested and proposed a method to optimally combine forecasts. This study spawne...

This paper provides guidance on how corporations should choose the optimal mix of "linear" and "non-linear" derivatives. Linear derivatives are products such as futures, forwards, and swaps, whose payoffs vary in linear fashion with changes in the un-derlying asset price or reference rate. Non-linear derivatives are contracts with option-like payoffs, including caps, floors, and swaptions. 2002 Morgan Stanley.

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We examine how corporations should choose their optimal mix of linear and nonlinear derivatives. We present a model in which a firm facing both quantity (output) and price (market) risk maximizes its expected profits when subjected to financial distress costs. The optimal hedging position generally is comprised of linear contracts, but as the levels of quantity and price-risk increase, the use of linear contracts will decline due to the risks associated with overhedging. At the same time, a substitution effect occursThe authors acknowledge the helpful comments and conversations we have had with Mike Rebello, Steve Smith, Alan Tucker, P. V. Viswanath, Jayant Kale, Tom Webster, and seminar participants at Pace University and the University of South Florida. They also are appreciative of the comments provided by an anonymous reviewer. Smithson (1998) for an excellent summary and also the seminal articles of DeMarzo and Duffie (1995), Froot, Scharfstein, and Stein (1993), and Smith and Stulz (1985. 2 A professional version of this article, with anecdotal and empirical support for our hypotheses, appears in Gay, Nam, and Turac (2002). toward the use of nonlinear contracts. The degree of substitution will depend on the correlation between output levels and prices. Our model also allows us to provide insight into the relation between a firm's derivatives usage and its transaction-cost structure.

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