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Trading Based on Knowing the Wasde Report in Advance

Author

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  • Milacek, Trent T.
  • Brorsen, B. Wade

Abstract

Past research shows that prices move in response to World Agricultural Supply and Demand Estimates (WASDE) reports immediately prior to and after a report. This research develops trading models based on knowing the next WASDE report in advance. This should help traders evaluate investments to predict information contained within the report and in determining how best to use such forecasts. The price-forecasting models use regressions against the ratios of ending stocks to use. Results show a steady increasing return to trading over the report month. The highest returns are produced by trading during the growing and harvest seasons.

Suggested Citation

  • Milacek, Trent T. & Brorsen, B. Wade, . "Trading Based on Knowing the Wasde Report in Advance," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 49(3).
    • Handle: RePEc:ags:joaaec:352117
    DOI: 10.22004/ag.econ.352117
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    References listed on IDEAS

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    1. Westcott, Paul C. & Hoffman, Linwood A., 1999. "Price Determination for Corn and Wheat: The Role of Market Factors and Government Programs," Technical Bulletins 33581, United States Department of Agriculture, Economic Research Service.
    2. Vogel, Fred A. & Bange, Gerald A., 1999. "Understanding USDA Crop Forecasts," USDA Miscellaneous 320799, United States Department of Agriculture.
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    1. is not listed on IDEAS
    2. Joshua Huang & Teresa Serra & Philip Garcia, 2021. "The Value of USDA Announcements in the Electronically Traded Corn Futures Market: A Modified Sufficient Test with Risk Adjustments," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 712-734, September.
    3. repec:hal:wpaper:hal-02149355 is not listed on IDEAS
    4. Etienne, Xiaoli L. & Farhangdoost, Sara & Hoffman, Linwood A. & Adam, Brian D., 2023. "Forecasting the U.S. season-average farm price of corn: Derivation of an alternative futures-based forecasting model," Journal of Commodity Markets, Elsevier, vol. 30(C).

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