US Soybean Market Forecasting Using Statistics & Machine Learning Techniques
Presentation Type
Poster
Student
Yes
Track
Finance/Insurance Application
Abstract
The agricultural product stock market is very stochastic and difficult to predict. The market is especially affected due to different political and economic policies. This year, the soybean trading market has been affected the most due to the trade war between the U.S. and China. According to USDA, 17% of the U.S. agriculture produce exports to China and 62% of those products were soybeans. Thus, the soybean market has a remarkable change from previous years. In this study, Long-Short Term Memory (LSTM), Time Series Regression model and GARCH model are explored to analyze the soybean market. Google trend and other factors are evaluated as important indicators to the market.
Start Date
2-11-2020 1:00 PM
US Soybean Market Forecasting Using Statistics & Machine Learning Techniques
Volstorff A
The agricultural product stock market is very stochastic and difficult to predict. The market is especially affected due to different political and economic policies. This year, the soybean trading market has been affected the most due to the trade war between the U.S. and China. According to USDA, 17% of the U.S. agriculture produce exports to China and 62% of those products were soybeans. Thus, the soybean market has a remarkable change from previous years. In this study, Long-Short Term Memory (LSTM), Time Series Regression model and GARCH model are explored to analyze the soybean market. Google trend and other factors are evaluated as important indicators to the market.