Soybean Futures Prices Research Articles

RV-FELM: Futures commodity price forecasting based on RIME-VMD algorithm coupled with FA-ELM

Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized. The results show that the MAPE(mean absolute percentage error) of one-step, three-step, and six-step methods for predicting crude oil prices are 0.48%, 0.66%, and 0.75%, respectively, and the MAPE of soybean prediction results are 0.22%, 0.27%, and 0.37%, respectively. The empirical results and ablation experiments show that it outperforms other benchmark models in terms of both horizontal and directional accuracy. Notably, it outperforms in predicting soybean futures prices, which demonstrates the ability of our model to better capture the characteristics of both the time and frequency domains of the series, to take sufficient consideration of the series characteristics, and to ensure robustness.

Analysis of the correlation between China’s soybean futures prices and import prices

ABSTRACT Soybeans play a critical role in China’s food system and hold significant importance in ensuring national food security. This study explores the correlation between soybean futures prices and international soybean import prices. It employs a time-series model to analyse the volatility of the soybean futures market. The results demonstrate a significant correlation between soybean futures prices and their historical returns, as well as international soybean import prices. Fluctuations in international soybean prices notably impact domestic soybean futures prices, and market information exhibits asymmetrical effects on futures prices. The study’s conclusions hold important implications for understanding the volatility of the soybean futures market and its response to changes in international soybean prices.

Strategies for improving food security under the impact of external grain price fluctuations: evidence from China

ABSTRACT The purpose of this study is to explore strategies for improving food security under the impact of external food price fluctuations. Using impulse response function, complex network analysis, and other methods, we studied the linkage between CBOT prices of soybeans, rice, wheat, corn, and Chinese futures prices from 2009 to 2022. This article analyzes the prominent price linkage relationships, network evolution characteristics, and development trends. The results show that CBOT soybean and China soybean futures prices, CBOT wheat and China soybean futures prices, and CBOT corn and China wheat futures prices are three prominent price linkage relationships. The three pairs of price linkage relationships are constantly changing over time and show a gradual weakening trend. The price of domestic soybean futures and CBOT soybean futures have a strong association. The sudden food crisis will strengthen the connection between international and Chinese grain futures prices. The above results demonstrate the importance of strengthening the independence of China’s futures markets. Based on the above analysis results, the paper put forward policy suggestions such as improving soybean yield levels, utilizing the futures market for risk management, and taking measures to respond to possible changes in Chinese wheat prices based on international corn futures prices.

Study on the Relationship between Chinese and American Soybean Futures Prices Based on GARCH and DCC-GARCH Models

Study on the Relationship between Chinese and American Soybean Futures Prices Based on GARCH and DCC-GARCH Models

FUTURES PRICES LINKAGES IN THE US SOYBEAN COMPLEX

This work investigates the linkages among the futures prices of soybeans, soybean meal, and soybean oil in the US. This has been pursued using a flexible methodology that allows modelling price relationships at different parts of their joint distribution. According to the empirical results, the markets are strongly connected in the vertical direction regardless of the sign and the size of shocks. The meal and oil prices maintain a negative relationship at the median and the upper quantiles but they are not connected under large negative shocks. The soybean market is a net transmitter of price risk to the other two markets while price shocks around the median tend to be transmitted with higher intensity relative to those at the extremes.

Soybean Futures Price Prediction Based on CNN-LSTM Model of Bayesian Optimization Algorithm

In recent years, the complex international environment and economic situation have made soybean futures prices increasingly unstable, which is not conducive to financial stability. Therefore, this paper uses a BO-CNN-LSTM model to accurately predict soybean futures prices and to manage price fluctuations for investors and governments. Firstly, LSTM network is employed to predict soybean futures prices using the local features extracted by CNN network. In addition, CNN-LSTM hyperparameters are optimally solved using Bayesian optimization algorithms. Finally, the constructed model is compared with BP neural network, LSTM model and CNN-LSTM model. This paper selects the basic daily data of the soybean futures contract No.1 of Dalian Commodity Exchange from 2014 to 2021 for research. According to the results, CNN-LSTM models based on Bayesian optimization algorithms perform best. Compared with the basic CNN-LSTM model, MAPE increased by 44.17%, RMSE increased by 24.61%, MAE increased by 41.48%, and R2 increased by 0.06%, which demonstrates Bayesian optimization's superiority.

Agricultural policy uncertainty and its impact on commodity markets

AbstractWe construct an agricultural policy uncertainty (APU) index from leading national and local newspapers in major agricultural states in the United States from January 1999 to September 2021. Our analysis shows that economic policy uncertainty Granger causes APU linearly in the short run, but the relationship is nonlinear and bidirectional in the long run. When economic policy uncertainty is considered, APU significantly reduces hedgers' net short positions and speculators' net long positions (except for corn hedgers) in the corn and soybean markets. APU also has a significant positive impact on the log returns of corn and soybean futures prices, consistent with the theory of storage where commodity returns and volatility are positively correlated.

Research on the Impact of El Nino Phenomenon on the Volatility of the U.S. Soybean Futures Price Yield

Frequent occurrences of the EI-Nino phenomenon have made the influence of ENSO on the commodity market a hot research topic. To further investigate the connection between climate change and commodities markets and to offer a benchmark for future economic growth This study focuses on the impact of El Nio on the futures price yield of U.S. soybeans to give a new research direction for academics and investors. This research employs the GARCH-Mean model to estimate returns, then applies the event study approach and parametric tests to see whether there are significant differences in the futures price yield for U.S. soybeans. Test results indicate that EI Nino events have not had a substantial impact on the yield of U.S. soybean futures prices during the recent EI Nino phenomenon. This conclusion indicates that climate change may not be the primary factor influencing the future price of U.S. soybeans, and researchers must consider other factors. In addition, as a result of technological advancement, the government may predict that investors had already prepared for the volatility of the commodity market.

Price multifractality and informational efficiency in the futures markets of the US soybean complex

This work investigates price multifractality and informational efficiency in the futures markets of the US soybean complex (soybeans, soybean meal, soybean oil, and the crush spread) using daily prices from 2015 to 2021 and Multifractal Detrended Fluctuation Analysis (MFDFA). The empirical findings suggest: First, none of the four series exhibited long-range dependence. They did, however, show considerable serial dependence locally. The futures prices of soybeans and soybean oil, and of the crush spread were locally anti-persistent (persistent) for large (small) fluctuations whereas the futures prices of soybean meal were persistent for all small and large fluctuations. Second, all markets in the US soybean complex exhibited some degree of informational inefficiency with that of the crush spread being less efficient relative to the other three. Overall, the results provide valuable information to investors as to whether trend-following or oscillatory trading strategies are more appropriate.

The Co-Movement between Non-GM and GM Soybean Prices in China: Evidence from Dalian Futures Market (2004-2014)

The price variability of agricultural commodities reached record levels in 2008, and again more recently in 2010. This raises concerns that this increased price volatility would be temporal or structural. There are two soybean futures contracts in China: non-GM and GM. With the emergence of the GM soybean contract in 2004, the components of non-GM futures price volatility might have changed. This study examines the volatility determinants as well as seasonality of non-GM and GM soybean futures prices traded in Dalian Commodity Exchange from 2005 to 2014. Also, we test the co-movement between these two soybeans markets. We analyze the volatility by incorporating changes in important economic variables into the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedastic (DCC-GARCH) model. This research provides statistical evidence that the futures prices of soybeans in China are being influenced by the increasing consumption of soybeans, the import quantity of soybean, the trading volume in futures market and weather. We also find spillover effect from non-GM to GM in soybean markets. A better understanding of the volatility determinants provides important additional information for various market participants, including commodity traders, hedgers, arbitrageurs, exchanges and regulatory agencies.

The Effects of IBGE and USDA Announcements on BM&FBOVESPA Soybean and Corn Futures Prices

This paper analyzes the impact of USDA and IBGE crop forecast announcements in the Brazilian corn and soybean futures market. Futures prices were obtained from BM&FBOVESPA from 2009 to 2014 and announcements days were collected from IBGE and USDA historical reports. Expected value of absolute price changes (EABS) of four days before and four days after were calculated. Univariate and multivariate tests were performed to assess if the information on the reports affected prices. Results using EABS show that the high illiquidity of soybean March contract does not allow prices to reflect information and corn futures prices are influenced only by USDA announcements. Results also show that Brazilian market participant expectations tend to overestimate both USDA and IBGE forecasts. The conclusion of this work is that USDA announcements bring new information only for corn futures market and the lack of liquidity in the market might deteriorate the information transfer capacity of corn and soybean futures prices

International linkages of the Chinese futures markets

The Chinese futures markets are among the fastest growing futures markets in the world. In terms of trading volume, the Chinese soybean futures market is the world's second largest, while China's copper and aluminum futures markets are the third largest in the world. The size of the Chinese futures markets, however, is not matched by the academic research on them. This article is the first to study the relationship between the Chinese and world futures markets of copper, aluminum, soybean and wheat, using Johansen's cointegration test, error correction model, the Granger causality test and impulse response analyses. We find that the futures prices in the Shanghai Futures Exchange are cointegrated with the futures prices on the London Metal Exchange (LME) for copper and aluminum. We also find that a cointegration relationship exists for Dalian Commodity Exchange and Chicago Board of Trade (CBOT) soybean futures prices, but no such relationship for Zhengzhou Commodity Exchange and CBOT wheat futures prices....

Do Agricultural Commodity Prices Respond to Bans against Bioengineered Crops?

Bans against bioengineered food ingredients by major food companies have been broadly reported and have been often taken to signal demand shifts away from commodities in favor of identity‐preserved non‐bioengineered crops. Yet, the impacts of such bans have not been quantified and, indeed, the market size for non‐bioengineered crops remains unknown. We tested whether either the Chicago Board of Trade soybean futures prices (commodity) or the Tokyo Grain Exchange non‐GMO soybean futures prices (identity preserved market) responded to firm bans of bioengineered food ingredients. Neither the Chicago Board of Trade soybean futures prices nor the Tokyo Grain Exchange non‐GMO soybean futures prices respond to bans.

Economic Criteria for Evaluating Commodity Price Forecasts

AbstractForecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

Reaction of Wheat, Corn, and Soybean Futures Prices to USDA “Export Inspections” Reports

Reaction of Wheat, Corn, and Soybean Futures Prices to USDA “Export Inspections” Reports

An ENSO Signal in Soybean Futures Prices

Abstract An example of socioeconomic repercussions of the El Nino-Southern Oscillation (ENSO) is examined. Multichannel singular spectrum analysis, a variant of principal component analysis useful in isolating the spatial and temporal variability associated with anharmonic oscillations, is applied to normalized monthly mean time series of soybean futures prices and the Southern Oscillation index. The method isolates the variability common to the two time series from the remaining variability and noise. It identifies the low- and high-frequency, quasi-biennial modes of ENSO as part of this variability.