In this paper, the multifractal detrended fluctuation analysis (MF-DFA) method is used to identify the multifractal structure of in the Chicago Board of Trade (CBOT) soybean futures and quantitatively describe the inefficiency and nonlinearity of the market. The data is the daily price of CBOT soybean futures from 3 January 2000 to 20 December 2019, with a total of 5025 trading days. The empirical results also show that the perspective based on MF-DFA can explain the market’s nonlinear, long-range correlation, predictability and other financial anomalies. At the same time, the prediction of price change direction and risk degree of the market are further studied. It is pointed out that multifractal characteristics are generated under the joint action of fat-tail distribution and long-range correlation. Investors can make use of these market characteristics to make arbitrage possible. Finally, based on the empirical results, some policy suggestions are put forward: strengthening rational investment education, strengthening supervision, reducing information asymmetry and other measures to improve market efficiency.

Nonlinear analysis and prediction of soybean futures | Tao Yin, Yiming Wang | Agricultural Journals

This paper mainly studied the chaotic characteristics and prediction of WTI crude oil monthly price time series from January 1980 to June 2017. Meanwhile, we analyzed whether the major shock of the financial crisis in July 2008 would break the chaotic character of the time series. In addition, when using the largest lyapunov exponent to determine chaotic characteristics, the robustness test of the largest lyapunov exponent was carried out using bootstrap method. Then, we utilized three types of prediction models (ANN+Chaos-type models, Chaos-type model and ANN-type models) to predict the price of crude oil in different months. And we found that the prediction accuracy of ANN-type model is lower than the other type models. This indicated that the accuracy of the prediction with ANN model under the model misspecification is not high because the time series of WTI crude oil price has chaotic characteristics. At last, we constructed a new predictive model, namely HWP-CHAOS model, to compare the prediction accuracy of the above three type models, and discovered the best prediction model among these models is HWP-CHAOS model.