Agricultural futures trading has a history of 170 years and the earliest agricultural futures exchange is the Chicago futures exchange. China’s agricultural product futures market was established in the 1990s. After many years of development, it initially covered the four major agricultural product futures that are grain, cotton, oil, and sugar. The value of the agricultural products futures market is reflected in three aspects:

1) It is a good guide for farmers to choose what kind of crops to grow and at what price to sell their crops.

2) The futures market effectively allows relevant enterprises to maintain and avoid risks. Most domestic corn and soybean business enterprises participate in hedging transactions and avoid the business risks caused by price fluctuations through futures.

3) The price information of the futures market reflects the market expectation and is the scientific reference signal of national macro-control.

In agricultural products, corn has complex attributes. It has grain ration attributes, industrial crop attributes, and forge crop attributes. The relevant proportion of corn is shown in Figure 1 and Figure 2.

Various prediction methods have been suggested, which can be classified into statistical and machine learning methods. Traditional statistical methods provide great prediction results under the linear assumption. For example, linear regression model [1] , autoregressive integrated moving average model (ARIMA) [2] , autoregressive conditional heteroskedasticity (ARCH) [3] , generalized autoregressive conditional heteroskedasticity (GARCH) [4] , and vector autoregressive regression (VAR) [5] . Because the financial time series is complicated, dynamic, and non-linear, various approaches which belong to the machine learning field have been employed to build the prediction model. Support vector machines (SVMs) [6] and deep learning type methods [7] [8] [9] [10] are widely used.

The influencing factors related to the price of corn futures include corn yield, climate factors, production cost, inventory, import and export volume, the price of soybean and wheat as substitutes, the development of animal husbandry, interest rate, and exchange rate, relevant national policies, crude oil, and so forth. It is challenging to establish an effective relationship between the corn price and quantifying the relevant influencing factors.

With the development of the Internet, Internet search data has vital timeliness and comprehensive coverage, and information comes from real economic individuals [11] - [15] . After realizing the advantages of Internet search volume, scholars have taken the Internet search index as a proxy variable for investors’ attention. In this study, we seek to investigate the explanations of exchange rate movements from the perspective of investor attention, which is widely used in asset pricing and market efficiency. It is a relatively new perspective to study the price of China’s agricultural futures market from the perspective of behavioral finance. It mainly analyzes the impact of investors’ attention on the agricultural futures market and studies how the search volume of the Baidu index affects the agricultural futures market, which has specific reference significance for predicting agricultural futures market price.

The second section of this article is the data description, and an in-sample analysis is shown in Section 3. Based on the VAR model, we found that the Baidu index is the Granger reason for corn futures price, and a VAR model including the Baidu index and corn futures price is established. Parameter significance analysis shows that investors’ attention significantly impacts corn futures prices. We also lead in other control variables that impact the price of corn futures in relevant studies, such as soybeans, pork, corn starch, and crude oil. The results show that investors’ attention can still be an influential explanatory variable. The final out-of-sample prediction in Section 4 shows that the prediction accuracy of the VAR model is effectively improved after introducing the Baidu index. In the fourth section, we use the Baidu index of “corn starch” and “corn price”, two keywords other than “corn”, and carry out the Granger causality test. The numerical results show that the Baidu index obtained using similar keywords is still the Granger reason for the corn futures price.

In this section, we will briefly describe the selection of data sources and variables. Firstly, the data used in this study are all from the WIND (https://www.wind.com.cn/) database, and we select weekly corn futures price data from January 2, 2017, to October 24, 2021. In order to eliminate the instability of time series, we use logarithm and difference data processing methods. Secondly, since Baidu is the largest search engine in China, which has extensive and professional characteristics in information collection, we choose the Baidu index (https://index.baidu.com/v2/index.html#/) as the proxy variable of investors’ attention. The Baidu index directly reflects the size of specific keywords in Baidu web search. We selected several keywords closely related to the corn market, including “corn”, “corn price”, and so on. According to the Granger causality test results, we choose the keyword “corn” with the highest correlation with corn futures to conduct the follow-up research.

Finally, we select several closely related factors to carry out the following research, including pork price, soybean futures price, wheat futures price, international crude oil futures price, and cornstarch futures price. The reasons for selecting these factors are as follows. First, the impulse response model to study the factors affecting corn price fluctuations shows that pork price is the main factor affecting corn price. Moreover, corn accounts for more than 50% of hogs’ feed costs. Secondly, wheat and soybean are the competitive commodities of corn, so the price change is bound to have a certain influence on corn. Third, corn starch is a by-product of corn, and the two are closely related. Fourthly, corn is the raw material of energy ethanol, and ethanol has a substitution relationship with crude oil, so international crude oil futures will also impact the return of corn futures.

Some basic descriptive statistics of corn futures price, investor attention, and the control variables are shown in Table 1. According to the data in Table 1, we can find that the time series of corn futures returns, investor attention on “corn”, and relevant market variables are volatile.

The VAR model is one of the models dealing with the analysis and prediction of multiple related economic indicators. The VAR model is good at predicting the interrelated time series system and analyzing the dynamic impact of random disturbances on variable systems so as to explain the impact of various economic shocks on the formation of economic variables. In this paper, we will use the VAR model to analyze the corn futures price and the time series involved in Table 1.

VAR theory requires that each variable in the model is stationary. The ADF unit root test was applied to the time series of corn futures price and the Baidu index after transferring the data to log returns and proceeding with the difference transform. ADF test results shown in Table 2 indicated that VAR modeling analysis can be carried out.

In this section, we conduct an in-sample analysis. First, divide the data into two parts at the time of August 29, 2021, for in-sample and out-sample analysis. We use the Granger causality test and impulse response analysis to study the impact relations between the Baidu index and corn futures price. Finally, the VAR models based on multiple interrelated time series are established.

We conduct an out-of-sample analysis on the following two models, which belong to the rolling window prediction method [13] . According to this prediction method, we can obtain the price of corn futures in the t + h week, as shown in Equations (3) and (4).

C F ^ t + h = α ^ 0 + ∑ i = 1 2 α ^ i C F t + h − i + ∑ i = 1 2 b ^ i A T T t + h − i , (3)

C F ^ t + h = α ^ 1 + ∑ i = 1 2 α ^ i C F t + h − i + ∑ i = 1 2 b ^ i A T T t + h − i + ∑ i = 1 2 c ^ i C t + h − i + ∑ i = 1 2 d ^ i A T T t + h − i ⋅ C t + h − i . (4)

The coefficients of Equations (3) and (4) are obtained from the in-sample analysis and update in each prediction. The forecast horizon h represents the certain week, the investor would like to estimate. After h is given, the model will give the price after h weeks, using data from 0 to t.

We analyze and assess the accuracy of different predictive models by calculating out-of-sample R squared ( R o o s 2 ), mean squared forecast error (MSFE), MSFE-adjusted statistic. The R o o s 2 indicates the proportion of reduction in MSFE for using the predictive models compared with the benchmark model. A positive R o o s 2 indicates that the forecasting performance of the predictive models outperforms the benchmark model. The R o o s 2 is obtained from the following equation:

R o o s 2 = 1 − ∑ k = t + h T ( R k − R ^ k ) 2 ∑ k = t + h T ( R k − R ¯ k ) 2 , (5)

where T is the numbers of out-of-sample. R k is the real value of carbon futures return. R ^ k contains the return forecasted by the above-mentioned Equations (3) and (4). R ¯ k denotes the predicted return of the benchmark forecast model. The historical average model is the bench mark model in our out-sample analysis, which is also adopted by other researches [13] . The model is given by Equation (6).

R ¯ k + 1 = 1 k ∑ s = 1 k R s , (6)

where R ¯ k + 1 represents the forecast value of the benchmark model, and R s means the real value. As for the MSFE, it can obtain from Equation (7).

MSFE = ∑ k = t + h T ( R k − R ^ k ) 2 T − h + 1 . (7)

The MSFE-adjusted statistic is a one-sided (upper-tail) measure to test the null hypothesis that the MSFE of the historical average model is less than or equal to that of the predictive models incorporated with investor attention. The alternative hypothesis is that the MSFE of the historical average model is more than that of the predictive models. Specifically, the MSFE-adjusted statistic is measured by the following equation:

MSFE adj = MSFE a − MSFE b + ∑ k = t + h T ( R ^ k , a − R ^ k , b ) 2 T − h + 1 , (8)

where MSFE_a and MSFE_b denote the MSFE statistics of the predictive models incorporated investor attention and the historical average model, respectively. R ^ k , a and R ^ k , b represent the forecast value of carbon futures return of the predictive models and the historical average model, respectively.

This study believes that investor attention is an important variable that can explain and predict the returns of corn futures. However, all results are calculated based on the Baidu index keyword “corn”. To ensure the study’s accuracy, we conducted robustness tests to explore whether the experimental results would produce different conclusions due to the change of keywords.

We search other keywords, i.e. “corn price” and “corn starch” during the same period from January 02, 2017 to August 29, 2021. Then, we adopt the same data pre-processing method, conduct the Granger causality test for different keywords and establish the VAR model in order to show that corn futures returns are still significantly affected by investor attention. The results of correlation regression are shown in Table 9 and Table 10. The results of the Granger causality test show that the change of keywords does not change the explanatory power of attention. As can be seen from the VAR results, investor attention can indeed

Note: *, **, *** denote significance at 10%, 5% and 1% level, respectively.

Note: *, **, *** denote significance at 10%, 5% and 1% level, respectively.

have a significant impact on corn futures returns. Here, we find that the lag is not the same as before, which is not surprising, but probably because different keywords give investors different feelings. The above results show that investor attention is a pricing factor that can not be ignored in the corn futures market, which deserves more attention.

In this paper, we combine the studies of agricultural economics and behavioral finance and provide significant evidence to show investors’ attention impacts the price of corn futures. The Granger causality test and the significance test of coefficients in the VAR method indicate that the Baidu index, treated as the proxy variable of investors’ attention, can be used to analyze the corn futures price. It is a relatively new perspective to study the price of China’s agricultural futures market from the perspective of behavioral finance. This work has specific reference significance for the prediction of agricultural futures market prices.

Our future research remains into two parts: one is to use the feature extraction or feature selection method of machine learning to analyze whether investors’ attention has an impact on the price of agricultural products; the other is to use the current popular deep learning method to analyze the prediction accuracy of agricultural product prices before and after the consideration of investors’ attention.

This work was supported by the National Natural Science Foundation of China (52275262 and 11701337), and the National Natural Science Foundation of Shandong Province (ZR2022MA009).

The authors declare no conflicts of interest regarding the publication of this paper.

Zhang, L., Zhang, Y.P., Sun, L. and Cheng, J.W. (2023) The Impact of Investor Attention on China’s Corn Futures Price. Journal of Mathematical Finance, 13, 144-158. https://doi.org/10.4236/jmf.2023.132009