Abstract
Stocks markets play their financial roles of price shocks and hedging just when they are proficient. The imperative highlights of productive market are that one cannot make extraordinary profit from the stocks markets. This research investigates whether China wheat futures price can be predicted by employing artificial intelligence neural network. This would add to our knowledge whether wheat futures market is resourceful and would enable traders, sellers, and investors to improve cost-effective trading strategy. We utilize the traditional financial model to forecast the wheat futures price and acquire out of sample point estimates. We additionally assess the robustness of our outcomes by applying several alternative forecasting techniques such as artificial intelligence with one hidden layer and autoregressive integrated moving average (ARIMA) model. Furthermore, the statistical significance of our point estimation was further tested through the Mariano and Diebold test. Considering random walk forecast as the bench mark, we used a number of economic indicators, trader’s expectation towards futures prices, and lagged value of futures price of wheat in order to forecast the evaluation of wheat futures price. The computable significance of out of sample estimations recommends that our ANN with one hidden layer has the best anticipating presentation among all the models considered in this exploration and has the estimating power in foreseeing wheat futures returns. Furthermore, this investigation discovers that the futures price of wheat can be predicted, and the wheat futures market of China is not productive.
Highlights
Commodity futures have pulled in a lot of consideration as of late in light of the fact that they encourage value revelation and permit supporting next to alter in product spot costs
We find that the estimated value of RMSE for the conventional model, ARMA (1,1,1), ARMA (1,1,2), neural network, and random walk are 0.7301, 0.7975, 0.6986, 0.6937, and 0.7487, respectively. e artificial neural network has the smallest value of RMSE
It suggests that the artificial neural network (ANN) model is a superior indicator than for conventional model, ARMA (1, 1, 2), and random walk model. is finding is contradicted with the investigation done by [34, 35] for India while steady with an examination direct by [16] utilizing the APSO-support vector regression (SVR) method
Summary
Commodity futures have pulled in a lot of consideration as of late in light of the fact that they encourage value revelation and permit supporting next to alter in product spot costs. Among these couple of studies, the authors in [10] analyzed the consistency of agricultural product and money futures market and found that farming, metal, and cash prospects returns can be all around anticipated utilizing instrumental factors, for example, depository charge yields, value profit yields, and so on. The authors in [23] applied random forest (RF) and support vector machine (SVM) to study the primary relationships between the features and the performances of the candidate models In these investigations, various examinations have analyzed the consistency of the rural items prospects costs fundamentally, and every one of these investigations has focused on the US and hardly any other futures markets.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
