Abstract: This paper discusses how to raise efficiency of predicting the Chinese futures market correlation coefficient. First, the predicted periods are divided by major events and the predictabilities between different periods are compared at the same time. Second, on this basis, an automatic machine learning framework, AutoGluon is applied to compare the predictive ability between different deep learning models such as LSTM and GRU. Results demonstrate that: (1) Compared by LSTM and GRU, AutoGluon can indeed raise efficiency of predicting. (2) The changes of prediction error between different periods can explain the influence of major events happened in futures market. (3) Although the predictive ability of many models decline over time, the performance of XGBoost is relatively stable, which can provide useful tools for market participants.

YangChun Xiong, ZiXuan Pan and BaiFu Chen, “Can the Futures Market be Predicted-Perspective based on AutoGluon” International Journal of Advanced Computer Science and Applications(IJACSA), 13(3), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130302

@article{Xiong2022,
title = {Can the Futures Market be Predicted-Perspective based on AutoGluon},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130302},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130302},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {3},
author = {YangChun Xiong and ZiXuan Pan and BaiFu Chen}
}