DeepValidity: deep convolutional neural networks for multi-factors forecasting

Zhilin Zhu

doi:10.1117/12.2646361

23 August 2022 DeepValidity: deep convolutional neural networks for multi-factors forecasting

Zhilin Zhu

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233014 (2022) https://doi.org/10.1117/12.2646361
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

This paper builds a novel deep learning model to test factor effectiveness as a basis for stock return prediction. The model uses LSTM modules to capture deeper connections between factors and stock returns other than only linear relationships and create a novel index for testing factor effectiveness. And based on the high effective factors extracted, a LSTM network is constructed to predict stork returns. The result is compared with the traditional method that depends on IC (Information Coefficient) to capture a strong linear relationship between variables. The model outperforms the traditional IC model both in the prediction accuracy and the reduced model training epochs. The ability to extract more effective information from data is an important implication for the business world, especially the stock forecast since insufficient data is often a large obstacle.

Citation Download Citation

Zhilin Zhu "DeepValidity: deep convolutional neural networks for multi-factors forecasting", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233014 (23 August 2022); https://doi.org/10.1117/12.2646361

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