Automatic Financial Feature Construction

Jie Fang, Shutao Xia, Jianwu Lin and Yong Jiang

Abstract: In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to conduct its evolution process. In this paper, we propose a new framework based on neural network, alpha discovery neural network (ADNN). In this work, we made several contributions. Firstly, in this task, we make full use of neural network overwhelming advantage in feature extraction to construct highly informative features. Secondly, we use domain knowledge to design the object function, batch size, and sampling rules. Thirdly, we use pre-training to replace the GP evolution process. According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process. Experiment shows that ADNN can remarkably produce more diversified and higher informative features than GP. Besides, ADNN can serve as a data augmentation algorithm. It further improves the the performance of financial features constructed by GP.

Date: 2019-12, Revised 2020-10
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1912.06236

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