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2024

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation

Gao, Yuan, Chen, Haokun, Wang, Xiang et al.

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Machine learning models have demonstrated remarkable efficacy and efficiency in a wide range of stock forecasting tasks.

However, the inherent challenges of data scarcity, including low signal-to-noise ratio (SNR) and data homogeneity, pose significant obstacles to accurate forecasting.
To address this issue, we propose a novel approach that utilizes artificial intelligence-generated samples (AIGS) to enhance the training procedures.
In our work, we introduce the Diffusion Model to generate stock factors with Transformer architecture (DiffsFormer).

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation · Around

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