  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 is initially trained on a large-scale source domain,
incorporating conditional guidance so as to capture global joint distribution.
When presented with a specific downstream task, we employ DiffsFormer to
augment the training procedure by editing existing samples. This editing step
allows us to control the strength of the editing process, determining the
extent to which the generated data deviates from the target domain. To evaluate
the effectiveness of DiffsFormer augmented training, we conduct experiments on
the CSI300 and CSI800 datasets, employing eight commonly used machine learning
models. The proposed method achieves relative improvements of 7.2% and 27.8% in
annualized return ratio for the respective datasets. Furthermore, we perform
extensive experiments to gain insights into the functionality of DiffsFormer
and its constituent components, elucidating how they address the challenges of
data scarcity and enhance the overall model performance. Our research
demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture
to mitigate data scarcity in stock forecasting tasks.
