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.

利用人工智能生成样本（AIGS）增强训练过程，提出了一种新颖的方法，利用 Diffusion Model 生成具有 Transformer 架构的股票因子（DiffsFormer）。通过编辑现有样本来控制生成数据与目标领域的差异程度，以缓解数据稀缺问题，并通过在 CSI300 和 CSI800 数据集上的实验证明了该方法在股票预测任务中的有效性。

DiffsFormer：基于股票因子增强的扩散变换器

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation

A broad class of stochastic volatility models are defined by systems of
stochastic differential equations. While these models have seen widespread
success in domains such as finance and statistical climatology, they typically
lack an ability to condition on historical data to produce a true posterior
distribution. To address this fundamental limitation, we show how to re-cast a
class of stochastic volatility models as a hierarchical Gaussian process (GP)
model with specialized covariance functions. This GP model retains the
inductive biases of the stochastic volatility model while providing the
posterior predictive distribution given by GP inference. Within this framework,
we take inspiration from well studied domains to introduce a new class of
models, Volt and Magpie, that significantly outperform baselines in stock and
wind speed forecasting, and naturally extend to the multitask setting.

提出了一种将随机波动模型更改为具有特殊协方差函数的分层高斯过程模型的方法，使其可以根据历史数据产生真正的后验分布，同时还将基于大量已研究的领域的启发式设计用于多任务学习，实现了比基线模型更好的股票和风速预测。